Computers & Geosciences最新文献_第4页

Conditioned 3D DeepKriging with locally varying anisotropy 具有局部变化各向异性的条件三维深克里格法

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

Computers & Geosciences

Pub Date : 2025-11-07 DOI: 10.1016/j.cageo.2025.106075

Gamze Erdogan Erten, Jeff Boisvert

Deep neural networks (DNNs) are powerful tools for spatial modeling tasks but they often struggle to capture spatial autocorrelation and accurately reproduce observed data, which are crucial in geoscience applications. While traditional methods like Kriging address these challenges effectively, DNNs typically treat spatial coordinates as standard features, missing the full potential of spatial relationships. A Conditioned DeepKriging (C-DK) methodology is proposed to overcome these limitations, which builds on the DeepKriging (DK) model architecture created by Chen et al., (2020). C-DK integrates Locally Dependent Moments (LDM) to ensure reproduction of observed values at sampled locations without increasing computational complexity. An embedding layer of spatial coordinates constructed with kernel basis functions is utilized as features in the DNN, and the resulting model is merged with LDM estimates based on local reliability. A second contribution is the addition of locally varying anisotropy (C-DK+LVA), which improves the ability to model complex geological features by incorporating LVA into the model. LVA parameterizes the spatial continuity of a domain using a vector field. Shortest-path distance (SPD) features are employed to encode the effects of LVA, replacing the Euclidean radial basis function (RBF) embedding used in the original DK model. This adaptation allows the model to incorporate directional continuity structures. To support 3D applications, both the Euclidean RBF embedding and SPD computations are extended to 3D. The proposed models are validated on 2D and 3D datasets and yield performance metrics comparable to Ordinary Kriging (OK). Moreover, C-DK+LVA outperforms both C-DK and OK+LVA in scenarios with significant variation in anisotropy. The proposed methodologies require no assumptions of stationarity or linearity and they eliminate the need for variogram calculations, enabling an automated estimation process.

深度神经网络（dnn）是空间建模任务的强大工具，但它们往往难以捕获空间自相关并准确重现观测数据，这在地球科学应用中至关重要。虽然像Kriging这样的传统方法可以有效地解决这些挑战，但dnn通常将空间坐标视为标准特征，错过了空间关系的全部潜力。为了克服这些限制，提出了一种条件DeepKriging （C-DK）方法，该方法建立在Chen等人（2020）创建的DeepKriging （DK）模型架构之上。C-DK集成了局部依赖矩（LDM），以确保在不增加计算复杂性的情况下再现采样位置的观测值。在深度神经网络中，利用核基函数构造的空间坐标嵌入层作为特征，并将得到的模型与基于局部可靠性的LDM估计相融合。第二个贡献是增加了局部变化的各向异性（C-DK+LVA），通过将LVA纳入模型，提高了模拟复杂地质特征的能力。LVA使用向量场参数化域的空间连续性。利用最短路径距离（SPD）特征对LVA效果进行编码，取代了原始DK模型中使用的欧几里得径向基函数（RBF）嵌入。这种适应性使模型能够纳入定向连续性结构。为了支持3D应用，欧几里得RBF嵌入和SPD计算都扩展到3D。所提出的模型在2D和3D数据集上进行了验证，其性能指标与普通克里格（OK）相当。此外，在各向异性变化显著的情况下，C-DK+LVA的性能优于C-DK和OK+LVA。所提出的方法不需要假设平稳性或线性，并且它们消除了对变异函数计算的需要，从而实现了自动估计过程。

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引用次数: 0

Integrating Variational Auto-Encoders (VAEs) and spatial interpolation for improving rock mass domaining in open pit mines 结合变分自编码器（VAEs）和空间插值改善露天矿岩体域

