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The fact-condition statement plays a significant role in the geological papers, where geological theory or phenomenon as a fact is constrained by its precondition. This constraint relation conveys a lot of key information in geological papers, but it has been ignored in most of studies on the information extraction. Although the rule-based algorithms have been proposed in the previous studies to extract the relationship between facts and conditions, but their low accuracy limit usability. Therefore, this paper introduces a series of mechanisms and methods to improve the extraction accuracy. We firstly utilize three kinds of pooling for extracting the semantic features of fact-condition tuples to find that balanced semantic representations can more accurately reflect their relations. In addition, semantic correlation between facts and conditions is also designed to mine their relations via the measurement of semantic similarity or semantic distance. Based on the above, the Deep Structured Semantic Model with Multi-Grained Representation (MGR-DSSM) is proposed. In this model, the Deep Structured Semantic Model (DSSM) is utilized two individual projections for denoising semantic correlation between facts and conditions, while the Multi-Grained Representation (MGR) is designed for encoding their semantic correlation at multiple granularities. Furthermore, the linear MGR-DSSM with SciBERT achieves the best result with 70.41% of accuracy and 74.55% of F1 score, obviously outperforming other baseline models. Finally, the linear MGR-DSSM with SciBERT is adopted to extract super relation from 722 geological paper abstracts, and the geological knowledge graph with fact-condition statement is built. With more accurate relation between fact and condition, the information validity of geological knowledge graph is enhanced. • The relation between facts and conditions play an essential role in the geological paper, the extraction of which is studied for the first time. • A series of mechanisms are proposed to improve the accuracy of fact-condition relation extraction. • The MGR-DSSM is proposed, which obviously outperforms other baselines in super relation extraction. [ABSTRACT FROM AUTHOR]

The integration of deep learning techniques from the field of image generation into digital rock analysis has resulted in substantial advancements. However, generating high-quality and highly controllable heterogeneous digital rocks still presents challenges and necessities. This paper reports a digital rock reconstruction method using a multi-condition denoising diffusion probabilistic model (MCDDPM). This method has the diffusion and denoising processes inherent in the diffusion model, ensuring high-quality reconstructed samples. Meanwhile, various physical property parameters are embedded as conditions in the network layers of the model at multiple scales, enhancing the conditional control ability of this method. We compared the porosity, pore size distribution, and two-point correlation coefficient under the same conditions, as well as the evaluation indicators of different methods. The comparative results provide convincing evidence for the accuracy of our proposed digital rock reconstruction method. In addition, studies on the control effect of different conditions on reconstructed samples also demonstrate the effectiveness of our strategy. Therefore, MCDDPM can reconstruct reasonable heterogeneous digital rocks based on combinations of physical property conditions, which provides effective technical support for expanding the library of heterogeneous digital rock samples and even achieving zero-shot learning of digital rocks. • The real numerical rock possesses strong heterogeneity, and the method proposed in this paper can effectively reconstruct heterogeneous digital rock. • Compared to other deep learning network models, the reconstruction effect of the method proposed in this paper is at a high level. • The method we propose supports multiple physical property conditions as input. [ABSTRACT FROM AUTHOR]

Spike is a type of abnormal waveform observed in strong motion records. It is characterized by a sudden and significant increase in peak acceleration, exceeding the theoretical range, and being greatly uncoordinated with the surrounding record points. The spike severely affects the reliability of subsequent analysis of strong motion records. This paper conducts research based on the strong motion records of the Turkey-Syria earthquakes in 2023. It focuses on the spike represented as Peak Ground Acceleration (PGA) and proposes a spike recognition model for strong motion records using Bayesian-optimized Support Vector Machine (SVM). Firstly, a preprocessing method for strong motion records based on adaptive waveform scaling is proposed. This method enables the adaptive normalization of strong motion records with different scales, thereby eliminating the influence of different scales on the accuracy of manual annotations. Furthermore, a new method for characterizing strong motion records is proposed. It captures shape features (two-dimensional) through one-dimensional data, enabling the spatial distribution of strong motion records to be represented by a shape feature vector. Finally, a Bayesian optimization-based improved SVM model is proposed. It utilizes the feature extraction method proposed in this paper for the identification of spikes in strong motion records. The experimental results show that the proposed model in this paper achieved excellent performance, with an F1-score of 0.868 and an AUC value of 0.93 on the test set. • A normalization method for strong motion records at different scales is proposed. • Proposed a numerical computation-based method for spike feature extraction. • Developed a spike identification model based on Support Vector Machine. • Optimized our model's hyperparameters using bayesian optimization. • Achieved good results on the dataset of Turkey-Syria earthquakes. [ABSTRACT FROM AUTHOR]

The particle-tracking method for transporting radionuclides in multicomponent ocean medium (water and multifractional suspended and deposited sediments) is considered using a probabilistic approach for simulating interaction processes between several states of radioactivity. The state transformations as a result of reactions of the first order were described using the master equation for the probability of the particle being in the given state. Transition probabilities between all possible states can be obtained from the numerical solution of the matrix master equation that is derived in the paper. In the first approximation, the Euler method was used to obtain a solution for the next time step. This approach can be applied to any linear system of equations describing phase transitions with any number of states, but it requires small values of the transition probabilities to ensure only a single-phase change during one time step. The paper also focuses on deriving the Lagrangian interface conditions between the water column and bottom deposition. To apply the probabilistic approach, the boundary conditions were considered as the reaction terms in a thin near-bottom interface layer in which boundary conditions were converted into source terms. For this layer, the corresponding master equation was derived to obtain transitional probabilities for particle states. The developed approaches were tested on numerical and analytical solutions of two test cases. It was found that the optimal thickness of the interface layer must be larger than the maximum vertical displacement of the particle during the one-time step, but it must be small enough to approximate the condition of uniform distribution of concentration in this layer. • Particle-tracking method for the transport of radionuclides in ocean is considered. • Master equation is derived to describe all possible states of contaminant. • Bottom boundary conditions were considered as the reaction terms in interface layer. • Optimal thickness of the interface layer was chosen. • The approach was tested on a numerical and analytical solutions for two test cases. [ABSTRACT FROM AUTHOR]

Geological reports house a wealth of geological domain knowledge and expert experience knowledge, and the efficient extraction of geological entity relations from these texts holds great significance in constructing a geological knowledge graph. To address challenges such as overlapping entity relations, data imbalance, and the limited incorporation of contextual features in existing deep learning-based entity relation extraction methods, this paper proposes a relational graph convolutional network pointer model based on the pre-trained model of Robustly Optimize Bidirectional Encoder Representation from Transformers Pre-training Approach (RoBERTa). Specifically, our approach tackles the issue of overlapping entity relationships by identifying the primary entity and the subordinate entity associated with a specific relationship. Additionally, by incorporating a relationship graph convolutional network, we can capture multiple relationships among entities. Moreover, we leverage the Biaffine layer to accurately handle entity annotation boundaries, thereby enhancing the precision of entity classification. To address the issue of imbalanced data, we adopt the Focal Loss function during experiments. Furthermore, we construct a geological entity relation dataset comprising 24 types of relationships, which is generated by collecting seven regional geological reports and employing semi-automatic annotation techniques. Finally, the proposed algorithm was tested and comparison with several baseline models, the results demonstrate the superior performance of the proposed model in this paper. • In this paper, a geological entity relationship dataset is constructed. • In this paper, we propose a convolutional pointer network model for relational maps based on the pre-trained RoBERTa model and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Oil source correlation can be used to identify the origin of crude oil by linking crude oil to source rocks; however, the manual methods, which are limited by the sample or parameter quantity or imbalanced datasets, are facing uncertainties. Although the existing multivariate statistical techniques can alleviate this problem, they are facing difficulties in processing imbalanced datasets and quantifying source beds. Therefore, a novel oil-source correlation analysis model called SVM-SelectKBest combining a support vector machine (SVM) with a feature selection algorithm to mitigate the common issue of dataset imbalance in oil-source correlations is proposed in this paper. The SVM-SelectKBest offers advantages over normal SVM by dynamically selecting the most relevant features and fine-tuning model parameters to achieve higher accuracy and better generalizability in complex datasets. SVM compensates for class imbalances by heavily penalizing the misclassification of the minority class, and SelectKBest streamlines the feature set to enhance SVM's effectiveness on critical variables. Furthermore, a shallow neural network (SensoryAttentionNet) is introduced into the proposed model to address the issue of quantifying the source bed proportions in crude oil. The result show that SVM-SelectKBest has better performance in identifying key geochemical parameters and discriminating oil source correlation, its accuracy in unbalanced datasets is improved by near 40% compared to SVM. The model obtains 25 key geochemical parameters such as C17 n-heptadecane, Pr pristane, and C18 n-octadecane, it achieves F1 scores of 1.0 on the training, validation, and test sets. SensoryAttentionNet also performs robustly, with a low variance of 0.05 between its predicted and actual values. All the results indicate the effectiveness of the proposed method in dealing with the imbalance problem in oil-source source correlation datasets and in determining the proportional contribution of source beds in crude oil. • A support vector machine model combined with feature selection is presented. • The issue of dataset imbalance in oil-source correlations is mitigated. • A neural network for quantifying the proportion of source beds is presented. • The challenge of quantifying the proportion of source beds is resolved. • The Selection of geochemical parameters are optimized by feature selection. [ABSTRACT FROM AUTHOR]

