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3D geological structure inversion from Noddy-generated magnetic data using deep learning methods
Computers & Geosciences ( IF 4.4 ) Pub Date : 2021-01-22 , DOI: 10.1016/j.cageo.2021.104701
Jiateng Guo , Yunqiang Li , Mark Walter Jessell , Jeremie Giraud , Chaoling Li , Lixin Wu , Fengdan Li , Shanjun Liu

Using geophysical inversion for three-dimensional (3D) geological modeling is an effective way to model underground geological structures. In this study, we propose and investigate a 3D geological structure inversion method using convolutional neural networks (CNNs). This method can quickly predict the parameters of a geological structure for constructing a 3D model. First, we sample the geological model space by generating millions of 3D geological models and their corresponding magnetic images. The dataset we use to train CNN classification and regression models includes faults, folds, tilts, tilt-faults and fold-faults. The classification model is used to judge the classification of geological structures. The regression model is used to predict the attitudes of geological structures. The method is applied to synthetic data and real survey data, and the results show that geological structures can be recovered effectively. The classification accuracy is approximately 100%, and the regression accuracy of different structures is mostly between 80% and 97%.



中文翻译:

使用深度学习方法从Noddy生成的磁数据中进行3D地质构造反演

将地球物理反演用于三维(3D)地质建模是对地下地质结构进行建模的有效方法。在这项研究中,我们提出并研究了使用卷积神经网络(CNN)的3D地质构造反演方法。该方法可以快速预测用于构建3D模型的地质结构的参数。首先,我们通过生成数百万个3D地质模型及其相应的磁像来对地质模型空间进行采样。我们用于训练CNN分类和回归模型的数据集包括断层,褶皱,倾斜,倾斜断层和褶皱断层。分类模型用于判断地质构造的分类。回归模型用于预测地质构造的态度。该方法适用于综合数据和真实调查数据,结果表明,该地质构造可以有效恢复。分类精度约为100%,不同结构的回归精度大多在80%至97%之间。

更新日期:2021-02-12
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