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

Computers & Geosciences

Pub Date : 2025-11-05 DOI: 10.1016/j.cageo.2025.106074

Yakin Hajlaoui , Jean-François Plante , Richard Labib , Michel Gamache

This study presents a novel method for identifying spatial zones with consistent rock hardness in open-pit mining, a process known as rock mass domaining. The objective is to leverage drilling sensor data to automate the classification of subsurface materials, thereby enhancing blasting efficiency and geological interpretation. The proposed approach combines a variational autoencoder – a type of deep generative model used for dimensionality reduction – with a learnable spatial interpolation mechanism that captures directional trends and geological continuity. Drilling measurements such as penetration rate, torque, weight on bit, and rotation speed are used to infer a latent indicator of rock hardness. This feature is spatially interpolated using a differentiable inverse distance weighting model, compatible with neural network backpropagation. Three neural architectures are compared: fully connected, convolutional, and radial basis function networks. The models were trained and tested on 40 drilling patterns from an iron mine in northern Quebec. The radial basis function variant with an inverse quadratic kernel achieved the best overall performance, with a median domain accuracy of 0.88 and an average pooled standard deviation of 0.31, indicating high spatial cohesion and internal cluster consistency. The approach also demonstrated strong alignment with expert-defined lithological domains and effective detection of disturbed collar zones near the surface. Model training was stable across architectures, and sensitivity analysis confirmed the robustness of hyperparameter choices. While limitations such as Gaussian priors in latent space restrict full modeling of geological multimodality, the framework remains extensible to future enhancements incorporating geological constraints. In summary, this method integrates deep representation learning with spatial modeling to provide interpretable, geologically meaningful domaining. It reduces sensitivity to noisy measurements and enables automated, data-driven mine planning and subsurface characterization.

本研究提出了一种新的方法来识别露天开采中具有一致岩石硬度的空间区域，这一过程被称为岩体域。目标是利用钻井传感器数据自动分类地下材料，从而提高爆破效率和地质解释。提出的方法结合了变分自编码器（一种用于降维的深度生成模型）和可学习的空间插值机制，该机制可以捕获方向趋势和地质连续性。钻速、扭矩、钻头重量和旋转速度等钻井测量数据被用来推断岩石硬度的潜在指标。该特征使用可微逆距离加权模型进行空间插值，与神经网络反向传播兼容。比较了三种神经结构：全连接、卷积和径向基函数网络。这些模型在魁北克省北部一个铁矿的40种钻孔模式上进行了训练和测试。具有反二次核的径向基函数变体获得了最佳的综合性能，中位数域精度为0.88，平均池标准差为0.31，表明了较高的空间凝聚力和内部聚类一致性。该方法还证明了与专家定义的岩性区域的高度对准，以及对地表附近受干扰的接箍区域的有效检测。模型训练在不同架构下是稳定的，敏感性分析证实了超参数选择的鲁棒性。虽然潜在空间中的高斯先验等限制限制了地质多模态的完整建模，但该框架仍然可以扩展到将来包含地质约束的增强。总之，该方法将深度表示学习与空间建模相结合，提供可解释的、有地质意义的域。它降低了对噪声测量的灵敏度，实现了自动化、数据驱动的矿山规划和地下表征。

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引用次数: 0

HiHa: Introducing hierarchical harmonic decomposition to implicit neural compression for atmospheric data 在大气数据的隐式神经压缩中引入层次谐波分解

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

Computers & Geosciences

Pub Date : 2025-11-04 DOI: 10.1016/j.cageo.2025.106078

Zhewen Xu , Baoxiang Pan , Xiaohui Wei , Hongliang Li , Dongyuan Tian , Zijian Li , Changzheng Liu

The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide, which requires an efficient compression method. However, traditional compression algorithms exhibit limited efficiency in compressing atmospheric data. As an emerging technique, Implicit Neural Representation (INR) has recently gained significant momentum and shows great potential for the compression of diverse atmospheric data. Nevertheless, it presents significant challenges in addressing the complex spatio-temporal characteristics and variability. Therefore, we propose Hierarchical Harmonic decomposition implicit neural compression (HiHa) for atmospheric data. HiHa firstly segments the data into multi-frequency signals through harmonic decomposition, and then tackles each harmonic with a frequency-based hierarchical compression module consisting of sparse storage, multi-scale INR and iterative decomposition sub-modules. We additionally design a temporal residual compression module to accelerate compression by utilizing temporal continuity. Experiments depict that HiHa can achieve: (1)

27 \times

compression in 308 s, error within 1e-5; (2)

244 \times

compression in 43 s, error within 1e-3. The results outperform both mainstream compressors and other INR-based methods, and demonstrate that using HiHa in existing data-driven models can achieve the same accuracy as raw data.

