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Fossil studies are crucial for understanding species evolution and uncovering the dynamic structure of the Earth. However, the examination and interpretation of fossil specimens is inherently complex and time-consuming, particularly when dealing with thin sections where microfossils and non-fossil structures often coexist. This study presents a comparative analysis of image classification models, specifically CNN, ResNet, VGG, DenseNet, and EfficientNet, aimed at enhancing the detection and classification of fossil specimens. We developed a custom Convolutional Neural Network (CNN) architecture tailored to the identification of benthic foraminifera using the Endless Forams dataset. The custom CNN achieved a training accuracy of 99% and a validation accuracy of 88%, indicating its robustness. ResNet-50 and VGG-16 models achieved average accuracy scores of 90% and 86%, respectively, demonstrating their comparative effectiveness. Furthermore, ResNet-50 and VGG-16 models were identified as particularly effective due to their advanced capabilities in handling high-dimensional data and capturing detailed image features. Our findings provide a comprehensive understanding of each model’s performance, supported by rigorous statistical evaluation, offering insights into their strengths and limitations within the domain of fossil image classification.

Land subsidence is an environmental geological phenomenon mainly caused by groundwater overexploitation. Long-term overexploitation of groundwater not only causes compaction of aquifer thickness and surface deformation but also leads to the loss of aquifer water storage capacity. The skeleton water storage coefficient (Sk) is an important parameter for evaluating the water storage capacity of aquifer groups. This article proposes a new research framework for obtaining the Sk of different aquifer groups: combining permanent scatter for SAR interferometry technology and a multiscale geographic weighted regression model to obtain subsidence information for different aquifer groups, inverting the Sk of different aquifer groups from the spatial scale, and discussing the deformation characteristics of soil layers under different water head change modes to evaluate the deformation and water storage characteristics of different aquifer groups. This framework is applied to the land subsidence region of the Beijing Plain. We calculated that the settlement proportions of different compression layer groups were 14.75%, 23.65%, 33.44%, and 28.16%. Due to the different lithological compositions and groundwater exploitation of different aquifers, the Sk values exhibit different spatial distribution characteristics. With the continuous development of subsidence, the water storage performance of the aquifer group is continuously declining. These findings contribute to managing the sustainable use of groundwater resources and controlling subsidence. It is demonstrated that the research framework proposed in this article can serve as an effective tool for obtaining settlement information and the Sk of different aquifer groups. [ABSTRACT FROM AUTHOR]

Lithology identification is the research basis in oil and gas reservoir exploration and is critical for the formation characterization and reservoir development. Traditional lithofacies identification methods rely on the knowledge and experience of geologists and are usually done manually. With the development of deep learning technology and its application in the field of geophysics, lithofacies identification based on deep-learning approach has attracted great attention in recent years. Well logging data have obvious sequence characteristics. Therefore, we propose to use a bidirectional long and short-term memory (BiLSTM) neural network to learn long-term information for more effective lithology facies classification. In addition, we also perform correlation analysis on the input well logging curves and conduct median filter at different scales according to the correlation degree to extract the geological features within data itself and discard the interference of noise. The raw data-based lithofacies identification can reflect the noise resistance of the neural network model to some extent, while the filtered data are more beneficial for the model to extract the geological features correlated with lithofacies and provide accurate classification results. We validate our proposed framework by applying it to a case study from the Council Grove gas reservoir located in Kansas. Furthermore, we compare the effect of input data and network model on the identification results. The experimental results show that the proposed lithofacies identification method has higher classification accuracy. [ABSTRACT FROM AUTHOR]

Global Ecosystem Dynamics Investigation (GEDI) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital elevation models (DEMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DEM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors published by NASA. Within forests, GeoGEDI improved the root mean square error (RMSE) of ground elevation in Landes by 26.8% (0.34 m) and by 13.3% (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6% (3.82 m) and 36.2% (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been widely used in seismic inversion. Since the label data obtained in production are actually a small amount of 1-D well-log data, most DL-based seismic inversion is 1-D trace-to-trace method based on poststack seismic data. However, the 1-D algorithm does not take seismic data spatial correlation into consideration and the results lack of stability with good lateral continuity. Meanwhile, it is still a challenge to obtain high-precision multiparameter inversion results simultaneously for prestack inversion. To mitigate the above issues, we propose a 2-D multitask full attention U-Net (FAUNet) for prestack three-parameter inversion. The proposed network can capture geological structural features in shared information and allow each task to automatically learn task-specific attention weights in a separate branch. The full attention mechanism fuses the shared features and the internal features of each task in stages and pays attention in both channels and feature maps. Moreover, a joint hybrid loss function based on mean squared error (mse) and structural similarity (SSIM) is used to further enhance the continuity and resolution of inversion results. To obtain sufficient training samples, we generate a large number of small sample patches by sliding window both laterally and vertically with equidistant around the wells. To obtain stable inversion results, we incorporate the initial models and feed them into the network together with seismic data as input. Experiments on synthetic and field data show that our proposed method can simultaneously obtain three-parameter inversion results with high vertical resolution and transverse continuity. [ABSTRACT FROM AUTHOR]

The topography of the seafloor is highly correlated with the local gravity through intrinsically nonlinear relationships across a particular wavelength band. The purpose of this study is to compare a fully connected deep neural network (FC-DNN) and a convolutional neural network (CNN) with the gravity-geological method (GGM) to determine whether deep learning can provide superior predictions of bathymetry. We include the short-wavelength gravity (SG) and geological models as training parameters, and assess the performance of different models and parameter combinations using various inputs. Compared with the CNN method, the FC-DNN with the SG as an input reduces the standard deviation (STD) of bathymetry differences from 118.6 m to about 73.5 m. The FC-DNN with SG reduces the STD of bathymetry differences by up to 13.3% compared with the conventional GGM. Furthermore, we demonstrate that the addition of geological information alongside the SG does not significantly enhance the accuracy. Power spectral density analysis suggests that the FC-DNN is superior for predicting wavelengths shorter than 6 km. [ABSTRACT FROM AUTHOR]

