Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Potential field data interpretation to detect the parameters of buried geometries by applying a nonlinear least-squares approach

Potential field data interpretation to detect the parameters of buried geometries by applying a... The detection of buried geometrical model parameters is vital to full interpretation of potential field data, especially that related to gravity and/or self-potential anomalies. This study introduced a proposed non-linear least-squares algorithm for solving a combined formula for gravity and self-potential anomalies due to simple geometric shapes. This proposed algorithm was relied upon delimiting the origin anomaly value and two symmetric anomaly values with their equivalent distances along with the anomaly profile in order to invert the buried geometry model parameters. After that, a root mean square error (μ-value) for each parameter value at different postulated shape factor was assessed. The μ-value was considered as a benchmark for detecting the true-values of the subsurface geometry structures. The efficacy and rationality of the proposed approach were revealed by numerous synthetic cases with and without random noise. Furthermore, the sensitivity analysis between shape factor and μ-value were investigated on synthetic gravity and self-potential data. It was evident that the inverted parameters were reliable with the genuine ones. This proposed method was tested on samples of gravity data and self-potential data taken from Senegal and USA. To judge the satisfaction of this approach, the results gained were compared with other available geological or geophysical information in the published literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Acta Geodaetica et Geophysica" Springer Journals

Potential field data interpretation to detect the parameters of buried geometries by applying a nonlinear least-squares approach

Download PDF
20 pages

Loading next page...
 
/lp/springer-journals/potential-field-data-interpretation-to-detect-the-parameters-of-buried-T7Zo8iGZvO

References (60)

Publisher
Springer Journals
Copyright
Copyright © Akadémiai Kiadó 2021
ISSN
2213-5812
eISSN
2213-5820
DOI
10.1007/s40328-021-00337-5
Publisher site
See Article on Publisher Site

Abstract

The detection of buried geometrical model parameters is vital to full interpretation of potential field data, especially that related to gravity and/or self-potential anomalies. This study introduced a proposed non-linear least-squares algorithm for solving a combined formula for gravity and self-potential anomalies due to simple geometric shapes. This proposed algorithm was relied upon delimiting the origin anomaly value and two symmetric anomaly values with their equivalent distances along with the anomaly profile in order to invert the buried geometry model parameters. After that, a root mean square error (μ-value) for each parameter value at different postulated shape factor was assessed. The μ-value was considered as a benchmark for detecting the true-values of the subsurface geometry structures. The efficacy and rationality of the proposed approach were revealed by numerous synthetic cases with and without random noise. Furthermore, the sensitivity analysis between shape factor and μ-value were investigated on synthetic gravity and self-potential data. It was evident that the inverted parameters were reliable with the genuine ones. This proposed method was tested on samples of gravity data and self-potential data taken from Senegal and USA. To judge the satisfaction of this approach, the results gained were compared with other available geological or geophysical information in the published literature.

Journal

"Acta Geodaetica et Geophysica" – Springer Journals

Published: Apr 12, 2021

There are no references for this article.