Inversion of gravity full tensor gradient data based on U-Rnet network

被引：0
|
作者：
Qi R. [1 ]
Li H. [2 ]
Hu J. [3 ]
Luo S. [4 ]
机构：
[1] Department of Basic Courses, Naval University of Engineering, Hubei, Wuhan
[2] College of Electrical Engineering, Naval University of Engineering, Hubei, Wuhan
[3] United Imaging Surgical Technology Co. ，Ltd, Shanghai
[4] School of Mathematics and Physics, China University of Geosciences, Hubei, Wuhan
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2024年 / 59卷 / 02期
关键词：
forward simulation; gradient tensor; gravity inversion; U-Rnet network; Vinton Salt Dome;
D O I：
10.13810/j.cnki.issn.1000-7210.2024.02.015
中图分类号：
学科分类号：
摘要：
Gravity inversion is one of the important means to obtain the spatial structure and physical properties of underground geological bodies through surface information，and each gravity gradient component represents different geological body information. Gravity inversion combined with gravity gradient components can better reflect the shape and distribution of underground abnormal bodies. In this paper，a neural networkbased algorithm for gravity full tensor data inversion is proposed. The U-Rnet network is applied to three-dimensional gravity full tensor data inversion. In order to test the effectiveness of the algorithm，six representative models are used for simulation experiments，and inversion results with clear boundaries and sparsity are obtained. Firstly，by comparing the inversion results of L2 and Tversky loss functions，it is found that the inversion results corresponding to Tversky loss functions can clearly represent the boundary position of the model. Then，by comparing the inversion results of different gradient tensor combinations，the results of four tests show different inversion accuracy on three directions（x，y，z），and the test 4 shows the lowest fitting error. Finally，the proposed method is applied to the FTG data of Vinton Salt Dome in Texas，USA，and the inversion results are consistent with the real geological information. © 2024 Editorial office of Oil Geophysical Prospecting. All rights reserved.
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页码：331 / 342
页数：11
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