Computers & Geosciences最新文献

{"title":"ScoreInver: 3D seismic impedance inversion based on scoring mechanism","authors":"Xinyuan Zhu ,&nbsp;Timing Li ,&nbsp;Kewen Li ,&nbsp;Guangyue Zhou ,&nbsp;Ruonan Yin","doi":"10.1016/j.cageo.2025.105896","DOIUrl":"10.1016/j.cageo.2025.105896","url":null,"abstract":"<div><div>In recent years, the introduction of deep learning has significantly advanced the field of seismic impedance inversion (SII). However, existing methods generally rely heavily on large volumes of expensive well logs, limiting their broader applicability, particularly in scenarios beyond mature or synthetic data. To reduce the dependency on well logs in deep learning-based SII research, this paper proposes a 3D data-driven SII approach based on the pseudo-labeling strategy in semi-supervised learning, termed the ScoreInver framework. The core of the ScoreInver framework lies in the design and training of a Scorer, which can precisely select high-quality pseudo-labels from seismic data, thereby enhancing data utilization and extracting geological information while minimizing the need for extensive well logs. This framework is highly versatile, capable of seamless integration into various semi-supervised learning architectures. Experimental results demonstrate that, when using only 9 well logs as training samples on synthetic data, the semi-supervised learning architectures based on the ScoreInver framework significantly outperforms traditional supervised learning methods, with improvements of 3.3% in Structural Similarity Index (SSIM) and a reduction of 29.1% in Mean Squared Error (MSE). Moreover, tests on field data reveal that the application of the ScoreInver framework yields more robust and reliable results, further validating its effectiveness and practicality in real-world exploration environments.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"198 ","pages":"Article 105896"},"PeriodicalIF":4.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429942","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":"Hybrid Newton method for the acceleration of well event handling in the simulation of CO2 storage using supervised learning","authors":"Antoine Lechevallier ,&nbsp;Sylvain Desroziers ,&nbsp;Thibault Faney ,&nbsp;Eric Flauraud ,&nbsp;Frédéric Nataf","doi":"10.1016/j.cageo.2025.105872","DOIUrl":"10.1016/j.cageo.2025.105872","url":null,"abstract":"<div><div>Geological storage of CO₂ is an essential instrument for efficient Carbon Capture and Storage policies. Numerical simulations provide the solution to the multi-phase flow equations that model the behavior of the CO₂ injection site. However, numerical simulations of fluid flow in porous media are computationally demanding: it can take up to several hours on a HPC cluster in order to simulate one injection scenario for a large CO₂ reservoir if we want to accurately model the complex physical processes involved. This becomes a limiting issue when performing a large number of simulations, e.g. in the process of ‘history matching’. Well events, such as opening and closure, cause important numerical difficulties due to their instant impact on the pressure and saturation unknowns. This often forces a drastic reduction of the time step size to be able to solve the non-linear system of equations resulting from the discretization of the continuous mathematical model. However, these specific well events in a simulation have a relatively similar impact across space and time. We propose a proof of concept methodology to alleviate the impact of well events during the numerical simulation of CO₂ storage in the underground by using a machine-learning based non-linear preconditioning. We complement the standard fully implicit solver by predicting an initialization of Newton’s method directly in the domain of quadratic convergence using supervised learning. More specifically, we replace the initialization in pressure by a linear approximation obtained through an implicit solver and we use a Fourier Neural Operator (FNO) to predict the saturation initialization. Furthermore, we present an open-source Python framework for conducting reservoir simulations and integrating machine-learning models. We apply our methodology to two test cases derived from a realistic CO₂ storage in saline aquifer benchmark. We reduce the required number of Newton iterations to handle a well opening by 53% for the first test case, i.e required number of linear system to solve and by 38% for the second test case.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105872"},"PeriodicalIF":4.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387528","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":"Linear filter theory for the forward Laplace transform and its use in calculating 1D EM responses","authors":"Niels Bøie Christensen","doi":"10.1016/j.cageo.2025.105865","DOIUrl":"10.1016/j.cageo.2025.105865","url":null,"abstract":"<div><div>The linear filter theory has previously been used for designing digital filters that allow Hankel and Fourier transforms to be calculated as discrete convolutions between sampled values of the kernel function and a set of filter coefficients. In this paper the linear filter theory is used to design filters for the forward Laplace transform that permit rapid and accurate calculations. Furthermore, it is shown that it is possible to estimate the computational errors. It is demonstrated that, in several cases, the Laplace transform developed in this paper can be used in the calculation of electromagnetic responses, traditionally calculated using Fast Hankel Transform filters. It is shown that for many instrument configurations, the Laplace transform approach is faster that the Fast Hankel Transform.