Computers & Geosciences最新文献

Multimodal feature integration network for lithology identification from point cloud data 从点云数据中识别岩性的多模态特征集成网络

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-13 DOI: 10.1016/j.cageo.2024.105775

Ran Jing , Yanlin Shao , Qihong Zeng , Yuangang Liu , Wei Wei , Binqing Gan , Xiaolei Duan

Accurate lithology identification from outcrop surfaces is crucial for interpreting geological 3D data. However, challenges arise due to factors such as severe weathering and vegetation coverage, which hinder achieving ideal identification results with both accuracy and efficiency. The integration of 3D point cloud technology and deep learning methodologies presents a promising solution to address these challenges. In this study, we propose a novel multimodal feature integration network designed to distinguish various rock types from point clouds. Our network incorporates a multimodal feature integration block equipped with multiple attention mechanisms to extract representative deep features, along with a hierarchical feature separation block to leverage these features for precise segmentation of points corresponding to different lithologies. Furthermore, we introduce a specialized loss function tailored for rock type identification to enhance network training. Through experiments involving point cloud sampling strategies and loss function evaluation, we identify the optimal network configuration. Comparative analyses against baseline methods demonstrate the superiority of our proposed network across diverse study areas reconstructed from UAV images and laser scanner data, exhibiting improved visual appearance and metric values (Accuracy = 0.978, mean Accuracy = 0.895, mean IoU = 0.857). These findings underscore the efficacy of the multimodal feature integration network as a promising approach for lithology identification tasks in various digital outcrop models derived from heterogeneous data sources.

从露头表面准确识别岩性对于解释地质三维数据至关重要。然而，由于严重风化和植被覆盖等因素，实现理想的识别结果的准确性和效率都受到了阻碍。三维点云技术与深度学习方法的结合为应对这些挑战提供了一种前景广阔的解决方案。在本研究中，我们提出了一种新型多模态特征集成网络，旨在从点云中区分各种岩石类型。我们的网络包含一个多模态特征集成块，配备多种注意机制以提取具有代表性的深度特征，以及一个分层特征分离块，利用这些特征精确分割对应不同岩性的点。此外，我们还为岩石类型识别引入了专门的损失函数，以加强网络训练。通过点云采样策略和损失函数评估实验，我们确定了最佳网络配置。与基线方法的对比分析表明，我们提出的网络在由无人机图像和激光扫描仪数据重建的不同研究区域中具有优势，显示出更好的视觉外观和度量值（准确度 = 0.978，平均准确度 = 0.895，平均 IoU = 0.857）。这些发现强调了多模态特征集成网络的功效，它是一种很有前途的方法，可用于从异构数据源获得的各种数字露头模型中的岩性识别任务。

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引用次数: 0

A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network 基于改进型密集卷积网络的二维磁图谱深度学习反演方法

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-12 DOI: 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.

磁电反演（MT）是MT数据解释的重要手段。利用深度学习技术进行MT反演因其不局限于初始模型、避免陷入局部最优解以及处理海量数据的强大能力而备受关注。然而，如何获得高度可靠的深度学习反演结果仍是一个挑战。本文提出了一种基于改进型密集卷积网络（DenseNet）的二维（2-D）MT反演方法，旨在提高二维深度学习MT反演结果的可靠性。首先，在建立样本集时，使用 MARE2DEM 计算二维 MT 前向响应。然后，通过在密集连接块中采用深度可分离卷积代替标准卷积，并嵌入注意力机制，提出了改进的 DenseNet。深度可分离卷积将标准卷积操作分为深度卷积和点卷积，从而有效捕捉输入数据的空间特征和通道之间的相关性。同时，注意力机制允许网络对数据序列中的不同元素赋予不同程度的重要性（或注意力），从而增强了关键特征提取能力。这种设计不仅保留了 DenseNet 固有的特征重用功能，缓解了 DenseNet 的梯度消失问题，还进一步提高了网络性能。改进后的 DenseNet 的优化网络参数通过在训练集上的训练获得，而验证集则用于调整超参数和评估模型性能。最后，利用合成数据和现场数据对所提出的二维深度学习方法进行了验证。合成数据的实验结果表明，使用所提算法得到的反演结果的可靠性得到了提高，使用 TE 和 TM 模式数据得到的反演结果比使用单一模式数据得到的反演结果更准确。野外数据反演结果表明，所提出的二维 MT 深度学习反演方法能有效探测地下电阻率结构，具有良好的应用前景。

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引用次数: 0

Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network 利用卷积神经网络去除山区 InSAR 干涉图中的大气噪声

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-08 DOI: 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.

