Computers & Geosciences最新文献

Shallow water area extraction method for multispectral remote sensing imagery based on adaptive object NDWI thresholding 基于自适应地物NDWI阈值的多光谱遥感影像浅水区域提取方法

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.cageo.2026.106135

Zhipeng Dong , Yanxiong Liu , Yikai Feng , Kai Guo , Yilan Chen , Yanli Wang

Shallow water area extraction from multispectral remote sensing images is a key component of satellite derived bathymetry (SDB). With the respect to the issues of susceptibility to image noise and difficulty in accurately setting spectral extraction thresholds during the extraction process in shallow water areas, the paper proposes a shallow water area extraction method for multispectral remote sensing images based on adaptive object NDWI thresholding. First, the image is segmented to generate superpixel objects using the simple linear iterative clustering algorithm, and the normalized difference water index (NDWI) is calculated for each object. Second, the optimal threshold for NDWI in shallow water areas is obtained based on an object adaptive threshold calculation algorithm, and the initial shallow water area is extracted based on the optimal NDWI threshold. Finally, the initial shallow water area is refined using a region growing algorithm. The proposed method is compared with some state-of-the-art shallow water area extraction algorithms using six islands and near-shore areas under different environmental conditions. The experimental results show that the proposed method outperforms other shallow water area extraction algorithms, and can accurately extract the shallow water area around the islands and coastal zones under different environmental conditions.

从多光谱遥感影像中提取浅水区域是卫星衍生测深（SDB）的关键组成部分。针对浅水区多光谱遥感图像在提取过程中易受图像噪声影响、难以准确设置光谱提取阈值等问题，提出了一种基于自适应目标NDWI阈值的多光谱遥感图像浅水区提取方法。首先，采用简单线性迭代聚类算法对图像进行分割生成超像素目标，并计算每个目标的归一化差水指数（NDWI）；其次，基于目标自适应阈值计算算法获得浅水区NDWI的最优阈值，并根据最优NDWI阈值提取初始浅水区；最后，利用区域增长算法对初始浅水区域进行细化。利用六个岛屿和近岸区域在不同的环境条件下，将本文提出的方法与现有的浅水区域提取算法进行了比较。实验结果表明，该方法优于其他浅水区提取算法，可以在不同环境条件下准确提取岛屿周围和海岸带浅水区。

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引用次数: 0

Adaptive variational fusion of multi-source lunar digital elevation models based on curvature regularization 基于曲率正则化的多源月球数字高程模型自适应变分融合

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.cageo.2026.106107

Siyi Qiu , Zhen Ye , Yusheng Xu , Jia Qian , Rong Huang , Tao Tao , Huan Xie , Xiaohua Tong

A good lunar Digital Elevation Model (DEM) is critical for successful lunar landings and exploration missions, providing essential terrain data for landing site selection and mission planning. However, existing lunar DEMs face significant limitations, such as insufficient resolution, incomplete data coverage, and errors arising from observational and processing methods. These limitations hinder their effectiveness in high-precision terrain analysis and practical applications. To address these challenges, this study proposes an adaptive regularized variational model with curvature smoothing constraints, aiming to robustly fuse multi-source DEMs with varying resolutions, noise levels, and data voids. The proposed model incorporates two key innovations: a weighted data fidelity term that dynamically adjusts based on slope anomaly detection and ensures accuracy consistency across multi-scale DEMs, and a curvature-constrained total variation regularization term that suppresses noise and artifacts while preserving terrain details. The variational problem is solved iteratively using the Alternating Direction Method of Multipliers, enabling the seamless integration of multi-source DEMs. Experimental validations on simulated and real lunar south pole datasets demonstrate the superior performance of the proposed method, achieving significant improvements in noise suppression, artifact removal, void filling, and terrain detail preservation. The results indicate that the proposed method is capable of generating high-quality, seamless DEMs with enhanced spatial consistency and accuracy.