大型气候模式的快速发展产生了在全球范围内存储和传输海量大气数据的需求，这就需要一种高效的压缩方法。然而，传统的压缩算法在压缩大气数据方面表现出有限的效率。作为一种新兴的技术，内隐神经表示（INR）在大气数据压缩方面发展迅速，显示出巨大的潜力。然而，它在解决复杂的时空特征和变异性方面提出了重大挑战。因此，我们提出了大气数据的层次谐波分解隐式神经压缩（HiHa）方法。HiHa首先通过谐波分解将数据分割成多频信号，然后利用由稀疏存储、多尺度INR和迭代分解子模块组成的基于频率的分层压缩模块处理每个谐波。我们还设计了一个时间残余压缩模块，利用时间连续性来加速压缩。实验表明，HiHa可以在308 s内实现27倍的压缩，误差在1e-5以内；(2) 43 s压缩244x，误差在1e-3以内。结果优于主流压缩器和其他基于inr的方法，并证明在现有数据驱动模型中使用HiHa可以达到与原始数据相同的精度。

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引用次数: 0

Spectral-diagonalization-based matrix exponential integration for efficient and stable solutions of full-Bloch equations in surface NMR 基于光谱对角化的矩阵指数积分法求解表面核磁共振全bloch方程的高效稳定解

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

Computers & Geosciences

Pub Date : 2025-11-03 DOI: 10.1016/j.cageo.2025.106073

Tingting Lin , Qingyue Wang , Chuandong Jiang , Chunpeng Ren , Yunzhi Wang , Liang Wang

Surface nuclear magnetic resonance (SNMR) is a geophysical extension of nuclear magnetic resonance (NMR) that enables non-invasive mapping of subsurface hydrogeological properties by measuring the relaxation response of groundwater hydrogen nuclei. Accurately modeling the transient spin dynamics in SNMR requires solving the full-Bloch equations under Earth’s geomagnetic field, where magnetic field inhomogeneities, multicomponent relaxation, and nonlinear pulsed excitations introduce significant mathematical and computational challenges. We present a spectral-diagonalization-based matrix exponential integration (SD-MEI) algorithm for efficient and stable solutions of full-Bloch equations in SNMR. Conventional explicit numerical methods exhibit cumulative discretization errors and escalating computational costs due to step-size dependence and finite precision limitations. SD-MEI integrates spectral diagonalization with matrix exponential operations, replacing iterative computations with a single eigendecomposition of the system matrix. This approach achieves parameter-robust computational complexity while maintaining numerical stability across broad

B_{1}

field strengths (10⁻¹⁰ T to 10⁻⁵ T) and relaxation times (10 ms to 1000 ms). Validated for steady-state free precession (SSFP) dynamics in heterogeneous geomagnetic environments, the method enables high-accuracy modeling of transient magnetization evolution with large time steps. The framework advances SNMR efficient forward modeling and inversion while optimizing protocols by resolving critical limitations in existing numerical and analytical approaches.

地表核磁共振（SNMR）是核磁共振（NMR）的地球物理延伸，通过测量地下水氢核的弛豫响应，可以实现地下水文地质性质的非侵入式测绘。精确地模拟SNMR中的瞬态自旋动力学需要求解地球地磁场下的full-Bloch方程，其中磁场的不均匀性、多分量弛豫和非线性脉冲激励带来了重大的数学和计算挑战。提出了一种基于谱对角化的矩阵指数积分（SD-MEI）算法，用于SNMR中全bloch方程的高效稳定解。由于步长依赖和有限精度的限制，传统的显式数值方法具有累积的离散误差和不断上升的计算成本。SD-MEI将光谱对角化与矩阵指数运算相结合，用系统矩阵的单一特征分解取代了迭代计算。该方法实现了参数鲁棒性计算复杂性，同时保持了宽B1场强（10−10 T至10−5 T）和松弛时间（10 ms至1000 ms）的数值稳定性。该方法在非均质地磁环境下进行了稳态自由进动（SSFP）动力学验证，实现了大时间步长瞬态磁化演化的高精度建模。该框架通过解决现有数值和分析方法的关键限制，提高了SNMR的有效正演建模和反演，同时优化了协议。

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引用次数: 0

Three-dimensional inversion method based on multi-source fused physical information networks for leachate distribution in landfills 基于多源融合物理信息网络的垃圾填埋场渗滤液分布三维反演方法

