利用磁电数据和深度学习算法估算三维电阻率分布

IF 4.2 2区 地球科学 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Pub Date : 2024-09-13 DOI:10.3390/rs16183400
Xiaojun Liu, James A. Craven, Victoria Tschirhart, Stephen E. Grasby
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引用次数: 0

摘要

在本研究中,我们介绍了一种基于深度学习(DL)的工作流程,用于对磁电uric(MT)数据进行三维(3D)地球物理反演。通过使用全连接神经网络框架(U-Net),我们得出了三维电阻率模型与地表观测到的电场/磁场响应之间的数学联系。受计算机硬件功能的限制,电阻率模型是通过随机行走技术生成的,以扩大神经网络模型的泛化范围,并利用 15,000 个配对数据集对其进行训练和验证。网格搜索用于选择最优配置参数。有了参数调整阶段的最佳模型框架,在模型训练/验证过程中,指标显示出稳定的收敛性。在测试阶段,利用不列颠哥伦比亚省米格山地区的模拟合成数据和实际 MT 数据,将训练有素的模型用于预测电阻率分布。使用合成模型的噪声输入数据验证了模型预测的可靠性。计算结果可用于重建电阻率异常体的位置和形状趋势,从而验证了基于 DL 的三维反演算法的稳定性和性能,并展示了其潜在的实际应用价值。
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Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm
In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications.
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来源期刊
Remote Sensing
Remote Sensing REMOTE SENSING-
CiteScore
8.30
自引率
24.00%
发文量
5435
审稿时长
20.66 days
期刊介绍: Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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