In this paper, we present a new open-source software "Open-AMA" developed to investigate atmospheric circulation dynamics and environmental research. Open AMA presents an integral package to conduct several air mass analyses. It appears to be a powerful, versatile software package developed to meet the needs of researchers using python and C++ in order to facilitate and speed up working time. This software seamlessly integrates new models for source identification based on air trajectories and ambient air pollution concentration data and enhances certain existing ones. Beyond source identification, it offers a rich array of functionalities for making it automatic, quick and easy to get access many kinds data including gridded meteorological data, trajectory calculations, synoptic parameter extraction from back-trajectories. All this functionalities can be used through a user-friendly graphical interface. Open-AMA can be a significant leap forward in air quality research and analysis, empowering researchers with the tools they need to make informed decisions and address pressing environmental and public health challenges and enhance understanding of pollutant origins in the atmosphere. • Open-AMA is an Open sources software for atmospheric circulation research. • Regional Correlation Analysis model. • Adaptive k-means clustering. • 3D-PSCF and 3D-CWT source–receptor models. • Meteorological data processing. [ABSTRACT FROM AUTHOR]

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ES-MDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for uncertainty quantification. However, there are significant challenges related to the computational cost, Gaussian assumptions, and its application for categorical model variables. Recent developments have proposed to integrate ES-MDA with Variational Autoencoder (VAE) to reparametrize facies into a continuous feature space and reduce computational costs. Despite promising results, it has introduced new limitations concerning network convergence, spatial correlation loss, and manipulation of the latent space. In this paper, a Fully Convolutional Variational Autoencoder (FCVAE) was proposed to outperform the VAE in terms of preserving spatial information, requiring fewer network parameters, presenting higher dimension reduction and yielding better facies estimation. The objective of the application example is to draw sound conclusions based on a 2D synthetic application, where the results of both methods can be compared with reference facies for quality evaluation. • Fully Convolutional VAE to reparametrize facies models. • Latent space with spatial correlation. • Seismic Facies Inversion with ES-MDA and FCVAE. • Pre-salt synthetic dataset with four facies. • Comparison between ES-MDA-FCVAE and ES-MDA-VAE. [ABSTRACT FROM AUTHOR]

Data-driven three-dimensional (3D) mineral prospectivity modeling (MPM) employs diverse 3D exploration indicators to express geological architecture and associated characteristics in ore systems. The integration of 3D geological models with 3D computational simulation data enhances the effectiveness of 3D MPM in representing the geological architecture and its coupled geodynamic processes that govern mineralization. Despite variations in modality (i.e., data source, representation, and information abstraction levels) between geological models and simulation data, the cross-modal gap between these two types of data remains underexplored in 3D MPM. This paper presents a novel 3D MPM approach that robustly fuses multimodal information from geological models and simulation data. Acknowledging the coupled and correlated nature of geological architectures and geodynamic processes, a joint fusion strategy is employed, aligning information from both modalities by enforcing their correlation. A joint fusion neural network is devised to extract maximally correlated features from geological models and simulation data, fusing them in a cross-modality feature space. Specifically, correlation analysis (CCA) regularization is utilized to maximize the correlation between features of the two modalities, guiding the network to learn coordinated and joint fused features associated with mineralization. This results in a more effective 3D mineral prospectivity model that harnesses the strengths from both modalities for mineral exploration targeting. The proposed method is evaluated in a case study of the world-class Jiaojia gold deposit, NE China. Extensive experiments were carried out to compare the proposed method with state-of-the-art methods, methods using unimodal data, and variants without CCA regularization. Results demonstrate the superior performance of the proposed method in terms of prediction accuracy and targeting efficacy, highlighting the importance of CCA regularization in enhancing predictive power in 3D MPM. • Casting 3D mineral prospectivity modeling (MPM) as a deep multimodal fusion problem. • Joint fusion network for integrating features of 3D geological models and simulation data. • Canonical correlation analysis regularization to guide network fusion of the cross-modal gap. • Maximizing the correlation of multimodal information boosts the performance of 3D MPM. [ABSTRACT FROM AUTHOR]

It is of very great significance to achieve accurate solution of equidistant points/equiratio line between two points at a large regional or global scale. At present, no effective method has been found for equidistant/equiratio line between two points on the Earth ellipsoid. In large scale space, the solution plane of equidistant/equiratio points between two points on the map cannot meet the requirements of high-precision space analysis, due to the existence of map projection deformation. The key to solving the above problems is to establish a method generating the equidistant/equiratio line based on solution to geodesic problem, and implement the entire calculation process on the Earth ellipsoid. Based on the Karney's direct and inverse formulas, the paper uses the pendulum method to design a cohesive method for calculating the equidistant/equiratio line between two points on the Earth ellipsoid. Several specific examples are selected for experimental verification, which proves that this algorithm can solve the problem of calculating equidistant/equiratio line of two points of different scale line in short distance, medium distance and long distance, and the precision of solving coordinate space is less than 0.00001 m, which can meet the requirements of long distance, wide range and high-precision spatial analysis. • A unified and efficient algorithm to calculate the equidistant/equiratio line between two points on the Earth ellipsoid. • The calculation is completely processed on the earth ellipsoid, and it can completely get rid of the influence of map projection. • The results can meet the high-precision requirements of spatial analysis on the Earth ellipsoid. [ABSTRACT FROM AUTHOR]

The survey setup of 3D magnetotelluric inversion is typically chosen to best reflect the underlying geological structure. Typically, the individual observation azimuths are distinct from this preferred survey layout orientation; in fact, measured electric and magnetic fields are aligned with local geomagnetic North. When that happens, either data preprocessing (rotation) is required before the inversion may be performed, or the orientation of the modeling grid is aligned with the data orientations. None of these approaches is optimal, possibly resulting in a reduction of data quality, the loss of statistical accuracy of the error estimates, or in unnecessary grid cells, resulting in increased computation times. In situations when the data quality may be improved by allowing distinct individual site orientations, such as when cultural noise or faulty instrumentation cause a degradation of one of the modes, data loss due to rotation is especially detrimental. In this paper, we present a modified 3D magnetotelluric inversion algorithm, based on the ModEM software, that allows for direct interpretation of observations with arbitrary data orientation angles (ModEM ADORA). In ModEM ADORA, the modeling responses are rotated to the observation orientation angles after each forward calculation and before the data misfit is evaluated. This allows for direct interpretation of observation data with distinct orientations at each site and (as needed for some historical data) at each frequency. The derivative of the penalty functional is computed by pre-multiplying the derivatives of the data functionals (or the complete Jacobian matrix) by the relevant sparse block-diagonal rotation operators. We describe the principle and implementation of ModEM ADORA, and provide a collection of synthetic and real data tests for validation and an analysis of effectiveness of the ADORA option of ModEM. We show that by decoupling modeling grid and sensor orientations, ModEM ADORA supports both computational efficiency and ease of use, as well as allows for the maximum quality and quantity of inverted data. • Modeling and observation coordinate systems can now be set arbitrarily for 3D magnetotelluric inversion. • More high-quality data can be incorporated into inversion by choosing optimal data orientation. • Solve for fewer inversion model parameters by rotating the modeling coordinate system to site distribution. • Free for academic use. [ABSTRACT FROM AUTHOR]