The seismic coherence attribute is one of the most typically used seismic discontinuity feature detection technologies, which is widely used in fault detection, channel boundary characterization, and other occasions. The eigen-structure-based coherence algorithm possesses the property of stability by using the eigenvalues of seismic data’s covariance matrix, but in the algorithm, only the eigenvalues are used to calculate the final coherence value, and the information contained in eigenvectors is ignored. Through analysis, it is found that the elements in the eigenvectors represent the energy differences of seismic traces. By directly measuring the difference of different elements in an eigenvector, the energy differences between different seismic records can be effectively measured. Since the seismic data on both sides of some discontinuous boundaries, such as channel boundaries, principally show amplitude differences, the coherence properties based on eigenvectors can better characterize them. Through theoretical analysis, model testing, and case application, this letter illustrates the reliability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Gaussian pyramid (GP) has the property of decomposing the 2-D data into multiple scales. It has been successfully applied in image processing, but rarely used in seismic data processing and interpretation. Specifically, seismic data are not just a 2-D image in one time slice or horizon. Moreover, the pattern of geological body varies rapidly in 3-D spaces. Hence, the classical GP method exists a limitation in processing 3-D seismic data. Especially in fracture detection, the 2-D isotropic Gaussian kernel used in GP tends to blur the fracture details. In this letter, to match the high-dimensional seismic data, we propose a structure-oriented adaptive Gaussian pyramid (SOA-GP) algorithm, which expands the 2-D GP method to the 3-D situation. In this case, to eliminate the influence of transverse change of wave impedance and consider stratigraphic characteristics, the 2-D Gaussian filter is also substituted by the 3-D structure-adaptive anisotropic Gaussian kernel, which is constructed by instantaneous phase-based gradient structure tensor (GST) method. Meanwhile, seismic attributes are applied to the multiresolution seismic data decomposed by the SOA-GP method to realize multiscale fracture detection. Finally, we apply this new 3-D SOA-GP method to field data. It demonstrates that the multiscale decomposition of the new method could produce more details of fracture and less disturbance from noise. [ABSTRACT FROM AUTHOR]

Multifrequency attribute analysis is an effective tool to delineate sand bodies with different thicknesses. Conventionally, red–green–blue (RGB) blending technique is often used to fuse three frequency components for depicting reservoir thicknesses, that is, the low-, medium-, and high-frequency components. However, the seismic signal is a typically broadband signal, while RGB blending can only fuse three frequency components. Moreover, how to select these three specific frequency components is also a difficult and unsolved task. In this study, we suggest a multifrequency attribute analysis workflow for delineating sand bodies. First, we introduce the S-transform (ST) to extract multifrequency components of the analyzed seismic data. Then, the t-distributed stochastic neighbor embedding (t-SNE)-based workflow for fusing multifrequency components is proposed, which is used to capture the local structural features of the analyzed high-dimensional data and reveal the global structures simultaneously. Afterward, we adopt a synthetic trace and a 3-D field data volume to test the effectiveness of the proposed workflow. Compared with the contrastive methods, our workflow performs better in delineating the spatial distribution and thicknesses of sand bodies, which benefits further well deployment. [ABSTRACT FROM AUTHOR]

To solve the problem of collaborative air–ground target search and localization in large outdoor environments, we adopt a distributed layered architecture and propose a corresponding distributed estimation algorithm, which can produce accurate estimates of interested target position. The air–ground system consists of a single unmanned aerial vehicle (UAV) and several ground platforms. First, for the distributed layered architecture, this article develops unbalanced simple convex combination algorithm using one-shot measurements from the single UAV and all ground platforms to generate global estimates of the stationary target state. The novelty of the proposed algorithm is that it considers the differences in sensor capabilities between the aerial and ground platforms. Then, performance analysis is described to show that the proposed algorithm is obviously superior to the traditional existing convex combination algorithms. Finally, the efficacy of this algorithm is further demonstrated by the simulation experiments and actual geolocation tests. [ABSTRACT FROM AUTHOR]

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (RSCD), which identifies the changed and unchanged pixels from a registered pair of remote sensing images, has enjoyed remarkable success recently. However, locating changed objects with fine structural details is still a challenging problem in RSCD. In this article, we propose a novel RSCD network via temporal feature interaction and guided refinement (TFI-GR) to solve this issue. Specifically, unlike previous methods, which just employ one single concatenation or subtraction operation for bi-temporal feature fusion, we design a temporal feature interaction module (TFIM) to enhance interaction between bi-temporal features and capture temporal difference information at diverse feature levels. Afterward, a guided refinement modules (GRMs), which aggregates both low- and high-level temporal difference representations to polish the location information of high-level features and filter the background clutters of low-level features, is repeatedly performed. Finally, the multilevel temporal difference features are progressively fused to generate change maps for change detection. To demonstrate the effectiveness of the proposed TFI-GR, comprehensive experiments are performed on three high spatial resolution RSCD datasets. Experimental results indicate that the proposed method is superior to other state-of-the-art change detection methods. The demo code of this work is publicly available at https://github.com/guanyuezhen/TFI-GR. [ABSTRACT FROM AUTHOR]

This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This letter aims to provide an analysis of selecting the optimal coil position under a dynamic transmitting coil (Tx-coil) to improve the performance of a dual-receiver magnetic resonance wireless power transfer (MR-WPT) system. The expressions of power transfer efficiency (PTE) and optimal load are derived using equivalent circuit model (ECM), and the relationship between coils position and PTE under cross-coupling is discussed; it proves the existence of an optimal transfer distance to achieve the maximum PTE for a symmetric receivers (Rxs) system. The system has a dynamic Tx-coil position variation range under optimal load of maintaining stable and efficient power transfer when the Tx-coil moves within this range. Finally, the theoretical results are verified through experiments in the laboratory. Under the optimal operation, a stable overall efficiency above 75% can be achieved for a system when the Tx-coil moves within this range that the gap of receivers at 100 mm. [ABSTRACT FROM AUTHOR]

