Applied Computing and Geosciences最新文献_第3页

Using explainable machine learning to identify controlling factors of low-permeability beach-bar and turbidite reservoir quality 利用可解释性机器学习识别低渗透滩坝和浊积岩储层质量控制因素

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-10-11 DOI: 10.1016/j.acags.2025.100299

Sixuan Wu , Dongyu Zheng , Yijun Wang , Runlin Teng , Tian Yang

Low-permeability reservoirs have become increasingly important targets for hydrocarbon exploration in lacustrine basins. However, complex pore-throat structures and the influence of diagenesis may impede our understanding of reservoir quality. Additionally, due to their similarly low permeability and porosity, identifying different types of low-permeability reservoirs—especially common beach-bar and turbidite deposits in lacustrine basins—is challenging. In this study, we applied an explainable machine learning (ML) model using mercury injection parameters of beach-bar and turbidite sandstone deposits in the Dongying Depression, Bohai Bay Basin, China, to classify the two groups of sandstone deposits and investigate the most influential factors in classifying them. Unlike conventional statistical or “black-box” ML approaches, our method integrates the full suite of pore-throat parameters while identifying the most influential features for classification. The model achieved an overall accuracy of 80 % in classifying the two deposit types. It shows that turbidite deposits have higher porosity and permeability than beach-bar deposits, mainly due to lower cementation and increased dissolution. This higher porosity and permeability in turbidite sandstones is likely caused by the release of organic acids from surrounding organic-rich source rocks, which promote dissolution, and by the infilling of organic matter that hinders cementation. In addition to permeability and porosity, our study finds that specific surface area is a key parameter for differentiating the two deposit types. A smaller specific surface area indicates higher macro-porosity, which benefits permeability. Overall, our explainable ML model not only accurately classifies beach-bar and turbidite sandstone reservoirs but also identifies the factors that control reservoir quality.

低渗透储层已成为湖泊盆地油气勘探的重要目标。然而，复杂的孔喉结构和成岩作用的影响可能阻碍我们对储层质量的认识。此外，由于它们的渗透率和孔隙度都很低，因此识别不同类型的低渗透储层（特别是在湖泊盆地中常见的滩坝和浊积岩沉积）是一项挑战。以渤海湾盆地东营凹陷滩坝砂岩和浊积砂岩为研究对象，采用可解释机器学习（ML）模型，对两组砂岩进行了压汞参数分类，并探讨了影响其分类的主要因素。与传统的统计或“黑箱”机器学习方法不同，我们的方法集成了全套孔喉参数，同时确定了最具影响力的分类特征。该模型对两种矿床类型的分类总体准确率达到80%。浊积岩比滩坝具有更高的孔隙度和渗透率，其主要原因是胶结作用减弱，溶蚀作用增加。浊积砂岩的高孔隙度和渗透率可能是由于周围富含有机物的烃源岩释放有机酸促进溶蚀作用，而有机质的充填则阻碍了胶结作用。研究发现，除渗透率和孔隙度外，比表面积也是区分两种矿床类型的关键参数。比表面积越小，宏观孔隙度越高，有利于提高渗透率。总的来说，我们的可解释ML模型不仅可以准确地对滩坝和浊积砂岩储层进行分类，还可以识别控制储层质量的因素。

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引用次数: 0

Leveraging generative AI and hyperspectral drill-core imaging for fully automated mineral mapping of unconventional reservoir 利用生成式人工智能和高光谱岩心成像技术实现非常规储层的全自动矿物制图

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-10-11 DOI: 10.1016/j.acags.2025.100298