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"198 ","pages":"Article 105865"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419744","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":"Efficient computation on large regular grids of higher-order spatial statistics via fast Fourier transform","authors":"Dany Lauzon ,&nbsp;Sebastian Hörning","doi":"10.1016/j.cageo.2025.105878","DOIUrl":"10.1016/j.cageo.2025.105878","url":null,"abstract":"<div><div>The complex spatial structures of natural variables are often caused by geological, physicochemical, meteorological, and biological processes that have shaped the emergence of the fields. The typical prediction of the spatial distributions of these phenomena is based on second-order geostatistical models. However, this approach has limitations, given the high complexity, non-Gaussian distributions, and nonlinear spatial connectivity models inherent in geological systems. Recently, researchers have suggested using higher-order spatial statistics, based on two- and three-point spatial statistics, to better capture spatial phenomena. Nevertheless, applying these methods requires intense numerical calculations, particularly in the case of extensive geostatistical models, and becomes especially intricate when utilized for conditioning realizations, such as in inverse problems. Spatial asymmetries and higher-order spatial cumulants, as well as their generalizations, are important higher-order statistics for characterizing non-Gaussian features. In this study, we focus on third-order statistics derived from two- and three-point spatial statistics. A MATLAB program has been developed to compute efficiently these spatial statistics using the FFT algorithm. The overall approach of these programs draws inspiration from the method successfully used for the fast calculation of variograms and cross-covariances using FFT. We recall the methodology associated with the computation of direct- and cross-variograms using FFT, as well as transiograms for categorical data. Codes are created to process regular grid data, whether it is complete or incomplete. Post-processing tools have been added to help geomodelers visualize the results. Using the FFT method is faster and delivers the same results as conventional spatial methods for this type of data. These programs are particularly valuable tools for geostatistical modeling and estimation when higher-order statistics are present in the spatial structures of natural variables, providing an efficient solution to the computational challenges associated with such applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"198 ","pages":"Article 105878"},"PeriodicalIF":4.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419293","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":"Deep learning contribution to the automatic picking of surface-wave dispersion for the characterization of railway earthworks","authors":"J. Cárdenas ,&nbsp;A. Burzawa ,&nbsp;N. Radic ,&nbsp;L. Bodet ,&nbsp;R. Vidal ,&nbsp;K. Diop ,&nbsp;M. Dangeard ,&nbsp;A. Dhemaied","doi":"10.1016/j.cageo.2025.105883","DOIUrl":"10.1016/j.cageo.2025.105883","url":null,"abstract":"<div><div>Railway Trackbed (RT), which collectively describes the subgrade structures that support rail tracks, is of great importance to the effective maintenance and rehabilitation of the rail network. Therefore, a comprehensive understanding of the mechanical condition of Railway Earthwork (RE) is necessary. The development of non-destructive and efficient methods for the characterization of REs is a priority. Previous studies have investigated the potential of surface waves for the characterization of RE. Preliminary results indicate that this approach is effective, particularly when using high yield acquisition setup such as landstreamer. However, these instruments generate an amount of data that necessitates optimized and automated processing. The potential of Deep Learning (DL) to automate the processing of surface wave data is being explored. In this study, the primary objective is to identify the energy maxima and propagation modes in dispersion images. A supervised convolutional neural network (CNN), designated as ‘U-Net’, was selected to perform segmentation tasks. This model, called ‘U2-pick’, integrates two U-net architectures: one for maxima identification and another for propagation mode identification. The training dataset was constructed using synthetic data that is representative of a French High-Speed-Line (HSL) RE structure. The preliminary outcomes on the synthetic datasets are encouraging, demonstrating accurate identification of energy maxima and mode classification. However, the predictions made on field datasets revealed that while the energy peaks were identified with a high degree of accuracy, the mode assignment proved to be less satisfactory, especially in the case of higher modes. Finally, a comparison of the accuracy and picking time was performed using more standard tools like maxima search, semi-automatic, and Machine Learning (ML) tools.