A search vehicle routing algorithm is presented, inspired by thermalling in glider operations, for use in unmanned aerial vehicle (UAV) search routing problems where radio frequency signal noise affects target geolocation. We conduct ground testing experiments and observe that signal strength significantly deviates from its mean value — 10-20% variation in our experiments — at each distance epoch away from a target of interest. The thermalling algorithm uses straight-line segments and relatively simple guidance logic to generate routes that appear promising for fixed-wing, short endurance and autonomous UAV routing applications. We compare the simulated performance of the thermalling algorithm to a stochastic gradient descent algorithm using 1,000 randomly generated problem instances and show that, when considering signal readings and distance traveled as performance metrics, the thermalling algorithm performs better in 100% and 37% of problem instances respectively. [ABSTRACT FROM AUTHOR]

The gravity gradient data have higher horizontal resolution and highlight the shallow sources relative to gravity data; therefore, the comprehensive measurement of gravity and its vertical gradient anomalies is commonly used to reveal the density structure of planets. To obtain higher resolution density results by the joint inversion of large-scale gravity and its gradient, we propose a density-weighted and cross-gradient constrained (DWCG) joint inversion method in the sphere coordinate, which improves the inversion accuracy by introducing a density structural constraint provided by the gravity inversion result. Furthermore, the block-Toeplitz Toeplitz-block (BTTB) structure is used to finish the DWCG joint inversion, which effectively accomplishes a high-efficiency calculation and is more practical for performing the inversion of large-scale data. Theoretical modeling experiments show that the DWCG joint inversion method can effectively improve the resolution and identify the distribution of deep and shallow field sources, and the accuracy is improved by nearly 40% compared to the regularization inversion method of gravity data. Simultaneously, the DWCG joint inversion method has certain noise immunity. Finally, we apply the DWCG joint inversion method to the lunar data to obtain the density distribution and further revealed the Moho relief by nonlinear inversion based on the density results. The results show that the high-density structure corresponds to the uplift of the mantle, and a large number of meteorite impacts caused mantle flow, which is shown as a clear impact basin and ring anomaly on the topography and anomaly map. [ABSTRACT FROM AUTHOR]

This article introduces the extended timing annotation dataset (ETAD) product for Sentinel-1 (S-1) which was developed in a joint effort of German Aerospace Center (DLR) and European Space Agency (ESA). It allows to correct range and azimuth timing of S-1 images for geophysical effects and for inaccuracies in synthetic aperture radar (SAR) image focusing. In combination with the precise orbit solution, these effects determine the absolute geolocation accuracy of S-1 SAR images and the relative collocation accuracy of repeat pass image stacks. ETAD contains the gridded timing corrections for the tropospheric and ionospheric path delays, the tidal-based surface displacements, and the SAR processing effects, all of which are computed for each data taken using standard models from geodesy and auxiliary atmospheric data. The ETAD product helps S-1 users to significantly improve the geolocation accuracy of the S-1 SAR products to better than 0.2 m and offers a potential solution for correcting large-scale interferometric phase variations. The product layout and product generation are described schematically. This article also reports first the results for different SAR techniques: first, the improvement in geolocation accuracy down to a few centimeters by verification of accurately surveyed corner reflector positions in the range–azimuth plane; second, the well-established offset-tracking technique, which is used for systematic ice velocity monitoring of ice sheets and glaciers, where ETAD can reduce velocity biases down to subcentimetric values; and third, the correction of atmospheric phase contributions in wide-area interferograms used for national and European ground motion services. These early results proof the added value of the ETAD corrections and that the product design is well-suited to be integrated into the processing flows of established SAR applications such as absolute ranging of targets, speckle/feature tracking, and interferometry. [ABSTRACT FROM AUTHOR]

Polarized scanning atmospheric corrector (PSAC) onboard the HuanjingJianzai-2 (HJ-2) A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by nine full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor for polarization observations. An automatic coastline inflection method (CIM) is implemented for PSAC geolocation error estimation. Over five months of globally middle or low latitude coastline area measurements are used to obtain statistical result. The results of the comparison with the Global Self-Consistent, Hierarchical, High-Resolution Geography (GSHHG) Database show that the PSAC geolocation error is smaller than 0.38 ground sample distance (GSD) or 3.25 km in 95% confidence level. In cross-track direction, the geolocation error estimation is affected by the instrument sampling characteristics, such as spatial response function (SRF). Thus, the correction method is proposed by establishing relationship between measurement radiance in CIM and offset proportion of PSAC GSD. The biases are obviously reduced after correction. [ABSTRACT FROM AUTHOR]

This article presents a novel reconstructed model for the delayed load frequency control (LFC) schemes considering wind power, which aims to improve the computational efficiency for PID controllers while retaining their dynamic performance. Via fully exploiting system states influenced by time delays directly, this novel reconstructed method is proposed with a controller isolated. Hence, when the PID controllers are unknown, the stability criterion based on this model can resolve controller gains with less time consumed. For given PID gains, this model can be employed to establish criteria for stability analysis, which can realize the tradeoff between the calculation accuracy and efficiency. The case study is first based on a two-area traditional LFC system to validate the merits of a novel reconstructed model, including accurately estimating the influence of time delay on system frequency stability with increased computational capability. Then, under traditional and deregulated environments, case studies are carried out on the two-area and three-area schemes, respectively. Through the novel reconstructed model, the efficiency of obtaining controller parameters is highly improved while their robustness against the random wind power, tie-line power changes, inertial reductions, and time delays remains almost unchanged. [ABSTRACT FROM AUTHOR]