Izzul Qudsi , Hilary Corlett , Ardiansyah Koeshidayatullah

The acquisition of hyperspectral imagery on core samples significantly enhances the ability of geoscientists to conduct preliminary reservoir characterization particularly for unconventional exploration. Hyperspectral analysis is particularly powerful in providing qualitative and quantitative mineral distribution maps on geological samples. Information about mineral distribution on unconventional shale cores (e.g., clay-dominated vs calcite-dominated) may aid in determining zones of sweet spots for hydraulic fracturing. Conventionally, the mapping of mineralogy in hyperspectral imagery requires considerable human involvement, ranging from the extraction of mineral endmembers, process of defining the representative reflectance spectra signature of every minerals in the hyperspectral dataset, to the selection of parameters for classification algorithms. We used a generative deep learning approach with modified Deep Embedded Clustering (DEC) and variational autoencoders (VAE) to automate mineral classification in hyperspectral imagery of Devonian unconventional reservoir cores from the Western Canada Sedimentary Basin. The cores are characterized by nodular carbonates and Si- and clay-rich shales with interbedded detrital carbonates, covering the upper portion of the Waterways Formation, the entire Majeau Lake and Duvernay formations, and a portion of the lower Ireton Formation. Our study demonstrates that the proposed generative AI approach is effective in qualitatively and quantitatively identifying key mineral endmembers, including calcite, non-clay silica-rich minerals, and clay minerals (illite and muscovite) and their mixtures. In addition, the algorithm is also able to extract and identify the spectral variation of the silica-rich minerals based on its organic content. The VAE + DEC algorithm was able to capture not only the thin interbedded layers but also its complex mineralogy that aligns well with findings from previous research on the studied formations. In addition, the spatial distribution of organic content variation is also identified correctly and matched with the laboratory measurements. Therefore, the proposed workflow emerges as a promising, less labor-intensive alternative for lithological mapping and brief rock properties analysis from hyperspectral datasets, offering potential further applications in unconventional reservoir drill core data.

获取岩心样品的高光谱图像大大提高了地球科学家对储层进行初步表征的能力，特别是在非常规勘探中。高光谱分析在提供地质样品的定性和定量矿物分布图方面特别强大。关于非常规页岩岩心上矿物分布的信息（例如，以粘土为主还是方解石为主）可能有助于确定水力压裂的最佳区域。通常，高光谱图像中的矿物制图需要大量的人工参与，从矿物端元的提取，定义高光谱数据集中每种矿物的代表性反射光谱特征的过程，到分类算法参数的选择。采用基于改进的深度嵌入聚类（DEC）和变分自编码器（VAE）的生成式深度学习方法，对加拿大西部沉积盆地泥盆纪非常规储层岩心的高光谱图像进行了自动矿物分类。岩心以碳酸盐结节状、富硅泥页岩和碎屑碳酸盐互层为特征，覆盖水道组上部、整个Majeau Lake组和Duvernay组以及下部部分Ireton组。我们的研究表明，所提出的生成式人工智能方法在定性和定量识别关键矿物端元方面是有效的，包括方解石、非粘土富硅矿物、粘土矿物（伊利石和白云母）及其混合物。此外，该算法还可以根据其有机含量提取和识别富硅矿物的光谱变化。VAE + DEC算法不仅能够捕获薄互层，而且能够捕获其复杂的矿物学，这与之前对所研究地层的研究结果很好地吻合。此外，有机含量变化的空间分布也得到了正确的识别，并与实验室测量结果相匹配。因此，所提出的工作流程是一种很有前途的、劳动强度较低的替代方法，可以从高光谱数据集进行岩性测绘和简短的岩石性质分析，为非常规油藏钻芯数据的进一步应用提供了潜力。

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引用次数: 0

Fully differentiable Lagrangian convolutional neural network for physics-informed precipitation nowcasting 物理信息降水临近预报的完全可微拉格朗日卷积神经网络

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-10-01 DOI: 10.1016/j.acags.2025.100296