一个好的月球数字高程模型（DEM）是成功的月球着陆和探测任务的关键，为着陆点选择和任务规划提供必要的地形数据。然而，现有的月球dem面临着明显的局限性，如分辨率不足、数据覆盖不完整以及观测和处理方法引起的误差。这些限制阻碍了它们在高精度地形分析和实际应用中的有效性。为了解决这些挑战，本研究提出了一种具有曲率平滑约束的自适应正则化变分模型，旨在鲁棒融合具有不同分辨率、噪声水平和数据空洞的多源dem。该模型包含两个关键创新：基于坡度异常检测动态调整的加权数据保真度项，确保跨多尺度dem的精度一致性；曲率约束的总变差正则化项，在保留地形细节的同时抑制噪声和伪影。采用乘法器交替方向法迭代求解变分问题，实现了多源dem的无缝集成。在模拟和真实月球南极数据集上的实验验证表明，该方法在噪声抑制、伪影去除、空隙填充和地形细节保存等方面取得了显著的进步。结果表明，该方法能够生成高质量、无缝的dem，并提高了空间一致性和精度。

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引用次数: 0

A spatiotemporal deep learning framework integrating CNN-BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province 基于CNN-BiLSTM和注意力机制的云南省GRACE数据降尺度时空深度学习框架

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.cageo.2026.106117

Yang He , Qi Chen , Zhifang Zhao , Dayu Cai , Liu Ouyang , Xiaoxiao Zhang , Yu Gao , Junrong Zhou

The Gravity Recovery and Climate Experiment (GRACE) dataset has emerged as a pivotal tool for quantifying terrestrial water storage (TWS) anomalies at regional scales. However, its coarse spatial resolution (∼3°) introduces substantial uncertainties in localized hydrological analyses. To overcome this limitation, we developed a spatiotemporal deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory networks (BiLSTM), enhanced by a time-space attention mechanism. Applied to Yunnan Province, China, this framework achieved a tenfold resolution enhancement (1°–0.1°), preserving high consistency with raw GRACE data (cc = 0.94). Validation against independent datasets demonstrated a 6–15 % improvement in Coefficient of Determination (R²) over conventional downscaling methods, while maintaining moderate to strong correlations (r = 0.53–0.74) with WGHM products and river-lake water level data. Multivariate analysis revealed statistically significant couplings between downscaled TWS variations and key environmental drivers, including soil moisture (SoilMoi), land surface temperature (LST), evapotranspiration (E), the Normalized Difference Vegetation Index (NDVI), and precipitation (TP). The refined GRACE Drought Severity Index (GRACE-DSI) exhibited enhanced synchronization with the Standardized Precipitation Evapotranspiration Index (SPEI), showing a >10 % increase in correlation coefficients compared to pre-downscaling values. This methodological advancement enabled precise spatiotemporal characterization of drought dynamics during the 2002–2023 period, particularly capturing the 2009–2012 extreme drought and 2019–2021 pluvial anomalies with sub-basin spatial fidelity. Our framework provides an operational solution for high-resolution hydrological monitoring, offering critical insights for adaptive water resource management in topographically complex regions.

重力恢复和气候实验（GRACE）数据集已成为量化区域尺度陆地储水（TWS）异常的关键工具。然而，其粗糙的空间分辨率（~ 3°）给局部水文分析带来了很大的不确定性。为了克服这一限制，我们开发了一个时空深度学习框架，该框架协同集成了卷积神经网络（cnn）和双向长短期记忆网络（BiLSTM），并通过时空注意机制得到增强。应用于中国云南省，该框架实现了10倍的分辨率增强（1°-0.1°），保持了与原始GRACE数据的高度一致性（cc = 0.94）。对独立数据集的验证表明，与传统的降尺度方法相比，该方法的决定系数（R2）提高了6 - 15%，同时与WGHM产品和河湖水位数据保持中等到强的相关性（r = 0.53-0.74）。多变量分析显示，缩小尺度的TWS变化与土壤湿度（SoilMoi）、地表温度（LST）、蒸散发(E)、归一化植被指数（NDVI）和降水（TP）等关键环境驱动因子之间存在显著的耦合关系。改进后的GRACE干旱严重指数（GRACE- dsi）与标准化降水蒸散指数（SPEI）的同同性增强，相关系数比降尺度前增加了10%。这一方法的进步使得2002-2023年期间干旱动态的精确时空特征得以实现，特别是捕获了2009-2012年极端干旱和2019-2021年降水异常，具有子流域空间保真度。我们的框架为高分辨率水文监测提供了一个可操作的解决方案，为地形复杂地区的适应性水资源管理提供了重要见解。