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

Computers & Geosciences

Pub Date : 2025-11-02 DOI: 10.1016/j.cageo.2025.106077

Xiaochen Sun , Ya Xu , Changxin Nai , Jingcai Liu

Groundwater pollution caused by high-concentration harmful leachate leakage from landfills has become a global environmental problem. The combined observation of multi-source geophysical data can offer a more comprehensive and multi-faceted view of underground conditions. With improved detection capability, the quantity and diversity of multi-source data pose significant challenges to landfill leachate imaging. With rapid development of deep learning, a novel approach can be realized for the fusion inversion of multi-source geophysical data. However, predictions from purely data-driven deep learning models can be physically inconsistent or unreliable, leading to poor generalization performance of network models. We propose a fusion neural network, PI-FusNet, based on physical information, to characterize leachate distribution in landfills by fusing resistivity and self-potential data. PI-FusNet designs a loss function that incorporates electric field partial differential loss in addition to traditional mean square error loss. This ensures that the network follows the distribution pattern of data samples while conforming to the physical law described by the partial differential equation. Consequently, resistivity and self-potential data obtained from different observation methods converge into the same electric field space. In numerical simulations, PI-FusNet performed better on evaluation metrics than the pure data-driven network and the smooth model, with the lowest RMSE (0.1054) and MAE (0.3775) and the highest SSIM (0.9276) and UIQI (82.8266). Therefore, it is evident that PI-FusNet can accurately characterize the distribution of pollutants, whether in single or multiple contaminated areas. Field verification demonstrates that PI-FusNet can more accurately reconstruct the diffusion and distribution process of leachate in soil.

垃圾填埋场高浓度有害渗滤液泄漏造成的地下水污染已成为一个全球性的环境问题。多源地球物理资料的联合观测，可以更全面、更多方位地了解地下状况。随着检测能力的提高，多源数据的数量和多样性对垃圾渗滤液成像提出了重大挑战。随着深度学习技术的迅速发展，为多源地球物理数据的融合反演提供了一种新的方法。然而，纯数据驱动的深度学习模型的预测可能在物理上不一致或不可靠，导致网络模型的泛化性能较差。本文提出了一种基于物理信息的融合神经网络PI-FusNet，通过融合电阻率和自电位数据来表征垃圾填埋场渗滤液的分布。PI-FusNet设计了一个损失函数，除了传统的均方误差损失外，还包含电场偏微分损失。这保证了网络既符合数据样本的分布规律，又符合偏微分方程所描述的物理规律。因此，不同观测方法获得的电阻率和自电位数据收敛到同一电场空间。在数值模拟中，PI-FusNet的评价指标优于纯数据驱动网络和平滑模型，RMSE（0.1054）和MAE（0.3775）最低，SSIM（0.9276）和UIQI（82.8266）最高。因此，很明显，PI-FusNet可以准确地表征污染物的分布，无论是在单个还是多个污染区域。现场验证表明，PI-FusNet可以更准确地重建渗滤液在土壤中的扩散和分布过程。

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引用次数: 0

Exploring the role of model classification, complexity, and selection in volcanic hazard forecasting 探讨模型分类、复杂性和选择在火山灾害预测中的作用

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

Computers & Geosciences

Pub Date : 2025-10-30 DOI: 10.1016/j.cageo.2025.106070

Emmy Scott, Melody Whitehead, Jonathan Procter

This review examines the current landscape of computational volcanic hazard models, focusing on their creation and application, for a diverse set of end-users’ short-term and long-term forecasting requirements. We provide a comprehensive classification of volcanic hazard models, categorising them according to their theoretical foundations. This is central to understanding the diversity of hazard characterisation and simulation approaches, from empirical models to computationally demanding physics-based numerical models. The classification framework helps contextualise the strengths and limitations of different models and their suitability for specific forecasting demands. We discuss the fundamental principles behind model construction, considering factors such as input parameters, conceptual frameworks, and the incorporation of uncertainties. We also synthesise existing literature on model testing, covering aspects such as model verification, validation, calibration, and benchmarking, and provide a systematic and transparent framework for model selection, considering data availability, computational constraints, and specific forecasting needs. We explore the balance between model complexity, computational efficiency, and accuracy, addressing the uncertainties inherent in both input parameters and model processes. A key focus is the role of input parameters in forecasting and the need to select models that are detailed enough to capture essential hazard dynamics, yet simple enough to minimise error and computational costs.

