2,489 results on '"seismic data"'
Search Results
2. Chicxulub impact tsunami megaripples, imaged in 3D seismic data: Distribution and characteristics on the northern Gulf of Mexico shelf and slope
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Kinsland, Gary L., Zhang, Rui, Burr, Rika, and Klug, Stephen
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- 2025
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3. Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks
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Jin, Han, Liu, Cai, and Guo, Zhiqi
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- 2024
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4. Difference-Enhanced Learning of the Deep Semantic Segmentation Networks for First Break Picking
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Wen, Zhongyang, Ma, Jinwen, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Witbrock, Michael, editor, and Tian, Qi, editor more...
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- 2025
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5. Effective First-Break Picking of Seismic Data Using Geometric Learning Methods.
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Wen, Zhongyang and Ma, Jinwen
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MACHINE learning , *DEEP learning , *SUPERVISED learning , *ELECTRONIC data processing , *CURVE fitting - Abstract
Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In this paper, we introduce a strategy of optimizing certain geometric properties of the target curve for first-break picking which can be implemented in both unsupervised and supervised learning modes. Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the target curve and the fitting region energy are minimized together. It is interpretable, and its effectiveness and robustness are demonstrated by the experiments on real world seismic data. We further investigate three schemes of combining it with human interaction, which is shown to be highly useful in assisting data annotation or correcting picking errors. In the case of supervised learning especially for deep learning(DL) models, we add a curve loss term based on the target curve geometry of first-break picking to the typical loss function. It is demonstrated by various experiments that this curve regularized loss function can greatly enhance the picking quality. [ABSTRACT FROM AUTHOR] more...
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- 2025
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6. Optimizing Site Selection for Construction: Integrating GIS Modeling, Geophysical, Geotechnical, and Geomorphological Data Using the Analytic Hierarchy Process.
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Wahba, Doaa, Omran, Awad A., Adly, Ashraf, Gad, Ahmed, Arman, Hasan, and El-Bagoury, Heba
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ANALYTIC hierarchy process , *POISSON'S ratio , *MULTIPLE criteria decision making , *GEOGRAPHIC information systems , *SEISMIC wave velocity - Abstract
Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson's ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world. [ABSTRACT FROM AUTHOR] more...
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- 2025
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7. Intelligent characterization of ultra-deep carbonate strike-slip fault zones based on 3DResNeSt-UNet: a case study of the YueMan Block in the Tarim Basin.
- Author
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Hu, Jin and Xu, Shouyu
- Abstract
The strike-slip fault zones (SSFZ) in the ultra-deep carbonate rocks of the Tarim Basin provide crucial space for the migration and accumulation of oil and gas. Traditional 3D seismic interpretation methods have limitations in identifying SSFZ. This study proposes a seismic interpretation method based on the 3DResNeSt-UNet to identify SSFZ more effectively. This network integrates the 3DUNet and ResNeSt modules, using synthetic 3D seismic data and its corresponding label data as inputs for training. The synthetic seismic data incorporates geological knowledge and geological body parameters. Initially, by combining field outcrop observations, logging, and seismic data, the geological patterns of SSFZ are summarized. Guided by these geological patterns and based on the geological body parameters extracted from logging and seismic data, synthetic 3D seismic data and its corresponding label data are generated. The results indicate that the accuracy of the 3DResNeSt-UNet model on the training data exceeds 98%. The trained model achieves good recognition results on the seismic data of the YueMan block. Compared with traditional seismic interpretation results, the model’s recognition accuracy is significantly improved and more aligned with geological understanding. Overall, the 3DResNeSt-UNet provides a new effective method for identifying SSFZ and has great potential for application in similar seismic interpretation scenarios. [ABSTRACT FROM AUTHOR] more...
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- 2025
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8. A self-supervised missing trace interpolation framework for seismic data reconstruction.
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Li, Ming, Yan, Xue-song, and Hu, Cheng-yu
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ARTIFICIAL neural networks , *SIGNAL-to-noise ratio , *SEISMIC prospecting , *SUPERVISED learning , *WEIGHT training , *DEEP learning - Abstract
Reconstruction of missing seismic traces is one of the key steps in seismic data processing. Deep neural network-based interpolation methods for seismic trace reconstruction have attracted much attention in recent years. However, the currently widely used supervised-based deep neural network interpolation models require the support of labeled data, which is difficult to obtain in real seismic exploration. In this study, we propose a novel unsupervised deep learning interpolation framework SSTI (Self-supervised Seismic Trace Interpolation), designed to address the limitations of existing supervised-based models by avoiding the need of labeled data. SSTI employs a unique combination of two semi-supervised models: temporal ensembling model and mean teacher model. The temporal ensembling model and mean teacher model jointly predict the missing traces by integrating the outputs and weights of each training epoch through Exponential Moving Average method, respectively. Furthermore, a joint loss function that combines consistency loss and self-supervised loss is designed to guide the training process of SSTI. We evaluated the interpolation performance of SSTI on synthetic and field seismic data. Comparative analyses demonstrate that our proposed method outperforms the traditional theory-guided methods in terms of mean square error, peak signal to noise ratio, structural similarity, and quality of the reconstructed traces. Notably, although it does not achieve the interpolation performance of supervised deep learning methods, SSTI has shown great potential since there is only a marginal performance difference between SSTI and supervised-based methods. Our findings suggest that SSTI presents a promising advancement in the field of unsupervised deep learning methods for seismic trace interpolation. The proposed architecture holds great potential for recovering the missing and corrupted traces at a low cost in field seismic exploration. [ABSTRACT FROM AUTHOR] more...
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- 2024
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9. Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting.
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Jafari, Alireza, Fox, Geoffrey, Rundle, John B., Donnellan, Andrea, and Ludwig, Lisa Grant
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GRAPH neural networks ,EARTHQUAKES ,TRANSFORMER models ,DEEP learning ,TIME series analysis ,LOGARITHMS - Abstract
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams. [ABSTRACT FROM AUTHOR] more...
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- 2024
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10. Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data.
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Zwartjes, Paul and Jewoo Yoo
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ARTIFICIAL intelligence ,MACHINE learning ,CONVOLUTIONAL neural networks ,GEOPHYSICISTS ,INFORMATION storage & retrieval systems - Abstract
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping. Auto-encoder neural networks with convolutional layers have been applied to this problem, with encouraging results, but the problem of generalization to unseen data remains. Most published works have used supervised learning with training data constructed from field seismic data or synthetic seismic data generated based on measured well logs or based on seismic wavefield modelling. This leads to satisfactory results on datasets similar to the training data but requires re-training of the networks for unseen data with different characteristics. In this work seek to improve the generalization, not by experimenting with network architecture (we use a conventional U-net with some small modifications), but by adopting a different approach to creating the training data for the supervised learning process. Although the network is important, at this stage of development we see more improvement in prediction results by altering the design of the training data than by architectural changes. The approach we take is to create synthetic training data consisting of simple geometric shapes convolved with a seismic wavelet. We created a very diverse training dataset consisting of 9000 seismic images with between 5 and 300 seismic events resembling seismic reflections that have geophysically motived perturbations in terms of shape and character. The 2D U-net we have trained can boost robustly and recursively the dominant frequency by 50%. We demonstrate this on unseen field data with different bandwidths and signalto- noise ratios. Additionally, this 2D U-net can handle non-stationary wavelets and overlapping events of different bandwidth without creating excessive ringing. It is also robust in the presence of noise. The significance of this result is that it simplifies the effort of bandwidth extension and demonstrates the usefulness of autoencoder neural network for geophysical data processing. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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11. Intelligent characterization of ultra-deep carbonate strike-slip fault zones based on 3DResNeSt-UNet: a case study of the YueMan Block in the Tarim Basin
- Author
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Jin Hu and Shouyu Xu
- Subjects
Tarim Basin ,Ultra-deep carbonate rocks ,Strike-slip fault zones (SSFZ) ,3DResNeSt-UNet ,Seismic data ,Petroleum refining. Petroleum products ,TP690-692.5 ,Petrology ,QE420-499 - Abstract
Abstract The strike-slip fault zones (SSFZ) in the ultra-deep carbonate rocks of the Tarim Basin provide crucial space for the migration and accumulation of oil and gas. Traditional 3D seismic interpretation methods have limitations in identifying SSFZ. This study proposes a seismic interpretation method based on the 3DResNeSt-UNet to identify SSFZ more effectively. This network integrates the 3DUNet and ResNeSt modules, using synthetic 3D seismic data and its corresponding label data as inputs for training. The synthetic seismic data incorporates geological knowledge and geological body parameters. Initially, by combining field outcrop observations, logging, and seismic data, the geological patterns of SSFZ are summarized. Guided by these geological patterns and based on the geological body parameters extracted from logging and seismic data, synthetic 3D seismic data and its corresponding label data are generated. The results indicate that the accuracy of the 3DResNeSt-UNet model on the training data exceeds 98%. The trained model achieves good recognition results on the seismic data of the YueMan block. Compared with traditional seismic interpretation results, the model’s recognition accuracy is significantly improved and more aligned with geological understanding. Overall, the 3DResNeSt-UNet provides a new effective method for identifying SSFZ and has great potential for application in similar seismic interpretation scenarios. more...