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引用次数: 0

Convolutional surrogate for 3D discrete fracture–matrix tensor upscaling 三维离散裂缝矩阵张量升级的卷积代理

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.cageo.2026.106105

Martin Špetlík, Jan Březina

Modeling groundwater flow in three-dimensional fractured crystalline media requires capturing the spatial heterogeneity introduced by fractures. Direct numerical simulations using fine-scale discrete fracture–matrix (DFM) models are computationally demanding, particularly when repeated evaluations are needed. We aim to use a multilevel Monte Carlo (MLMC) method in the future to reduce computational cost while retaining accuracy. When transitioning between accuracy levels, numerical homogenization is used to upscale the impact of the hydraulic conductivity of sub-resolution fractures. To reduce the computational cost of conventional 3D numerical homogenization, we develop a surrogate model that predicts the equivalent hydraulic conductivity tensor,

K^{e q}

, from a voxelized 3D domain representing a tensor-valued random field of matrix and fracture hydraulic conductivities. Fracture properties, including size, orientation, and aperture, are sampled from distributions informed by natural observations. The surrogate architecture combines a 3D convolutional neural network with feed-forward layers to capture both local spatial patterns and global interactions. Three surrogates are trained on data generated by discrete fracture–matrix (DFM) simulations, each corresponding to a different fracture-to-matrix conductivity ratio. Their performance is evaluated across varying fracture network parameters and correlation lengths of the matrix field. The trained surrogates achieve high prediction accuracy (

NRMSE < 0.22

) in a wide range of test scenarios. To demonstrate practical applicability, we compare conductivities upscaled by numerical homogenization and by our surrogates in two macro-scale problems: computation of equivalent tensors of hydraulic conductivity and prediction of outflow from a constrained 3D area. In both cases, the surrogate-based approach preserves accuracy while substantially reducing computational cost. Surrogate-based upscaling achieves speedups exceeding

100 \times

when inference is performed on a GPU.

在三维裂隙晶体介质中模拟地下水流动需要捕捉裂缝引入的空间非均质性。使用精细尺度离散裂缝矩阵（DFM）模型进行直接数值模拟需要大量的计算量，特别是在需要重复评估的情况下。我们的目标是在未来使用多层蒙特卡罗（MLMC）方法来降低计算成本，同时保持准确性。在精度等级之间转换时，采用数值均质化方法提高了亚分辨率裂缝水力导流性的影响。为了降低传统三维数值均匀化的计算成本，我们开发了一个代理模型，从代表矩阵和裂缝水力导度的张量值随机场的体素化三维域预测等效水力导率张量Keq。裂缝性质，包括尺寸、方向和孔径，都是从自然观测的分布中采样的。代理架构结合了3D卷积神经网络和前馈层，以捕获局部空间模式和全局交互。在离散裂缝-基质（DFM）模拟生成的数据上训练三个代理，每个代理对应不同的裂缝-基质导电性比。通过不同的裂缝网络参数和矩阵场的相关长度来评估它们的性能。经过训练的代理在广泛的测试场景中实现了很高的预测精度（NRMSE<0.22）。为了证明实际的适用性，我们在两个宏观尺度问题中比较了通过数值均匀化和我们的替代品升级的电导率：水力电导率的等效张量的计算和从受限的3D区域流出的预测。在这两种情况下，基于代理的方法都保持了准确性，同时大大降低了计算成本。当在GPU上执行推理时，基于代理的升级可以实现超过100倍的加速。

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引用次数: 0

An open source FORTRAN subroutine for calculation of TEM responses and derivatives from 1D models 一个开源的FORTRAN子程序，用于计算TEM响应和一维模型的导数

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.cageo.2025.106102

Niels B. Christensen , Anders V. Christiansen , Esben Auken , Nikolaj Foged

In this paper, an open source FORTRAN subroutine for the calculation of transient electromagnetic responses of one-dimensional earth models in the quasi-static approximation is presented. The code accommodates the most common TEM instruments configurations used today and includes the modelling of the effect of the system response, i.e. the influence of instrument properties on the responses. It also provides an option for calculating the derivatives of the response with regard to the model parameters of a one-dimensional earth model. Furthermore, induced polarisation effects can be included in the forward responses. The paper presents the considerations behind its creation and outlines the details of the computational elements used in its realisation. The subroutine is intended as a building block that can be included in other programs, specifically as an external computational resource for speeding up calculations in higher order language modelling and inversion codes.