Peter Pavlík , Martin Výboh , Anna Bou Ezzeddine , Viera Rozinajová

This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a Lagrangian Double U-Net for Physics-Informed Nowcasting, that draws from existing extrapolation-based nowcasting methods. It consists of a U-Net that dynamically produces mesoscale advection motion fields, a differentiable semi-Lagrangian extrapolation operator, and an advection-free U-Net capturing the growth and decay of precipitation over time. Using our approach, we successfully implement the Lagrangian convolutional neural network for precipitation nowcasting in a fully differentiable and GPU-accelerated manner. This allows for end-to-end training and inference, including the data-driven Lagrangian coordinate system transformation of the data at runtime. We evaluate the model and compare it with other related AI-based models both quantitatively and qualitatively in an extreme event case study. Based on our evaluation, LUPIN matches and even exceeds the performance of the chosen benchmarks, opening the door for other Lagrangian machine learning models.

本文提出了一种降水临近预报的卷积神经网络模型，该模型将数据驱动学习与物理知识相结合。我们提出LUPIN，一个拉格朗日双u网，用于物理信息临近预报，它借鉴了现有的基于外推的临近预报方法。它由一个动态产生中尺度平流运动场的U-Net、一个可微的半拉格朗日外推算子和一个捕捉降水随时间增长和衰减的无平流U-Net组成。利用我们的方法，我们以完全可微和gpu加速的方式成功地实现了降水临近预报的拉格朗日卷积神经网络。这允许端到端训练和推理，包括在运行时对数据进行数据驱动的拉格朗日坐标系转换。我们在一个极端事件案例研究中评估了该模型，并将其与其他相关的基于人工智能的模型进行了定量和定性的比较。根据我们的评估，LUPIN达到甚至超过了所选基准的性能，为其他拉格朗日机器学习模型打开了大门。

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引用次数: 0

Interpretable ore classification using SHAP-enhanced LightGBM: A case study from the Qiaomaishan deposit, China 基于shap增强LightGBM的可解释矿石分类——以乔麦山矿床为例

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-26 DOI: 10.1016/j.acags.2025.100295

Mingming Zhang , Xiaoyuan Wang , Cong Chen , Jing Ding , Xinsuo Zhou , Jiangyanyu Qu

Accurate ore classification is essential for geological exploration and mineral resource assessment, particularly in geologically complex settings. This study presents an interpretable classification framework that integrates the Light Gradient Boosting Machine (LightGBM) algorithm with post hoc model interpretation using SHapley Additive exPlanations (SHAP). The framework is applied to geochemical and spatial data from the Qiaomaishan Cu-S polymetallic deposit in Anhui Province, China. A dataset comprising 1588 samples-each containing concentrations of 29 geochemical elements along with 3D spatial coordinates-was used to train and evaluate 5 machine learning models: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), LightGBM, and CatBoost. Among these, LightGBM achieved the best performance, with an average F1 score of 0.990 across 10 ore and lithological categories. An ablation experiment further confirmed the critical role of spatial coordinates, as removing them led to a notable drop in classification accuracy, particularly for underrepresented ore types. To interpret the LightGBM model, SHAP analysis was used to quantify feature contributions, identifying key geochemical elements such as W, Sr, Ca, and Fe as significant drivers of classification-consistent with the known mineralogical characteristics of the deposit. Moreover, SHAP beeswarm plots provided insights into the direction and magnitude of each feature's influence, enhancing the model's geological interpretability. SHAP-based feature selection further improved classification performance for underrepresented classes, including copper–sulphur–tungsten ore and copper ore. The proposed framework demonstrates strong potential for facilitating automated ore identification and supporting data-driven decision-making in complex geological environments.