Seismic data denoising (SDD) plays an important role in obtaining high-quality data for subsequent seismic imaging and inversion. However, traditional SDD methods still present several disadvantages such as parameter selection difficulties, high computational costs, and a strong dependence on the experience of processing personnel. Deep learning (DL)-based SDD methods can overcome these issues to a certain extent, having the advantages of high computational efficiency and negligible dependency on the experience of processing personnel. However, existing DL-based SDD methods typically adopt a specific single-network architecture, which can be further improved in terms of training efficiency and denoising quality. In this paper, we propose a two-step prediction denoising method based on the combination of two identical or different network architectures for improving the accuracy and efficiency of DL-based SDD methods. Compared with existing SDD methods, the two-step prediction denoising method is more capable of extracting feature information at multiple scales because multiple network architectures are combined in a single denoising task, thus improving the training efficiency and denoising accuracy. In this study, we implemented the two-step prediction denoising method using an improved denoising convolutional neural network (DnCNN) and a nested U-shaped fully convolutional network (U-Net + +). Several synthetic numerical examples based on three different types of noise were denoised, with the results indicating that the proposed method effectively improves the accuracy and efficiency of the DL-based SDD method. Finally, a test using field data was performed to demonstrate the real-world applicability of the novel two-step prediction denoising method. • A modified DnCNN using a different architecture is constructed. • A network-based two-step prediction method is proposed to denoise seismic data. • Examples illustrate the accuracy and feasibility of the method. [ABSTRACT FROM AUTHOR]

Pore-scale analysis and characterization of reservoir rocks provide valuable information for the definition and management of underground hydrogen storage and CO 2 storage or sequestration. This article presents an optimized implementation of the A* algorithm, the most popular pathfinding method in the presence of obstacles. In this context, the algorithm is applied to recognize the minimum length connected paths through each flow direction of 3D images of a porous medium representative of a reservoir rock. The identification of the main paths allows the characterization of the pore space and the calculation of fundamental petrophysical properties such as tortuosity and effective porosity, which can be used for permeability estimation. Compared to other algorithms available for pore-scale characterization, such as the pore centroid, A* provides a better approximation of the pore space available for the flow and, therefore, a reliable characterization of the petrophysical properties. On the other hand, path identification is significantly consuming in terms of time and memory. In this paper, an efficient and optimized implementation based on C++/OpenMP programming language is presented. The proposed implementation aims to the analysis of large-scale models profiting from parallelization, memory optimization, and enhanced managing of dead paths. Three test cases of increasing sizes are presented, to analyze the advantages and the disadvantages of the algorithm as the number of explored points increases. The 3D binary images analyzed are related to a synthetic domain (1 million voxels) and two actual sandstone samples (about 4 and 64 million voxels respectively). The code is validated against a Matlab serial implementation, showing a significant improvement in efficiency. Remarkable test cases of several millions of voxels were afforded, overcoming the memory and execution slowness issues. Moreover, the proposed implementation is suitable for large pore-scale models run in HPC environments. [Display omitted] • Computer-aided geometric characterization of porous media images. • Pathfinding algorithm implementation applied to Digital Rocks Physics. • Parallel computing optimization for native serial algorithms. • Validation on real sample binary representations. • Geometrical properties computation for underground storage. [ABSTRACT FROM AUTHOR]

This paper presents a model framework based on a deep learning algorithm using Faster R-CNN to improve the accuracy and efficiency of detecting long-distance levee hazards. The proposed method aims to assist technicians in identifying hazards in resistivity inversion profiles, thereby reducing the manual workload and extracting valuable information from a large volume of data using deep learning techniques. To this end, we constructed a dataset of levee hazards using inversion images of electrical resistivity imaging data obtained from numerical simulations, indoor experiments, and actual levee hazards. Subsequently, we utilized image augmentation techniques to expand the dataset and enhance the model's performance, resulting in a total of 2000 labeled hazard images. The network was then trained from scratch by setting the hyperparameters. the trained weight parameters and network architecture were saved for future use. The model's performance was evaluated using standard evaluation metrics, including precision, recall, F1 score, and average precision (AP). The proposed model demonstrated robustness and adaptability, achieving high precision and accuracy with an AP value of up to 100%. Furthermore, we validated the method using three cases, and the results indicated its superiority over traditional manual identification-based or outlier analysis detection of levee hazards in practical scenarios. This study emphasizes the significant advantages of deep learning techniques over the traditional methods, including improved accuracy, automation, speed, and scalability. It suggests that intelligent detection based on deep learning can accurately identify and locate hazards in levees. • A levee hazard detection model is built on the Faster R–CNN for position and prediction. • The model replaces manual identification, enhancing efficiency and accuracy in identifying hazards. • The self-made dataset, constructed from resistivity images, is augmented to improve model performance. • The model achieves excellent precision and accuracy, with an AP value of up to 100%. • Case study is carried out to verify the intelligent hazard identification method. [ABSTRACT FROM AUTHOR]

To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge- and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathematical feature information from terms in Chinese geological text to construct a Term Combination Probability Model (TCPM) for Chinese word combinations by integrating the combination features of geological terms and the Chinese writing styles under zero-sample conditions. The TCPM was used to extract geological terms with high combination characteristics as a user-defined dictionary, and then a geological corpus was constructed by using a general domain word segmentation method based on this dictionary. After a small amount of manual review and optimization, the geological corpus was trained with a BiLSTM-CRF model to segment Chinese geological text. The proposed method in this paper was tested using a regional geological survey report set in Henan Province, and the precision, recall, and F1-score of the method are 92.65%, 92.53%, and 92.59%, respectively. The experimental results demonstrated that this method, combined with the inherent knowledge features of geological text and the machine learning method, can assist in expanding the core dictionary for Chinese geological text segmentation based on zero-sample, and can improve the segmentation precision of Chinese geological text compared to simple general word segmentation methods or machine learning methods. • TCPM with no sample can reflect the structural features from professional terms in geological field texts. • Chinese word segmentation with custom dictionary from terms extracted by TCPM is better than general segmentation methods. • Combining TCPM with machine learning can improve the accuracy in word segmentation for Chinese geological text. [ABSTRACT FROM AUTHOR]

Deep Learning is an effective tool to invert the underground electrical conductivity structure. In this study, we build a new 8-layer residual neural network (ResNet1D-8) for audio magnetotelluric (AMT) data inversion based on the deep learning theory. In terms of the network structure, the degradation of the model with the increase of depth is effectively avoided by adding shortcut connections. Meanwhile, adding a batch normalization layer greatly improves the model training speed and network generalization ability. This study uses the parallel technology to quickly generate millions of samples, which effectively reduces the computational time and provides a large number of high-quality samples for deep learning model training. Compared with the simulated annealing algorithm, the neural network model in this paper has the advantages of high reliability, short inversion time, and strong model generalization ability. Moreover, we add Gaussian noise to the data of testing samples, and inversion results show that the model has good robustness. The inversion test is carried out on the field measured AMT data set collected in the Dachaidan area of Qinghai Province, China. The results show that the ResNet1D-8 residual neural network model established in this paper can effectively invert the underground electrical structure. • A new residual neural network model (ResNet1D-8) for 1-D MT inversion is constructed. • A uniform stochastic model and parallel techniques are used to rapidly generate samples. • The generalizability and robustness of ResNet1D-8 are verified through numerical experiments. [ABSTRACT FROM AUTHOR]

Multibeam sonar data are a valuable tool for seafloor mapping and geological studies. However, the presence of outliers in multibeam data can distort the results of analyses and reduce the accuracy of seafloor maps. In this paper, we define a weighting function based on the performance of various outlier detection techniques (ODTs) for detecting outliers in multibeam data, which calculates an outlier probability score for each sounding. Our results show that each ODT has its own strengths and weaknesses, and that a combination of outlier detection techniques is promising to improve reproducibility, explainability and the accuracy of the detection process. To address the challenge of detecting outliers in multibeam data, we propose a weighted outlier detection function that outperforms individual outlier detection techniques in terms of precision, recall and F1 scores by considering their strengths and combining them in a way that accounts for variations in the data. The function detects various types of outliers with high precision and recall values, resulting in valuable improvements in outlier detection performance for multibeam data. Overall, our proposed workflow has the potential to significantly improve the way multibeam data cleaning is performed, with the weighted outlier detection function being applied first, detecting most of the outlier automatically, followed by a domain-expert review of a small group of soundings whose automatic outlier labeling is not unequivocal. • Developed innovative outlier detection for precision in seabed analysis. • Weighted function outperforms in precision, recall, F1 for seabed mapping. • Enhanced geoscientific data quality, preserving sonar data integrity. • Supports sustainable management with accurate seabed mapping accuracy. • Discussed adaptability and limitations, advocating further research. [ABSTRACT FROM AUTHOR]

