Improved adaptive thin-layer inversion for semi-airborne transient electromagnetic method

Author:

Xian Yougong1,Lan Riyan1,Liu Yuchao23,Li Dunren4,Yang Jing23,Sun Huaifeng235ORCID

Affiliation:

1. Guangxi XinFaZhan Communication Group Co., Ltd , Nanning 530029 , China

2. Geotechnical & Structural Engineering Research Center, Shandong University , Jinan 250061 , China

3. Laboratory of Earth Electromagnetic Exploration, Shandong University , Jinan 250061 , China

4. Guangxi Communications Design Group Co., Ltd , Nanning 530029 , China

5. Advanced Exploration and Transparent City Innovation Center, Shandong Research Institute of Industrial Technology , Jinan 250061 , China

Abstract

ABSTRACT To improve the resolution of electromagnetic inversion for thin layers, electromagnetic one-dimensional inversion was studied. The smooth conductivity model produced by Occam's inversion cannot accurately represent the information of the subterranean thin resistive layers, leading to erroneous inversion findings. The existing thin resistive layers’ inversion method sets the model constraint term at the thin resistive layers to 0, resulting in abrupt changes in resistivity values. Given these problems, we proposed an adaptive roughness matrix calculation method to improve the thin, low-resistive-layer resolution. The resistivity difference between neighboring layers of the updated inversion model determines the roughness matrix, allowing for the realization of adaptive inversion of the thin layer. It achieves semi-airborne transient electromagnetic enhanced adaptive thin-layer inversion and automatically manages the model constraint term. The calculation of the synthetic model demonstrates that the improved adaptive thin-layer inversion method does not need to know the thin, low-resistive layers information in advance. The model can produce appropriate inversion results regardless of the presence of thin, low-resistive layers. Finally, the drilling results are consistent with the inversed appearance of the semi-airborne transient electromagnetic field data. Other geophysical adaptive thin resistive layers inversion can also benefit from this paper's findings.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Management, Monitoring, Policy and Law,Industrial and Manufacturing Engineering,Geology,Geophysics

Reference26 articles.

1. Machine learning based fast forward modelling of ground-based time-domain electromagnetic data;Bording;Journal of Applied Geophysics,2021

2. Detection capability of grounded electric source TEM for thin layer;Chen;Geophysical and Geochemical Exploration (in Chinese),2015

3. Occam's inversion: a practical algorithm for generating smooth models from electromagnetic sounding data;Constable;Geophysics,1987

4. Airborne electromagnetic systems - 50 years of development;Fountain;Exploration Geophysics,1998

5. Transient electromagnetic inversion for thin layer with low resistivity;Guo;Geophysical and Geochemical Exploration (in Chinese),2017

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3