Computers & Geosciences最新文献

{"title":"Digital rock physics: Calculation of effective elastic properties of heterogeneous materials using graphical processing units (GPUs)","authors":"Yury Alkhimenkov","doi":"10.1016/j.cageo.2024.105749","DOIUrl":"10.1016/j.cageo.2024.105749","url":null,"abstract":"<div><div>An application based on graphical processing units (GPUs) applied to 3-D digital images is described for computing the linear anisotropic elastic properties of heterogeneous materials. The application can also retrieve the property contribution tensors of individual inclusions of any shape. The code can be executed on professional GPUs as well as on a basic laptop or personal computer Nvidia GPUs. The application is extremely fast: a calculation of the effective elastic properties of volumes consisting of about 7 million voxel elements (191³) takes less than 4 s of computational time using a single A100 GPU; 3 min for 100 million voxel elements (479³) using a single A100 GPU; 14 min for 350 million voxel elements (703³) using a single A100 GPU. Several comparisons against analytical solutions are provided. In addition, an evaluation of the anisotropic effective elastic properties of a 3-D digital image of a cracked Carrara marble sample is presented. The software can be downloaded from a permanent repository Zenodo, the link with a doi is given in the manuscript.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105749"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659019","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 novel algorithm for identifying arrival times of P and S Waves in seismic borehole surveys","authors":"P. Anbazhagan,&nbsp;Sauvik Halder","doi":"10.1016/j.cageo.2024.105746","DOIUrl":"10.1016/j.cageo.2024.105746","url":null,"abstract":"<div><div>The arrival times of P and S waves, originating from earthquakes, diverse seismic tests, and events, are crucial geotechnical parameters. Derived from the inversion of these travel times, V_P (P-wave velocity) and V_S (S-wave velocity) are pivotal in geotechnical engineering, correlating directly with dynamic soil properties and enabling calculations of Poisson's Ratio (<strong>ν</strong>), Young's modulus (E), Shear modulus (μ), and Bulk modulus (B). Both V_P and V_S are crucial for evaluating soil behaviour under various conditions, aiding in modelling soil for settlement, wave propagation, seismic wave interaction, liquefaction potential analysis, seismic response analysis, and many more. The selection of arrival times for seismic tests, including Crosshole, Downhole, and Uphole tests, is done manually, which is time-consuming and potentially erroneous. To address this issue, various algorithms have been developed to automate the picking process. Some of these algorithms use wavelet transforms and Bayesian information criteria, while others use machine learning techniques such as artificial neural networks. These methods vary in terms of their accuracy, yet each one possesses inherent limitations when it comes to processing data with different levels of signal-to-noise ratio. The advancement of automated algorithms for determining arrival times is an ongoing and dynamic field of research. Apart from the existing research focused on determining the arrival time of P waves, there is a dearth of studies investigating the detection of S wave arrival times. To fill this gap, this study proposes new approaches for detecting both P and S wave arrival time(s). One approach entails the utilization of an iterative optimization algorithm to accurately fit a curve to the leading edge of the P waveform. The arrival time is determined by calculating a fraction relative to the highest point obtained from the fitted peak. The second approach entails identifying the exact moment of the S wave's arrival by determining the points of intersection between the oppositely polarized S waveforms. These methods provide a promising approach for automatically detecting both P and S wave arrival time(s), which has the potential to improve the precision and efficiency in picking up arrival time(s).</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105746"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554641","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":"Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network","authors":"George Brencher ,&nbsp;Scott T. Henderson ,&nbsp;David E. Shean","doi":"10.1016/j.cageo.2024.105771","DOIUrl":"10.1016/j.cageo.2024.105771","url":null,"abstract":"<div><div>Atmospheric noise in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscures real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, a reliable technique for atmospheric correction in high-relief terrain is increasingly important. We developed and implemented a statistical machine learning atmospheric correction approach that relies on the differing spatial and topographic characteristics of slow-moving periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data, does not require external atmospheric reanalysis data, and can correct both stratified and turbulent atmospheric noise.