The parameters of fractures, such as fracture geometries, placement, and conductivity, are critical for the production of horizontal wells in tight gas reservoirs. Current studies on fracture parameter optimization, especially those using the optimization algorithms, are often based on the assumptions that the fractures are uniformly placed with equal length and conductivity. However, non-uniform designs of fracture spacing have been proved to be effective in reducing fracture interference as well as increasing the well production, especially with strong rock heterogeneity. In addition, most of the current literature focuses on identifying optimal hydraulic fracture geometries without considering the fracture propagation process, which may oversimplify the evaluation of completion cost and result in fracture geometries that cannot be realized in practice. In this paper, an automatic optimization process, which could efficiently couple the fracture propagation, is proposed to optimize the fracture parameters. The genetic algorithm (GA) with penalty function method is used to optimize the constraint and non-linear problem. A modified PKN fracture propagation model is established to quickly predict the hydraulic fracture geometries. Fracture locations and fluid injection volume are sequentially optimized with consideration of heterogeneous rock properties. Model parameters are taken from typical tight gas horizontal wells in Jinqiu gas field in Sichuan Province, China. The results show the importance of permeability on both fracture length and production in tight gas well. The lower the permeability, the more significant the production increase after fracture location optimization. More fractures tend to be placed in area with small permeability and fewer fractures are required in highly permeable regions. With an increase in rock heterogeneity, the distribution of fractures becomes more uneven. Conversely, increasing the number of fractures can lead to a more even distribution of fractures. More fluid should be injected in low-permeability regions according to the optimization results. The optimization method is applied to a practical tight gas well in Sichuan Basin and the cumulative gas production can be increased by 6.5% to 8% when compared with the initial uniform designs. • A modified PKN model is proposed for quick prediction of fracture propagation, validated against numerical simulations. • GA is used to optimize fracture locations by considering rock heterogeneity's impact on fracture propagation and production. • Sequential optimization of fracture placement and injection volume is conducted with its reliability verified. [ABSTRACT FROM AUTHOR]

The acquisition of rock property information is at the core of regional geological survey and mineral exploration, but hand-crafted feature-based methods are heavily influenced by human prior knowledge and have limited transferability. End-to-end deep learning techniques, exemplified by convolutional neural networks (CNNs), have attained significant accomplishments in the domain of image classification. However, previous end-to-end CNN-based methods are hard to focus on image critical areas, and they also cannot make full use of global alignment dependency of the rocks. In this paper, rock image classification via a RockS2Net is proposed. The RockS2Net framework adopts a Siamese architecture, characterized by two branches that share parameters, enabling the efficient extraction of both global and local features. This approach facilitates the extraction of global features from entire images and focuses on critical areas to extract local features. The architecture of spatial transformer network (STN) is introduced to transform microscopic images of rock sections to their critical areas. By fusing local features and global features, the properties obtained from microscopic images of rock sections can be more accurately predicted. To test the robust generalizability of the proposed method, the constructed CHN-Rock images dataset is used for experiments and evaluation. Experimental results show that the accuracy of the proposed RockS2Net on the CHN-Rock image dataset is 2–3% higher than that of other rock image classification networks. • The proposed RockS2Net can effectively extract local features of the critical areas and global features from microscopic images of rock sections. • The proposed SGD block, which can strengthen the transfer of features and reduces the loss of features, fuses the two-stream global-local features, and discriminative features of the rock sections can be obtained. • The proposed STN blocks capture key regions by transforming microscopic images of rock sections to reduce the influence of irrelevant features on rock image classification. • The introduced idea of extended convolution is applied to the framework for solving the information loss caused by rock image resolution reduction and down sampling in rock image classification problems. [ABSTRACT FROM AUTHOR]

For layered models with a low-velocity half-space, Rayleigh waves can exhibit inversely dispersive patterns and thus are advantageous for providing useful information on the seismic properties of such structures. However, the calculation of dispersion curves usually encounters some difficulties such as root skipping or numerical instability due to the presence of leaky modes. The full-wavefield method and spectral element method can offer better solutions but at the cost of a significant computational effort. For practical applications, most researchers treat the zeros of the real part of the secular function as an approximation to the leaky modes, which may lead to relatively large errors that cannot be ignored. Instead, in this paper, we propose a simple and efficient method to approximate the leaky mode based on the local minima of the absolute value of the secular function. Compared with the dispersion curve generated from the real part of the secular function, the proposed method can provide a more accurate approximation. We further apply a new efficient Monte Carlo search technique to two synthetic datasets and a permafrost example. Results show that our forward and inverse methods are effective, efficient, and robust. Moreover, our numerical experiments indicate that the modified Thomson-Haskell method is more suitable for calculating leaky modes compared with the fast delta method and the generalized reflection/transmission method, and that the inversion results are less sensitive to the effect of the limited spread length on the measured dispersion curves. The present study emphasizes the high efficiency of the proposed method in imaging permafrost structures and foundation features of buildings or roads. • A fast solver for calculating the apparent modes due to a low-velocity half-space. • The modified Thomson-Haskell method is more suitable for calculating the leaky modes. • Application for imaging permafrost structures. [ABSTRACT FROM AUTHOR]

Like many research areas, tsunami research has plenty of related topics that lack unified terminology. Some particular sub-topics may not have enough attention or do not share the same terminology as different views on the phenomenon. This issue can be tackled by an ontology that puts knowledge from different related topics into a formal structure that connects concepts with relationships. This paper proposes the development process of Tsunami-Related Ontology (TRO) that would aid the research in this field. The proposed semi-automatic ontology development methodology applies to any research field and does not require specific algorithms or programming skills to achieve its goal. This paper particularly focuses on three research gaps related to tsunami that are expected to benefit significantly from an ontology: meteorological tsunami, community resilience, and physical vulnerability. For these topics, the created ontology provides a formal taxonomy that links individual concepts to equivalent or related concepts, providing an easy-to-understand overview of the area. • Semi-automatic developmental method of a tsunami-related ontology (TRO) is proposed. • TRO unifies terms of (meteo)tsunami, community resilience and physical vulnerability. • TRO facilitates the search of tsunami-related documents. • TRO recommends related concepts on the basis of the user's query. • The ability of TRO to propose suitable related concepts is illustrated by a use case. [ABSTRACT FROM AUTHOR]

The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algorithm has been proposed in this paper to improve its accuracy and efficiency of parameter estimation with low SNR. The NMLM algorithm can also converge globally under certain conditions and converge quadratically under the error-bound condition, which updates based on the gain ratio and determines the damping factor based on the gradient value. An experimental investigation has been conducted under different SNRs and the amount of data, and the results indicate the new algorithm can accurately and quickly estimate the parameters of underground metal targets under the lower SNRs and smaller amounts of data. More specifically, the estimation accuracy of position, principal axes polarizability, and orientation of the metal target can be increased by up to 1.6 c m , 0.00013, and 2.31 degree, respectively. This paper also verifies that the new algorithm finds the optimal solution for standard numerical problems with low errors and a finite number of iterations. • A new LM algorithm is proposed to improve the accuracy and convergence of the result. • The performances are analyzed by comparing conventional and NMLM algorithms. • The performance of the NMLM algorithm on the common optimization problem is analyzed. [ABSTRACT FROM AUTHOR]

Seismic data are often characterized by low quality due to noise contamination or missing traces. Convolutional neural networks are popular in dealing with denoising and interpolation. However, fixed-size convolution kernels have limited feature extraction range, and popular networks aim at either denoising or interpolating. In this paper, we propose a Swin Transformer convolutional residual network (SCRN) for simultaneous denoising and interpolation. We step-by-step show on synthetic datasets that SCRN can effectively denoise, interpolate, or denoise and interpolate simultaneously. Following this, the trained simultaneous denoising and interpolation model is directly used to handle multi-tasks on the field datasets. We assess the reconstruction effect of SCRN based on synthetic and field datasets. Comparison methods include both supervised ones (DnCNN, Unet and RIDNet) and unsupervised ones (DDUL, MultiResUNet and DL-POCS). Experimental results show that SCRN outperforms the counterparts in terms of (1) quantitative evaluation indices, (2) event continuity and weak signal retention, (3) generalization seismic data ability to suppress various noise levels and interpolate different sampling rates, and (4) preservation of first-arriving edge signals. These results indicate that SCRN can effectively reconstruct seismic data. • Global and local feature fusion helps fully utilize seismic data's local similarity. • Swin Transformer shares temporal information, maintaining the continuity of the events. • Convolutional Neural Networks focus on high-correlation information, preserving weak signals. • Simultaneous denoising and interpolation networks complete the multi-task. • The diversity of the training set enables better generalization of the model. [ABSTRACT FROM AUTHOR]