准确的矿石分类对地质勘探和矿产资源评价至关重要，特别是在地质复杂的环境中。本研究提出了一个可解释的分类框架，该框架将光梯度增强机（LightGBM）算法与使用SHapley加性解释（SHAP）的事后模型解释相结合。将该框架应用于安徽乔麦山铜硫多金属矿床的地球化学和空间数据。一个包含1588个样本的数据集-每个样本包含29种地球化学元素的浓度以及3D空间坐标-用于训练和评估5种机器学习模型：支持向量机（SVM），多层感知器（MLP），随机森林（RF）， LightGBM和CatBoost。其中，LightGBM表现最佳，10个矿石和岩性类别的F1平均得分为0.990。消融实验进一步证实了空间坐标的关键作用，因为去除它们会导致分类精度显著下降，特别是对于代表性不足的矿石类型。为了解释LightGBM模型，使用SHAP分析来量化特征贡献，确定关键的地球化学元素，如W、Sr、Ca和Fe，作为分类的重要驱动因素，与已知的矿床矿物学特征一致。此外，SHAP蜂群图提供了对每个特征影响的方向和大小的见解，增强了模型的地质可解释性。基于shap的特征选择进一步提高了代表性不足的类别（包括铜硫钨矿和铜矿）的分类性能。所提出的框架在促进复杂地质环境下的自动化矿石识别和支持数据驱动决策方面具有强大的潜力。

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引用次数: 0

Enhancing SAM-based digital rock image segmentation via edge-semantics fusion 利用边缘语义融合增强基于sam的数字岩石图像分割

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-20 DOI: 10.1016/j.acags.2025.100292

Ziqiang Wang , Zhiyu Hou , Danping Cao

The Segment Anything Model (SAM) demonstrates strong segmentation capabilities. However, its application to digital rock images faces challenges from subtle transitions between matrix minerals and pore structures, as well as inherent heterogeneity, which result in mis-segmentation and discontinuities that affect petrophysical characterization and numerical modeling of subsurface reservoir properties. To address these challenges, we introduce ESF-SAM (Edge-Semantics Fusion-SAM), a novel approach that enhances SAM's segmentation fidelity by integrating edge and semantic features. Specifically, in ESF-SAM, semantic features from SAM's image encoder are processed through an edge decoder enhanced by progressive dilated convolutions to extract detailed structural boundaries. The resulting edge and original semantic features are adaptively fused through a dual-attention mechanism, where spatial gating attention dynamically balances their contributions across locations, and channel attention recalibrates feature importance to enrich the representation. This spatial–channel attention framework enriches feature representations, enabling targeted fine-tuning within the SAM decoder and thereby preserving global segmentation capability while significantly improving local boundary delineation in two-phase segmentation tasks. Experimental results demonstrate that ESF-SAM improves segmentation detail, leading to more accurate derivation of key rock properties such as elastic modulus and pore geometry parameters, with results that more closely align with labeled data compared to the original SAM. Trained on only a small number of annotated sandstone images, ESF-SAM effectively adapts to the target domain and exhibits strong generalization when applied to carbonate rock images without additional fine-tuning. This work exemplifies how integrating priors into foundation models can substantially enhance their applicability to complex scientific imaging tasks.

分段任意模型（SAM）展示了强大的分段能力。然而，将其应用于数字岩石图像面临着基质矿物和孔隙结构之间的微妙过渡以及固有的非均质性的挑战，这些挑战导致了错误的分割和不连续性，从而影响了岩石物理表征和地下储层性质的数值模拟。为了解决这些挑战，我们引入了ESF-SAM（边缘语义融合-SAM），这是一种通过整合边缘和语义特征来提高SAM分割保真度的新方法。具体而言，在ESF-SAM中，来自SAM图像编码器的语义特征通过渐进式扩展卷积增强的边缘解码器进行处理，以提取详细的结构边界。通过双注意机制自适应融合生成的边缘和原始语义特征，其中空间门控注意动态平衡其在不同位置上的贡献，通道注意重新校准特征的重要性以丰富表征。这种空间通道注意框架丰富了特征表示，使SAM解码器能够进行有针对性的微调，从而在保留全局分割能力的同时显著改善了两阶段分割任务中的局部边界描绘。实验结果表明，与原始的SAM相比，ESF-SAM改善了分割细节，可以更准确地推导出关键的岩石属性，如弹性模量和孔隙几何参数，结果与标记数据更接近。仅在少量带注释的砂岩图像上进行训练，ESF-SAM可以有效地适应目标域，并且在无需额外微调的情况下应用于碳酸盐岩图像时表现出很强的泛化能力。这项工作举例说明了如何将先验整合到基础模型中可以大大提高它们对复杂科学成像任务的适用性。

