Computers & Geosciences最新文献

{"title":"Million-core scalable 3D anisotropic reverse time migration on the Sugon exascale supercomputer","authors":"Sihai Wu ,&nbsp;Jiubing Cheng ,&nbsp;Jianwei Ma ,&nbsp;Tengfei Wang ,&nbsp;Xueshan Yong ,&nbsp;Yang Ji","doi":"10.1016/j.cageo.2024.105754","DOIUrl":"10.1016/j.cageo.2024.105754","url":null,"abstract":"<div><div>Reverse time migration (RTM) plays a crucial role in high-resolution seismic imaging of the Earth’s interior. However, scaling it across millions of cores in parallel to process large-scale seismic datasets poses significant computational challenges, because the conventional storage solutions are insufficient to deal with the I/O and memory bottlenecks. To address this issue, we present a highly scalable 3D RTM algorithm for vertically transverse isotropic (VTI) media, optimized for the Sugon exascale supercomputer, utilizing over 1,024,000 cores with optimal weak-scaling efficiency. Through cache optimizations tailored for the new deep computing unit (DCU) accelerator architecture, our approach achieves a maximum speedup of 6x compared to conventional methods on a single accelerator. Moreover, based on the lossy compression and boundary-saving techniques, we reduce storage requirements by 266 times, which allows for the effective utilization of million-core computing resources and ensures scalability efficiency when handling large-scale datasets for complex geophysical tasks. Finally, when applied to a industrial dataset, the method demonstrates robust scalability and high efficiency, making it well-suited for large-scale geophysical exploration.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105754"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding","authors":"Bahman Abbassi,&nbsp;Li-Zhen Cheng","doi":"10.1016/j.cageo.2024.105768","DOIUrl":"10.1016/j.cageo.2024.105768","url":null,"abstract":"<div><div>Understanding deformation networks, visible as curvilinear lineaments in images, is crucial for geoscientific explorations. However, traditional manual extraction of lineaments is expertise-dependent, time-consuming, and labor-intensive. This study introduces an automated method to extract and identify geological faults from aeromagnetic images, integrating Bayesian Hyperparameter Optimization (BHO), Principal Component Wavelet Analysis (PCWA), and Hysteresis Thresholding Algorithm (HTA). The continuous wavelet transform (CWT), employed across various scales and orientations, enhances feature extraction quality, while Principal Component Analysis (PCA) within the CWT eliminates redundant information, focusing on relevant features. Using a Gaussian Process surrogate model, BHO autonomously fine-tunes hyperparameters for optimal curvilinear pattern recognition, resulting in a highly accurate and computationally efficient solution for curvilinear lineament mapping. Empirical validation using aeromagnetic images from a prominent fault zone in the James Bay region of Quebec, Canada, demonstrates significant accuracy improvements, with 23% improvement in <em>F</em>_<em>β</em> Score over the unoptimized PCWA-HTA and a marked 300% improvement over traditional HTA methods, underscoring the added value of fusing BHO with PCWA in the curvilinear lineament extraction process. The iterative nature of BHO progressively refines hyperparameters, enhancing geological feature detection. Early BHO iterations broadly explore the hyperparameter space, identifying low-frequency curvilinear features representing deep lineaments. As BHO advances, hyperparameter fine-tuning increases sensitivity to high-frequency features indicative of shallow lineaments. This progressive refinement ensures that later iterations better detect detailed structures, demonstrating BHO's robustness in distinguishing various curvilinear features and improving the accuracy of curvilinear lineament extraction. For future work, we aim to expand the method's applicability by incorporating multiple geophysical image types, enhancing adaptability across diverse geological contexts.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105768"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)","authors":"Hadi Farhadi ,&nbsp;Hamid Ebadi ,&nbsp;Abbas Kiani ,&nbsp;Ali Asgary","doi":"10.1016/j.cageo.2024.105742","DOIUrl":"10.1016/j.cageo.2024.105742","url":null,"abstract":"<div><div>Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105742"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive constraint-guided surrogate enhanced evolutionary algorithm for horizontal well placement optimization in oil reservoir","authors":"Qinyang Dai ,&nbsp;Liming Zhang ,&nbsp;Peng Wang ,&nbsp;Kai Zhang ,&nbsp;Guodong Chen ,&nbsp;Zhangxing Chen ,&nbsp;Xiaoming Xue ,&nbsp;Jian Wang ,&nbsp;Chen Liu ,&nbsp;Xia Yan ,&nbsp;Piyang Liu ,&nbsp;Dawei Wu ,&nbsp;Guoyu Qin ,&nbsp;Xingyu Liu","doi":"10.1016/j.cageo.2024.105740","DOIUrl":"10.1016/j.cageo.2024.105740","url":null,"abstract":"<div><div>In the face of escalating global energy demands, this study introduces an Adaptive Constraint-Guided Surrogate Enhanced Evolutionary Algorithm (ACG-EBS) for optimizing horizontal well placements in oil reservoirs. Addressing the complex challenge of maximizing oil production, the ACG-EBS integrates geological, engineering, and economic considerations into a novel optimization framework. This algorithm stands out for its adept navigation through a complex and discrete decision space of horizontal well placements, an area where traditional methods often encounter challenges. Key innovations include the Adaptive Constraint Initialization Mechanism (ACIM) and the Evolutionary Constraint-Tailored Candidate Refinement strategy (ECTCR), which collectively elevate the feasibility of candidate solutions. An enhanced balance strategy harmonizes comprehensive and niche surrogate models, optimizing the balance between exploration and exploitation. Through testing on both two-dimensional and three-dimensional reservoir models, the ACG-EBS has proven highly effective in identifying optimal well placements that align with field deployment realities and maximize economic returns. This contribution significantly supports the ongoing evolution of oilfield development optimization, showcasing the algorithm's potential to enhance oil production and economic outcomes.