The interest in the study of electromagnetic propagation through planetary atmospheres is briefly discussed. Special attention is devoted to extremely-low-frequency fields in the Earth's atmosphere for its global nature and possible applications to climate monitoring studies among others. In the Earth's case, the system can be considered as a spherical electromagnetic shell resonator in which two concentric and large conducting spheres with a radius around 6300 km are separated by a very small distance of around 100 km, the atmosphere height. A numerical solution using the Transmission Line Method is proposed. The classical spherical-coordinate description is easy to use, however, the important difference in the dimensions along the three coordinate directions causes high numerical dispersion in the results. A Cartesian scheme with equal node size for all directions is used to reduce this undesired dispersion. A pre-processing stage is the key point introduced to lessen the resulting high demand of memory and time calculation and make the solution feasible. A parallelized Fortran code together with pre- and post-processing Python programs to ease the user interface are provided with this work. Details on the Fortran code and the Python modules are included both in the paper and the source codes to allow the use and modifications by other researchers interested in electromagnetic propagation through planetary atmospheres. The program allows calculation of the time evolution of the electromagnetic field at any point in the atmosphere. It includes the possibility of considering multiple time-dependent sources and different homogeneous and inhomogeneous conductivity profiles to model different situations. Profiles to study day-night asymmetries or locally perturbed profiles which have been attributed to earthquakes in the literature are implemented, for instance. • A parallel algorithm is used to study the electromagnetic propagation in the Earth's atmosphere. • A TLM scheme is used for the numerical modeling of electromagnetic propagation. • Schumann resonances can be obtained in a full 3D electromagnetic model. • The implementation uses Cartesian coordinates to reduce numerical dispersion. [ABSTRACT FROM AUTHOR]

The Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which is capable of diluting the salinity. This can be attributed to the cyclic flow of high salinity water (≥ 35 psu), coming from Arabian sea and entering BoB from the south, which moves northward and mixes with this fresh water. The movement of this high salinity water has been studied and analyzed in previous work (Singh et al., 2022). This paper extends the computational methods and analysis of salinity movement. Specifically, we introduce an advection based feature definition that represents the movement of high salinity water, and describe algorithms to track their evolution over time. This method allows us to trace the movement of high salinity water caused due to ocean currents. The method is validated via comparison with established observations on the flow of high salinity water in the BoB, including its entry from the Arabian Sea and its movement near Sri Lanka. Further, the visual analysis and tracking framework enables us to compare it with previous work and analyze the contribution of advection to salinity transport. • Novel feature definitions for salinity transport based on advection. • Algorithms for computing and tracking this feature in the ocean. • Visual analysis of high salinity transport due to advection. • Comparison of salinity movement in the Bay of Bengal against existing methods. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy is decisive for accurately characterizing rock samples for geological and mineral applications, since slight differences in mineral composition can be easily recognized in spectral signatures. Although, instrument limitations prevent high spatial resolution pixels scanned along hundreds of spectral channels, which considerably reduces the data capabilities. Multi frame Super-Resolution (SR) techniques can overcome this issue by retrieving high-quality images using purely computational methods. However, these approaches still have important drawbacks because of inherent uncertainties when estimating the spatial variation between frames, which strongly penalize the spectral information, essential for lithology. In this paper we propose an assisted framework to provide fully controlled motion parameters of the imaging process, skipping the intricate registration task. Essentially, we use a stepping device to successively change the position of rock samples for each new frame acquisition. With accurate knowledge about motion parameters, we were able to overcome the uncertainties regarding spatial registration between frames. Therefore, a reasonable number of equations connecting the low-resolution frames and the super resolved image could be used to solve the optimization problem. Extensive experiments proved that the proposed assisted method achieved best performances (both qualitatively and quantitatively) compared with similar approaches. Moreover, detailed spectral analysis showed almost absolute consistency between the original low-resolution image and the super-resolved result, which is crucial to guarantee reliable mineral characterization. • Hyperspectral images of rock samples can be enhanced using super-resolution principles. • Shifted assisted approach can overcome the traditional multi-frame registration problem. • Spectral analysis showed absolute spectral consistency between original and super-resolved result. • The result achieves a new level where no spectral losses are found on super-resolution enhancement of rock samples imagery. [ABSTRACT FROM AUTHOR]

This paper presents an alternative way of simulating 3D facies models using conditional generative adversarial networks (GANs) by reconstructing the 3D volume upward with customised thickness from generated 2D slices. To mimic natural fluvial reservoirs, GANs need to learn the complicated facies' lateral and vertical sequences, connectivity patterns and spatial correlation, e.g. the distribution of channel deposits and lateral accretion packages, including channel lags and point bars in a meandering fluvial system. 3D GAN modelling remains challenging, despite some success in 3D, due to the increased data and GAN model complexity caused by the 3D training data. For example, a 3D facies model with a larger number of voxels introduces more complex geobody shapes and requires 3D convolutions that increases the computational burden compared to 2D convolutions. Our proposed conditional GAN-based framework predicts 2D upper layers slice by slice to build 3D facies models, mimicking the sedimentary process in a purely aggrading system, which is less computationally costly than generating the facies models used as training data and generating facies models directly in 3D from a GAN. Each upper layer is predicted conditionally to a lower layer that contains the facies distributions in all layers below the target using several unique training enhancements. All those methods help prevent the simulated 3D models from gradually losing realism and the preset sedimentary settings vertically while inferring the plausible evolution of the channel system. The results demonstrate that the trained conditional generator, a SPADE generator, can work as a simulator to produce unconditional 3D meandering fluvial facies models by inputting a user-defined spatial correlation strength between layers and the number of layers needed (thickness in metres) without post-processing or retraining. This framework's performance was evaluated using a connectivity metric towards the range of avulsion scenarios that represent a range of depositional uncertainty. • We propose a conditional GAN-based 3D reconstruction approach called FluvalGAN_3DR. • FluvalGAN_3DR allows customising the model thickness without re-training. • We developed a solution to tackle the decay of realism during 3D reconstruction. • We developed a novel way of avoiding GAN to have a biased sense of the avulsion rate. • FluvalGAN_3DR created varied avulsion scenarios covering various sand connectivity. [ABSTRACT FROM AUTHOR]

The 3D modeling method based on parallel geological cross-sections is suitable for modeling large-scale bedrock geological bodies. However, there are usually more branching, pinch-out strata, deformation structures (such as faults and folds), and the correspondence problem of geological boundaries between adjacent cross-sections. These problems have become the most crucial obstacle in the current application of 3D geological modeling based on parallel geological cross-sections. To this end, this paper proposes a topological reasoning automatic processing method for the consistency of parallel geological cross-sections to obtain topologically consistent adjacent cross-sections efficiently. The method involves (1) Stratigraphic inference based on the distance and topological relationships, transforming strata with m:n relationships into three relatively simple cases of 1:1, 1:n, and 1:0; (2) Splitting branching strata based on the Voronoi diagram, transforming strata with 1:n relationships into 1:1 relationships; (3) Dealing pinch-out strata based on the method of filling virtual strata, transforming strata with 1:0 relationships into 1:1 relationships (4) Topologically consistent processing of stratigraphic boundaries based on automatic recognition of no-corresponding arcs and addition of virtual stratum. Based on experiments in the Longtoushan Hill area in Jiangsu Province, China, and the southeast side of Chandler Mountain, Alabama, USA, this method can effectively replace the many manual processes such as determining the location of pinch-out adding auxiliary cross-sections and performing branching strata correspondence in the current application of 3D geological modeling based on arc segments. The geological cross-sections processed based on this method have an exemplary data structure and consistent topological relationships. The 3D geological modeling algorithm based on parallel cross-sections has the advantages of low algorithm complexity, high automation, and stable modeling quality. • An automated method for topology-consistent processing of parallel geological cross-sections was developed. • The strata with corresponding complex relationships can be converted into one-to-one corresponding arc segment sets. • The method lays a sound data foundation for 3D modeling using parallel cross-sections. [ABSTRACT FROM AUTHOR]

Marine controlled-source electromagnetic (CSEM) method has proved its potential in detecting highly resistive hydrocarbon bearing formations. To image the subsurface resistivity, this paper presents the first application of a frequency domain CSEM inversion approach using fictitious wave domain timestepping modelling. Each inversion iteration requires the gradient of the misfit with respect to resistivity computed by the product between the forward and adjoint fields. Simulation of the adjoint field using fictitious time modelling is challenging as it requires the time domain adjoint source functions while only a few discrete frequencies of the data residual are available for the inversion. To overcome this issue, we formulate a regularized linear inverse problem to estimate a long time series from very few frequency samples. It can then be solved using linear optimization technique in matrix-free manner. Instead of computing adjoint source time function one by one at each receiver location, a basis function implementation has been designed, such that the linear inversion can be solved only once and then reused every time to construct all time-domain adjoint sources. The method allows computing all frequencies of the EM fields in one go without heavy memory and computational overhead. Numerical examples are employed to demonstrate the application of our method. • The first CSEM inversion using fictitious wave domain timestepping modelling is developed. • The adjoint source time function is estimated by solving a regularized linear inverse problem. • The basis functions are computed matrix-free, once and reused for all receivers. • The numeric examples demonstrate its application to practical 3D CSEM inversions. [ABSTRACT FROM AUTHOR]