To reduce all kinds of noise interference in the transient electromagnetic signal, and improve the resolution of abnormal resistance in detection area. In this paper, a method based on Slime Mould Algorithm (SMA) optimized Variational Mode Decomposition (VMD) combined with Wavelet Threshold Denoising (SMA-VMD-WTD) was proposed to identify and eliminate the noise. Firstly, the slime mould algorithm (SMA) was used to select the key parameters in VMD. Then, the signal was adaptively decomposed by VMD according to the optimized parameters to obtain certain modal components, and divided them into effective signal components and noise components. Finally, the effective signal components were further processed by Wavelet Threshold Denoising (WTD) and reconstructed to obtain the transient electromagnetic signal with noise removal. The simulation signal tests with different noise intensity were built, and this method was compared with Ensemble Empirical Mode Decomposition (EEMD), WTD, unoptimized VMD and SMA to optimize VMD for reducing noise analysis of simulation tests. The results showed that SMA-VMD-WTD was more effective in identifying and eliminating transient electromagnetic signal noise. The method was applied to a geological exploration area, which verified the field applicability of this method. The results of the simulation tests and field test showed that SMA-VMD-WTD was a better denoising method for transient electromagnetic signal. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) receivers are vulnerable to all types of interferences due to the inherently low power of GNSS signals. Whereas GNSS jammers aim at denial-of-service attacks, GNSS spoofers bring even more risks, since they can fabricate a fake position and/or time without recognizing it. This paper presents a direct position determination method to improve the localization accuracy of GNSS spoofing attacks, which is called despreading direct position determination (DS-DPD). The localization algorithm utilizes a prior knowledge of the satellites’ code sequences, which in turn provides significant gains in localization accuracy. Firstly, the received signal model is established, which takes the time delay, Doppler shift, direction-of-arrival (DOA), and modulation codes into account. Then, the maximum likelihood (ML) criterion is utilized to construct the objective function of DS-DPD. Finally, the location parameters of the spoofers can be obtained through a two-dimensional spectral search of the objective function. Numerical simulations verify the localization performance improvement of the proposed DS-DPD algorithm. Especially when the interference-to-noise ratio (INR) approaches −30dB, the localization accuracy could be improved by more than ten times due to its full utilization of the delay, Doppler shift, DOA, and code sequences information. [ABSTRACT FROM AUTHOR]

Subsidence of the gob area is an important geological problem in coal mines. Such events not only pose potential safety risks, but also can seriously impair the subsequent mining of coal mines. Therefore, detection of gob subsidence is important to ensure sustainable coal mining. Due to the complex seismic and geological conditions in the gob subsidence area, it is difficult to accurately identify the gob area by using traditional three-dimensional (3-D) seismic detection method. Herein, a new, model-driven gob detection method based on four-dimensional (4-D) seismic data is proposed. According to the proposed strategy, geological models before and after coal seam mining are firstly established, and synthetic data are generated through forward modeling. Then, the prestack time migration (PSTM) sections of synthetic data are used to retrospectively analyze the seismic reflection characteristics of the gob area, and the gob area is identified according to the variations of reflection characteristics and velocity fields caused by the gobs. Next, the characteristics of gob areas in synthetic data are used to guide the identification of the gob area in real seismic data. Meanwhile, a 3-D stratum stripping technique is used to finely depict the gob area. According to the method, the scope of the caving zone, the height of the overburden fractured zone in the gob area, and the geological structure of the lower coal seam in the gob area can be ascertained. This article presents the successful use of the proposed method in detecting the gob area from a real coal mine. [ABSTRACT FROM AUTHOR]

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote-sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based locations. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems. [ABSTRACT FROM AUTHOR]

Machine learning inversion (ML-inversion) has been widely used in the interpretation of geophysical data. However, because the transient electromagnetic (TEM) is not sensitive to the response of the small-size or large-depth abnormal body, there are some problems, such as poor imaging accuracy and low resolution when directly processing raw TEM data with machine learning (ML) method. To solve this problem, we propose to use the pseudo seismic wavelet (PSW) data obtained from the TEM wave field transformation to perform TEM ML-inversion. Simulation and example application verify that compared with the TEM data, the PSW data has higher recognition sensitivity to the electrical changes of underground anomalies, and the ML-inversion based on the PSW data can significantly improve the TEM imaging accuracy. Our study demonstrates that the PSW data can be used to achieve TEM ML-inversion, which may provide a new way for the further combination of electromagnetic data processing and ML technology. [ABSTRACT FROM AUTHOR]

The study of surnames for a given population, together with their distribution and spatial patterns identification, has been a long-standing problem in the fields of human biology, public health, and social sciences. The ancestry inferred from surname information can be a useful means to understand the dynamics of human populations. This knowledge allows us to characterize geographically the ethnicity of populations, and to understand the complex relationships between identity, migration, and health issues in a demographic view. However, in most cases, a detailed geolocalization of this data can be a daunting task. We propose a visual analytic tool that summarizes the heterogeneous surname and geographic information collected from Argentinean electoral rolls. This tool allows a massive data analysis, and facilitates interdisciplinary studies about population dynamics related to ancestry, migration, and health. It also offers an easy-to-use interface that allows interactive exploration of isonymy and surname origins, their distribution, and spatial trends in a high population density context. [ABSTRACT FROM AUTHOR]

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge. [ABSTRACT FROM AUTHOR]