- Published
- 2025
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- View/download PDF
12. High resolution 3-D seismic and sequence stratigraphy for reservoir prediction in ‘Stephi’ field, offshore Niger Delta, Nigeria
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Oluwatoyin Abosede Oluwadare, Adetayo Femi Folorunso, Olusola Raheemat Ashiru, and Stephanie Imabong Otoabasi-Akpan
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Sequence stratigraphy ,Reservoir characterization ,Offshore ,Niger Delta ,Well logs ,Seismic data ,Medicine ,Science - Abstract
Abstract The Offshore Niger Delta, Nigeria, stands as a dynamic geological marvel, known for its intricate processes and extensive hydrocarbon reservoirs. This study employed an integrated approach, utilizing 3D seismic data and well logs, to conduct a thorough analysis of sequence stratigraphy and reservoir characterization in the pursuit of optimizing hydrocarbon exploration in the region. The study focused on the NW–SE trending Miocene depocenters, which predominantly comprises alternating sandstone and thick shale layers within the Agbada Formation. These reservoir units showcased stacked shallow marine fluvial–deltaic sediments, separated by significant marine shale units. Within the study area, two hydrocarbon-bearing reservoirs were identified and named: R1 and R2. Petrophysical analysis identified R2 as the most promising reservoir, with a permeability of 1184 × 10− 3 µm2, 85% hydrocarbon saturation, porosity of 0.30, and effective porosity of 0.27. Fault structural analysis uncovered that hydrocarbons are trapped within a network of growth faults within the wave-dominated Niger Delta depositional system. From the sequence stratigraphic interpretation, four depositional sequences were delineated between the depths of 2030–3417 m, and are bounded by five sequence boundaries. Integrated seismic facies analysis revealed high-energy feeder systems likely supplying sediments from river sources to offshore locations. These integrated findings provide essential insights to inform resource management, exploration strategies, understanding of reservoir distribution, and structural intricacies within the complex offshore Niger Delta, Nigeria, providing valuable information. The depositional environment helped in the understanding of the stratigraphic traps which are prospects in the study area. This together with the associated reservoir quality allowed accurate prediction for potential reservoir facies and will further improve the field development plans. more...
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- 2024
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13. Structural and Tectonic Zoning of Paleozoic Deposits of Western Taimyr
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E. A. Zyza, E. E. Polek, and I. S. Igonin
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paleozoic deposits ,taimyr ,hydrocarbon resources ,seismic data ,tectonic zoning ,Geology ,QE1-996.5 - Abstract
Based on an actual seismic geological model, a structural-tectonic zoning of the Paleozoic complex of Western Taimyr was carried out, including the territory of the South Taimyr monocline, where Paleozoic deposits are hidden under the Mesozoic sedimentary cover. Zoning was carried out for tectonic elements of different orders: regional, supra-order, structures of the 1st, 2nd, 3rd orders, local uplifts. When compiling a structural-tectonic scheme, an analysis of previous tectonic schemes by various authors and currently available materials was performed. In addition, much attention is paid to the main faults of Western Taimyr.All available geological and geophysical data reflecting the tectonic structure of the study area was used, including structural maps of the top of the Paleozoic complex and horizons reflecting it’s internal structure, 2D CDP seismic sections, maps of potential fields (magnetic and gravity), maps of dips and azimuths of reflecting horizons, thickness maps, published and archive papers on this topic, including tectonic and geological maps.As a result, an updated structural-tectonic diagram of the Paleozoic deposits of Western Taimyr was compiled, characterized by a high degree of detail, the fault model of the region was generalized and significantly refined, all tectonic elements and structures were assigned their own names, taking into account the results of previous studies, positive structures of the 1st order, which represent potential zones of oil and gas accumulation in the Paleozoic complex. more...
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- 2024
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14. Strong Interference Elimination in Seismic Data Using Multivariate Variational Mode Extraction.
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Yu, Zhichao, Tan, Yuyang, and Lv, Yiran
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NOTCH filters , *TIME-frequency analysis , *SIGNAL-to-noise ratio , *DATA extraction , *SIGNALS & signaling - Abstract
Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals. [ABSTRACT FROM AUTHOR] more...
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- 2024
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15. CBM reservoir thickness prediction using the seismic nonlinear stochastic inversion method.
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Chen, Fangbo, Iqbal, Ibrar, Wang, Jiabao, Zhang, Tianyu, Yang, Yang, and Chen, Meng
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SEISMIC waves , *GEOPHYSICAL prospecting , *COALBED methane , *FACIES , *PERMEABILITY - Abstract
Due to the complex reservoir conditions and rapid changes in lithological facies in seismic exploration, predicting coalbed methane (CBM) reservoirs is quite challenging. Conventional inversion methods are not highly effective at predicting reservoir thickness, and cannot keep up with current demands. Our aim is to demonstrate how seismic data can be used to forecast coal thickness, as well as the distribution and orientation of subtle structures that may be linked to enhanced permeability zones. In this study, we used a nonlinear stochastic inversion method based on making full use of seismic data and constraining it with known information, such as drilling and logging analysis. This method has been successfully applied in a mining area in Wuxiang County in the southeastern part of Shanxi Province. Compared with the actual geological data, it is found that the prediction accuracy is consistent with the drilling results, and the distribution of the predicted CBM reservoir thickness is consistent with the geological information. Furthermore, due to the randomness of the subsurface medium, the accuracy of reservoir prediction can be increased by observing the target layer seismic reflection wave amplitude, frequency, and other properties. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
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16. Seismic Random Noise Attenuation Using DARE U-Net.
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Banjade, Tara P., Zhou, Cong, Chen, Hui, Li, Hongxing, Deng, Juzhi, Zhou, Feng, and Adhikari, Rajan
- Subjects
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CONVOLUTIONAL neural networks , *MICROSEISMS , *SIGNAL-to-noise ratio , *IMAGE denoising , *IMAGING systems in seismology - Abstract
Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted. [ABSTRACT FROM AUTHOR] more...