</div><div>Using Sentinel-1 data from 2017 to 2022, we trained a convolutional neural network (CNN) on observed atmospheric noise from 330 short-baseline interferograms and observed displacement signals from time series inversion of 1322 interferograms. We applied our trained CNN to correct 251 additional interferograms over an out-of-region application area, which were inverted to create displacement time series. We used the Rocky Mountains in New Mexico, Colorado, and Wyoming as our training, validation, testing, and application areas. When applied to our testing dataset, our correction offered performance improvements of 131%, 208%, and 68% in structural similarity index measure over corrections using atmospheric reanalysis data, phase correlation with topography, and high-pass filtering, respectively. The CNN-corrected time series reveals previously obscured kinematic behavior of rock glaciers and other features in the application dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105771"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659533","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":"New fast imaging techniques for electrical source transient electromagnetic data: Approaches and application","authors":"Yu-lian Zhu ,&nbsp;Wei-ying Chen ,&nbsp;Wan-ting Song ,&nbsp;Si-xu Han","doi":"10.1016/j.cageo.2024.105770","DOIUrl":"10.1016/j.cageo.2024.105770","url":null,"abstract":"<div><div>The rapid imaging of electrical source transient electromagnetic (TEM) data involves two essential processes: the calculation of apparent resistivity and the conversion of time to depth. Traditionally, the definition of full-time apparent resistivity is defined by considering solely the vertical magnetic field, which is predicated on the monotonic relationship between the resistivity and the electromagnetic field response. Based on the concept of peak time, we have developed distinct methodologies for calculating the apparent resistivity for both the horizontal electric field (<em>e</em>_x) and the vertical induced voltage (<em>v</em>_z), which demonstrated accuracy across the entire time range examined. We also introduced a formula to address discrepancies in apparent resistivity arising from the non-dipole size effect of the source, thereby ensuring that the algorithm can adapt to any transmitting and receiving configuration. Furthermore, we provided straightforward and precise time-depth conversion equations applicable to both <em>e</em>_<em>x</em> and <em>v</em>_<em>z</em>, which facilitate the rapid imaging of observational data. Multiple numerical examples were employed to illustrate the effectiveness and robustness of this approach. Finally, we applied this imaging technique to the data processing of actual measured data from a survey area conducted in Ningxia Province, and the imaging results accurately reflected the distribution of the electrical structure of the subsurface strata. The innovative imaging technique presented in this study holds considerable potential for the expedited processing and analysis of ground-based and semi-aerial electrical source transient electromagnetic survey data, which are widely employed in contemporary applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105770"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659030","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 two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network","authors":"Nian Yu ,&nbsp;Chenkai Wang ,&nbsp;Huang Chen ,&nbsp;Wenxin Kong","doi":"10.1016/j.cageo.2024.105765","DOIUrl":"10.1016/j.cageo.2024.105765","url":null,"abstract":"<div><div>Magnetotelluric (MT) inversion is an important means of MT data interpretation. The use of deep learning technology for MT inversion has attracted much attention because it is not limited to the initial model, avoids falling into local optimal solutions, and has the strong ability to process large amounts of data. However, obtaining highly reliable deep learning inversion results remains a challenge. In this paper, we have proposed a two-dimensional (2-D) MT inversion method based on the improved Dense Convolutional Network (DenseNet), with the aim of improving the reliability of the 2-D deep learning MT inversion results. First, the MARE2DEM is used to compute the 2-D MT forward responses when establishing the sample set. Then, an improved DenseNet is proposed by incorporating depthwise separable convolution in lieu of standard convolution within dense connection blocks, and embedding the attention mechanism. Depthwise separable convolution splits the standard convolution operation into depthwise and pointwise convolution, effectively capturing spatial features of input data and correlations between channels. Meanwhile, attention mechanism allows the network to assign varying degrees of importance (or attention) to different elements in a sequence of data, thus enhancing its ability of key feature extraction. This design not only retains the inherent feature reuse and alleviates gradient vanishing