Geological and geophysical interpretation is characterised by large and localised datasets that are extremely expensive to acquire. There are clear advantages in applying deep learning techniques to such datasets, but this requires a large amount of suitable data for effective training. Creation of training data can be time-consuming, but novel countermeasures such as Generative Adversarial Networks (GANs) are an enticing alternative with the potential to alleviate this issue by providing synthetic data that is representative of the real data. This paper details an investigation of the potential of GANs for the augmentation of labelled seismic facies from a mass transport complex in training a convolutional neural network (CNN) for facies classification. The study adopts a specific GAN approach, known as the Conditional Style-Based Logo GAN (LoGANv2), because of its capability to generate conditioned output with improved training stability. By using LoGANv2 to synthesise examples that mimic the behaviour of the real data, based on a 3D seismic dataset from the North Carnarvon Basin in Australia, the accuracy of the traditional CNN for facies classification improved from 38% (the benchmark result where no augmentation was applied) to 52%, 61%, 94% and 74% for four trials with different degrees of augmentation. The results show that increasing training size, either through manual annotation (trial 2) or standard manipulations such as image rotation (trial 3 and trial 4), improves the performance of LoGANv2 and hence the CNN classification. The experiments indicate that although standard manipulation can increase the diversity of the training dataset, the balance between diversity and consistency within the datasets is also important, and this should be optimized for LoGANv2 to achieve better performance. In addition, we explored the adaptability of the proposed LoGANv2-CNN approach to unlabelled samples from another survey to demonstrate the robustness and flexibility of the model. • A LoGANv2-assisted CNN model was developed for augmentation and classification of seismic facies. • With LoGANv2 proving its capacity to augment different types of seismic facies with minimal manual labelling, the synthetic facies generated and the real ones are passed down to a CNN for further classification. • The streamlined process demonstrates that, with little manual annotation, the generation of synthetic seismic facies could lead to a better understanding of the underlying geological structures. [ABSTRACT FROM AUTHOR]

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. As an additional feature, our method permits to backtrace the spectral bands that most contribute to the estimation of a given variable. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution. In fact, the proposed method achieves an overall R 2 of about 0.75. In detail, pH CaCl 2 and CaCO 3 are predicted with an R 2 above 0.90, pH H 2 O , OC, N, clay, sand, CEC, silt are predicted with an R 2 between 0.89 and 0.70, K and P with an R 2 respectively of 0.54 and 0.37. The proposed method overcomes, in terms of R 2 , the state of the art of about 15% on the LUCAS dataset and of about 24% on the PRISMA simulated dataset. • A flexible convolutional neural network based system to estimate multiple soil properties at once. • A Neural Architecture Search procedure to find the best configuration of the proposed system. • Investigation of band importance for each variable's estimation. • Analysis of the dependency among the estimated variables. • Evaluation of the adoption of the proposed method on data from a simulated satellite sensor. [ABSTRACT FROM AUTHOR]

The magnetotelluric (MT) data processing is often a time-consuming job due to the manual inspection of the time series and removal of noisy segments. Use of different data selection tools combined with the robust estimation of the impedances has enabled the automation of MT data processing to a large extent. In this paper, we introduce an open-source Python package, named 'SigMT' for the automated MT data processing. This Python package offers Python scripts to estimate MT impedances and tipper data from the raw time series data. The overview of the SigMT package explaining different steps involved in the processing is discussed in this paper. Different data selection tools such as Mahalanobis distance based-, coherency threshold based-, polarization direction based-selection tools are integrated in the package. The incorporation of different data selection tools with robust estimation technique delivers best estimates of the impedances. SigMT is applied to the MT data from Himalaya with different selection tools and found to be yielding satisfactory results. • Open-source Python package for magnetotelluric data processing. • Automated processing of data, no need of any manual time series editing. • Availability of different data selection tools. • Robust estimation of the magnetotelluric impedance values. [ABSTRACT FROM AUTHOR]

Estimation of fault-model parameters is a challenge faced by researchers. Traditional statistical tools rely on a velocity field derived from precise coordinates of control-points in GNSS networks, changing over-time. This requires dealing with geodetic datum and deformation-monitoring datum, while handling ill-conditioned state models. This paper describes an algorithm for evaluating full fault-model parameters all at once using a feature-voting technique, while relying on precise GNSS vectors computed with scientific GNSS processing software. The algorithm bypasses the datum issues and deals with numerical instabilities using perturbed data replications. Dealing with gross-errors is an embedded attribute of the technique, and noise is an intrinsic feature needed to cope with the ill-conditioned equation-set problem with respect to the low-frequency nature of the data. Thus, the geodetic datum and deformation monitoring datum becomes irrelevant for this algorithm. Simulations of a locked fault-model, based upon a true solved epoch, derive proper parameter values for a preselected fault-model scenario, including simulations with noise. A case study relies on the northern section of the Dead-Sea fault, on 10 northern sites in Israel's continuously operating permanent stations, based on four true-data epochs of computed vectors in order to evaluate Dead-Sea fault model parameters. Similarity is shown between computed fault-model parameters using the presented feature-voting technique and those estimated by traditional processes; with respect to other researchers' work, at the same area over a similar time period, carried out by a traditional statistical least-squares adjustment procedure, including efforts in gross-error filtering and coping mechanisms with the biases caused from both datum issues. The feature-voting based methodology for analyzing fault-model parameters, using precise GNSS vectors and not precise coordinates; enables simultaneous full fault-model parameter computation while avoiding biases originating in deformation-monitoring datum and other error factors. • Technique bypasses major error-factors: Datum Defect and monitoring-datum setup. • Gross errors are treated in the voting-process – not gaining votes. • New measurements introduced without special treatment even when network changes. • Evaluating all model parameters together. • Presented methodology can't detect rigid motion of the entire monitoring-network. [ABSTRACT FROM AUTHOR]

Accurate forecasting is required for the effective risk management of drought disasters. Many machine learning- and deep learning-based models have been proposed for drought forecasting, however, they cannot handle the temporal and/or spatial dependencies in the input data, causing unexpected forecasting results. In order to solve the challenging issue, in this paper we proposed the Heterogeneous Spatio-Temporal Graph (HetSPGraph), for drought forecasting. It includes three major layers: spatial aggregations including inter and intra aggregations, temporal aggregation, and a forecasting network. The main function of HetSPGraph is to learn the dynamic spatiotemporal correlations between the regions and to further predict the drought in different regions, based on which accurate drought forecasting can be achieved. Experimental forecasting results of the Standardized Precipitation Evapotranspiration Index (SPEI) in China indicated that the HetSPGraph model outperformed the traditional baseline methods including the Long Short-Term Memory model (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), Gated Recurrent Unit (GRU), Spatio-Temporal Graph Convolutional Networks (STGCN) and Geographic-Semantic-Temporal Hypergraph Convolutional Network (GST-HCN). Even for long-term forecasting (12 months), more accurate forecasting results, with the coefficient of determination R 2 higher than 0.89, can also be obtained by HetSPGraph compared to the other three models. The proposed HetSPGraph model has the potential for wider use in forecasting drought and other natural disasters. • A novel HetSPGraph approach with three layers is proposed for drought forecasting. • Dynamic spatiotemporal correlation of droughts at multi-time scales can be described by HetSPGraph. • The HetSPGraph model outperformed conventional Deep Learning models for drought forecasting. • The HetSPGraph model can keep its stable accuracy at long-term forecasting period (12 months). [ABSTRACT FROM AUTHOR]