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- 2024
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17. Full‐waveform inversion as a tool to predict fault zone acoustic properties.
- Author
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Alghuraybi, Ahmed M., Bell, Rebecca E., Jackson, Christopher A.‐L., Sim, Melissa, and Jin, Shuhan
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FAULT zones , *EARTHQUAKE hazard analysis , *CARBON sequestration , *GEOTHERMAL resources , *SEISMIC wave velocity - Abstract
Understanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three‐dimensional seismic reflection data can image the geometries of faults in the sub‐surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale‐gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full‐waveform inversion P‐wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high‐quality post‐stack depth–migrated seismic reflection and full‐waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post‐stack depth migrated and full‐waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full‐waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P‐wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P‐wave velocity anomalies within fault zones and the modern‐day stress orientation. Faults with high P‐wave velocity are the ones that are perpendicular to the present‐day maximum horizontal stress orientation and are likely under compression. Faults with lower P‐wave velocity are the ones more parallel to the present‐day maximum horizontal stress orientation and are likely in extension. We propose that these P‐wave velocity anomalies may indicate differences in how 'open' and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower VP) and therefore may provide a promising proxy for fault transmissibility. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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18. Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data.
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Chen, Gui, Liu, Yang, Zhang, Mi, Sun, Yuhang, and Zhang, Haoran
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MATRIX decomposition , *COMPLETE graphs , *SENSITIVITY analysis , *ALGORITHMS , *LOW-rank matrices - Abstract
Low-rank approximation has emerged as a promising technique for recovering five-dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank robustness remains a critical pursuit. We introduce a low-rank approximation method by leveraging the complete graph tensor network (CGTN) decomposition and the learnable transform (LT), referred to as the LRA-LTCGTN method, to simultaneously denoise and reconstruct 5D seismic data. In the LRA-LTCGTN framework, the LT is employed to project the frequency tensor of the original 5D data onto a small-scale latent space. Subsequently, the CGTN decomposition is executed on this latent space. We adopt the proximal alternating minimization algorithm to optimize each variable. Both 5D synthetic data and field data examples indicate that the LRA-LTCGTN method exhibits notable advantages and superior efficiency compared to the damped rank-reduction (DRR), parallel matrix factorization (PMF), and LRA-CGTN methods. Moreover, a sensitivity analysis underscores the remarkably stronger robustness of the LRA-LTCGTN method in terms of rank without any optimization procedure with respect to rank, compared to the LRA-CGTN method. [ABSTRACT FROM AUTHOR] more...
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- 2024
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19. In-situ stress regime analysis and mechanical earth modeling of the southwestern sector of the Zagros folded belt, SW Iran: applications for acid fracturing in Ilam and Sarvak carbonate formations.
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Mesbahi, Fatemeh, Kadkhodaie, Ali, and Wood, David A.
- Abstract
The studied oilfield in the Zagros folded belt accommodates hydrocarbons in the Cretaceous carbonate rocks of the Ilam and Sarvak Formations. The compressional tectonic regime of the study area is reflected in the presence of the NW-SE trending active Ch.1 and Ch.2 thrust fault-related anticlines and associated NW-SE trending reverse faults. The NW-SE trending reverse faults have formed a pop-up structure in the crest of the Ch.2 anticline dividing it into 3 sectors (A, B, and C) with different geomechanical characteristics. Sector B as the high-strain central part of the pop-up structure despite Sectors A and B as the low-strain parts (in the footwall of the reverse faults forming the pop-up structure), is considered a high-risk region for performing acid fracturing stimulation of the reservoirs. In seismic time slices at the Gachsaran level, ChF.1 and ChF.2 faults with NE-SW trend display sinistral and dextral displacement respectively, representing tear faults related to the basal thrust of the Zagros Fore-dip Fault. Horizontal and vertical stress magnitudes for the study area were derived from well-log data. The horizontal stress orientations were established from the dominant directions of the induced fractures and breakouts observed from the FMI log data and are in ~ 030° (NE-SW) and ~ 120° (NW-SE), respectively. Based on these determined horizontal stress orientations, the expected induced fracture propagation direction is ~ 030° and their opening would likely occur in the direction ~ 120°. Based on this deduced stress information, the optimal azimuthal perforation would be from 000° to 060° for acid-fracture stimulation of Ilam and Sarvak reservoirs. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
20. Using microtremor data to obtain dynamic properties of soils in the Veracruz-Boca del Rio metropolitan areaMendeley Data
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José E. Barradas-Hernández, Sergio Márquez-Domínguez, Franco Antonio Carpio-Santamaria, Alejandro Vargas-Colorado, Abigail Zamora-Hernández, and Roberto Rivera-Baizabal
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Seismic data ,H/V spectral ratio ,Site effect ,One-dimensional stratigraphic model ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Science (General) ,Q1-390 - Abstract
The data presented here are the result of microtremor measurements at 44 points in three different soil types classified according to their fundamental vibration frequencies, on the metropolitan area of Veracruz-Boca del Río, Mexico. These Data are raw and was obtained using a GÜRALP 6TD model broadband orthogonal triaxial seismometer with an integrated 24-bit digitizer with a minimum recording time of 30 min and a recording rate of 100 samples per second (sps). The microtremor records were used to construct the H/V spectral ratios using the method of Nakamura. These H/V spectral ratios are a good approximation of the transfer function between the vibration waves in the sediment and the rigid stratum. Therefore, they can be used to construct seismic microzonation maps, seismic intensity maps and spectra for designing seismic resistant structures. One-dimensional stratigraphic soil models were obtained by processing the H/V spectral ratios. The relevant data from these models are layer thickness, primary wave velocities (Vp), secondary wave velocities (Vs) and density. These models represent a mathematical approximation of the soil structure that can be used to dynamically classify it according to Mexican technical codes. more...
- Published
- 2025
- Full Text
- View/download PDF
21. Subsurface Geosciences Learning in Virtual Reality: A Case Study in Central Luconia Province, Malaysia
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Grisel Jiménez, Abdul Halim Latiff, and Katja Schulze
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virtual reality ,geoscience education ,seismic data ,petroleum geoscience ,3D virtual environments ,Dynamic and structural geology ,QE500-639.5 - Abstract
The recent proliferation of Extended Reality (XR) applications in geoscience education and research has opened new avenues for the enhanced visualization and analysis of the Earth’s geodata. This study specifically explores the benefits for teaching when supplementing industry standard software packages, such as Paleoscan, Petrel, and JewelSuite, with 3D visualization in XR. The teaching focuses on but is not limited to an understanding of subsurface seismic and well data. During this study, the undergraduate Petroleum Geoscience students transitioned from 2D computer screen visualizations to immersive XR tools. The dataset selected for teaching focuses on the subsurface carbonate EX field in the South China Sea. The EX-field in Central Luconia is located 100–300 km from Sarawak’s coastline in water depths of 60–140 m. It includes a post-stacked time- 3D seismic cube linked to wells, allowing students to work with seismic data, adjust scales, and conduct preliminary seismic analysis. The findings revealed a significant improvement in respondents’ skills in comprehending and analysing seismic and core data, enhancing the overall learning experience in Petroleum Geoscience. This paper also examines the students’ feedback on their learning experiences during virtual subsurface visualization throughout their university degree in geoscience. For evaluating learning success, we used an approach that merges quantitative and qualitative data, The students’ perceptions were assessed through anonymous quantitative surveys and questions. The analysis of student responses emphasizes the valuable learning experience offered by 3D virtual environments designed for realistic first-person navigation and freedom of movement, like a real field experience. The results highlight the potential of virtual subsurface visualization for imparting essential skills to geosciences. more...