of DenseNet but also further enhances network performance. The optimized network parameters for the improved DenseNet are obtained by training on the training set, while the validation set is used to adjust hyperparameters and evaluate model performance. Finally, the proposed 2-D deep learning approach is verified by using both synthetic and field data. Experimental results with synthetic data show that the reliability of inversion results obtained by using the proposed algorithm is improved, and the inversion results obtained by using both TE- and TM-mode data is more accurate than those obtained by using the single mode data. The inversion results of field data show that the proposed 2-D MT deep learning inversion approach can effectively detect the subsurface resistivity structure and has a good application prospect.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105765"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659031","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":"Shale sample permeability estimation using fractal parameters computed from TransUnet-based SEM image segmentation","authors":"Kaili Liu ,&nbsp;Jianmeng Sun ,&nbsp;Han Wu ,&nbsp;Xin Luo ,&nbsp;Fujing Sun","doi":"10.1016/j.cageo.2024.105745","DOIUrl":"10.1016/j.cageo.2024.105745","url":null,"abstract":"<div><div>Microscopic pore structure forms the foundation for studying shale gas adsorption and transport mechanisms and for establishing geological models. However, most current methods for analyzing microporous structure through physical experiments are time-consuming and labor-intensive. Hence, there is a need to automate pore segmentation and extract pore microstructural information from shale SEM images quickly and accurately. This will significantly enhance the efficiency of digital rock analysis and related computational simulations. This study used scanning electron microscopy (SEM) images of shale from a certain region in China to investigate the relationship between the microscopic structure of shale pores and the macroscopic permeability. Firstly, a semantic image segmentation model called TransUnet, based on deep learning, was used to segment the pore images and extract the micro-pore structure parameters. Then, the relationship between the macroscopic permeability parameters and the micro-pore structure was analyzed using a fractal apparent permeability calculation model. Finally, the permeability of the shale was calculated to improve the efficiency of geological exploration and reduce experimental costs. The experimental results show that this study provides an effective image processing method for the SEM quantification of shale microstructure and extraction of permeability parameters.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105745"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659014","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":"Application of polynomial type elastic outer boundary conditions in fractal composite reservoir seepage model","authors":"Xiaoxu Dong ,&nbsp;Yu Peng ,&nbsp;Wenjing Li ,&nbsp;Ying Liang ,&nbsp;Yu Wang ,&nbsp;Zheng Zeng","doi":"10.1016/j.cageo.2024.105764","DOIUrl":"10.1016/j.cageo.2024.105764","url":null,"abstract":"<div><div>In this paper, the elastic function in the explanation of elastic outer boundary condition is regarded as polynomial functions of space variable <span><math><mrow><mi>r</mi></mrow></math></span> and time variable <span><math><mrow><mi>t</mi></mrow></math></span>, and this is incorporated into the analysis of fractal composite reservoirs. The Laplace space solution the fractal composite reservoir models, which have polynomial elastic outer boundary conditions, is achieved through a modified method of similarity construction and the Gaver-Stehfest numerical inversion technique is used to derive the semi-analytical solutions for the models in actual space. Next, the polynomial elastic function is turned into a first-order function about time variable. Curves of pressure in non-dimensional well bottom under different quadratic pressure gradient terms and primary control factors are drawn by using MATLAB software and their impact on non-dimensional well bottom are analyzed. It is proved that the three impractical outer boundary conditions are only a particular case of the polynomial elastic outer boundary conditions. The research in this paper expands the discussion scope of elastic outer boundary conditions, and has strong reference significance.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105764"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586652","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":"Geothermal modeling in complex geological systems with ComPASS","authors":"A. Armandine Les Landes ,&nbsp;L. Beaude ,&nbsp;D. Castanon Quiroz ,&nbsp;L. Jeannin ,&nbsp;S. Lopez ,&nbsp;F. Smai ,&nbsp;T. Guillon ,&nbsp;R. Masson","doi":"10.1016/j.cageo.2024.105752","DOIUrl":"10.1016/j.cageo.2024.105752","url":null,"abstract":"<div><div>In deep geothermal reservoirs, faults and fractures play a major role, serving as regulators of fluid flow and heat transfer while also providing feed zones for production wells. To accurately model the operation of geothermal fields, it is necessary to explicitly consider objects of varying spatial scales, from the reservoir scale itself, to that of faults and fractures, down to the scale of the injection and production wells.