The comprehensive access to road traffic patterns in the continuously growing urban areas is key to achieve a sustainable mobility. However, the inherent complexity of urban traffic poses many challenges to achieve this goal, including: i) the need to integrate heterogeneous views of road traffic (such as speed limits, jam size, delay, throughput) from available sources; ii) the complex spatiotemporal intricacies of geolocalized speed and loop counter data; iii) the need to mine congestion patterns robust to the inherent traffic variability and unexpected occurrence of events, taking also into consideration the varying degrees of congestion severity; and iv) the need to guarantee the statistical significance and interpretability of the target patterns. In the context of our work, a road traffic pattern is a recurrent congestion profile (w.r.t. speed limits, jam extent and flow) that can span multiple locations and time periods within a day. Biclustering, the discovery of coherent subspaces (local patterns) within real-valued data, has unique properties of interest, being positioned to unravel such traffic patterns, while satisfying the aforementioned challenges. Despite its relevance, the potentialities of applying biclustering in mobility domains remain unexplored. This work proposes a structured view on why, when and how to apply biclustering for mining traffic patterns of road mobility, a subject remaining largely unexplored up to date. Using the city of Lisbon as a guiding case, we illustrate the relevance of biclustering geolocalized speed data and loop counter data. The gathered results confirm the role of biclustering in comprehensively finding statistically significant and actionable spatiotemporal associations of road mobility. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) use geographic information to provide a variety of benefits to users—most ubiquitously, through users’ mobile devices (phones and smart wearables). While such information offers value—through navigation apps, for example—it contains troves of personal data that can be exploited against users. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic (CSEM) method is crucial for detecting and locating underground anomalies and structures. However, it is challenging to interpret the field data with multifrequency via 3-D CSEM inversion. To fully excavate and utilize the valuable information of CSEM data at different frequencies, we propose an efficient algorithm for 3-D multifrequency CSEM (MFCSEM) inversion based on rational Krylov (RK) subspace. Within the framework of our algorithm, we first use the three-term Lanczos recursion to construct the RK basis matrix quickly; thus, the fast MFCSEM forward modeling can be realized via the RK approximation. Subsequently, we present a novel cyclic projection and correction (CPC) algorithm to solve the MFCSEM adjoint forward problems. Finally, the nonlinear conjugate gradient (NLCG) method is adopted to seek a solution to this nonlinear inverse problem. We demonstrate the excellent performance of our algorithm by synthetic and field datasets. The inversion results show that our algorithm is computationally efficient, resulting in considerable speedup compared with the conventional method. Our algorithm provides a new idea that would significantly improve the efficiency of MFCSEM inversion. [ABSTRACT FROM AUTHOR]

Existing deep learning-based seismic data denoising methods mainly involve supervised learning, in which a denoising network is trained using a large amount of noisy input/clean label pairs. However, the scarcity of high-quality clean labels in practice limits the applicability of these methods. Recently, the blind spot (BS) strategy in the field of image processing has attracted extensive attention. Under the assumption that the noise is statistically independent and the true signal exhibits some correlation, the BS strategy allows us to estimate a denoiser from the noisy data itself. In this article, we study the application of the BS strategy to the random noise attenuation of seismic data and propose an unsupervised blind spot network (BSnet) method. Specifically, considering the characteristics of the random noise, we improve the commonly used Unet network and design two types of randomly mask operators to deal with Gaussian white noise and bandpass noise. Synthetic and real data experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Deep subspace clustering (DSC) has achieved remarkable performances in the unsupervised classification of hyperspectral images. However, previous models based on pixel-level self-expressiveness of data suffer from the exponential growth of computational complexity and access memory requirements with an increasing number of samples, thus leading to poor applicability to large hyperspectral images. This article presents a neighborhood contrastive subspace clustering (NCSC) network, a scalable and robust DSC approach, for unsupervised classification of large hyperspectral images. Instead of using a conventional autoencoder, we devise a novel superpixel pooling autoencoder to learn the superpixel-level latent representation and subspace, allowing a contracted self-expressive layer. To encourage a robust subspace representation, we propose a novel neighborhood contrastive regularization to maximize the agreement between positive samples in subspace. We jointly train the resulting model in an end-to-end fashion by optimizing an adaptively weighted multitask loss. Extensive experiments on three hyperspectral benchmarks demonstrate the effectiveness of the proposed approach and its substantial advancement of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In civil or military applications, a transmitter on Earth may utilize different means of communications. A typical example is the naval craft, which often radiates very high frequency signals received by a satellite system and high frequency signals received by land-based observers. This article concentrates on the combination of land-based and satellite-based over-the-horizon (OTH) geolocations. A quadratic constrained weighted least-squares optimization model for the combined geolocation is established and a differentiable exact penalty solution is developed to locate the OTH transmitter in an iterative manner, which is shown to have robust convergence performance. Theoretical analysis is presented, which involves the Cramér–Rao bound for combined geolocation under the constraint of ellipsoidal Earth model and the closed-form expression of mean square error for the proposed method. Numerical examples are provided to validate our theoretical analysis and illustrate that the proposed method outperforms other geolocation methods in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

The morphology of isolated barchan dunes on Mars and Earth may shed light on the dynamic conditions that form them, their migration direction and the physical properties of the sediments composing them. Prior to this study, dune fields have been largely analyzed manually from aerial and satellite imagery, as automatic detection techniques are often not sufficiently accurate in outlining dunes. Here, we employ an instance segmentation neural network to detect and outline isolated barchan dunes on Mars and Earth. We train and test the model on martian targets using Mars reconnaissance orbiter (MRO) context camera (CTX) images, and find it sufficiently accurate (mAP=77% on the test dataset) to characterize dune field dynamics. Using our trained model, we detect and map the global distribution of barchan dunes relative to previously mapped dune fields, and find that barchan dunes are more abundant in the northern hemisphere than in the southern hemisphere. These contrasting abundances of barchans may reflect latitudinally dependent wind regimes, sediment supply, or sediment availability.