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105740"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent fault prediction with wavelet-SVM fusion in coal mine","authors":"Chengyang Han ,&nbsp;Guangui Zou ,&nbsp;Hen-Geul Yeh ,&nbsp;Fei Gong ,&nbsp;Suzhen Shi ,&nbsp;Hao Chen","doi":"10.1016/j.cageo.2024.105744","DOIUrl":"10.1016/j.cageo.2024.105744","url":null,"abstract":"<div><div>Fault prediction in coal mining is crucial for safety, and recent technological advancements are steering this field towards supervised intelligent interpretation, moving beyond traditional human-machine interaction. Currently, support vector machine (SVM) predictions often rely on seismic attribute data; however, the poor quality of some fault data characteristics hampers their predictive capability. To localize the fault based on original seismic data and improve SVM prediction we propose the W-SVM algorithm, which integrates wavelet transform and SVM. Through wavelet transform, we localize fault features in seismic data, which are then used for SVM prediction. Validation using real data confirms the feasibility of the W-SVM approach. The W-SVM model successfully identifies 34 known faults. Beyond achieving high prediction accuracy, the model exhibits improved stability and generalization. The difference among the evaluation metrics for training, validation, and testing is within 5%. Moreover, this study localizes the response of faults through wavelet transform, simplifies the dataset preparation process, improves computational efficiency, and increases overall applicability. This advancement further promotes the development of intelligent identification of faults in coal mines.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105744"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SwinInver: 3D data-driven seismic impedance inversion based on Swin Transformer and adversarial training","authors":"Xinyuan Zhu ,&nbsp;Kewen Li ,&nbsp;Zhixuan Yang ,&nbsp;Zhaohui Li","doi":"10.1016/j.cageo.2024.105743","DOIUrl":"10.1016/j.cageo.2024.105743","url":null,"abstract":"<div><div>As deep learning becomes increasingly prevalent in seismic impedance inversion, 3D data-driven approaches have garnered substantial interest. However, two critical challenges persist. First, existing methodologies predominantly rely on Convolutional Neural Networks (CNNs), which, due to the inherent locality of convolutional operations, are inadequate in capturing the global context of seismic data. This limitation notably hinders their performance in inverting complex subsurface structures, such as salt bodies. Second, the current inversion frameworks are prone to overfitting, particularly when trained on limited seismic datasets. To address these challenges, we propose SwinInver, a novel backbone network that integrates the Swin Transformer as its fundamental unit, coupled with a high-resolution network design to facilitate comprehensive global modeling of intricate subsurface structures. Furthermore, we incorporate adversarial training to enhance the inversion process and effectively mitigate overfitting. Experimental evaluations demonstrate that SwinInver significantly surpasses conventional CNN-based approaches in both synthetic and field data scenarios, providing a more accurate and reliable framework for seismic impedance inversion.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105743"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid inversion algorithm to obtain the resistivity of the uninvaded zone based on the array induction log","authors":"Xinmin Ge ,&nbsp;Mohmmed Ishag ,&nbsp;Haiyan Li ,&nbsp;Jundong Liu ,&nbsp;Cuixia Qu ,&nbsp;Badreldein Mohamed","doi":"10.1016/j.cageo.2024.105766","DOIUrl":"10.1016/j.cageo.2024.105766","url":null,"abstract":"<div><div>This study investigates the impact of the drilling mud invasion on the borehole-measured resistivity. The primary objective is to retrieve the true resistivity of the formation, which helps in identifying different fluids in the reservoir. To achieve this goal, We proposed a hybrid inversion approach integrating the Levenberg-Marquardt and Markov Chain Monte Carlo algorithms with a five-parameter formation resistivity model. Synthetic and real-world data are utilized to assess the method's robustness and reliability. The simulated result indicated that the method is reliable when the data noise level is less than 5%.</div><div>The method applied to real-world data revealed that the resistivity profile on the water zone showed a slight increase in the inverted resistivity from measured resistivity. Meanwhile, in the oil zone, the calculated resistivity revealed a high deviation from the measured resistivity, indicating the effects of mud invasion. The introduced methods are only applicable when the invasions of mud occur within the range of the logging tool's depth of investigation. Moreover, the method may give no reliable result when the invasion exceeds the tool's investigation depth. It indicates its limitation.