在干涉合成孔径雷达（InSAR）得出的地表形变估算值中，大气噪声往往会掩盖真实的位移信号，尤其是在山区。由于气候变化对山区冰冻圈的影响尤为严重，因此在高折射地形中采用可靠的大气校正技术变得越来越重要。我们开发并实施了一种统计机器学习大气校正方法，该方法依赖于缓慢移动的冰川地貌和大气噪声的不同空间和地形特征。我们的校正应用于 InSAR 数据的原始空间和时间分辨率，不需要外部大气再分析数据，并且可以校正分层和湍流大气噪声。利用 2017 年至 2022 年的哨兵-1 号数据，我们对来自 330 张短基线干涉图的观测到的大气噪声和来自 1322 张干涉图的时间序列反演的观测到的位移信号训练了一个卷积神经网络（CNN）。我们将训练有素的 CNN 应用于校正区域外应用区域的另外 251 张干涉图，并对这些干涉图进行反演，以创建位移时间序列。我们将新墨西哥州、科罗拉多州和怀俄明州的落基山脉作为训练区、验证区、测试区和应用区。与使用大气再分析数据、与地形的相位相关性和高通滤波进行校正相比，我们的校正方法在测试数据集上的性能分别提高了 131%、208% 和 68%。经过 CNN 校正的时间序列揭示了应用数据集中以前被掩盖的岩石冰川运动学行为和其他特征。我们灵活、稳健的方法可用于校正任意 InSAR 数据，以分析一系列科学和工程应用中的微妙地表形变信号。

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引用次数: 0

Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding 曲线线性提取：贝叶斯优化主成分小波分析和滞后阈值法

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-07 DOI: 10.1016/j.cageo.2024.105768

Bahman Abbassi, Li-Zhen Cheng

Understanding deformation networks, visible as curvilinear lineaments in images, is crucial for geoscientific explorations. However, traditional manual extraction of lineaments is expertise-dependent, time-consuming, and labor-intensive. This study introduces an automated method to extract and identify geological faults from aeromagnetic images, integrating Bayesian Hyperparameter Optimization (BHO), Principal Component Wavelet Analysis (PCWA), and Hysteresis Thresholding Algorithm (HTA). The continuous wavelet transform (CWT), employed across various scales and orientations, enhances feature extraction quality, while Principal Component Analysis (PCA) within the CWT eliminates redundant information, focusing on relevant features. Using a Gaussian Process surrogate model, BHO autonomously fine-tunes hyperparameters for optimal curvilinear pattern recognition, resulting in a highly accurate and computationally efficient solution for curvilinear lineament mapping. Empirical validation using aeromagnetic images from a prominent fault zone in the James Bay region of Quebec, Canada, demonstrates significant accuracy improvements, with 23% improvement in F_β Score over the unoptimized PCWA-HTA and a marked 300% improvement over traditional HTA methods, underscoring the added value of fusing BHO with PCWA in the curvilinear lineament extraction process. The iterative nature of BHO progressively refines hyperparameters, enhancing geological feature detection. Early BHO iterations broadly explore the hyperparameter space, identifying low-frequency curvilinear features representing deep lineaments. As BHO advances, hyperparameter fine-tuning increases sensitivity to high-frequency features indicative of shallow lineaments. This progressive refinement ensures that later iterations better detect detailed structures, demonstrating BHO's robustness in distinguishing various curvilinear features and improving the accuracy of curvilinear lineament extraction. For future work, we aim to expand the method's applicability by incorporating multiple geophysical image types, enhancing adaptability across diverse geological contexts.

