Pub Date : 2025-12-01Epub Date: 2024-11-06DOI: 10.1016/j.cageo.2024.105749
Yury Alkhimenkov
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.
{"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 (191<sup>3</sup>) takes less than 4 s of computational time using a single A100 GPU; 3 min for 100 million voxel elements (479<sup>3</sup>) using a single A100 GPU; 14 min for 350 million voxel elements (703<sup>3</sup>) 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}
Pub Date : 2025-12-01Epub Date: 2024-11-08DOI: 10.1016/j.cageo.2024.105771
George Brencher , Scott T. Henderson , David E. Shean
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.
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.
{"title":"Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network","authors":"George Brencher , Scott T. Henderson , 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}
Pub Date : 2025-12-01Epub Date: 2024-10-23DOI: 10.1016/j.cageo.2024.105746
P. Anbazhagan, Sauvik Halder
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, VP (P-wave velocity) and VS (S-wave velocity) are pivotal in geotechnical engineering, correlating directly with dynamic soil properties and enabling calculations of Poisson's Ratio (ν), Young's modulus (E), Shear modulus (μ), and Bulk modulus (B). Both VP and VS 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).
源于地震、各种地震试验和事件的 P 波和 S 波的到达时间是至关重要的岩土参数。VP（P 波速度）和 VS（S 波速度）是岩土工程中的关键参数，直接与动态土壤特性相关，可用于计算泊松比 (ν)、杨氏模量 (E)、剪切模量 (μ) 和体积模量 (B)。VP 和 VS 对于评估土壤在各种条件下的行为至关重要，有助于为沉降、波传播、地震波相互作用、液化潜力分析、地震响应分析等方面的土壤建模。地震测试（包括横孔、井下和井上测试）的到达时间选择需要人工完成，既费时又可能出错。为了解决这个问题，人们开发了各种算法来实现挑选过程的自动化。其中一些算法使用小波变换和贝叶斯信息标准，另一些则使用人工神经网络等机器学习技术。这些方法的准确性各不相同，但在处理不同信噪比的数据时，每种方法都有其固有的局限性。用于确定到达时间的自动算法的发展是一个持续且充满活力的研究领域。除了现有的以确定 P 波到达时间为重点的研究外，还缺乏对 S 波到达时间检测的研究。为了填补这一空白，本研究提出了检测 P 波和 S 波到达时间的新方法。其中一种方法是利用迭代优化算法将曲线精确拟合到 P 波的前缘。通过计算与拟合峰值最高点相对的分数来确定到达时间。第二种方法是通过确定相对极化的 S 波形之间的交点来确定 S 波到达的确切时刻。这些方法为自动检测 P 波和 S 波的到达时间提供了一种可行的方法，有可能提高拾取到达时间的精度和效率。
{"title":"A novel algorithm for identifying arrival times of P and S Waves in seismic borehole surveys","authors":"P. Anbazhagan, 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<sub>P</sub> (P-wave velocity) and V<sub>S</sub> (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<sub>P</sub> and V<sub>S</sub> 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}
Pub Date : 2025-12-01Epub Date: 2024-11-07DOI: 10.1016/j.cageo.2024.105770
Yu-lian Zhu , Wei-ying Chen , Wan-ting Song , Si-xu Han
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 (ex) and the vertical induced voltage (vz), 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 ex and vz, 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.
电源瞬变电磁（TEM）数据的快速成像涉及两个基本过程：视电阻率的计算和时间到深度的转换。传统上，全时视电阻率的定义仅考虑垂直磁场，其前提是电阻率与电磁场响应之间的单调关系。基于峰值时间的概念，我们开发了不同的方法来计算水平电场（ex）和垂直感应电压（vz）的视电阻率，这些方法在整个考察时间范围内都表现出了准确性。我们还引入了一个公式，以解决源的非偶极子尺寸效应引起的视电阻率差异，从而确保算法能够适应任何发射和接收配置。此外，我们还提供了适用于 ex 和 vz 的直接而精确的时间深度转换方程，这有助于观测数据的快速成像。我们采用了多个数值示例来说明这种方法的有效性和稳健性。最后，我们将该成像技术应用于宁夏某测区实测数据的数据处理，成像结果准确反映了地下地层电性结构的分布。本研究提出的创新成像技术在加快处理和分析当代广泛应用的地面和半航空电源瞬变电磁勘测数据方面具有相当大的潜力。
{"title":"New fast imaging techniques for electrical source transient electromagnetic data: Approaches and application","authors":"Yu-lian Zhu , Wei-ying Chen , Wan-ting Song , 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><sub>x</sub>) and the vertical induced voltage (<em>v</em><sub>z</sub>), 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><sub><em>x</em></sub> and <em>v</em><sub><em>z</em></sub>, 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}
Pub Date : 2025-12-01Epub Date: 2024-11-12DOI: 10.1016/j.cageo.2024.105765
Nian Yu , Chenkai Wang , Huang Chen , Wenxin Kong
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.
{"title":"A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network","authors":"Nian Yu , Chenkai Wang , Huang Chen , 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}
Pub Date : 2025-12-01Epub Date: 2024-10-28DOI: 10.1016/j.cageo.2024.105745
Kaili Liu , Jianmeng Sun , Han Wu , Xin Luo , Fujing Sun
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.