- Published
- 2024
- Full Text
- View/download PDF
22. Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data
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Paul Zwartjes and Jewoo Yoo
- Subjects
Resolution ,Seismic data ,u-Net ,Bandwidth extension ,Synthetic data ,Geography (General) ,G1-922 ,Information technology ,T58.5-58.64 - Abstract
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping. Auto-encoder neural networks with convolutional layers have been applied to this problem, with encouraging results, but the problem of generalization to unseen data remains. Most published works have used supervised learning with training data constructed from field seismic data or synthetic seismic data generated based on measured well logs or based on seismic wavefield modelling. This leads to satisfactory results on datasets similar to the training data but requires re-training of the networks for unseen data with different characteristics. In this work seek to improve the generalization, not by experimenting with network architecture (we use a conventional U-net with some small modifications), but by adopting a different approach to creating the training data for the supervised learning process. Although the network is important, at this stage of development we see more improvement in prediction results by altering the design of the training data than by architectural changes. The approach we take is to create synthetic training data consisting of simple geometric shapes convolved with a seismic wavelet. We created a very diverse training dataset consisting of 9000 seismic images with between 5 and 300 seismic events resembling seismic reflections that have geophysically motived perturbations in terms of shape and character. The 2D U-net we have trained can boost robustly and recursively the dominant frequency by 50%. We demonstrate this on unseen field data with different bandwidths and signal-to-noise ratios. Additionally, this 2D U-net can handle non-stationary wavelets and overlapping events of different bandwidth without creating excessive ringing. It is also robust in the presence of noise. The significance of this result is that it simplifies the effort of bandwidth extension and demonstrates the usefulness of auto-encoder neural network for geophysical data processing. more...
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- 2024
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23. CONTRIBUTION OF SEISMIC AND GEOTHERMAL DATA ANALYSIS TO THE ASSESSMENT OF THE HYDROCARBON POTENTIAL OF THE CENTRAL BASIN OF THE D.R. CONGO
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Marlin Agolo Monza, Joel Etshekodi Lohadje, Franck Tondozi Keto, Raphael Matamba Jibikila, and Néhémie Bikayi Tshiani
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seismic data ,envelope attribute ,isobaths ,geothermal gradient ,source rock maturity ,Technology (General) ,T1-995 ,Science - Abstract
This paper presents an evaluation of the hydrocarbon potential of the Cuvette Centrale basin in the Democratic Republic of Congo (DRC) using an integrated approach that combines seismic and geothermal data. The envelope attribute of seismic data was used to identify different rocks of the petroleum system, including potential gas-prone zones. The interpretation of seismic profiles helped to delineate geological units and determine their lithology. Isobath maps based on seismic data revealed the presence of grabens and anticlines, which are favorable geological structures for hydrocarbon accumulation. Analysis of the geothermal gradient and temperature evolution in the formations allowed us to establish source rock maturity maps, highlighting two distinct zones: an overmature zone favorable for gas and a mature zone favorable for oil. These results suggest a strong hydrocarbon potential in the Cuvette Centrale basin. more...
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- 2024
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24. Estimation of Reservoir Fracture Properties from Seismic Data Using Markov Chain Monte Carlo Methods.
- Author
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Feng, Runhai, Mosegaard, Klaus, Mukerji, Tapan, and Grana, Dario
- Subjects
- *
MARKOV chain Monte Carlo , *GEOMETRIC modeling , *GEOLOGICAL modeling , *FLUID flow , *ROCK properties - Abstract
The knowledge of fracture properties and its geometrical patterns is often required for the analysis of mechanical and flow properties in fractured reservoirs, as fracture characterization plays a critical role in the optimization of hydrocarbon production or estimation of storage capacity of subsurface reservoirs. A stochastic method based on a Markov chain Monte Carlo (MCMC) algorithm is proposed to estimate fracture properties using a rock physics model for fractured rocks. Two implementations are presented: a Metropolis algorithm based on a Gaussian prior distribution and an extended Metropolis algorithm with an informative prior obtained from multiple-point statistics simulations. The results are compared to a Bayesian analytical approach where the solution is based on a linearized approximation of the rock physics model. The novelty of the proposed approach is the use of a training image, that is, a conceptual geological model, to account for the spatial distribution of the fractures. Two fracture properties are considered, namely fracture density and aspect ratio, and the spatial distribution and geometrical characteristics of fractures are also investigated to understand the connectivity patterns that control fluid flow. The MCMC approach with a training image is more computationally demanding but provides geometrical models of the spatial distribution of fractures. The inversion results show that the prediction accuracy of fracture density and aspect ratio obtained by the MCMC methods is similar to the one obtained with the analytical approach, and that the MCMC methods provide a reliable assessment of the posterior uncertainty as well. [ABSTRACT FROM AUTHOR] more...
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- 2024
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25. Optimizing Parameters for Earthquake Prediction Using Bi-LSTM and Grey Wolf Optimization on Seismic Data.
- Author
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Shidik, Guruh Fajar, Pramunendar, Ricardus Anggi, Purwanto, Hasibuan, Zainal Arifin, Dolphina, Erlin, Kusumawati, Yupie, and Sriwinarsih, Nurul Anisa
- Subjects
MATHEMATICAL optimization ,ERROR rates ,STATISTICAL significance ,ANALYSIS of variance ,DISASTERS - Abstract
Earthquakes pose a significant threat to societies worldwide, underscoring the urgent need for advanced prediction technologies. This study introduces an optimization technique aimed at reducing the error rate in earthquake prediction by selecting the most suitable parameters for a Bi-LSTM (Bidirectional Long Short-Term Memory) model. Despite Bi-LSTM's promising outcomes, variations in parameters can impact performance, necessitating careful parameter selection. This research employs Grey Wolf Optimization (GWO) to optimize parameters and evaluates its effectiveness against other group optimization approaches to identify the most efficient parameters for earthquake prediction. Additionally, a multiple input multiple output (MIMO) architecture is implemented to enhance prediction accuracy. The evaluation results demonstrate that GWO outperforms other optimization techniques, achieving a reduced loss score of 0.364. The ANOVA method yields a p-value approaching 0, indicating statistical significance. This study contributes to the development of early warning systems for earthquake disasters by emphasizing the importance of parameter optimization in earthquake prediction and showcasing the effectiveness of Bi-LSTM and GWO methodologies. [ABSTRACT FROM AUTHOR] more...
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- 2024
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26. Reporting Impulsive Noise from Underwater Explosions Using Seismic Data
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Mustonen, Mirko, Klauson, Aleksander, Lepper, Paul, Section editor, Popper, Arthur N., editor, Sisneros, Joseph A., editor, Hawkins, Anthony D., editor, and Thomsen, Frank, editor
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- 2024
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27. Suppressing random noise in seismic signals using wavelet thresholding based on improved chaotic fruit fly optimization
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Feng Yang, Jun Liu, Qingming Hou, and Lu Wu
- Subjects
Random noise ,Seismic data ,Denoising ,Wavelet threshold ,Chaotic fruit fly optimization ,Telecommunication ,TK5101-6720 ,Electronics ,TK7800-8360 - Abstract
Abstract Suppressing random noise in seismic signals is an important issue in research on processing seismic data. Such data are difficult to interpret because seismic signals usually contain a large amount of random noise. While denoising can be used to reduce noise, most denoising methods require the prior estimation of the threshold of the signals to handle random noise, which makes it difficult to ensure optimal results. In this paper, we propose a wavelet threshold-based method of denoising that uses the improved chaotic fruit fly optimization algorithm. Our method of selects uses generalized cross-validation as the objective function for threshold selection. This objective function is optimized by introducing an adjustment coefficient to the chaotic fruit fly optimization algorithm, and the optimal wavelet threshold can then be obtained without any prior information. We conducted denoising tests by using synthetic seismic records and empirical seismic data acquired from the field. We added three types of noise, with different average signal-to-noise ratios, to synthetic seismograms containing noise with original intensities of − 5, − 1, and 4 dB, respectively. The results showed that after denoising, the signal-to-noise ratios of the three types of noise increased to 7.12, 10.04, and 14.26, while the mean-squared errors in the results of the proposed algorithm decreased to 0.006, 0.0031, and 0.0012, respectively. more...
