Joint Euler deconvolution of multi-component gravity gradient data and software design

SUN Bo-Xuan; HOU Zhen-Long; Zhou Wen-Yue; GONG En-Pu; Zheng Yu-Jun; CHENG Hao

doi:10.11720/wtyht.2022.1500

2022 Vol. 46, No. 5

Article Contents

Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(5): 1241-1250

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SUN Bo-Xuan, HOU Zhen-Long, Zhou Wen-Yue, GONG En-Pu, Zheng Yu-Jun, CHENG Hao. 2022. Joint Euler deconvolution of multi-component gravity gradient data and software design. Geophysical and Geochemical Exploration, 46(5): 1241-1250. doi: 10.11720/wtyht.2022.1500

Citation:

SUN Bo-Xuan, HOU Zhen-Long, Zhou Wen-Yue, GONG En-Pu, Zheng Yu-Jun, CHENG Hao. 2022. Joint Euler deconvolution of multi-component gravity gradient data and software design. Geophysical and Geochemical Exploration, 46(5): 1241-1250. doi: 10.11720/wtyht.2022.1500

Joint Euler deconvolution of multi-component gravity gradient data and software design

1. School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China；
2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

Received Date: 03 September 2021

Revised Date: 20 October 2022

Publish Date: 03 January 2023

Abstract

Abstract

Compared with Euler deconvolution, joint Euler deconvolution of multi-component gravity gradient data features higher calculation accuracy and inversion resolution. To eliminate the divergent solutions of calculation, different screening methods must be used in the application of joint Euler deconvolution of multi-component gravity gradient data, making the calculation process cumbersome. It is evident that effective screening methods and developing a piece of easy-to-use and visual software can improve the accuracy, convenience, and effects of the joint Euler deconvolution of multi-component gravity gradient data. Therefore, this study proposed the joint Euler deconvolution of gravity gradient data with the constraint of edge detection based on correlation coefficients. Moreover, on the design principles of the intuitive interfaces, practical functions, and concise codes, this study designed a software system with functions such as data/file management, two-dimensional/three-dimensional visualization, edge detection, and joint Euler deconvolution of multi-component gravity gradient data using Python language and its function library to meet the requirements of algorithm flow and functions. The accuracy of calculations and the practicability of the software have been verified through a theoretical model and tests of measured data, proving that the software designed in this study can improve the application effects.
- gravity gradient data /
- Euler deconvolution /
- edge detection /
- software design

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References

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