Salt body extraction plays an important role in the analysis of salt structures and the exploration of oil and gas. Seismic attributes and edge detection algorithms, which require manual effort, are the conventional methods of extracting salt boundaries from seismic images. Convolutional neural networks (CNNs) have become the state-of-the-art automatic segmentation method for seismic interpretation. However, the fully automatic results of the extraction of salt boundaries may still need to be modified to become accurate and robust enough for practical production. We present a novel deep-learning-based interactive segmentation method for extracting salt boundaries. To incorporate the interaction points into our method, we transform positive and negative points into two Euclidean distance maps (EDMs), which are combined with seismic images to train our CNN model. The model is composed of a U-net and a pyramid pooling module (PPM), and it is trained on the Tomlinson Geophysical Services (TGS) Salt Identification Challenge dataset. Then, we use a graph cut algorithm to refine the likelihood maps predicted by our CNN model and, subsequently, update the salt boundaries. Some field examples show that the proposed method outperforms fully automatic CNN methods with a higher matching degree of the ground truth. [ABSTRACT FROM AUTHOR]

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset. [ABSTRACT FROM AUTHOR]

The ground-source airborne time-domain electromagnetic (GATEM) method is an effective electromagnetic exploration technology. The actual geological medium has rough characteristics; however, the current inversion methods for GATEM data are mostly based on homogeneous medium and extract only resistivity information. In this article, a neural network (NN) is served as extracting the two parameters of resistivity and roughness for rough medium. The structural parameter selection of NN has no fixed formula and is often related to experience. The NN has difficulty converging to the target accuracy if the structural parameters are not selected properly. To realize high-precision inversion of GATEM data, this article introduces Rademacher complexity to limit the generalization error and improve the generalization ability of the NN. Above all, a sample set of the GATEM response, resistivity, and roughness of the rough medium is established. In the next place, a fully connected NN structure is constructed, and a highly generalized NN is obtained by using Rademacher complexity. Then the mapping relationships are established through training, and the NN method is served as inverting the resistivity and roughness. The initial NN and the highly generalized NN are used to invert the GATEM response of rough medium for typical geological models. The results of the highly generalized NN based on Rademacher complexity are closer to the real models. The method is applied to the GATEM field data in Zhuxianzhuang, Anhui Province, China, and the results are consistent with the geological data. [ABSTRACT FROM AUTHOR]

This article investigates the coordination of a class of underactuated systems subject to limited energy supply and channel bandwidth and aims to stabilize system states and exclude Zeno behaviors simultaneously. First, by means of event-triggered (E-T) and quantized techniques, several novel quantized sampled-data-based E-T schemes are constructed, which only require discrete-time controller updates and partial quantized states, and thus efficiently mitigate the control and communication workloads. Then, in order to further lower the communication consumptions, several new triggered sampled-data-based communication rules under fixed and switched networks are established, where the communications are performed only at some specific instants and, thus, the ideally continuous-time signal transmission among neighbors can be avoided. Note that sufficient criteria for achieving the coordination of the underactuated systems are derived in terms of the Lyapunov–Krasovskii functional method. Finally, numerous simulations are carried out to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Road collapse and underground water inrush accidents pose a serious threat to urban safety and development. Transient electromagnetic (TEM) method is an effective geophysical method for detecting urban underground space. However, due to the complexity of urban geological environment, the conventional TEM detection methods are difficult to meet the needs of efficient and high-resolution detection of urban underground space. To solve this problem, an adaptive high-resolution (AHR) TEM detection method based on pseudoseismic wavelet transform technology is proposed in this study. The simulation results show that compared with the conventional technologies, the AHR detection method can more accurately reflect the resistivity distribution information in complex geological environment. The proposed method is implemented to detect an urban underground cavity, and the imaging results are consistent with the actual results. The research results will provide technical support for the early warning of road collapse and water inrush in urban industrial development. [ABSTRACT FROM AUTHOR]

Recently, intelligent data processing and interpretation based on deep learning (DL) have received considerable attention. Training data are vital for DL-based approaches. In geosciences, researchers have been facing a significant obstacle, i.e., the absence of authoritative and representative open data for training and testing artificial neural networks (ANNs). Although open-source works in geosciences are increasing, the quantity is currently limited. With the aid of the Society of Exploration Geophysicists (SEG) 2020 Machine Learning (ML) Blind-Test Challenge Data, we open source a package for DL-based seismic facies classification (SFC). We regard SFC as translating the seismic data into the facies by training a flexible end-to-end encoding–decoding-style ANN “BridgeNet” revised from U-Net. Inspired by the residual network (ResNet), we further enhance the BridgeNet by inserting the identity shortcut connections, which can theoretically ease the notorious problem of vanishing/exploding gradients. The evaluated framework, however, is not restricted to SFC. We hope that it can provide some insights that help researchers to construct and train ANNs that yield reliable and robust results in their own tasks. We carry out benchmarking experiments to investigate some crucial factors impacting the ANN’s performance to elucidate how we obtained our current optimal results on the SEG 2020 ML Challenge Data. The accuracy and continuity of predicted facies along the training and testing sections indicate that the results are consistent with geologic sedimentation, verifying the generalization capability of the enhanced flexible end-to-end encoding–decoding-style BridgeNet. For SFC, we recommend a memory-saving sparse categorical cross-entropy (CC) loss function to improve the efficiency. The codes are available at https://doi.org/10.5281/zenodo.5787673. [ABSTRACT FROM AUTHOR]