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"198 ","pages":"Article 105883"},"PeriodicalIF":4.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395140","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":"An improved general method for measuring pore size distribution with digital images of porous media","authors":"Shuaibing Song,&nbsp;Tong Zhang,&nbsp;Qingyi Tu","doi":"10.1016/j.cageo.2025.105893","DOIUrl":"10.1016/j.cageo.2025.105893","url":null,"abstract":"<div><div>As one of the most fundamental and vital parameters for characterizing the pore structure characteristics of porous media materials, the accurate and efficient quantitative measurement of pore size distribution (PSD) has a wide and strong demand in various research fields. In this paper, by analogy with the pore size measurement principle of mercury intrusion porosimetry (MIP), an improved general method for measuring the PSD of porous media from their digital images is developed by using a cluster of predefined digital circular or spherical pores with different radius dimensions to pack the pore space step by step in an overlapping manner. To verify the validity and accuracy of the proposed method, a set of artificially generated pore structures with known PSD is used as the benchmark test dataset, and the results show that the PSD measured by the proposed method is consistent with the theoretical benchmark value. In addition, the pore structures of real porous media materials with large dimensions are selected as the test dataset as well, and the computational efficiency of the proposed method is comprehensively evaluated by comparing that of the existing advanced PSD measurement methods, the results of which show that the proposed method takes the least amount of time to complete the PSD measurement. In terms of accuracy and efficiency, the proposed method has good performance, indicating that it can be promoted as a general method for the PSD measurement of various porous media materials.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105893"},"PeriodicalIF":4.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387527","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":"Data space inversion for efficient predictions and uncertainty quantification for geothermal models","authors":"Alex de Beer ,&nbsp;Andrew Power ,&nbsp;Daniel Wong ,&nbsp;Ken Dekkers ,&nbsp;Michael Gravatt ,&nbsp;Elvar K. Bjarkason ,&nbsp;John P. O’Sullivan ,&nbsp;Michael J. O’Sullivan ,&nbsp;Oliver J. Maclaren ,&nbsp;Ruanui Nicholson","doi":"10.1016/j.cageo.2025.105882","DOIUrl":"10.1016/j.cageo.2025.105882","url":null,"abstract":"<div><div>The ability to make accurate predictions with quantified uncertainty provides a crucial foundation for the successful management of a geothermal reservoir. Conventional approaches for making predictions using geothermal reservoir models involve estimating unknown model parameters using field data, then propagating the uncertainty in these estimates through to the predictive quantities of interest. However, the unknown parameters are not always of direct interest; instead, the predictions are of primary importance. Data space inversion (DSI) is an alternative methodology that allows for the efficient estimation of predictive quantities of interest, with quantified uncertainty, that avoids the need to estimate model parameters entirely. In this paper, we illustrate the applicability of DSI to geothermal reservoir modelling. We first review the processes of model calibration, prediction and uncertainty quantification from a Bayesian perspective, and introduce data space inversion as a simple, efficient technique for approximating the posterior predictive distribution. We then introduce a modification of the typical DSI algorithm that allows us to sample directly and efficiently from the DSI approximation to the posterior predictive distribution, and apply the algorithm to two model problems in geothermal reservoir modelling. We evaluate the accuracy and efficiency of our DSI algorithm relative to other common methods for uncertainty quantification and study how the number of reservoir model simulations affects the resulting approximation to the posterior predictive distribution. Our results demonstrate that data space inversion is a robust and efficient technique for making predictions with quantified uncertainty using geothermal reservoir models, providing a useful alternative to more conventional approaches.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105882"},"PeriodicalIF":4.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377638","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":"GPR-FWI-Py: Open-source Python software for multi-scale regularized full waveform inversion in Ground Penetrating Radar using random excitation sources","authors":"Xiangyu Wang ,&nbsp;Hai Liu ,&nbsp;Xu Meng ,&nbsp;Hesong Hu","doi":"10.1016/j.cageo.2025.105870","DOIUrl":"10.1016/j.cageo.2025.105870","url":null,"abstract":"<div><div>Full Waveform Inversion (FWI) of Ground Penetrating Radar (GPR) is crucial for enhancing subsurface imaging, yet its applications often confronts computational and usability challenges. This paper introduces GPR-FWI-Py, a comprehensive 2D GPR FWI code package that addresses these challenges through a multi-scale strategy, a random excitation source strategy, and Total Variation (TV) regularization. Optimized for high-performance computing, the software is developed in pure Python, ensuring both high efficiency and accessibility. Key features include user-friendly design and readability, which empower users to easily adapt and maintain the software to meet specific project needs. Performance evaluations on layered and Over-Thrust models confirm that our strategies significantly improve FWI results. The modular architecture of GPR-FWI-Py not only simplifies the integration of the FWI algorithm into GPR imaging but also enhances adaptability by supporting the introduction of additional functionalities.