本文审查了计算火山灾害模型的现状，重点是它们的创建和应用，以满足各种最终用户的短期和长期预测需求。我们对火山灾害模型进行了综合分类，并根据其理论基础对其进行了分类。这是理解危险特征和模拟方法多样性的核心，从经验模型到计算要求高的基于物理的数值模型。分类框架有助于将不同模型的优势和局限性以及它们对特定预测需求的适用性联系起来。我们讨论了模型构建背后的基本原则，考虑了输入参数、概念框架和不确定性的结合等因素。我们还综合了关于模型测试的现有文献，涵盖了模型验证、验证、校准和基准测试等方面，并为模型选择提供了一个系统和透明的框架，考虑到数据可用性、计算约束和特定的预测需求。我们探索模型复杂性、计算效率和准确性之间的平衡，解决输入参数和模型过程中固有的不确定性。一个关键的焦点是输入参数在预测中的作用，以及需要选择足够详细的模型，以捕捉基本的危险动态，但又足够简单，以尽量减少误差和计算成本。

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引用次数: 0

PoreViT: Automated pore typing in carbonate rocks using vision transformers and neighborhood features PoreViT：利用视觉变压器和邻域特征对碳酸盐岩进行自动孔隙分型

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

Computers & Geosciences

Pub Date : 2025-10-30 DOI: 10.1016/j.cageo.2025.106071

Yemna Qaiser , Mohammed Ishaq , Mohammed Yaqoob , Mohammed Yusuf Ansari , Isaac Sujay , Talha Khan , Harris Rabbani , Juan Carlos Laya , P.J. Moore , Thomas Daniel Seers

The classification of pores into their intrinsic depo-diagenetic or petrophysical morphotypes is a fundamental practice within carbonate petrography, providing linkage between pore-scale textures and their associated petrophysical signatures and/or paragenetic histories. Typically, pore classification is performed manually in a qualitative/semi-quantitative manner, which is hampered by inefficiency, subjectivity, and a lack of scalability. Though aimed at addressing the limitations of manual pore classification, efforts to automate petrographic pore-typing through artificial intelligence and computer vision techniques are limited by the inability of models to classify pores into genetic classes solely based upon simplistic size and shape features, which have been the focus of the existing literature. To address this nuanced classification problem, we present PoreViT: a Vision Transformer (ViT) model used to classify macropores observed in thin-sections into their respective Lucia classes (interparticle, touching vug, separate vug). The core novelty of PoreViT lies in its Feature Fusion block, which integrates ViT features, enhanced by a Global Token Addition layer, with spatial features extracted from a Convolutional Neural Network (CNN). Critically, our classifier leverages neighborhood information to provide the model with localized pore system topology, recognizing that pore types need to be identified not just by shape but also by their local spatial context. Trained and tested using 4115 labels obtained from 25 high-resolution thin-section scans, PoreViT provides an accurate, automated classification of carbonate macropores, achieving precision and recall values of 0.92 and 0.93 (macro-F1 0.92) corresponding to absolute improvements of ＋4.0% and ＋4.0%, and relative gains of ＋4.54% and ＋4.5%, respectively, over the best-performing CNN model (DenseNet121). The high throughput pore-textural classification capabilities demonstrated herein offer unprecedented opportunities in the integrated quantitative characterization of carbonates.

将孔隙划分为其内在的沉积成岩或岩石物理形态是碳酸盐岩岩石学的基本实践，它提供了孔隙尺度结构与其相关的岩石物理特征和/或共生历史之间的联系。通常，孔隙分类以定性/半定量的方式手动进行，效率低、主观性强、缺乏可扩展性。虽然旨在解决人工孔隙分类的局限性，但通过人工智能和计算机视觉技术自动化岩石学孔隙分型的努力受到模型无法仅根据简单的大小和形状特征将孔隙划分为遗传类的限制，这已经成为现有文献的重点。为了解决这个细致入微的分类问题，我们提出了PoreViT: Vision Transformer （ViT）模型，用于将薄切片中观察到的大孔隙分类为各自的Lucia类（颗粒间、接触空隙、分离空隙）。PoreViT的核心新颖之处在于其特征融合块，该块集成了由全局令牌添加层增强的ViT特征，以及从卷积神经网络（CNN）中提取的空间特征。关键的是，我们的分类器利用邻域信息为模型提供局部孔隙系统拓扑，认识到孔隙类型不仅需要通过形状来识别，还需要通过其局部空间环境来识别。使用从25个高分辨率薄层扫描中获得的4115个标签进行训练和测试，PoreViT提供了准确的、自动的碳酸盐大孔隙分类，与表现最好的CNN模型（DenseNet121）相比，精度和召回率分别达到0.92和0.93 (macro-F1 0.92)，分别提高了+4.0%和+4.0%，相对增益分别为+4.54%和+4.5%。本文所展示的高通量孔隙结构分类能力为碳酸盐的综合定量表征提供了前所未有的机会。