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- 2024
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28. The North Tambey uplift history study using 3D seismic data
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Yu. A. Zagorovsky
- Subjects
formation thickness analysis ,seismic data ,north tambey uplift ,tambeyskoye natural gas field ,cenomanian ,overpressure ,tanopchinskaya formation ,Geology ,QE1-996.5 - Abstract
Paper shows the information about the geological and geophysical exploration of Tambeyskoye natural gas field located in the north of the Yamal Peninsula. The problems with mapping of natural gas deposits in Cretaceous and Jurassic formations are described. The results of formation thickness analysis are presented in order to explain the reasons for the unprecedented concentration of separate natural gas accumulations and the heterogeneous saturation of massive reservoirs in Cretaceous formations. The method of paleotectonic analysis is briefly described, the initial data are reported. Structural and isopach maps are presented. Structural elements and their evolution in Jurassic and Cretaceous time are presented. It was concluded that different structural elements of the work area transformed quite independently until the end of Cenomanian. The modern shape of North Tambey uplift was forming during the Neogene to Quarter age. Natural gas bearing reservoirs in Jurassic formation with the overpressure were reported. The young age of the North Tambey uplift, the unprecedented concentration of separate natural gas accumulations, the and the heterogeneous saturation of massive reservoirs in Cretaceous formations, overpressure in Jurassic formation – all these facts show that the Tambeyskoye natural gas field is under active gas accumulation. Hydrocarbon gases coming from deep Jurassic formations and it was not enough time for gas accumulations to be distributed over the reservoirs of Cretaceous. more...
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- 2024
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29. Hydrocarbon prospectivity of pinch-out zone of Jurassic deposits in Southwestern part of South Taimyr Monocline
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I. S. Igonin, E. A. Zyza, I. Yu. Kuleshova, P. E. Zherzhova, A. V. Kiryanina, and A. N. Bondarev
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structure-lithological traps ,filling of erosional incisions ,khabei licence block ,hydrocarbon resources ,seismic data ,Geology ,QE1-996.5 - Abstract
A comprehensive analysis has been conducted on the geological structure of the entire Jurassic interval within the southwestern part of the South Taymyr Monocline, which has allowed a more precise understanding of the structure of the Khabei gas field and the identification of prospective objects of similar geological pattern. The study utilized CDP 2D seismic data, covering a total length of 7835 kilometers, as well as results from exploratory and appraisal drilling. Based on visual analysis of temporal cross-sections, an assumption has been made regarding the correlation between reservoir development zones and paleo-channel systems of the Lower-Middle Jurassic deposits. In order to identify and locate such systems, an analysis of temporal cross-sections, maps of total Jurassic deposit thickness, and maps of dynamic attributes were conducted. As a result, areas of development of large paleo-channel systems (paleo-valleys) within the South Taymyr Monocline have been delineated through integration. These identified zones are characterized by relatively shallow burial depths of up to 2.5 km, the absence of abnormally high reservoir pressures (AHBP), and comparably high filtrationcapacity properties for Jurassic reservoirs (porosity – 22%, permeability – 25–50 mD).However, considering the complexity of these objects’ structures and the uncertainties caused by the low degree of geological-geophysical exploration in the study area, it is recommended to conduct additional seismic surveys (CDP 3D) to maturate the objects for exploration drilling. more...
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- 2024
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30. Quick Determination of Bolide Explosion Locations Using Seismic and Optic Data
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Díaz, Jordi, Trigo-Rodríguez, Josep M., Tapia, Mar, Ruiz, Mario, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, Gawad, Iman O., Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Bezzeghoud, Mourad, editor, Ergüler, Zeynal Abiddin, editor, Rodrigo-Comino, Jesús, editor, Jat, Mahesh Kumar, editor, Kalatehjari, Roohollah, editor, Bisht, Deepak Singh, editor, Biswas, Arkoprovo, editor, Chaminé, Helder I., editor, Shah, Afroz Ahmad, editor, Radwan, Ahmed E., editor, Knight, Jasper, editor, Panagoulia, Dionysia, editor, Kallel, Amjad, editor, Turan, Veysel, editor, Chenchouni, Haroun, editor, Ciner, Attila, editor, and Gentilucci, Matteo, editor more...
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- 2024
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31. A Multisynchrosqueezing-Based S-Transform for Time-Frequency Analysis of Seismic Data
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Liu, Wei, Zhai, Zhixing, and Fang, Zhou
- Published
- 2024
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32. Suppressing random noise in seismic signals using wavelet thresholding based on improved chaotic fruit fly optimization.
- Author
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Yang, Feng, Liu, Jun, Hou, Qingming, and Wu, Lu
- Subjects
OPTIMIZATION algorithms ,SIGNAL-to-noise ratio ,MICROSEISMS - Abstract
Suppressing random noise in seismic signals is an important issue in research on processing seismic data. Such data are difficult to interpret because seismic signals usually contain a large amount of random noise. While denoising can be used to reduce noise, most denoising methods require the prior estimation of the threshold of the signals to handle random noise, which makes it difficult to ensure optimal results. In this paper, we propose a wavelet threshold-based method of denoising that uses the improved chaotic fruit fly optimization algorithm. Our method of selects uses generalized cross-validation as the objective function for threshold selection. This objective function is optimized by introducing an adjustment coefficient to the chaotic fruit fly optimization algorithm, and the optimal wavelet threshold can then be obtained without any prior information. We conducted denoising tests by using synthetic seismic records and empirical seismic data acquired from the field. We added three types of noise, with different average signal-to-noise ratios, to synthetic seismograms containing noise with original intensities of − 5, − 1, and 4 dB, respectively. The results showed that after denoising, the signal-to-noise ratios of the three types of noise increased to 7.12, 10.04, and 14.26, while the mean-squared errors in the results of the proposed algorithm decreased to 0.006, 0.0031, and 0.0012, respectively. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
- View/download PDF
33. A Dynamic Extreme Value Model with Application to Volcanic Eruption Forecasting.
- Author
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Nguyen, Michele, Veraart, Almut E. D., Taisne, Benoit, Tan, Chiou Ting, and Lallemant, David
- Subjects
- *
VOLCANIC activity prediction , *EXTREME value theory , *BUSINESS forecasting , *GAUSSIAN distribution , *NATURAL disasters - Abstract
Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to accurately characterise extremes. This motivates the development of extreme value models for extreme event forecasting. In this paper, a dynamic extreme value model is proposed for forecasting volcanic eruptions. This is inspired by one recently introduced for financial risk forecasting with high-frequency data. Using a case study of the Piton de la Fournaise volcano, it is shown that the modelling framework is widely applicable, flexible and holds strong promise for natural hazard forecasting. The value of using EVT-informed thresholds to identify and model extreme events is shown through forecast performance, and considerations to account for the range of observed events are discussed. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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34. Time-reassigned multisynchrosqueezing of the S-transform for seismic time-frequency analysis.