微观孔隙结构是研究页岩气吸附和传输机制以及建立地质模型的基础。然而，目前通过物理实验分析微孔结构的方法大多耗时耗力。因此，需要从页岩扫描电镜图像中快速、准确地自动进行孔隙分割并提取孔隙微观结构信息。这将大大提高数字岩石分析和相关计算模拟的效率。本研究利用中国某地区页岩的扫描电子显微镜（SEM）图像，研究页岩孔隙微观结构与宏观渗透率之间的关系。首先，利用基于深度学习的语义图像分割模型 TransUnet 对孔隙图像进行分割并提取微观孔隙结构参数。然后，利用分形表观渗透率计算模型分析了宏观渗透率参数与微孔结构之间的关系。最后，计算出页岩的渗透率，从而提高地质勘探效率，降低实验成本。实验结果表明，本研究为 SEM 定量页岩微观结构和提取渗透率参数提供了一种有效的图像处理方法。
{"title":"Shale sample permeability estimation using fractal parameters computed from TransUnet-based SEM image segmentation","authors":"Kaili Liu , Jianmeng Sun , Han Wu , Xin Luo , 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}
Pub Date : 2025-12-01Epub Date: 2024-10-31DOI: 10.1016/j.cageo.2024.105764
Xiaoxu Dong , Yu Peng , Wenjing Li , Ying Liang , Yu Wang , Zheng Zeng
In this paper, the elastic function in the explanation of elastic outer boundary condition is regarded as polynomial functions of space variable and time variable , 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.
本文将弹性外边界条件解释中的弹性函数视为空间变量 r 和时间变量 t 的多项式函数，并将其纳入分形复合储层的分析中。通过改进的相似性构造方法实现了具有多项式弹性外边界条件的分形复合储层模型的拉普拉斯空间解，并利用 Gaver-Stehfest 数值反演技术得出了模型在实际空间的半解析解。然后，将多项式弹性函数转化为关于时间变量的一阶函数。利用 MATLAB 软件绘制了不同二次压力梯度项和主控因素下的非三维井底压力曲线，并分析了它们对非三维井底的影响。结果证明，三种不切实际的外边界条件只是多项式弹性外边界条件的一种特殊情况。本文的研究拓展了弹性外边界条件的讨论范围，具有很强的借鉴意义。
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Pub Date : 2025-12-01Epub Date: 2024-11-05DOI: 10.1016/j.cageo.2024.105752
A. Armandine Les Landes , L. Beaude , D. Castanon Quiroz , L. Jeannin , S. Lopez , F. Smai , T. Guillon , R. Masson
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.
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.
Its practical interest is demonstrated by two case studies in high-energy geothermal contexts.
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Pub Date : 2025-12-01Epub Date: 2024-11-06DOI: 10.1016/j.cageo.2024.105756
Yakin Hajlaoui , Richard Labib , Jean-François Plante , Michel Gamache
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.
可爆性指数（BI）是表示爆破时岩石抗破碎能力的指标。利用新技术，矿工现在能够在钻探时收集和计算不同深度的可爆性指数。在这项研究中，我们提出了一种方法，仅利用空间位置和先前钻孔的观察 BI 测量值来估算新区域多个深度的 BI。研究了空间插值技术。该研究引入了一种新的高斯过程（GPs）和反距离加权（IDW）处理方法。利用变分法确保数据与空间分量之间的适当拟合。控制各向异性的参数受限于所选的区间，以反映观察到的各向异性。采用反向传播梯度下降法进行优化。所提出的方法提高了 GP 和 IDW 预测 BI 的性能。讨论了所提出的 IDW 变体与单层神经网络之间的相似性。
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Pub Date : 2025-12-01Epub Date: 2024-11-02DOI: 10.1016/j.cageo.2024.105755
Guido Di Federico, Louis J. Durlofsky
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.
地质参数化需要使用一小套潜在变量来表示地质模型，并将这些变量映射到孔隙度和渗透率等网格块属性。参数化对于数据同化（历史匹配）非常有用，因为它既能保持地质的真实性，又能减少需要确定的变量数量。扩散模型是一类新的生成式深度学习程序，在图像生成任务中的表现优于以往的方法，如生成式对抗网络。扩散模型经过 "去噪 "训练，能够从随机噪声输入区域生成新的地质现实。潜在扩散模型是本研究中考虑的具体变体，它通过使用低维潜在变量来降低维度。本研究开发的模型包括一个用于降维的变异自动编码器和一个用于去噪的 U 型网络。我们的应用涉及有条件的二维三岩层（通道-岩层-泥浆）系统。结果表明，潜在扩散模型可提供与地理建模软件样本视觉上一致的现实。对涉及空间和流量响应统计的定量指标进行了评估，发现扩散生成的模型与参考现实之间基本一致。还进行了稳定性测试，以评估参数化方法的平稳性。然后将潜在扩散模型用于基于集合的数据同化。考虑了两个合成的 "真实 "模型。在这两种情况下，都能显著减少不确定性，P10-P90 后期预报与观测数据基本保持一致，后期地理模型也保持一致。
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