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引用次数: 0

Analyzing key controlling factors of shale reservoir heterogeneity in "thin" stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou “薄”地层条件下页岩储层非均质性控制因素分析——以黔北扶岩向斜五峰组—龙马溪组为例

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-20 DOI: 10.1016/j.acags.2025.100293

Ye Tao, Zhidong Bao, Fukang Ma

The Wufeng-Longmaxi Formation shales are key targets for shale gas exploration, but they are often studied as a single stratigraphic unit with limited analysis of internal differences. This study combines traditional geological methods with deep learning to compare the reservoir characteristics of the Wufeng Formation, the first member of the Longmaxi Formation (Long 1), and the second member of the Longmaxi Formation (Long 2), identifying the main controlling factors of differences. We found that: (1) The Wufeng Formation primarily develops siliceous shale lithofacies (S), mixed siliceous shale lithofacies (S-2), and clay siliceous shale lithofacies (S-3). Long 1 develops mixed siliceous shale lithofacies (S-2) and clay siliceous shale lithofacies (S-3), while Long 2 exhibits clay and siliceous mixed shale lithofacies (M-2) and siliceous clay shale lithofacies (CM-1). (2) The YOLO-v8 model demonstrates higher accuracy in shale pore type detection than the YOLO-v10 model, with a maximum mAP of 78.9 %. Using the YOLO-v8 model, it was found that S, S-2, and S-3 lithofacies are dominated by dissolution pores and organic pores with larger specific surface areas and porosities, while CM-1 and M-2 lithofacies are characterized by dissolution pores with smaller specific surface areas and porosities. (3) Based on evaluation indicators such as TOC content, BET surface area, porosity, brittleness index, and gas content, S and S-2 are classified as Class I lithofacies, S-3 as Class II lithofacies, and M-2 and CM-1 as Class III lithofacies. The main controlling factor for the heterogeneity of the shale reservoirs in the study area is lithofacies.

五峰组—龙马溪组页岩是页岩气勘探的重点靶区，但往往将其作为一个单一的地层单元进行研究，对其内部差异分析有限。本研究将传统地质方法与深度学习相结合，对龙马溪组一段（龙一段）与龙马溪组二段（龙二段）五峰组储层特征进行对比，找出差异的主控因素。研究发现：(1)五峰组主要发育硅质页岩岩相(S)、混合硅质页岩岩相（S-2）和粘土硅质页岩岩相（S-3）。龙1发育混合硅质页岩岩相（S-2）和粘土硅质页岩岩相（S-3），龙2发育粘土与硅质混合页岩岩相（M-2）和硅质粘土页岩岩相（CM-1）。(2) YOLO-v8模型对页岩孔隙类型的检测精度高于YOLO-v10模型，最大mAP值为78.9%。利用YOLO-v8模型发现，S、S-2和S-3岩相以溶蚀孔和有机质孔为主，具有较大的比表面积和孔隙度；CM-1和M-2岩相以溶蚀孔为主，比表面积和孔隙度较小。(3)根据TOC含量、BET表面积、孔隙度、脆性指数、含气量等评价指标，将S、S-2划分为ⅰ类岩相，S-3划分为ⅱ类岩相，M-2、CM-1划分为ⅲ类岩相。控制研究区页岩储层非均质性的主要因素是岩相。

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引用次数: 0

Using machine learning classifiers together with discrimination diagrams for validation of rock classification labels 利用机器学习分类器和判别图对岩石分类标签进行验证

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-19 DOI: 10.1016/j.acags.2025.100288

Malte Mues , Dennis Kraemer , David M. Ernst Styn

Rock classification based on chemical components is a common task in the geochemical domain. Literature recommends the Total Alkali and Silica (TAS) discrimination diagram for classifying igneous volcanic rocks by the sum of Na