- Author
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Liu, Wei, Liu, Yang, and Li, Shuangxi
- Subjects
- *
TIME-frequency analysis , *SIGNALS & signaling - Abstract
To accurately capture the time-frequency spectral anomaly, a novel time-frequency analysis (TFA) method, termed as time-reassigned multisynchrosqueezing S-transform (TMSSST), is proposed. In this study, we derive a N-order group delay (GD) estimator designed for frequency-domain S-transform to cope with the signal with fast varying instantaneous frequency (IF). By introducing an iterative reassignment procedure, the proposed TMSSST not only can produce a highly energy-concentrated time-frequency representation (TFR) but also can reconstruct the original signal with a high accuracy. Three synthetic signals are employed to validate the effectiveness of the proposed method by comparing with some classical TFA techniques such as S-transform (ST), synchrosqueezing S-transform (SSST) and time-reassigned synchrosqueezing S-transform (TSSST). It is shown that the TMSSST does a better job in addressing strongly frequency-varying signal. Application on field data further indicates the potential of highlighting subsurface geological structures and thus, facilitating seismic interpretation. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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35. An adaptive physics-informed deep learning method for pore pressure prediction using seismic data.
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Xin Zhang, Yun-Hu Lu, Yan Jin, Mian Chen, and Bo Zhou
- Subjects
- *
DEEP learning , *PETROLEUM engineering , *PETROLEUM production , *FLUID flow , *FEATURE extraction , *PROBLEM solving - Abstract
Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering. Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction. However, most of the traditional deep learning models are less efficient to address generalization problems. To fill this technical gap, in this work, we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data. Specifically, the new model, named CGP-NN, consists of a novel parametric features extraction approach (1DCPP), a stacked multilayer gated recurrent model (multilayer GRU), and an adaptive physics-informed loss function. Through machine training, the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction. The CGP-NN model has the best generalization when the physicsrelated metric λ = 0:5. A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels. To validate the developed model and methodology, a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability. The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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36. 基于变分模态分解的弹性参数核密度估计方法.
- Author
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朱鑫杰, 张宏兵, 曾繁鑫, and 祝新益
- Abstract
Probability density modelling is a crucial aspect of seismic stochastic simulation, and probability density estimation of the high frequency components of the elastic parameters determines the accuracy of high resolution seismic stochastic simulation results. To address inaccuracies in extracting high frequency components of elastic parameters, excessive constraints in a priori conditions for probability density modelling, and hierarchical design in conventional methods, an elastic parameter kernel density estimation method based on variational mode decomposition ( VMD) were proposed. Firstly, the VMD method was employed to conduct modal decomposition on the logging elastic parameter data, followed by the selection of high frequency terms within the intrinsic mode function (IMF) in order to acquire the high frequency component of the logging elastic parameter. Then the probability density model of high frequency components was obtained by using kernel density estimation hierarchical computation, and the model was used to generate random high-frequency components by random sampling to be superimposed on the seismic data next to the wells in order to enrich the high-frequency content of the seismic elasticity parameter data. The experimental results in Well 34 of the Pearl River Mouth Basin show that VMD effectively separates the high frequency components of the logging elastic parameters with a central frequency above 70 Hz. The kernel density estimation method with layered design highlights the statistical law of the high frequency components. After superimposing the random high frequency components, the high frequency components of the seismic elastic parameters above 70 Hz are obviously supplemented. This method provides a new idea for high-resolution stochastic earthquake simulation. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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37. Multi-Scale Acoustic Velocity Inversion Based on a Convolutional Neural Network.
- Author
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Li, Wenda, Wu, Tianqi, and Liu, Hong
- Subjects
- *
CONVOLUTIONAL neural networks , *SPEED of sound , *DEEP learning , *SURFACE waves (Seismic waves) - Abstract
The full waveform inversion at this stage still has many problems in the recovery of deep background velocities. Velocity modeling based on end-to-end deep learning usually lacks a generalization capability. The proposed method is a multi-scale convolutional neural network velocity inversion (Ms-CNNVI) that incorporates a multi-scale strategy into the CNN-based velocity inversion algorithm for the first time. This approach improves the accuracy of the inversion by integrating a multi-scale strategy from low-frequency to high-frequency inversion and by incorporating a smoothing strategy in the multi-scale (MS) convolutional neural network (CNN) inversion process. Furthermore, using angle-domain reverse time migration (RTM) for dataset construction in Ms-CNNVI significantly improves the inversion efficiency. Numerical tests showcase the efficacy of the suggested approach. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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38. Investigating Multiple Ionospheric Disturbances Associated with the 2020 August 4 Beirut Explosion by Geodetic and Seismological Data.
- Author
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Freeshah, Mohamed, Şentürk, Erman, Zhang, Xiaohong, Livaoğlu, Hamdullah, Ren, Xiaodong, and Osama, Nahed
- Subjects
- *
IONOSPHERIC disturbances , *BLAST effect , *BEIRUT Explosion, 2020 , *GRAVITY waves , *SPACE environment - Abstract
In this study, we investigated oscillatory disturbances in the atmosphere [from GNSS-derived ionospheric slant total electron content (STEC)] and in the solid Earth (from triaxial seismic data) following the 4 August 2020 Beirut port explosion in Lebanon. The ionospheric disturbances were investigated under meticulous observations of the space weather indices. The STEC sequences were analyzed by the Savitzky-Golay smoothing filter to check the ionospheric response to the blast. Our results showed that the induced wave structures have significant multiple ionospheric disturbances associated with the explosion epicenter. The research findings showed that the ionosphere responds to the severe blast with two time arrivals. The first time arrival was after the blast within a few minutes and had a low frequency. The second time arrival for the ionospheric disturbance was after > 2 h from the explosion time with high frequency compared with the first one. These ionospheric disturbances were associated with the time and space of the blast. The speed of induced waves in the northern direction of the explosion was slower than the waves in the western and the southern directions, respectively. Finally, the seismological data revealed that there were two major blasts in the Beirut port. The first blast triggered a severe blast after 5 s. Our results are a significant indication that the ionospheric disturbances are influenced by the acoustic gravity wave activity induced by the blast rather than by other random events. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
- Full Text
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39. Transformer and Convolutional Hybrid Neural Network for Seismic Impedance Inversion
- Author
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Chunyu Ning, Bangyu Wu, and Baohai Wu
- Subjects
Impedance inversion ,seismic data ,self-supervised learning ,transformer ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
The inversion of elastic parameters especially P-wave impedance is an essential task in seismic exploration. Over the years, deep learning methods have made significant achievements in seismic impedance inversion, and convolutional neural networks (CNNs) become the dominating framework relying on extracting local features effectively. In fact, the elastic parameters temporal correlation consists of local and global characteristics, with the latter as a general trend in vertical direction due to gravity and diagenesis (vertical mechanical compression). Therefore, considering the excellent performance in capturing global dependencies of Transformer, we design an improved transformer encoder, a transformer and convolutional hybrid neural network (trans-CNN), for seismic impedance inversion. The designed network not only has the ability of transformer capturing global features with the facilitation of parallel computing but also the advantage of extracting local features of CNNs. With sparse well log data as labels, it can infer the absolute impedance from seismic data without an initial model. We also devise a relative time interval prediction self-supervised task to assist the network in better extracting seismic data features without adding any labels. Therefore, a multitask framework composed of self-supervised and supervised learning is used to train the network. We first conduct experiments on the Marmousi2 and overthrust model. The prediction profiles show that the proposed trans-CNN has better inversion and transfer learning ability than several comparable networks. We then test the proposed network on a field data, the experiments further suggest that trans-CNN can obtain stable inversion results with better horizontal continuity and high vertical resolution. more...