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引用次数: 0

Dual watermarking algorithm for trajectory data based on vector decomposition 基于矢量分解的轨迹数据双水印算法

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

Computers & Geosciences

Pub Date : 2025-10-27 DOI: 10.1016/j.cageo.2025.106072

Heyan Wang , Luanyun Hu , Yuchen Hu , Changqing Zhu , Wang Zhang , Na Ren

Trajectory data security demands simultaneous copyright protection and spatiotemporal integrity authentication, yet existing watermarking methods struggle with irreconcilable trade-offs between robustness, reversibility, and temporally-aware tamper detection. Traditional approaches often introduce irreversible geometric distortions under affine transformations and lack mechanisms to validate timestamp authenticity, leaving critical vulnerabilities in precision-sensitive applications. This paper proposes a dual watermarking framework integrating geometric-invariant reversible embeddings and semi-fragile temporal authentication. The method decomposes trajectory coordinates into affine-invariant α/β coefficients using vector decomposition, enabling high-capacity copyright watermark embedding resilient to geometric attacks. Temporal integrity verification is achieved through Pearson-correlation analysis of timestamp sequences, detecting malicious deletions with complete accuracy while tolerating legitimate temporal shifts. A bidirectional quantization modulation scheme guarantees lossless coordinate recovery, reducing restoration errors to sub-microradian precision by synchronizing geometric invariants with relative distance preservation. Comprehensive evaluations across 68,632 real-world trajectories demonstrate superior performance: the framework achieves normalized correlations above 0.94 against 45° rotation/200 % scaling attacks, 21 % higher robustness than state-of-the-art methods under compression, and full-error detection of timestamp tampering. By unifying geometric invariance, temporal causality verification, and reversible modulation within a single architecture, this work establishes a new paradigm for dual-function security in spatiotemporal data management, with direct applicability to GIS.

轨迹数据安全需要同时保护版权和时空完整性认证，然而现有的水印方法在鲁棒性、可逆性和时间感知篡改检测之间难以调和。传统的方法通常会在仿射变换下引入不可逆的几何扭曲，并且缺乏验证时间戳真实性的机制，这在精度敏感的应用中留下了严重的漏洞。提出了一种结合几何不变可逆嵌入和半脆弱时间认证的双水印框架。该方法利用矢量分解方法将轨迹坐标分解为仿射不变的α/β系数，使高容量版权水印嵌入能够抵御几何攻击。时间完整性验证是通过时间戳序列的皮尔逊相关分析来实现的，在允许合法时间偏移的同时，可以完全准确地检测恶意删除。双向量化调制方案保证了无损的坐标恢复，通过同步几何不变量和相对距离保持将恢复误差降低到亚微弧度精度。对68,632个真实世界轨迹的综合评估显示了卓越的性能：该框架在45°旋转/ 200%缩放攻击下实现了0.94以上的归一化相关性，比最先进的压缩方法高21%的鲁棒性，以及对时间戳篡改的全错误检测。通过在单一架构中统一几何不变性、时间因果验证和可逆调制，本工作建立了时空数据管理双功能安全的新范式，可直接适用于GIS。

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引用次数: 0

RIPPL, a Python-based InSAR stack and tropospheric delay software package RIPPL，基于python的InSAR堆栈和对流层延迟软件包