本文给出了一个用于准静态近似下一维地球模型瞬变电磁响应计算的开放源码FORTRAN子程序。该规范适用于目前使用的最常见的瞬变电磁法仪器配置，并包括系统响应效果的建模，即仪器特性对响应的影响。它还提供了一种计算响应对一维地球模型参数的导数的方法。此外，诱导极化效应可以包含在正向响应中。本文介绍了其创建背后的考虑因素，并概述了其实现中使用的计算元素的细节。子程序旨在作为可以包含在其他程序中的构建块，特别是作为加速高阶语言建模和反转代码计算的外部计算资源。

引用次数: 0

Prediction of natural gamma and neutron porosity based on waveform structures of elastic parameters using a closed-loop deep learning fusion network 基于弹性参数波形结构的闭环深度学习融合网络自然伽马和中子孔隙度预测

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.cageo.2026.106122

Yingjie Ma , Gang Gao , Haojie Liu , Xiaoyan Zhai

Natural gamma (GR) and neutron porosity (NPHI) have been proven to be effective parameters for identifying and characterizing shale oil reservoirs in borehole geophysical studies. However, prediction of these petrophysical properties from elastic parameters presents considerable difficulties due to the complex nonlinear relationships between measurements of different physical properties of rocks, while relevant research literature remains limited. Conventional approaches typically employ multivariate regression techniques to transform P-wave velocity (Vp), S-wave velocity (Vs), and density (Den/ρ) into GR and NPHI. However, these regression methods are constrained during application due to their adoption of single-point mapping structures and linear mapping relationships, which consequently reduce prediction accuracy. To overcome these limitations, this study proposes an approach that predicts GR and NPHI by leveraging the waveform structures of data within a closed-loop deep learning fusion network. Input samples were constructed from the waveform structure of data to capture the temporal characteristics neglected by single-point inputs, thereby resolving the non-unique mapping to data. To address the complex nonlinear relationships, a fusion network of a Convolutional Neural Network (CNN), a Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism was established (STABGCN). This network design enables the extraction of sequential features along well trajectory, captures spatial patterns across the entire field area, and optimizes feature weighting through attention mechanisms. A closed-loop deep learning network was constructed and trained using a semi-supervised learning approach to improve its generalization capability by leveraging the abundance of unlabeled elastic parameter data. The Dongying Sag was selected as the study area for method validation and application, specifically, the feasibility of predicting GR and NPHI based on elastic parameters is systematically evaluated from three perspectives: the correlation between these parameters, the characteristics of their waveform structures, and how forward and inversion deep learning networks perform in prediction accuracy. Numerical and field data validation demonstrated that the proposed method significantly improved prediction accuracy. In model testing, the method proposed in this paper achieved R² values for GR and NPHI prediction were 0.7857 and 0.89 respectively, confirming the method's effectiveness in enhancing key petrophysical parameter prediction.

自然伽马（GR）和中子孔隙度（NPHI）在井眼地球物理研究中已被证明是识别和表征页岩油储层的有效参数。然而，由于岩石不同物性测量之间复杂的非线性关系，利用弹性参数预测岩石物性存在相当大的困难，相关研究文献仍然有限。传统方法通常采用多元回归技术将纵波速度（Vp）、横波速度（Vs）和密度（Den/ρ）转换为GR和NPHI。然而，这些回归方法由于采用单点映射结构和线性映射关系，在应用过程中受到了限制，从而降低了预测精度。为了克服这些限制，本研究提出了一种通过利用闭环深度学习融合网络中数据的波形结构来预测GR和NPHI的方法。根据数据的波形结构构建输入样本，捕捉单点输入忽略的时间特征，从而解决数据的非唯一映射问题。为了解决复杂的非线性关系，建立了卷积神经网络（CNN）、双向门控循环单元（BiGRU）和注意机制（STABGCN）的融合网络。这种网络设计能够沿着井眼轨迹提取序列特征，捕获整个油田区域的空间模式，并通过注意机制优化特征权重。利用丰富的未标记弹性参数数据，利用半监督学习方法构建和训练闭环深度学习网络，提高其泛化能力。选取东营凹陷为研究区进行方法验证与应用，从弹性参数之间的相关性、弹性参数的波形结构特征、正反演深度学习网络的预测精度三个方面系统评价了基于弹性参数预测GR和NPHI的可行性。数值和现场数据验证表明，该方法显著提高了预测精度。在模型测试中，本文方法预测GR和NPHI的R2值分别为0.7857和0.89，证实了该方法在增强关键岩石物性参数预测方面的有效性。