In the 3-D density inversion calculation of gravity data, the entire subsurface space is discretized into rectangular prisms, and the density value of each prism is calculated. However, the method of dividing the entire space often results in invalid calculations. In this study, we proposed a highly efficient density inversion method using a locally adaptive unstructured mesh. In this method, the inversion scope is reduced by using the feature that the zero value of the tilt angle method corresponds to the edge of the field source. In addition, the local inversion area is divided by unstructured meshes, which can better represent irregular geological bodies and undulating terrain. In the proposed method, the size of the grid cells is changed according to the value of the tilt angle to reduce the amount of mesh, which reduces the computational complexity and improves the computational efficiency. In addition, by introducing a volume weighting function, the sensitivity of grid cells of different sizes can be balanced. Through synthetic modeling experiments, we verified that the locally adaptive unstructured mesh method can improve the efficiency and accuracy of inversion, flexibly deal with undulating terrain, and obtain the distribution features of irregular bodies. We applied this method to gravimetric data of the North Qinling region of the Shaanxi province, which clearly shows the spatial distribution information of five high-density geological bodies, and the depth range of ore bodies formed is from 240 to 1481 m according to our inversion results. [ABSTRACT FROM AUTHOR]

Diffractions in the seismic data are associated with the small-scale subsurface structures, thus their separation and imaging are helpful in characterizing the underground discontinuities with a high resolution that cannot be reached by traditional reflection imaging methods. Traditional seismic slope-based diffraction separation methods are strongly affected by the accuracy and stability of the slope estimation methods, for example, the plane-wave destruction (PWD) method. When the local seismic slope is not properly estimated, the separated seismic diffraction waves suffer from the mixture between the reflection and diffraction energy due to their coupling in the slope map. We propose an automatic local rank-reduction (LRR) method to separate 3-D diffraction waves from zero-offset seismic data, based on which we conduct 3-D migration to output the diffraction images. Due to the difficulty in choosing the rank in each local 3-D window, we apply an adaptive strategy to obtain the optimal rank. The proposed LRR method with adaptively selected ranks (LRRA) is applied to several 3-D synthetic and field data examples and demonstrated to perform better than the traditional PWD, the LRR, and the global rank-reduction (GRR) methods. [ABSTRACT FROM AUTHOR]

Spatiotemporal fusion is commonly used in remote sensing to generate fine spatial–temporal resolution images. Among most present spatiotemporal fusion methods, the registration error results in a mismatch in the reflectance and class abundance information within the MODIS pixel to be analyzed during the spatiotemporal fusion. In this article, a new spatiotemporal fusion model, robust flexible spatiotemporal data fusion (RFSDAF), is proposed; in other words, this method is robust to registration errors. The RFSDAF method uses a multiscale fusion strategy that is adaptive to different degrees of coregistration error. It incorporates multiscale information, which extends the analysis of the reflectance and class fraction from per-MODIS pixel to inter-MODIS pixels, and it is robust to coregistration errors. This method does not require high-accuracy preprocession coregistration algorithms and can automatically reduce the effect of the registration error to a great extent. The RFSDAF method is compared with four spatiotemporal image fusion algorithms, and the effectiveness of the method in resolving the registration error is demonstrated using both a simulated dataset and two actual satellite datasets. The experimental results show that the RFSDAF method can better reduce the impact of the registration error than the spatial and temporal adaptive reflectance fusion model (STARFM) and FSDAF, which adopts the phase cross-correlation algorithm to preprocess and coregister the MODIS–Landsat image through real image experiments. Consequently, the RFSDAF method has a great robustness for real spatiotemporal image fusion with registration errors and has the potential for monitoring land surface dynamics. [ABSTRACT FROM AUTHOR]

5-D data is the original recorded form in the 3-D seismic acquisition, which includes sufficient information from all five dimensions. However, environmental and economic logistic difficulties often severely impact the data acquisition geometry, leading to raw data with missing traces and strong contaminating random noise. This deficiency often causes troubles in subsequent processing. Thus, an efficient interpolation and denoising method is required to recover useful signals. Unfortunately, practical applications of many existing reconstruction algorithms are limited by their intensive computational cost when applied to the 5-D data. Additionally, the stability of these algorithms is also challenged by complex geological structures, which often degrades the reconstruction performance. To seek solutions to the aforementioned problems, we design a simple and effective framework for fast reconstruction and denoising of undersampled 5-D seismic data via a two-step process. First, we prepare the initial model from the original recordings by constructing a 3-D gather at each common offset point. This step effectively interpolates the missing traces in 3-D common offset gathers by exploiting the data coherency in the adjacent areas (i.e., nearby mid-points). In the second step, the processed 5-D data is reorganized into 3-D common midpoint gathers, with each of them further sorted into a 2-D section according to absolute offset values. Then a conventional 2-D processing algorithm (e.g., F-X prediction, wavelet thresholding, or multichannel singular spectrum analysis) is invoked to filter the obtained 2-D section. The proposed workflow has a low overall computational cost and preserves signal fidelity. We use this framework to simultaneously denoise and interpolate the low-quality and extremely sparse seismic data. The synthetic and field examples both demonstrate the superb performance of the proposed framework in comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Small-loop transient electromagnetic (TEM) method can be used for urban underground space detection. Given the mutual inductance between transmitting and receiving coils, the early secondary field signals are contaminated by primary field signals. To compensate for the primary field, bucking coil technology has been utilized in the airborne TEM system due to attractive features of the technology, which include a compensation effect and a strong coupling strength between the receiving coil and the underground body. However, many problems arise when the bucking coil technology is used directly in a ground small-loop TEM system. One is that the size of the receiving coil is limited, resulting in insufficient bandwidth; the other is that the bucking coil brings the near-field effect, which causes the loss of shallow information. To solve these problems, in this article, we propose a new TEM structure with noncoplanar bucking compensation. Compared with the traditional design, the new structure overcomes not only the near-field effect but also reduces the constraint of the receiving coil size. Furthermore, the approach can suppress the primary field coupling more effectively, and has a better fault tolerance for the installation accuracy. The simulation and field experiment results verify the effectiveness and practicability of the new structure. [ABSTRACT FROM AUTHOR]