_{2}

O and K

_{2}

O in relation to SiO

_{2}

contents. This paper comparatively applies the TAS diagram and machine learning classification techniques to a collection of volcanic rocks from the GEOROC database. The study demonstrates a mismatch between the rock type labeled by experts in the database and rock types assigned by the TAS diagram. Despite this discrepancy, the experiments show that support vector machines are particularly promising for building decision systems for rock classification. Random forests, multi-layer perceptrons and K nearest neighbors are less suitable as rock classifiers in the study.

基于化学成分的岩石分类是地球化学领域的一项常见任务。文献推荐用总碱硅（Total Alkali and Silica， TAS）判别图，通过Na2O和K2O与SiO2含量的总和对火成岩进行分类。本文将TAS图和机器学习分类技术对比应用于GEOROC数据库中的火山岩集合。该研究表明，数据库中专家标记的岩石类型与TAS图分配的岩石类型之间存在不匹配。尽管存在这种差异，但实验表明，支持向量机在构建岩石分类决策系统方面特别有前途。随机森林、多层感知器和K近邻都不适合作为岩石分类器。

引用次数: 0

Automating fault detection in seismic data: integrating image processing with deep learning 地震数据故障自动检测：图像处理与深度学习的集成

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-15 DOI: 10.1016/j.acags.2025.100286

Ahmad Ashtari

Fault interpretation in seismic images is crucial for identifying fluid accommodation and flow migration pathways in the oil and gas industry. Several algorithms have been developed to calculate seismic attributes which help identify faults. Despite these advancements, challenges still remain in fault interpretation due to the complexity of fault networks, noise, and quality of seismic data. Hybrid seismic attributes extracted through artificial neural networks can enhance fault interpretation. In the case of neural network-based approaches used for geological feature extraction, picking precise samples for training neural networks is vital. In this study, an innovative method based on the Shi–Tomasi corner detection algorithm has been introduced to automatically pick fault samples on seismic data to be used as input to deep neural networks to predict faults. The method has been tested on two field seismic images that were acquired at different surveys. The field examples indicate that the trained neural networks could give a precise and clear estimation of faults with different azimuths. This proves the proposed sampling method can effectively provide a high-quality training data set for deep neural networks to automatically predict faults from seismic data.

在油气工业中，地震图像中的断层解释对于确定流体容纳和流动运移路径至关重要。已经开发了几种算法来计算地震属性，以帮助识别断层。尽管取得了这些进步，但由于断层网的复杂性、噪声和地震数据的质量，断层解释仍然存在挑战。通过人工神经网络提取混合地震属性，可以提高断层解释的精度。在用于地质特征提取的基于神经网络的方法中，为训练神经网络选择精确的样本是至关重要的。本文提出了一种基于Shi-Tomasi角点检测算法的创新方法，从地震数据中自动提取断层样本，作为深度神经网络的输入，进行断层预测。该方法已在不同勘探获得的两幅现场地震图像上进行了测试。现场算例表明，所训练的神经网络能准确、清晰地估计出不同方位的故障。这证明了该方法可以有效地为深度神经网络从地震数据中自动预测断层提供高质量的训练数据集。

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引用次数: 0

Neural network inversion of seismic wave velocities for vadose zone water content profile 含气带含水率剖面地震波速度的神经网络反演

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-01 DOI: 10.1016/j.acags.2025.100285

Quentin Didier, Victor Sauvage, Léna Pellorce, Rémi Valois, Slimane Arhab, Arnaud Mesgouez