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引用次数: 0

GravCHAW: A software framework for the assimilation of time-lapse gravimetry data in groundwater models GravCHAW：一个用于同化地下水模型中时移重力数据的软件框架

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.cageo.2026.106118

Nazanin Mohammadi , Hamzeh Mohammadigheymasi , Landon J.S. Halloran

We present an open-source python framework GravCHAW (Gravimetric Coupled Hydro Assimilation Workflow) for the assimilation of time-lapse gravimetry (TLG) data into numerical groundwater models. This framework enables quantitative exploration of the full potential of TLG in reducing hydrogeological data gaps. TLG is a non-invasive geophysical method that can be used to monitor spatiotemporal variability of groundwater storage changes. At the software’s core is a site-independent coupled hydrogravimetric model that accurately simulates TLG data. Using a range of advanced optimization and uncertainty analysis approaches in a Bayesian context, built around the hydrogravimetric model, the framework assimilates TLG data to estimate parameters, make predictions, and quantify uncertainty across diverse problem scales. In doing so, it accounts for both parameter priors and observation uncertainty, enabling a probabilistic uncertainty analysis. The framework can perform a coupled hydrogravimetric inversion assimilating TLG data individually or jointly with hydrological observations. To illustrate some of the core capacities of the framework, we apply it to a simple groundwater model and explore the propagation of observation uncertainty to parameter and model predictions. The results show that TLG can accurately estimate model parameters and significantly reduce uncertainty in parameters and predictions, both when assimilated individually and jointly with hydraulic head data, provided that the signal-to-noise (SNR) is sufficiently high. In this condition, while joint assimilation results in greater uncertainty reduction in our example case, TLG appears to have the most substantial contribution. GravCHAW will enable the reduction of uncertainty in groundwater models by integrating TLG data, which will be particularly impactful in data-poor situations.

我们提出了一个开源python框架GravCHAW（重力耦合水力同化工作流），用于将时移重力（TLG）数据同化到数值地下水模型中。该框架能够定量探索TLG在减少水文地质数据空白方面的全部潜力。TLG是一种非侵入性的地球物理方法，可用于监测地下水储量变化的时空变异性。该软件的核心是一个独立于站点的耦合水文重力模型，可以精确模拟TLG数据。在贝叶斯环境中使用一系列先进的优化和不确定性分析方法，围绕水重力模型构建，框架吸收TLG数据来估计参数，做出预测，并量化不同问题尺度的不确定性。在这样做时，它考虑了参数先验和观测不确定性，从而实现了概率不确定性分析。该框架可以单独或与水文观测联合进行同化TLG数据的耦合水文重力反演。为了说明该框架的一些核心能力，我们将其应用于一个简单的地下水模型，并探讨了观测不确定性对参数和模型预测的影响。结果表明，在信噪比足够高的情况下，无论是单独同化还是与水头数据联合同化，TLG都能准确估计模型参数，显著降低参数和预测的不确定性。在这种情况下，虽然联合同化在我们的例子中导致更大的不确定性降低，但TLG似乎有最实质性的贡献。GravCHAW将通过整合TLG数据来减少地下水模型的不确定性，这在数据匮乏的情况下将特别有影响力。

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引用次数: 0

EWoPe: A light Embeddable WOrkflow PErsistence tool for geoscientific pipeline reproducibility EWoPe：用于地球科学管道再现的轻量级可嵌入工作流持久性工具

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.cageo.2025.106099

Marianna Miola , Daniela Cabiddu , Simone Pittaluga , Micaela Raviola , Marino Vetuschi Zuccolini