- Published
- 2024
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40. Self-Supervised Attenuation Method Based on Similarity Comparisons for 3D Seismic Random Noise
- Author
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Jian Gao, Yixuan Gao, and Wanyue Gao
- Subjects
Seismic data ,random noise ,deep learning ,noise attenuation ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The improvement of the signal-to-noise ratio of seismic data is crucial for high-precision processing. The self-supervised denoising methods based on correlation differences are gaining attention due to their low cost of training data construction and the ability to build training data directly on test data. However, as the volume of data processed increases, these methods must maintain a denoising effect by adding extra processings. This increase in processing times can lead to a rise in total cost, making these methods less cost-effective compared to other deep learning methods for processing large data. Therefore, we improved these self-supervised methods by altering the training data construction process, thereby retaining its cost advantage. Specifically, we modified the selection process of zones used for training data construction. Through correlation analysis, these methods can obtain higher-quality zones from the original zone for training, indicating that the network needs to be trained only once to process any data in the original zone. Without the requirement for additional processing to ensure noise attenuation, these self-supervised methods can maintain their cost advantage when processing large data. We applied one of these self-supervised methods to synthetic and field examples to demonstrate the enhancement’s effectiveness. Experimental results show that the improved method performs as well as the conventional self-supervised method in suppressing random noise and constructing reflection events but with a significant cost advantage. more...
- Published
- 2024
- Full Text
- View/download PDF
41. Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences
- Author
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Jian Gao, Zhenchun Li, Min Zhang, Wanyue Gao, and Yixuan Gao
- Subjects
Seismic data ,random noise ,deep learning ,self-supervised learning ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Suppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the large zone into several smaller zones for individual training and processing, thereby achieving lower training costs. However, as the volume of seismic data that needs to be processed continues to increase, the cost advantage of this method becomes less apparent. This is because a larger data volume necessitates more independent training, ultimately increasing the overall training cost. Therefore, we propose a denoising method based on self-supervised learning to overcome the aforementioned problem. This method can directly acquire higher-quality training data from large zones by leveraging similarity differences, decreasing the need to divide the large zone into smaller parts for individual processing. As a result, it can effectively reduce the times for individual processing, leading to a decrease in the overall training cost. Compared to traditional denoising methods and self-supervised learning methods, the experimental results on both synthetic and field data demonstrate that the proposed denoising method exhibits superior performance in random noise attenuation and reduction in training costs. more...
- Published
- 2024
- Full Text
- View/download PDF
42. STRUCTURAL STYLE OF WABI FIELD, OFFSHORE NIGER DELTA NIGERIA, USING SEISMIC AND WELL-LOG DATA
- Author
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Amakiri, S., Uko, E. D and Tamunobereton-ari
- Subjects
well logs ,seismic data ,wabi field ,faults ,horizons ,structural style ,reservoirs ,niger delta ,Geology ,QE1-996.5 - Abstract
This study is focused on the interpretation of structural style in Wabi field in the Niger Delta Nigeria using seismic and well log data. From the results, faults and horizons correlated on Wabi wells tied perfectly to reflections on the seismic. The faulting pattern shows that the structural geometry over the Wabi field consist of an elongated N-W to S-E trending, collapsed crest, roll-over structure with two crests separated by a central saddle. Based on their lateral extent and throws Faults in Wabi Field is classified into boundary faults that confined the Wabi structure, synthetic and antithetic faults are within the crestal roll-over structure. The interplay of these faults, combined with the structural dip, provides the closure for the hydrocarbon accumulation within the field. Hydrocarbons in the field are encountered between 9,560 and 12,750ftss, and are contained within 3 stacked B, O, and R1 reservoirs in crestally collapsed rollover anticline. The reservoirs are predominantly shore face and are correlatable throughout the Field. Well correlation and sand analysis showed that sand R1 was the thickest sand unit by 320ft. Sand B is the thinnest sand unit in Wabi Field, it is 50ft. Results from this study provides interpretation and modelling of structural and stratigraphic interplays that would help in understanding of the feature and factors that control them reservoirs thereby creating room for predictive models that can be applied in other reservoirs at greater depth prospects. This helps to make informed decisions for optimum exploration and development of hydrocarbons in the field of study. more...
- Published
- 2023
- Full Text
- View/download PDF
43. Knowledge graphs for seismic data and metadata
- Author
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William Davis and Cassandra R. Hunt
- Subjects
Seismology ,Seismic data ,Knowledge graphs ,Ontologies ,Semantic models ,Geography. Anthropology. Recreation ,Geology ,QE1-996.5 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define a semantic model of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology. more...
- Published
- 2024
- Full Text
- View/download PDF
44. Spatiotemporal Characteristics of Tectonic Tremors in the Collisional Orogen of Taiwan.
- Author
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Ide, Satoshi and Chen, Kate Huihsuan
- Subjects
- *
OROGENIC belts , *TREMOR , *SEISMIC networks , *EARTH movements , *SEISMOGRAMS - Abstract
Taiwan offers a distinctive tectonic setting as a collisional orogen, ideal for studying tectonic tremors and the slow deformation process in the mountain‐building process. Using continuous seismic data at many stations, which have become available recently, and employing the envelope correlation method, we detected ∼7,000 tremor events from 2012 to 2022, with waveform characteristics similar to tectonic tremors worldwide. Beyond the previously known tremor zone beneath the southern Central Range, where newly detected tremors align along a low‐angle thrust plane, we identified several new tremor "hotspots" spanning 200 km along the mountain belt. These hotspots are situated at the termination of the subducting slabs and around the deep (25–50 km) extension of the Central Range fault, where repeating earthquakes occur at a depth of 10–25 km. Our findings suggest a strong linkage between the tremor generation mechanism and the mountain‐building process, potentially influenced by underground fluid and temperature anomalies. Plain Language Summary: Since around 2000, tectonic tremors have been discovered worldwide as geophysical phenomena strongly related to slow deformation and fluid movement inside the Earth. While a small cluster of tremors has been identified in Taiwan, where a rapid mountain‐building process occurs, the comprehensive distribution of tremors has remained unknown. Utilizing recently released seismic network records for all of Taiwan, we have clarified a broader pattern of tremor activity, previously unknown, in the region. The tremors are distributed planarly, corresponding to the mountain‐building processes and deep underground deformation in Taiwan. Key Points: Using newly available continuous seismograms, we identified ∼7,000 tectonic tremors in TaiwanSeveral tremor hotspots spanning 200 km were newly identified along the mountain beltTremor distribution suggests planer structures that are contributing to the rapid mountain‐building process [ABSTRACT FROM AUTHOR] more...
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- 2024
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45. Seismic data compression: an overview.
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Sebai, Dorsaf, Zouaoui, Manel, and Ghorbel, Faouzi
- Abstract
Seismic Data (SD) have been for several decades used as one of the main inspection and exploration tools in various fields, particularly petroleum industry and geoscience. However, seismic datasets are huge and involve many terabytes, whose handling and storage are expensive for industry activities and computers capabilities. Driven by the large volume of SD, several compression methods have been proposed the last five decades to significantly reduce the SD size, while aiming the highest possible preservation of rocks structural and lithological characteristics. Considering the importance of SD compression, this paper is expected to make the first overview of a large number of relevant state-of-the-art papers related to SD compression, where the papers are divided into two main classes, with respect to the approach they use to extract the SD relevant features. Our aim is to review recent achievements in SD compression, and to go over covering scope, key techniques and performances of the main representative methods on this topic. This, along with issues of these latter, can help raise some open challenges and future directions for upcoming SD compression efforts. [ABSTRACT FROM AUTHOR] more...
