基于U⁃Rnet的重力全张量梯度数据反演  

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

作　　者：祁锐 李厚朴[2] 胡佳心 罗莎 QI Rui;LI Houpu;HU Jiaxin;LUO Sha(Department of Basic Courses,Naval University of Engineering,Wuhan,Hubei 430033,China;College of Electrical Engi-neering,Naval University of Engineering,Wuhan,Hubei 430033 China;United Imaging Surgical Technology Co.,Ltd,Shanghai 200100,China;School of Mathematics and Physics,China University of Geosciences,Wuhan,Hubei 430074,China)

机构地区：[1]海军工程大学基础部,湖北武汉430033 [2]海军工程大学电气工程学院,湖北武汉430033 [3]上海联影智能医疗科技有限公司,上海200100 [4]中国地质大学(武汉)数理学院,湖北武汉430074

出　　处：《石油地球物理勘探》2024年第2期331-342,共12页Oil Geophysical Prospecting

基　　金：国家优秀青年科学基金项目“海洋大地测量”(42122025);国家自然科学基金项目“基于深度学习的大尺度重磁异常正反演研究”(42374174)联合资助。

摘　　要：重力反演是通过地表信息获取地下地质体空间结构与物理性质的重要手段之一。每个重力梯度分量反映不同的地质体信息,联合重力梯度分量进行重力反演能够更好地研究地下密度异常体的形态和分布。为此,提出基于神经网络的重力全张量梯度数据反演算法,将U⁃Rnet网络应用于重力全张量数据的三维反演问题。为了检验该算法的有效性,采用六种典型模型进行模拟实验,获得了具有清晰边界和稀疏的反演结果。首先,对比L2和Tversky两种损失函数的反演结果,后者的反演结果能更清晰地反映模型的边界位置;然后,对不同梯度张量组合进行反演,四组实验结果在三个方向(x、y、z)上具有不同的反演精度,组合四的误差最低;最后,将该方法应用于美国德克萨斯州文顿盐丘的FTG数据,反演结果与实际地质信息基本吻合。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 network⁃based algo⁃rithm 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 re⁃sults 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 diffe⁃rent 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.

关 键 词：梯度张量 U⁃Rnet网络 正演 重力反演 文顿盐丘 

分 类 号：P631[天文地球—地质矿产勘探]