Scientific workflows are essential in modern geoscientific research, where complex, multi-stage computational pipelines are used to analyze heterogeneous environmental data. Ensuring the reproducibility and traceability of these workflows is critical but often challenging due to their intricacy and evolving structure. We introduce EWoPe (Embeddable Workflow Persistence), a lightweight and embeddable methodology and C++ library designed to persist and reconstruct scientific workflows over time. Unlike existing workflow systems and provenance tools, our method adds minimal overhead to workflow execution: it does not automate or optimize processes, but instead ensures persistence through lightweight and structured metadata. EWoPe models workflows as directed acyclic graphs (DAGs), in which each data node is linked to computational tasks through metadata. The latter captures input–output dependencies, algorithm parameters, execution commands, and intermediate results, supporting full traceability and reproducibility of computational histories. EWoPe offers dual usability: as a standalone command-line tool or as an embeddable component within larger applications. We show its flexibility and applicability through a case study involving a complex workflow leading to subsurface reaction-transport modeling, starting from boreholes data. The EWoPe library is publicly available and designed to be extensible, making it suitable for a wide range of scientific domains, including geochemistry, geophysics, environmental engineering, and any other fields where transparency and data integrity are critical.

科学工作流程在现代地球科学研究中是必不可少的，在现代地球科学研究中，复杂的、多阶段的计算管道被用来分析异构环境数据。确保这些工作流的可再现性和可追溯性至关重要，但由于其复杂性和不断发展的结构，这往往具有挑战性。我们介绍了EWoPe（可嵌入工作流持久性），这是一种轻量级的可嵌入方法和c++库，旨在随着时间的推移持久化和重构科学工作流。与现有的工作流系统和来源工具不同，我们的方法为工作流的执行增加了最小的开销：它不自动化或优化流程，而是通过轻量级和结构化的元数据确保持久性。EWoPe将工作流建模为有向无环图（dag），其中每个数据节点通过元数据链接到计算任务。后者捕获输入-输出依赖关系、算法参数、执行命令和中间结果，支持计算历史的完全可跟踪性和可再现性。EWoPe提供了双重可用性：作为独立的命令行工具或作为大型应用程序中的可嵌入组件。我们通过一个案例研究展示了它的灵活性和适用性，该案例研究涉及一个复杂的工作流程，从钻孔数据开始，导致地下反应传输建模。EWoPe库是公开可用的，设计为可扩展的，使其适用于广泛的科学领域，包括地球化学，地球物理，环境工程，以及其他透明度和数据完整性至关重要的领域。

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引用次数: 0

Three-dimensional digital core reconstruction from 2D SEM images of heterogeneous shale samples 非均质页岩样品二维SEM图像三维数字岩心重建

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.cageo.2026.106119

Qian Feng , Xiaofeng Xu , Ren Wang , Wanzhong Shi , Qintao Guo , Bowei Yang , Zijie Zhang , Xiaoming Zhang , Mehdi Ostadhassan

The construction of 3D digital core models for heterogeneous shale samples presents significant challenges due to the multi-scale nature of these materials. In this study, we propose an optimized generative adversarial network (GAN) method, known as SliceGAN-RFB, to overcome these challenges by leveraging scanning electron microscopy (SEM) images for digital core reconstruction. The SliceGAN-RFB model entails key improvements over the traditional SliceGAN, including the use of the Sobel operator for gradient-based image subset extraction and the integration of the receptive field block (RFB) into the discriminator network to enhance multi-scale feature extraction. SliceGAN-RFB attempts multiple critical iterations (here, we call them critic iters) during the training phase to increase the quality of the generated digital core model. The results demonstrated that the digital cores generated by SliceGAN-RFB better resembled the true samples, where the clay minerals exhibited greater continuity. Additionally, the spatial distributions of the pores and pyrite within the digital core were found to be closer to those in the original two-dimensional SEM images. The two-point connectivity probability function (2 PC) curve further validated that the digital model generated by SliceGAN-RFB was more accurate and consistent with the original data than the SliceGAN was. Ultimately, the generation of digital cores by the SliceGAN-RFB is particularly important when dealing with heterogeneous materials such as shale in comparison with homogeneous materials such as sandstone or battery components. This enhanced capability enables the establishment of pore network models and flow simulations of shale and other heterogeneous materials, which are important in various fields, including hydrogen storage, carbon capture, utilization and storage (CCUS), and nuclear waste containment.