Accurate estimation of water saturation in the vadose zone is crucial for hydrological and agricultural applications. However, traditional seismic inversion methods often struggle with non-linearity and sensitivity to noise, limiting their generalisation capabilities. To address this challenge, we propose a neural network-based inversion approach that allows the assessment of the vertical distribution of water saturation from compressional (

v_{_{P}}

) and shear (

v_{_{S}}

) wave velocities. Specifically, our architecture is based on a regression model and incorporates an autoencoder layer to improve robustness against noise. As a result, this enhances its ability to invert complete saturation profiles and increases its adaptability to complex hydrological conditions. Furthermore, the model demonstrates strong performance across varying water table depths, with low error metrics and high resilience to input noise with a RMSE of 3.34 × 10⁻² and a R

^{2}

of 0.978 for 5% noise. Our current approach has been trained exclusively on noisy synthetic data. We plan to validate it in the near future against experimental field data we have recorded for an agricultural soil. Overall, this study establishes a foundation for future applications of deep learning in hydrogeophysical inversion and underscores the need for validation with real-world data.

准确估计渗透带含水饱和度对水文和农业应用至关重要。然而，传统的地震反演方法往往存在非线性和噪声敏感性等问题，限制了其泛化能力。为了应对这一挑战，我们提出了一种基于神经网络的反演方法，可以通过纵波速度（vP）和横波速度（vS）来评估含水饱和度的垂直分布。具体来说，我们的架构是基于一个回归模型，并结合了一个自编码器层，以提高对噪声的鲁棒性。因此，这增强了其反演完整饱和度剖面的能力，并提高了其对复杂水文条件的适应性。此外，该模型在不同的地下水位深度上表现出很强的性能，具有较低的误差指标和对输入噪声的高弹性，RMSE为3.34 × 10−2，对于5%的噪声，R2为0.978。我们目前的方法是专门针对有噪声的合成数据进行训练的。我们计划在不久的将来根据我们在农业土壤中记录的试验田数据来验证它。总的来说，这项研究为未来深度学习在水文地球物理反演中的应用奠定了基础，并强调了用现实世界数据验证的必要性。

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引用次数: 0

GPU-accelerated simulation of steady-state flow and particle transport in discrete fracture networks 离散断裂网络中稳态流动和粒子输运的gpu加速模拟

IF 3.2 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-09-01 DOI: 10.1016/j.acags.2025.100284

Tingchang Yin , Teng Man , Pei Zhang , Sergio Andres Galindo-Torres

Fracture networks in the subsurface can serve as the primary pathway for fluid flow, allowing for solute transport. This process is critical to various real-world applications, including resource extraction and contaminant migration in fractured rocks. We develop an open-source code called cuDFNsys to simulate flow and transport in discrete fracture networks (DFNs). Our code uses the mixed hybrid finite element method to solve the hydraulic head and velocity fields in DFNs, and the particle tracking method to simulate the movement of solute plumes. The GPU parallelization accelerates the generation of DFNs, identification of intersections between fractures, determination of elementary matrices, and motion of particles. We use several benchmarks to verify the accuracy of flow and transport simulation in cuDFNsys. Dispersion in a DFN is used to demonstrate examples of particle tracking. Performance analyses demonstrate that our code is well-suited for Monte Carlo iterations of DFN simulations, enabling physicists and geoscientists to study critical phenomena and phase transitions in fracture networks using percolation theory.

地下裂缝网络可以作为流体流动的主要通道，允许溶质运输。该过程对于各种实际应用至关重要，包括资源提取和裂缝岩石中的污染物运移。我们开发了一个名为cuDFNsys的开源代码来模拟离散裂缝网络（DFNs）中的流动和传输。我们的代码采用混合混合有限元法求解DFNs中的水头和速度场，采用粒子跟踪法模拟溶质羽流的运动。GPU的并行化加速了dfn的生成、裂缝间交点的识别、初等矩阵的确定和粒子的运动。我们使用几个基准来验证cuDFNsys中流量和传输模拟的准确性。DFN中的色散被用来演示粒子跟踪的例子。性能分析表明，我们的代码非常适合DFN模拟的蒙特卡罗迭代，使物理学家和地球科学家能够使用渗透理论研究裂缝网络中的关键现象和相变。

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