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- 2024
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46. How Did the Late Paleozoic to Early Mesozoic Tectonism Constrain the Carboniferous Stratigraphic Evolution in the Eastern Qaidam Basin, NW China?
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Zhong, Chang, Tang, Xiaoyin, and Wang, Jiaqi
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MESOZOIC Era , *PALEOZOIC Era , *CARBONIFEROUS Period , *OROGENIC belts , *LAND subsidence - Abstract
The eastern Qaidam Basin (EQB), along with its surrounding orogenic belts, witnessed complicated tectonic movements in the period from the late Paleozoic to the early Mesozoic. As strategic succeeding strata, the Carboniferous strata (CST) in the EQB have gradually become a research hotspot in recent years. However, the question of how tectonism controlled the tempo-spatial evolution of the CST has yet to be studied. To resolve these issues, we collated statistics related to unconformities, seismic interpretation, and basin modeling in this study. The results show that the structure of the CST was mostly controlled by NNE-striking faults, namely the Zongjia and Ainan Fault, in the period from the Carboniferous to the Triassic time. During the Carboniferous time, the sedimentation of the CST was controlled by medium-high angle potential normal faults. The CST experienced two stages of tectonic subsidence and subsequent burial: the highest average subsidence and burial rate of 45 m/Ma and 12 m/Ma occurred at 340~285 Ma, decreasing to 15 m/Ma and 7.5 m/Ma between 305 Ma and 250 Ma. However, the maximum burial (~5500 m) took place at ~250 Ma. From the end of the late Permian to the late Triassic (254~195 Ma), the overall exhumation rate of the CST has averaged 38.71 m/Ma, and 75 m/Ma in the southern margin of the Huobuxun Depression. The CST near the piedmont margins of the EQB suffered essential denudation at 254~195 Ma, resulting in small amounts of the residual CST. In these areas, the CST were deformed with a steepening dip during this time and were characterized by the combinations of syncline-anticlinal asymmetric folds with the high-angle interlimb. These findings indicated that the tempo-spatial evolution of the CST was possibly influenced by the sedimentary and tectonic transition, and was a combined response to Paleo-Tethys Ocean subduction, and arc-continental collisions since the late Paleozoic to early Mesozoic periods. [ABSTRACT FROM AUTHOR] more...
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- 2024
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47. Regularized deep learning for unsupervised random noise attenuation in poststack seismic data.
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Song, Chengyun, Guo, Shutao, Xiong, Chuanchao, and Tuo, Jiying
- Subjects
DEEP learning ,SIGNAL-to-noise ratio ,NOISE control ,ELECTRONIC data processing ,DATA scrubbing ,PROBLEM solving - Abstract
Deep learning methods achieve excellent noise reduction performances in seismic data processing compared with traditional methods. However, deep learning usually requires a large number of pairwise noisy-clean training data, which is an extremely challenging task. In this paper, an unsupervised approach without clean seismic data is proposed to suppress random noise. Seismic data is divided into odd and even traces, which serve as the input and output of the depth network, so that the proposed algorithm can be trained directly on the original data. What is more, the proposed method introduces two regularization terms to solve the over-smoothing problem caused by reconstruction of adjacent traces. The first term considers an ideal denoising network that does not cause oversmooth as a constraint, while the second term considers the structural information existing in seismic data. Experiments on synthetic post-stack data illustrate that the proposed method obtain a higher signal-to-noise ratio than the comparison methods. In the application of field post-stack seismic data, the proposed method can effectively maintain the seismic amplitude and generate good spectral characteristics. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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48. Earthquake time-series forecast in Kazakhstan territory: Forecasting accuracy with SARIMAX.
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Nurtas, Marat, Zhantaev, Zhumabek, and Altaibek, Aizhan
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EARTHQUAKE prediction ,BOX-Jenkins forecasting ,STANDARD deviations ,EMERGENCY management ,HAZARD mitigation - Abstract
This research paper presents an analytical approach to earthquake time-series forecasting using the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The objective of this study is to investigate the effectiveness of the SARIMAX model in earthquake forecasting by considering relevant exogenous variables, such as historical seismic activity, geological characteristics, and geodetic measurements. We start introducing the SARIMAX models, explaining its mathematical formulation and the incorporation of exogenous variables. The research methodology involves collecting earthquake time-series data from seismological databases and preprocessing the data for analysis. Various SARIMAX models are constructed and evaluated using statistical measures, such as root mean square error and mean absolute error, to assess their forecasting accuracy. Additionally, the impact of different exogenous variables on the predictive performance of the models is analyzed. The results of this research contribute to the field of earthquake prediction by demonstrating the applicability and efficacy of the SARIMAX model in capturing the temporal patterns and dynamics of seismic events. The practical reason of the results provides valuable information for decision-makers and stakeholders involved in disaster preparedness and mitigation strategies. [ABSTRACT FROM AUTHOR] more...
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- 2024
- Full Text
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49. Noise reduction method based on curvelet theory of seismic data.
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Zhao, Siwei, Zhen, Dayong, Yin, Xiaokang, Chen, Fangbo, Iqbal, Ibrar, Zhang, Tianyu, Jia, Mingkun, Liu, Siqin, Zhu, Jie, and Li, Ping
- Subjects
- *
NOISE control , *CURVELET transforms , *NOISE , *MICROSEISMS , *FAST Fourier transforms - Abstract
One of the significant challenges in processing and analyzing seismic data is the contamination of the seismic signal with noise from various sources. Coherent or incoherent noise, multiples, and other types of noise can all be eliminated using a number of techniques, but it is still complicated to attenuate random noise to the desired level. The non-homogeneous curvelet transform is first applied in this study, and the regularized calculation is then carried out using the inverted operator. Following the application of the linear calculation method, the threshold value is modified to remove the coefficient noise during the iteration process, allowing the homogeneous coefficients and noise-free seismic data to be obtained. We choose some real data as an example and apply the suggested curvelet theory to obtain homogeneous data in order to verify the denoising effect. Tests on real data show that, when the suggested approach interpolates sampled data to be homogeneous, random noise can be effectively reduced. The important benefit of this approach is that features like multi-resolution, and locality introduce minimal overlapping between coefficients representing signal and noise in curvelet domain. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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50. Assessment of spectral attributes in identifying gas hydrates in seismic data from the Pegasus Basin, offshore New Zealand.
- Author
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Jackson, Emily, Bedle, Heather, and Ha, Thang
- Abstract
Methane gas hydrates are formed in the subsurface along shallow ocean basins or in permafrost settings, and are commonly identified in the seismic data by the bottom-simulating reflector (BSR). Various methods have been employed in the past to measure gas hydrates from lab analyses, well log, or velocity data, but few studies have demonstrated methods to identify gas hydrates in seismic data when the BSR is sparse or lacking. One approach is to measure the expected attenuation caused by hydrates in the gas hydrate stability zone (GHSZ). Statistical attributes that measure the asymmetry of the seismic amplitude spectrum are applied to quantify the attenuation responses throughout the GHSZ. Although the study area does not contain well log data, there are numerous studies that confirm hydrates exist throughout the Pegasus Basin. These attributes, in addition to amplitude-related attributes, demonstrate that frequency-related variations are the major contributors to attenuation response, rather than seismic amplitude or geology effects. The spectral attribute results show that strong positive skewness and kurtosis variations above the high amplitude BSR is likely due to attenuation through an interval of hydrates. Negative skewness and kurtosis may correspond to an interval that does not contain hydrates, therefore suggesting that the GHSZ in the Pegasus Basin consists of discontinuous hydrates, rather than one continuous layer from ocean bottom to BSR. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
- Full Text
- View/download PDF
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