由于非均质页岩样品的多尺度性质，构建非均质页岩样品的三维数字岩心模型面临着巨大的挑战。在本研究中，我们提出了一种优化的生成对抗网络（GAN）方法，称为SliceGAN-RFB，通过利用扫描电子显微镜（SEM）图像进行数字核心重建来克服这些挑战。SliceGAN-RFB模型对传统的SliceGAN进行了关键改进，包括使用Sobel算子进行基于梯度的图像子集提取，以及将接收野块（RFB）集成到鉴别器网络中以增强多尺度特征提取。SliceGAN-RFB在训练阶段尝试多次关键迭代（在这里，我们称之为批评家），以提高生成的数字核心模型的质量。结果表明，SliceGAN-RFB生成的数字岩心与真实样品更接近，其中粘土矿物表现出更大的连续性。此外，数字岩心内孔隙和黄铁矿的空间分布与原始二维SEM图像更接近。两点连连概率函数（2 PC）曲线进一步验证了SliceGAN- rfb生成的数字模型比SliceGAN更准确，与原始数据更一致。最终，与砂岩或电池组件等均质材料相比，SliceGAN-RFB在处理页岩等非均质材料时产生的数字核心尤为重要。这种增强的能力可以建立页岩和其他非均质材料的孔隙网络模型和流动模拟，这在氢储存，碳捕获，利用和储存（CCUS）以及核废料遏制等各个领域都很重要。

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引用次数: 0

StripesCounter: A new image software for increment measurement in paleoclimate archives StripesCounter：一种用于古气候档案增量测量的新图像软件

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2026-03-01 Epub Date: 2026-01-09 DOI: 10.1016/j.cageo.2026.106104

Clara Boutreux , Patrick Brockmann , Mary Elliot , Matthieu Carré , Marc Gosselin

Most natural paleoclimate archives are accretionary material presenting periodic structures that bear environmental and/or chronological information. Here we present StripesCounter, an open access Python software designed for automated banding detection and measurement. As a study case, 16-year long profiles of daily growth increment measurements were conducted on a modern shell of the giant clam Tridacna gigas. High resolution images of shell thin sections were obtained using a confocal laser scanning microscopy and processed using StripesCounter. We demonstrate that StripesCounter provides highly reproducible and accurate results. The long time series of daily increments indicate that Tridacna gigas growth is strongly modulated by seasonal oceanographic variations, reflecting changes in sea surface temperature, precipitation, and salinity. Notably, growth profiles reveal semi-annual variations related to semi-annual variations in environmental factors, potentially linked to ENSO events. This automated growth increment analysis can be extended to other archives with cyclic structures, including tree rings, corals, and other biogenic or abiotic laminated materials. StripesCounter offers a powerful and accessible tool for generating long high-resolution, temporally explicit datasets, opening new perspectives for investigating rapid environmental changes across diverse ecosystems and geological timescales.

大多数自然古气候档案都是增生物质，呈现出带有环境和/或年代信息的周期性结构。在这里，我们介绍StripesCounter，一个开放访问的Python软件，设计用于自动条带检测和测量。作为一个研究案例，对现代巨型蛤壳进行了长达16年的每日生长增量测量。采用共聚焦激光扫描显微镜获得贝壳薄片的高分辨率图像，并使用StripesCounter进行处理。我们证明了StripesCounter提供了高重复性和准确的结果。长时间的日增量表明，季节性海洋变化对砗磲的生长有强烈的调节作用，反映了海面温度、降水和盐度的变化。值得注意的是，增长曲线揭示了与环境因素半年变化相关的半年变化，这可能与ENSO事件有关。这种自动化的生长增量分析可以扩展到其他具有循环结构的档案，包括树木年轮，珊瑚和其他生物或非生物层压材料。StripesCounter提供了一个功能强大且易于访问的工具，用于生成长时间高分辨率、时间明确的数据集，为研究不同生态系统和地质时间尺度的快速环境变化开辟了新的视角。

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