了解变形网络（在图像中表现为曲线线状）对于地球科学勘探至关重要。然而，传统的人工提取线状物的方法依赖于专业知识，耗时耗力。本研究结合贝叶斯超参数优化（BHO）、主成分小波分析（PCWA）和磁滞阈值算法（HTA），介绍了一种从航空磁场图像中提取和识别地质断层的自动化方法。在不同尺度和方向上使用的连续小波变换 (CWT) 可提高特征提取质量，而 CWT 中的主成分分析 (PCA) 则可消除冗余信息，集中处理相关特征。利用高斯过程代理模型，BHO 可自主微调超参数，以实现最佳的曲线模式识别，从而为曲线线状图绘制提供高精度和计算效率的解决方案。利用加拿大魁北克詹姆斯湾地区一个突出断层带的航空磁场图像进行的经验验证表明，该方法的精确度有了显著提高，与未优化的 PCWA-HTA 相比，Fβ 得分提高了 23%，与传统 HTA 方法相比，Fβ 得分明显提高了 300%，这突出表明了在曲线线状提取过程中融合 BHO 与 PCWA 的附加价值。BHO 的迭代特性可逐步完善超参数，增强地质特征检测。早期的 BHO 迭代可广泛探索超参数空间，识别代表深层线状的低频曲线特征。随着 BHO 的发展，超参数微调提高了对指示浅层线状的高频特征的灵敏度。这种逐步完善的过程确保了以后的迭代能更好地检测到细节结构，证明了 BHO 在区分各种曲线特征方面的鲁棒性，并提高了曲线线状提取的准确性。在未来的工作中，我们希望通过结合多种地球物理图像类型来扩展该方法的适用性，从而增强其在不同地质环境下的适应性。

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引用次数: 0

Novel empirical curvelet denoising strategy for suppressing mixed noise of microseismic data 抑制微地震数据混合噪声的新经验小曲线去噪策略

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-07 DOI: 10.1016/j.cageo.2024.105751

Liyuan Feng , Binhong Li , Huailiang Li , Jian He

We present a novel denoising strategy based on empirical curvelet transform (ECT) for noisy microseismic data. Our approach can simultaneously suppress high-frequency, low-frequency, and shared-bandwidth noises and preserve detailed information on the noisy microseismic data. Initially, we design a new threshold estimation method by adding a scale factor for ECT threshold denoising. Subsequently, we construct an adaptive parameter model employing the similarity standard deviation for the non-local means (NLM) algorithm. Then, we divide the coefficients obtained from the ECT decomposition into two sets based on the energy spectrum, subjecting each set to improved adaptive thresholding and improved NLM denoising algorithms. Eventually, we reconstruct the denoised signals using the empirical curvelet inverse transform. Our results demonstrate that under a signal-to-noise ratio (SNR) of

-

10 dB, the proposed strategy achieves a correlation coefficient of 0.9524, a root mean square error of 0.198, an SNR of 1.36 dB, and reduces the first arrival picking error to 0.00382 s. Furthermore, application on the real microseismic data further confirms that the proposed method can clarify the corresponding first arrival.

我们针对高噪声微地震数据提出了一种基于经验小曲线变换（ECT）的新型去噪策略。我们的方法可以同时抑制高频、低频和共享带宽噪声，并保留噪声微地震数据的详细信息。首先，我们设计了一种新的阈值估计方法，为 ECT 阈值去噪添加了一个比例因子。随后，我们利用非局部均值（NLM）算法的相似性标准偏差构建了一个自适应参数模型。然后，我们根据能谱将 ECT 分解得到的系数分成两组，每组都采用改进的自适应阈值和改进的 NLM 去噪算法。最后，我们使用经验小曲线逆变换重建去噪信号。结果表明，在信噪比（SNR）为 -10 dB 的条件下，所提出的策略实现了 0.9524 的相关系数、0.198 的均方根误差、1.36 dB 的信噪比，并将首次到达的选取误差降低到 0.00382 s。

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引用次数: 0

New fast imaging techniques for electrical source transient electromagnetic data: Approaches and application 电源瞬态电磁数据的新型快速成像技术：方法与应用

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-07 DOI: 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 (e_x) and the vertical induced voltage (v_z), which demonstrated accuracy across the entire time range examined. We also introduced a formula to address discrepancies in apparent resistivity arising from the non-dipole size effect of the source, thereby ensuring that the algorithm can adapt to any transmitting and receiving configuration. Furthermore, we provided straightforward and precise time-depth conversion equations applicable to both e_x and v_z, 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。

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引用次数: 0

Digital rock physics: Calculation of effective elastic properties of heterogeneous materials using graphical processing units (GPUs) 数字岩石物理学：利用图形处理器（GPU）计算异质材料的有效弹性特性

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-06 DOI: 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.