Developing a reliable and robust automatic earthquake detection system is quite a challenging and highly necessary task as two conditions can make this task a difficult one. First, earthquake detection systems may perform more poorly if they are employed in a region that is different from the one where the training database corresponds to. Systems trained with local databases are assumed to perform better. Nevertheless, these databases are usually limited. Second, the performance of such systems worsens when the SNR of the seismogram signals decreases. This paper proposes an end-to-end DNN-HMM based scheme to address these limitations, i.e. it does not require previous phase-picking, backed by engineered features and combined with duration modeling of states and seismic events. The proposed engine requires 10- or 15-times fewer parameters than state-of-the-art methods and therefore needs a smaller training database. Modeling duration can improve the noise robustness of the detection system significantly, particularly with limited training data; having a negligible increase in the number of training parameters. The system described here provides a F1-score 101% higher on average than schemes published elsewhere with Iquique and North Chile databases. It provides a reduction in F1-score equal to 10% when the average SNR is reduced by approximately 18 dB. This reduction in F1-score is at least half of the one observed with the state-of-the-art schemes in the same testing conditions. With respect to the detection of small earthquakes at short epicenter-station distances, the averaged precision provided by the DNN-HMM system with duration modeling is at least 5% higher than other systems. • Robust earthquake detection with a DNN-HMM system and duration modeling. • Models trained on local data are more accurate than those trained on global data. • Fewer trainable parameters requires smaller training databases. • Duration modeling increases the noise robustness of earthquake detection. • Engineering features softens the requirement of training data size. [ABSTRACT FROM AUTHOR]

A new risk-based tsunami early warning method is developed using Random Forest (RF) with an extensive tsunami monitoring network (S-net) deployed off the Northeastern region of Japan. To consider a wide range of possible tsunami waves that may occur in the future, the RF model is developed using simulated 4000 tsunami wave time series at the S-net sensors. The response variable is the total aggregate loss of buildings caused by the tsunami, and the explanatory variables include earthquake information (magnitude, epicenter latitude, and epicenter longitude) and tsunami wave amplitudes at the S-net sensors. Unlike the conventional tsunami early warning method for predicting the tsunami wave amplitude along the shoreline, the response variable adopted in this study is a tsunami risk metric that reflects the tsunami impact to people and assets in coastal areas. The RF model is suitable for predicting highly nonlinear and scattered tsunami loss due to its characteristics of nonparametric regression and ensemble learning. The result indicates that compared with conventional linear regression-based algorithms, RF predicts the tsunami loss significantly better, reducing the mean square error by 90%. Furthermore, RF does not rely heavily on earthquake information, making it useful for announcing early warning for tsunamis generated by other sources. • A new risk-based tsunami early warning algorithm is developed. • A random forest method is particularly suitable for forecasting highly nonlinear tsunami loss. • The random forest method outperforms a conventional method of multiple linear regression method. • A case study application considers an extensive ocean bottom sensor network in northeastern Japan (S-net). • The dataset and the MATLAB code are included in the paper. [ABSTRACT FROM AUTHOR]

In this paper we describe a novel data structure called Sector Lists (SLs) to represent discrete scalar fields equivalent to polygonal maps. The idea is to model scalar fields as a sum of elementary, wedge-shaped constant fields — what we call sectors. Sectors are organized in sorted lists in order to support an efficient processing using algorithms based on the sweeping plane paradigm. As a result, SLs combine properties often associated with rasters with those of general polygons, in the sense the borders of regions mapped to the same value can be represented exactly. Algorithms to convert, evaluate, add, transform and morph SLs are described herein, with proof-of-concept implementations publicly available. We also show how several operations commonly supported by raster and polygon-based representations can be computed with SLs. In particular, we include the results of two experiments that demonstrate the flexibility and efficiency of our prototype implementation. • We present the Sector List (SL) data structure for modeling polygonal maps. • Polygonal maps are represented as a sum of wedge-shaped called sectors. • SLs are an elegant and efficient alternative to polygon-based representations. • Results of an evaluation of SLs are shown using actual and artificial data sets. • Implementations of SLs and of all tests are included in the supplementary material. [ABSTRACT FROM AUTHOR]

Mineral image recognition is a challenging computer vision problem. Without external tools, even a human expert cannot distinguish some mineral species accurately. Previous research was mainly focused on processed mineral recognition. This is considered to be a simplified statement of a problem because processed minerals are more visually expressive. On the contrary, in a raw sample, the target mineral can appear in the form of thinly represented inclusions. In real life, the raw samples usually require automatic mineral species identification. Another difficulty in raw mineral recognition is the shortage of publicly available training and validation data. It is impossible to compare different deep learning approaches when the results are evaluated on dissimilar data. The main contribution of this paper is providing an open benchmark for zero-shot raw mineral visual recognition. Besides the evaluation-only zero-shot classification dataset, we publish subsets for segmentation, mineral size estimation, and few-shot classification. For all of the provided computer vision problems, we publish baseline solutions we offer for the community to beat. • Mineral recognition models need a unified benchmark. • Vision-language models can perform zero-shot on the mineral recognition domain. • CAM methods can produce mineral segmentation masks without segmentation data. • Zero-shot mineral segmentation models can produce trustworthy confidence scores. • Cross-dataset benchmarks prevent data leaks, ensuring research integrity. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) electromagnetic (EM) modeling is an important tool for geophysical applications. In EM induction methods the modified Helmholtz equation is often used to describe scattered or residual electric fields in three dimensions. Throughout this paper, a high order compact finite difference scheme for the solution of that equation for vertical magnetic dipole source (VMD) is presented. The approximation of the residual electric field intensity using a fourth order compact finite difference (FD) discretizer is achieved with the solution of a block linear system, the coefficient matrices are large and sparse with a particular structure, implying the application of a matrix-free Krylov subspace method for an efficient numerical solution. The proposed solver is being examined using a number of test problems in uniform and non-uniform grid spacing where numerical and analytical solutions for the homogeneous half-space cases are being compared. • A parallel algorithm for the solution of the modified Helmholtz equation used to describe scattered 3D electric fields for a VMD source • A 4th order compact finite difference numerical scheme is being used • The method presented proves to be efficient without the application of PMLs, preconditioning or divergence correction techniques • The implementation solves scattered fields using a uniform or a non-uniform grid and small-sized cells [ABSTRACT FROM AUTHOR]

In seismic exploration, reservoir prediction plays a significant role since it can reveal the characteristics of a reservoir through attribute analysis. Multi-attribute reservoir prediction models are the most commonly used methods that aim to establish a reasonable relationship between reservoir parameters and seismic attributes. Although many related approaches have been proposed, their results are unsatisfactory when given limited logging data. Meanwhile, they frequently fail to evaluate how reliable their predictions are. Given the prominent advantages of Bayesian methods to model uncertainty, we propose in this paper a Bayesian neural network (BNN) model to predict reservoir thickness and quantify uncertainty. In particular, we first combine attribute data with spatial information as auxiliary variables to forecast reservoir thickness using a neural network. Meantime, we capture both epistemic and aleatoric uncertainty of the BNN using a Bayesian approximation approach known as Monte Carlo dropout (MC-dropout). Finally, the BNN model is applied to one sparse logging dataset to predict the reservoir thickness as well as the uncertainty in our interested field area. Experimental results demonstrate that our proposed BNN is more appropriate for coping with geostatistical problems in comparison with other methods. • BNN framework is one of the few frameworks in the reservoir thickness prediction task. • BNNs are applied in multi-attribute fusion reservoir thickness prediction to improve the prediction accuracy of the sparse dataset, while capturing the uncertainty of the prediction. • Our BNN with MC-dropout inference aleatoric uncertainty as well as epistemic uncertainty with less computational overhead. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for restoring each grain's original shape from an image of a granular rock sample whose grains partially overlap with each other. Image-based grain shape restoration provides a promising way to understand the generation of the rock that involves sedimentary and diagenesis processes. The difficulties of this task are: 1) how to effectively identify the region of each grain that is partially overlapped with its neighbor grains; and 2) how to recover the overlapped part of a grain based on the residual part. To solve the first problem, a differential erosion strategy that combines concave-points-constraint and pore-skeleton-extension-erosion is proposed to overcome the under-partitioning issue that popularly occurs in the current erosion-dilation grain partitioning method. To address the second problem, closed B-spline interpolation is applied to recover the original shape of the grains based on the partially presented grain edges. The proposed method successfully processed some synthetic and real granular rock images and presented a competent ability to deal with the moderately compressed granular rock samples consisting of relatively high-roundness grains that each grain can be treated as a convex polygon. • Single grain shape can be restored effectively from rock image by proposed method. • Concave points are effective for separating the grain edges into edge segments. • Grain partitioning runs faster than Branch and Bound in edge segments reassemble. [ABSTRACT FROM AUTHOR]