This paper proposes a cyber-physical framework consisting of the secure and privacy-preserving distributed impulsive-based estimator (DIE) algorithm and the local nonlinear control (LNC) algorithm to solve the cooperative formation tracking problem of networked marine surface vehicles (NMSVs) with sampled-data interactions and external disturbances. In the cyber layer, a secure and privacy-preserving information exchange scheme is proposed based on the Paillier cryptosystem such that the states of the virtual leader can be estimated in a secure and privacy-preserving way. In the physical layer, the LNC algorithm is designed with the utilization of the estimators in the cyber layer to achieve the formation tracking for the NMSVs with disturbance rejection ability. Sufficient conditions for guaranteeing the convergence and the stability of the closed-loop system are derived based on systematic analysis. Finally, numerical simulations are performed on Cyber-Ships II to verify the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

Calculation of geomagnetically induced current (GIC) flowing through power system during the geomagnetic storm has attracted more attention recently. However, for high voltage power systems with transmission lines over hundreds or even thousands of kilometers, the earth model and geomagnetical field generally vary significantly. So, it is essential to take them into consideration using limited earth survey sites and geomagnetic observatories. To address this problem, a Kriging method is introduced in this paper to make earth model and geomagnetical field interpolations. It has the characteristic of spatial autocorrelation by considering not only the distances between predicted points and training points but also the distances between training points themselves. Finally, a case study of the Central China 1000 kV ultra-high voltage (UHV) grid is carried out to illustrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) applications that overlay the perception of the real world with digitally generated information are on the cusp of commercial viability. AR has appeared in several commercial platforms like Microsoft HoloLens and smartphones. They extend the user experience beyond two dimensions and supplement the normal 3D world of a user. A typical location-based multi-player AR application works through a three-step process, wherein the system collects sensory data from the real world, identifies objects based on their context, and finally, renders information on top of senses of a user. However, because these AR applications frequently exchange data with users, they have exposed new individual and public safety issues. In this paper, we develop ARSpy, a user location tracking system solely based on network traffic information of the user, and we test it on location-based multi-player AR applications. We demonstrate the effectiveness and efficiency of the proposed scheme via real-world experiments on 12 volunteers and show that we could obtain the geolocation of any target with high accuracy. We also propose three mitigation methods to mitigate these side channel attacks. Our results reveal a potential security threat in current location-based multi-player AR applications and serve as a critical security reminder to a vast number of AR users. [ABSTRACT FROM AUTHOR]

IP hijack attacks deflect traffic between endpoints through the attacker network, leading to man-in-the-middle attacks. Current detection solutions are only based on AS-level path analysis, while attacks that include data-plane manipulations may exhibit only geographic anomalies and preserve the AS-level route, or hide the problematic AS in the path. Thus, there is a need to develop data-plane analysis frameworks that examine the actual route packets traverse. We introduce here a deep learning system that examines the geography of traceroute measurements to detect malicious routes. We use multiple geolocation services, with various levels of confidence; each also suffers from location errors. Moreover, identifying a hijacked route is not sufficient since an operator presented with a hijack alert needs an indication of the cause for flagging out the problematic route. Thus, we introduce a novel deep learning layer, called Source-Aware Self-Attention (SASA), which is an extension of the attention mechanism. SASA learns each data source’s confidence and combines this score with the attention of each router in the route to point out the most problematic one. We validate our IP hijacking classification method using two router data types: coordinates and country location, and show that SASA outperforms the regular self-attention layer, using the same neural network architecture, and achieves extremely high accuracy. [ABSTRACT FROM AUTHOR]

The unsplit complex frequency-shifted perfectly matched layer (CFS-PML) has been widely used in the first-order wave equation in velocity and stress while rarely formulated for the wave equation recast as a second-order system in displacement. Among different variants of PML, the unsplit CFS-PML for the second-order wave equation enjoys better absorbing performance and numerical stability, compared to the traditional PML, due to the presence of the general form of CFS stretching factors, as well as higher computational efficiency over the split PMLs since it avoids wave equation order reduction and splitting the state variables into multiple directional components. This study aims to develop an unsplit CFS-PML scheme for the second-order wave equation and devote specific attention to fractional viscoacoustic simulation where fractional time derivatives are involved and hard to be reformulated into a first-order form. In the complex space, PML is typically regarded as an analytical continuation of the real coordinates; thus, we define an explicit coordinate stretching operator acting on the Laplacian operator. This stretching operator consists of several convolution terms; each of them can be efficiently resolved by a recursive convolution updating strategy. Viscoacoustic simulations on homogeneous Pierre Shale, Marmousi model, and 3-D SEG/EAGE overthrust model verify the feasibility and absorbing the performance of our proposed scheme. [ABSTRACT FROM AUTHOR]

Acoustic remote sensing with multibeam echo-sounder systems (MBESs) has been extensively used for coastal and ocean survey works. The imperfect integration of multibeam echo sounder and motion sensor can introduce integration errors, which manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed topographic maps. To address this issue, a precise method is developed to calibrate these integration errors based on a simplified georeferenced model. First, an equivalent attitude coordinate (EAC) system is defined to represent mutual transformation between equivalent attitudes and the beam launch vector; then, with the help of equivalent attitudes, a simplified footprint georeferenced model is deduced that considers the effect of the parameterized integration errors (including time delay, motion scale, yaw misalignment, and lever arm errors); finally, in a selected flat region, integration errors are inverted by regressing the bathymetric data to the corresponding fitted plane via the differential evolution (DE) algorithm. The results indicate that the proposed method can effectively eliminate wobbles in multibeam bathymetries caused by single or multiple integration errors in both shallow- and deep-water areas. By comparing the data before and after calibration with the in situ measurements, the accuracy of the calibrated shallow- and deep-water data is controlled within approximately 0.15% and 0.06% of the water depth, respectively. [ABSTRACT FROM AUTHOR]