介绍了一种基于图形处理单元（GPU）应用于三维数字图像的应用程序，用于计算异质材料的线性各向异性弹性特性。该应用程序还可以检索任何形状的单个夹杂物的属性贡献张量。代码既可以在专业 GPU 上执行，也可以在基本的笔记本电脑或个人电脑 Nvidia GPU 上执行。该应用程序运行速度极快：使用单个 A100 GPU 计算由约 700 万个体素（1913 个）组成的体积的有效弹性特性只需不到 4 秒钟；使用单个 A100 GPU 计算 1 亿个体素（4793 个）只需 3 分钟；使用单个 A100 GPU 计算 3.5 亿个体素（7033 个）只需 14 分钟。提供了与分析解决方案的若干比较。此外，还介绍了对卡拉拉裂纹大理石样品三维数字图像的各向异性有效弹性特性的评估。该软件可从永久存储库 Zenodo 下载，手稿中提供了带 doi 的链接。

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引用次数: 0

Backpropagation-based inference for spatial interpolation to estimate the blastability index in an open pit mine 基于反向传播推理的空间插值法估算露天矿的可爆性指数

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-06 DOI: 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。

引用次数: 0

Million-core scalable 3D anisotropic reverse time migration on the Sugon exascale supercomputer 在曙光超大规模超级计算机上实现百万核级可扩展三维各向异性反向时间迁移

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-05 DOI: 10.1016/j.cageo.2024.105754

Sihai Wu , Jiubing Cheng , Jianwei Ma , Tengfei Wang , Xueshan Yong , Yang Ji

Reverse time migration (RTM) plays a crucial role in high-resolution seismic imaging of the Earth’s interior. However, scaling it across millions of cores in parallel to process large-scale seismic datasets poses significant computational challenges, because the conventional storage solutions are insufficient to deal with the I/O and memory bottlenecks. To address this issue, we present a highly scalable 3D RTM algorithm for vertically transverse isotropic (VTI) media, optimized for the Sugon exascale supercomputer, utilizing over 1,024,000 cores with optimal weak-scaling efficiency. Through cache optimizations tailored for the new deep computing unit (DCU) accelerator architecture, our approach achieves a maximum speedup of 6x compared to conventional methods on a single accelerator. Moreover, based on the lossy compression and boundary-saving techniques, we reduce storage requirements by 266 times, which allows for the effective utilization of million-core computing resources and ensures scalability efficiency when handling large-scale datasets for complex geophysical tasks. Finally, when applied to a industrial dataset, the method demonstrates robust scalability and high efficiency, making it well-suited for large-scale geophysical exploration.

反演时间迁移（RTM）在地球内部高分辨率地震成像中起着至关重要的作用。然而，由于传统的存储解决方案不足以应对 I/O 和内存瓶颈，因此在数百万个内核上并行扩展以处理大规模地震数据集带来了巨大的计算挑战。为解决这一问题，我们针对垂直横向各向同性（VTI）介质提出了一种高度可扩展的三维 RTM 算法，该算法针对 Sugon 超大规模超级计算机进行了优化，利用超过 1,024,000 个内核实现了最佳弱扩展效率。通过为新的深度计算单元（DCU）加速器架构量身定制的高速缓存优化，我们的方法在单个加速器上实现了比传统方法快 6 倍的最大速度。此外，基于有损压缩和边界节省技术，我们将存储需求降低了 266 倍，从而实现了百万核计算资源的有效利用，并确保了在处理复杂地球物理任务的大规模数据集时的可扩展性效率。最后，在应用于工业数据集时，该方法表现出强大的可扩展性和高效率，非常适合大规模地球物理勘探。

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引用次数: 0

Geothermal modeling in complex geological systems with ComPASS 利用 ComPASS 进行复杂地质系统的地热建模

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2024-11-05 DOI: 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.

在深层地热储层中，断层和裂缝发挥着重要作用，它们是流体流动和热传递的调节器，同时也是生产井的进料区。为了准确模拟地热田的运行，有必要明确考虑不同空间尺度的对象，从储层本身的尺度到断层和裂缝的尺度，再到注水井和生产井的尺度。我们开发 ComPASS 地热流模拟器的主要目的是在使用通用非结构网格的流动和传热数值模型中考虑所有这些几何约束。在目前的状态下，该代码提供了非结构网格上多孔断裂介质中驱动成分多相热流的非线性方程时空离散的并行执行。它允许将断层和裂缝明确离散化为嵌入三维矩阵的二维混合对象。同样，油井被建模为由三维网格边缘离散化的一维图形，允许任意多分支油井。在高能地热领域的两个案例研究证明了这种方法的实用性。

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引用次数: 0