</div><div>Our main objective in developing the ComPASS geothermal flow simulator, was to take into account all of these geometric constraints in a flow and heat transfer numerical model using generic unstructured meshes. In its current state, the code provides a parallel implementation of a spatio-temporal discretization of the non-linear equations driving compositional multi-phase thermal flows in porous fractured media on unstructured meshes. It allows an explicit discretization of faults and fractures as 2D hybrid objects, embedded in a 3D matrix. Similarly, wells are modeled as one dimensional graphs discretized by edges of the 3D mesh which allows arbitrary multi-branch wells. The resulting approach is particularly flexible and robust in terms of modeling.</div><div>Its practical interest is demonstrated by two case studies in high-energy geothermal contexts.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105752"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659029","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":"Backpropagation-based inference for spatial interpolation to estimate the blastability index in an open pit mine","authors":"Yakin Hajlaoui ,&nbsp;Richard Labib ,&nbsp;Jean-François Plante ,&nbsp;Michel Gamache","doi":"10.1016/j.cageo.2024.105756","DOIUrl":"10.1016/j.cageo.2024.105756","url":null,"abstract":"<div><div>The blastability index (BI) is a measure that indicates the resistance of rock to fragmentation when blasting. With novel technologies, miners are now able to collect and calculate BI at different depths while drilling. In this research, we propose an approach to estimate the BI at multiple depths for new areas using only spatial locations and observed BI measurements of previously drilled holes. Spatial interpolation techniques are investigated. This study introduces a novel treatment for Gaussian Processes (GPs) and Inverse Distance Weighting (IDW). Variography is leveraged to ensure an appropriate fit between the data and the spatial component. The parameters controlling anisotropy are constrained to intervals chosen to reflect the observed anisotropy. Gradient descent with back-propagation is used for optimization. The proposed approach improves the performance of GP and IDW at predicting BI. The similarities between the IDW variant proposed and a single-layer neural network are discussed.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105756"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659021","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 models for parameterization of facies-based geomodels and their use in data assimilation","authors":"Guido Di Federico,&nbsp;Louis J. Durlofsky","doi":"10.1016/j.cageo.2024.105755","DOIUrl":"10.1016/j.cageo.2024.105755","url":null,"abstract":"<div><div>Geological parameterization entails the representation of a geomodel using a small set of latent variables and a mapping from these variables to grid-block properties such as porosity and permeability. Parameterization is useful for data assimilation (history matching), as it maintains geological realism while reducing the number of variables to be determined. Diffusion models are a new class of generative deep-learning procedures that have been shown to outperform previous methods, such as generative adversarial networks, for image generation tasks. Diffusion models are trained to “denoise”, which enables them to generate new geological realizations from input fields characterized by random noise. Latent diffusion models, which are the specific variant considered in this study, provide dimension reduction through use of a low-dimensional latent variable. The model developed in this work includes a variational autoencoder for dimension reduction and a U-net for the denoising process. Our application involves conditional 2D three-facies (channel-levee-mud) systems. The latent diffusion model is shown to provide realizations that are visually consistent with samples from geomodeling software. Quantitative metrics involving spatial and flow-response statistics are evaluated, and general agreement between the diffusion-generated models and reference realizations is observed. Stability tests are performed to assess the smoothness of the parameterization method. The latent diffusion model is then used for ensemble-based data assimilation. Two synthetic “true” models are considered. Significant uncertainty reduction, posterior P₁₀–P₉₀ forecasts that generally bracket observed data, and consistent posterior geomodels, are achieved in both cases.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105755"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659015","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}