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105870"},"PeriodicalIF":4.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377635","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 deep learning-based parametric inversion for forecasting water-filled bodies position using electromagnetic method","authors":"Lu Gan ,&nbsp;Rongjiang Tang ,&nbsp;Hao Li ,&nbsp;Fusheng Li ,&nbsp;Yunbo Rao","doi":"10.1016/j.cageo.2025.105881","DOIUrl":"10.1016/j.cageo.2025.105881","url":null,"abstract":"<div><div>The transient electromagnetic method (TEM) is extensively employed for identifying regions with low resistivity ahead of tunnel construction. Nevertheless, performing a 2D or 3D inversion of geo-electric models across the entire subsurface is impractical due to the limited space within the underground tunnel and the non-uniqueness associated with TEM inversion. We design a tunnel electromagnetic joint scan observation system and present a deep learning-based parametric inversion for improved tunnel electromagnetic imaging, designed specifically for tunnel prediction of water-filled structures. It utilizes a configuration wherein transmitters scan along the surface while receivers are positioned within the tunnel, employing time-domain and frequency-domain transmitters and a multi-component receiver. The DL model for the first time provides parametric imaging of two different view, forming a self-checking mechanism, which can help constrain the predictions and reduce the non-uniqueness of the inversion. Trained by synthetic data, our system shows impressive adaptability to predict the 3D spatial position of water-filled anomalies and strong robustness under different tunnel environments including metal inference and undulating terrain conditions.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105881"},"PeriodicalIF":4.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377636","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 veins recognition method for slope rock mass geological images in complex background noise","authors":"Niu Wen-jing ,&nbsp;Chen Yuan-xin ,&nbsp;He Ben-guo ,&nbsp;Chen Guang-mu ,&nbsp;Qiu Zhen-hua ,&nbsp;Fan Wen-yu","doi":"10.1016/j.cageo.2025.105885","DOIUrl":"10.1016/j.cageo.2025.105885","url":null,"abstract":"<div><div>The veins formed by magma intrusion often exist in the rock mass of slopes in the form of weak structural planes, thereby triggering landslide engineering disasters. Therefore, accurately identifying veins information from complex slope geological images is crucial. In light of this, this paper tackles the challenges encountered by traditional edge detection and deep learning algorithms in effectively detecting veins amidst complex background noise. These challenges include poor detection performance, low-quality manually annotated data, and the significant workload associated with manual annotation. To address these issues, we propose a vein intelligent recognition algorithm that combines OpenCV, AnyLabeling, and Mask R-CNN. By employing OpenCV, this study utilizes denoising and cropping techniques on the original images characterized by complex background noise. By preprocessing the raw dataset, it removes image noise and optimizes feature details within the images, thereby enhancing the quality of the dataset; Utilizing AnyLabeling for objective and automated annotation of veins information, this process eliminates the subjectivity associated with manual annotations, resulting in the creation of a high-quality library of veins image recognition samples; Building upon this foundation, we develop a Mask R-CNN model for veins contour segmentation. This model accurately and efficiently identifies veins information in complex geological images with background noise. Its successful application has been rigorously validated on a specific open-pit mine slope. The results indicate that this method successfully addresses the challenge of accurately identifying veins contours under complex background noise; The preprocessing technique, which combines OpenCV and AnyLabeling, demonstrates a significant enhancement in both the accuracy and efficiency of dataset annotation. This improvement contributes to heightened precision in the recognition results. Effectively discerned intricate veins details within the specific open-pit mine slope, resulting in an impressive 93.6% increase in MIoU value. This notable enhancement substantially improves the precision of slope stability calculation outcomes. The research findings are of significant importance for the analysis of veins-type geological hazards.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105885"},"PeriodicalIF":4.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377637","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}