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105766"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel empirical curvelet denoising strategy for suppressing mixed noise of microseismic data","authors":"Liyuan Feng ,&nbsp;Binhong Li ,&nbsp;Huailiang Li ,&nbsp;Jian He","doi":"10.1016/j.cageo.2024.105751","DOIUrl":"10.1016/j.cageo.2024.105751","url":null,"abstract":"<div><div>We present a novel denoising strategy based on empirical curvelet transform (ECT) for noisy microseismic data. Our approach can simultaneously suppress high-frequency, low-frequency, and shared-bandwidth noises and preserve detailed information on the noisy microseismic data. Initially, we design a new threshold estimation method by adding a scale factor for ECT threshold denoising. Subsequently, we construct an adaptive parameter model employing the similarity standard deviation for the non-local means (NLM) algorithm. Then, we divide the coefficients obtained from the ECT decomposition into two sets based on the energy spectrum, subjecting each set to improved adaptive thresholding and improved NLM denoising algorithms. Eventually, we reconstruct the denoised signals using the empirical curvelet inverse transform. Our results demonstrate that under a signal-to-noise ratio (SNR) of <span><math><mo>−</mo></math></span>10 dB, the proposed strategy achieves a correlation coefficient of 0.9524, a root mean square error of 0.198, an SNR of 1.36 dB, and reduces the first arrival picking error to 0.00382 s. Furthermore, application on the real microseismic data further confirms that the proposed method can clarify the corresponding first arrival.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105751"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal feature integration network for lithology identification from point cloud data","authors":"Ran Jing ,&nbsp;Yanlin Shao ,&nbsp;Qihong Zeng ,&nbsp;Yuangang Liu ,&nbsp;Wei Wei ,&nbsp;Binqing Gan ,&nbsp;Xiaolei Duan","doi":"10.1016/j.cageo.2024.105775","DOIUrl":"10.1016/j.cageo.2024.105775","url":null,"abstract":"<div><div>Accurate lithology identification from outcrop surfaces is crucial for interpreting geological 3D data. However, challenges arise due to factors such as severe weathering and vegetation coverage, which hinder achieving ideal identification results with both accuracy and efficiency. The integration of 3D point cloud technology and deep learning methodologies presents a promising solution to address these challenges. In this study, we propose a novel multimodal feature integration network designed to distinguish various rock types from point clouds. Our network incorporates a multimodal feature integration block equipped with multiple attention mechanisms to extract representative deep features, along with a hierarchical feature separation block to leverage these features for precise segmentation of points corresponding to different lithologies. Furthermore, we introduce a specialized loss function tailored for rock type identification to enhance network training. Through experiments involving point cloud sampling strategies and loss function evaluation, we identify the optimal network configuration. Comparative analyses against baseline methods demonstrate the superiority of our proposed network across diverse study areas reconstructed from UAV images and laser scanner data, exhibiting improved visual appearance and metric values (Accuracy = 0.978, mean Accuracy = 0.895, mean IoU = 0.857). These findings underscore the efficacy of the multimodal feature integration network as a promising approach for lithology identification tasks in various digital outcrop models derived from heterogeneous data sources.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105775"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent diffusion model for conditional reservoir facies generation","authors":"Daesoo Lee ,&nbsp;Oscar Ovanger ,&nbsp;Jo Eidsvik ,&nbsp;Erlend Aune ,&nbsp;Jacob Skauvold ,&nbsp;Ragnar Hauge","doi":"10.1016/j.cageo.2024.105750","DOIUrl":"10.1016/j.cageo.2024.105750","url":null,"abstract":"<div><div>Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas sector. Traditional two-point geostatistics, while foundational, often struggle to capture complex geological patterns. Multi-point statistics offers more flexibility, but comes with its own challenges related to pattern configurations and storage limits. With the rise of Generative Adversarial Networks (GANs) and their success in various fields, there has been a shift towards using them for facies generation. However, recent advances in the computer vision domain have shown the superiority of diffusion models over GANs. Motivated by this, a novel Latent Diffusion Model is proposed, which is specifically designed for conditional generation of reservoir facies. The proposed model produces high-fidelity facies realizations that rigorously preserve conditioning data. It significantly outperforms a GAN-based alternative. Our implementation on GitHub: <span><span>github.com/ML4ITS/Latent-Diffusion-Model-for-Conditional-Reservoir-Facies-Generation</span><svg><path></path></svg></span></div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105750"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}