This paper presents an innovative methodology to assimilate peak period into wave models at a local scale. The proposed methodology estimates the peak period by processing time stack images from a video monitoring system for assimilation into a wave energy balance spectral model. Assimilation of the wave peak period is performed by correcting the boundary conditions and replacing the directional spectra prescribed by SWAN when using a nesting scheme. This methodology represents a new procedure for assimilating wave peak periods in coastal areas with video system infrastructures. The wave modelling is performed using a three-mesh nesting scheme where the finer domain coincides with the local scale and the proposed assimilation methodology is applied. The results show that the model improves the estimation of the peak period across the whole domain. The shape of the spectrum obtained changes significantly in the inner domain, mainly for low frequencies. • Peak period from time stack image processing was assimilated into the wave models using a new novel method. • Assimilation of peak period in coastal arear was achieved with a low computational cost. • The proposed methodology is a very useful tool for hindcast modelling in swell dominated coastal areas. • The methodology proposed allow to increase the quality of Peak period in local wave numerical models. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are often used to establish a mapping between an input data set and a corresponding output. There are many applications that rely on quantifying the conditional distribution of the output given the input data set. This is often referred to as aleatoric uncertainty associated with variability of the outcome due to inherently random effects. In this paper, deep learning is used to quantify moments of the conditional distribution of a missing variable based on homotopic multivariate observations. The lambda distribution is then used to parametrize the conditional distribution based on the provided moments. Geostatistical quantification of spatial continuity complements the multivariate conditional distribution through Bayesian updating to inform multiple data imputation that accounts for the uncertainty associated with the missing variable(s). Geological data are often incomplete, and data imputation is an essential step to avoid excluding heterotopic data. The proposed data imputation framework trains multi layer perceptron (MLP) neural networks to characterize multivariate relationships inferred from homotopic training data. A case study is conducted using geological data from a lateritic Nickle deposit to demonstrate application of the proposed methodology. • A new methodology to quantify the conditional distribution of using deep learning. • Parametrize the conditional distribution using lambda distribution. • A new solution for fitting the lambda distribution parameters using deep learning. • A case study that uses the new methodology for multivariate data imputation. • Comparison with Gaussian mixture models as an alternative data imputation method. [ABSTRACT FROM AUTHOR]

This paper presents a generic framework for the numerical simulation of transformation-diffusion processes in complex volume geometric shapes. This work follows a previous one devoted to the simulation of microbial degradation of organic matter in porous system at microscopic scale using a graph based method. The pore space is represented by an optimal ball network. We generalized and improved the MOSAIC method significantly and thus yielded a much more generic and efficient numerical simulation scheme. We proposed to improve the numerical explicit scheme presented in a previous paper by updating the valuated graph in parallel instead of sequentially. From this parallel numerical explicit scheme, we derived an implicit numerical scheme that very significantly reduced the computational cost of the simulation of the diffusion process. We validated our method by comparing the results to the ones provided by classical Lattice Boltzmann Method (LBM) within the context of microbial decomposition simulation. For the same datasets, we obtained similar results in a significantly shorter computing time (i.e., 10–15 min) than the prior work (several hours). Besides the classical LBM method takes around 3 weeks computing time. This paper presents through details the algorithmic and mathematical schemes used in a previous paper. • Generic simulator of transformation-diffusion processes at the micro-scale. • Representing pore space using compact, robust and intrinsic geometrical modelling allows considerable computing time gain. • Explicit numerical scheme by parallel updating of the graph instead of sequential. • Implicit numerical scheme on the network of geometrical primitives representing pore space (attributed relational graph) to simulate diffusion processes. • Huge computing time gain: 1/10 compared to graph model using explicit numerical scheme, 1/400 compared to classical Lattice Boltzman Method (LBM). [ABSTRACT FROM AUTHOR]

Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate(e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today's climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration's daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations. • Interoperable knowledge graphs advances integrating and updating multi-source data. • OpenStreetMap data enhance the geographical features of the knowledge graph data. • Exploring the knowledge graph structures visually is necessary for non-experts. [ABSTRACT FROM AUTHOR]

This scientific review paper aims at challenging a common point of view on metadata as a necessary evil and something mandatory to the data creating and dataset publishing process. Metadata are instead presented as a crucial element to ensure the findability of data services and repositories. This paper describes a way through four levels of metadata management and publication, from default unstructured data, through schema-based metadata with literal values and/or URIs, towards linked open (meta)data providing explicit linkage between reliable data resources. Such research was conducted within the European Union's project PoliVisu. Special attention is given to the following: (1) guidance on publication aimed at the broad audience of search engine users and (2) the publication of geo (meta)data not only via standard technologies, such as the OGC Catalogue Service for Web and open data portals, but also through leading search engines (that are Schema.org-based). • Metadata developed in line with open linked data concept are scarce in geosciences. • The presented paradigm revokes an artificial border between data and metadata. • Open linked (meta)data are advertised attractively in search engines. • Multiple-times more users can be attracted, both, within as well beyond geosciences. • Incremental implementations approach is a novel one towards open linked (meta)data. [ABSTRACT FROM AUTHOR]

Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - " Py thon for E arth O bservation". Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences. • Highlight 1: A Python package for Earth observation processing chains for change detection is presented. • Highlight 2: Data can be processed in near-real-time whenever a new satellite image is acquired. • Highlight 3: The satellite change detection algorithm informs the user of detected change events. • Highlight 4: An application to a forest in Kenya is presented to demonstrate the software. • Highlight 5: This software is used by the Kenya Forest Service for monitoring deforestation from Sentinel-2. [ABSTRACT FROM AUTHOR]

A crossover refers to the intersection of two satellite ground tracks. Crossovers are required for performing crossover differences adjustment to remove the orbit error and for establishing the time series of elevation in surfaces, which is why crossovers are important for satellite altimetry measurements of ice sheet or ice shelf elevation changes. How to extract more crossovers precisely is the objective of this paper. On the basis of the traditional method of solving crossovers and computer graphics, this paper proposes an improved algorithm called improved rapid rejection and straddle test (IRST) for computing the position of the crossover point. This algorithm efficiently and accurately searches for two points in ascending pass and two points in descending pass that can form a crossover, and then computes crossovers' geolocation. By using CryoSat-2 satellite altimeter data, we conducted our study on the Antarctic Ross ice shelf and Filchner–Ronne ice shelf using the fixed iteration (FI) algorithm, rapid rejection and straddle test (RST) algorithm, and the IRST algorithm. Results show that IRST is superior to the two other algorithms in terms of the number of crossovers, geolocation accuracy and computational efficiency. These advantages are more noticeable in the border areas between the ice shelf and the mountain with large terrain slope, which solves the problem that crossovers are less in these areas with poor-quality data coverage. • The improved algorithm computes more crossover points efficiently. • Crossover points computed by the improved algorithm with high geolocation accuracy. • More crossover points cover on the steep areas using the improved algorithm. [ABSTRACT FROM AUTHOR]

Conditional Nonlinear Optimal Perturbation (CNOP) method is an effective way to study the predictability of oceanic and climatic events. A framework combining the Feature Extraction method and Intelligent Algorithm (FEIA) is frequently used to solve CNOP because it is gradient-free and scalable at high-dimensional scales. However, the fixed latent subspace of FEIA framework makes it challenging to achieve both the quality of solutions and the solving efficiency. To overcome this bottleneck, this paper proposes Dimension Shifting based Intelligent Algorithm (DSIA) framework to solve CNOP in the large-scale model. DSIA framework adopts the dimension shifting strategy, which dynamically shifts search particles in different low-dimensional spaces. To verify the feasibility of DISA framework, we take a Regional Ocean Modeling System (ROMS) model of double-gyre variation as an experimental case. In experiments, we figure out that the selection of feature extraction method and the shifting dimension set are two influential factors for the performance of DSIA framework. Besides, in comparative experiments, DSIA framework yields better objective function values and more valid CNOP than FEIA framework. Moreover, convergence experiments demonstrate DSIA framework can solve CNOP with an appropriate number of function evaluations and has better convergence performance. In conclusion, experimental results prove that DSIA improves both quality of CNOP and solving efficiency. • We design Dimension Shifting based Intelligent Algorithm framework to solve Conditional Nonlinear Optimal Perturbation (CNOP). • Dimension shifting strategy can balance the quality of solutions with the solving efficiency. • The framework is applied to a Regional Ocean Modeling System model of double-gyre variation. • Settings affecting the framework's performance are analyzed. • The framework shows higher quality of CNOP and better solving efficiency than FEIA framework. [ABSTRACT FROM AUTHOR]

Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize large-pores and fine-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and fine-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media. • An efficient multiscale reconstruction algorithm of porous media is proposed. • Joint modeling of high- and low- resolution 3D structures with different FoV. • The algorithm can perform multiscale reconstruction without regard to the input size. • The fitting curve of pore shape factor-pore radius distribution is proposed. • The effectiveness and applicability of the algorithm are demonstrated by experiments. [ABSTRACT FROM AUTHOR]