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

Computers & Geosciences

Pub Date : 2025-10-22 DOI: 10.1016/j.cageo.2025.106069

G. Mulder, F.J. van Leijen, P. Lopez-Dekker, R.F. Hanssen

Interferometric Synthetic Aperture Radar (InSAR) has a wide range of applications, including the monitoring of solid-earth and cryospheric geophysical processes and the monitoring of the built environment. The use of InSAR for atmospheric applications is less thoroughly developed. To perform such analyses the atmospheric phase delay of the SAR signal between different overpasses is used, which needs to be disentangled from other phase constituents, such as displacements and topography, which requires stack processing of large data volumes. Typically, initial atmospheric delays are predicted using existing numerical weather prediction (NWP) models, but InSAR processing and NWP model delay estimation software are not well integrated. Here we present a pure Python-based software package that integrates the automatic downloading and processing of InSAR and NWP model data to create time-series of unwrapped InSAR interferograms and InSAR equivalent tropospheric delays from NWP models. By combining the geometry of the InSAR radar signals with different NWP model datasets the tropospheric delays can accurately be derived on a pixel by pixel basis.

干涉合成孔径雷达（InSAR）具有广泛的应用，包括固体地球和冰冻圈地球物理过程的监测以及建筑环境的监测。InSAR在大气应用方面的应用发展得不太彻底。为了进行这种分析，使用了不同立交桥之间SAR信号的大气相位延迟，需要将其与其他相位成分（如位移和地形）分离，这需要对大数据量进行叠加处理。通常，使用现有的数值天气预报（NWP）模式预测初始大气延迟，但InSAR处理和NWP模式延迟估计软件没有很好地集成。本文提出了一个基于python的软件包，该软件包集成了InSAR和NWP模型数据的自动下载和处理，以创建来自NWP模型的解包裹InSAR干涉图的时间序列和InSAR等效对流层延迟。通过将InSAR雷达信号的几何形状与不同的NWP模式数据集相结合，可以精确地逐像元导出对流层延迟。

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引用次数: 0

openKARST: A novel open-source flow simulator for karst systems openKARST：一个新颖的开源岩溶系统流动模拟器

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

Computers & Geosciences

Pub Date : 2025-10-20 DOI: 10.1016/j.cageo.2025.106066

Jannes Kordilla, Marco Dentz, Juan J. Hidalgo

We introduce the open-source Python-based code openKARST for flow in karst conduit networks. Flow and transport in complex karst systems remain a challenging area of hydrogeological research due to the heterogeneous nature of conduit networks. Flow regimes in these systems are highly dynamic, with transitions from free-surface to fully pressurized and laminar to turbulent flow conditions and Reynolds numbers often exceeding one million. These transitions can occur simultaneously within a network, depending on conduit roughness properties and diameter distributions. openKARST solves the transient dynamic wave equation using an iterative scheme and is optimized through an efficient vectorized structure. Transitions from free-surface to pressurized flows in smooth and rough circular conduits are realized via a Preissmann slot approach in combination with an implementation of the Darcy–Weisbach and Manning equations to compute friction losses. To mitigate numerical fluctuations commonly encountered in the Colebrook–White equation, the dynamic switching from laminar to turbulent flows is modeled with a continuous Churchill formulation for the friction factor computation. openKARST supports common boundary conditions encountered in karst systems, as and includes functionalities for network import, export and visualization. The code is verified via comparison against several analytical solutions and validated against a laboratory experiment. Finally, we demonstrate the application of openKARST by simulating a synthetic recharge event in one of the largest explored karst networks, the Ox Bel Ha system in Mexico.

本文介绍了基于python的开放源代码openKARST岩溶管道网络中的流动。由于管道网络的非均质性，复杂岩溶系统的流动和输运仍然是水文地质研究的一个具有挑战性的领域。这些系统中的流动状态是高度动态的，从自由表面到全压，层流到湍流状态的转变，雷诺数通常超过一百万。根据导管的粗糙度和直径分布，这些转变可以同时发生在管网中。openKARST采用迭代格式求解瞬态动力波动方程，并通过有效的矢量化结构进行优化。通过Preissmann槽法结合Darcy-Weisbach和Manning方程计算摩擦损失，实现了光滑和粗糙圆形管道中从自由表面到加压流动的过渡。为了减轻Colebrook-White方程中经常遇到的数值波动，用连续Churchill公式模拟了从层流到湍流的动态转换，以计算摩擦系数。openKARST支持喀斯特系统中常见的边界条件，包括网络导入、导出和可视化功能。该代码通过与几个分析解的比较和与实验室实验的验证来验证。最后，我们通过模拟墨西哥Ox Bel Ha系统中最大的岩溶网络之一的综合补给事件来演示openKARST的应用。

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