李泽林,
姚长利^,,
郑元满
地下信息探测技术与仪器教育部重点实验室, 中国地质大学(北京), 北京 100083

基金项目: 国家高技术研究发展计划重大项目"深部矿产资源探测技术"第13课题(2014AA06A613)资助


详细信息

作者简介: 李泽林, 男, 1990年生, 博士, 毕业于中国地质大学(北京).现主要从事重磁处理反演研究.E-mail:zelin.lee@foxmail.com

通讯作者: 姚长利, 主要从事重磁勘探理论与方法技术研究.E-mail:clyao@cugb.edu.cn

中图分类号: P631

收稿日期:2018-07-05
修回日期:2019-07-29
上线日期:2019-10-05

3D inversion of gravity data using L_p-norm sparse optimization

LI ZeLin,
YAO ChangLi^,,
ZHENG YuanMan
Key Laboratory of Geo-detection(China University of Geosciences, Beijing), Ministry of Education, Beijing 100083, China



More Information

Corresponding author: YAO ChangLi,E-mail:clyao@cugb.edu.cn

MSC: P631

--> Received Date: 05 July 2018
Revised Date: 29 July 2019
Available Online: 05 October 2019


摘要
摘要:三维密度反演已经成为重力数据定量解释的常规方法，但由于重力数据本身并没有深度分辨率，为了减少由此引起的重力反演的非唯一性，常用的手段是引入额外的先验信息.本文提出了一种重力三维稀疏反演（以下简称稀疏反演）方法，该方法通过求解物性上下界约束时的L_p范数（0 ≤ p ≤ 1）稀疏优化问题，来获得具有尖锐边界的解.与传统的L₂范数反演方法相比，稀疏反演方法可以更加有效地利用已知的物性信息，获得深度分辨率更高的反演结果.此外，我们也分析了稀疏反演方法与二值、三值反演算法的等价性以及在实际应用中需要注意的问题.最后，通过模型试验以及矿区实测数据反演验证了稀疏反演方法的有效性.
关键词: 三维重力反演/
稀疏/
L_p范数/
物性信息

Abstract:Three-dimensional inversion for density distribution has become a common tool for quantitative interpretation of gravity data in recent years. However, as gravity data lacks depth resolution, such inversion has severe non-uniqueness. To reduce this problem, the most common approach is to introduce extra prior information. To further perfect this tool, we propose a 3D sparse gravity inversion method which minimizes an L_p-norm (0 ≤ p ≤ 1) of the density model subject to bound constraints. Compared with the traditional L₂-norm inversion approach, our method permits to use known physical property information more effectively and produce solutions characterized by sharp boundaries and high depth resolution. To better understand our method, we analyze the equivalence between our method and binary or ternary inversion. Moreover, we also point out several issues of this method that need to be noticed. The validity of our method is tested by both synthetic-and real-data examples.
Key words:Three-dimensional gravity inversion/
Sparse/
L_p-norm/
Physical property information



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