Anomaly shape inversion via model reduction and PSO

Fernández Muñiz, Zulima and Pallero, Jose L. G. and Fernández Martínez, Juan Luis (2020). Anomaly shape inversion via model reduction and PSO. "Computers & Geosciences", v. 140 ; pp. 1-12. ISSN 0098-3004. https://doi.org/10.1016/j.cageo.2020.104492.

Description

Title: Anomaly shape inversion via model reduction and PSO
Author/s:
  • Fernández Muñiz, Zulima
  • Pallero, Jose L. G.
  • Fernández Martínez, Juan Luis
Item Type: Article
Título de Revista/Publicación: Computers & Geosciences
Date: 19 April 2020
ISSN: 0098-3004
Volume: 140
Subjects:
Freetext Keywords: Gravimetry; Anomaly detection; Inversion; RR-PSO; Uncertainty analysis
Faculty: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Department: Ingeniería Cartográfica y Topografía
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Most of the geophysical inverse problems in geophysical exploration consist in detecting, locating and outlining the shape of geophysical anomalous bodies imbedded into a quasi-homogeneous background by analyzing their effect in the geophysical signature. The inversion algorithm that is currently used creates a very fine mesh in the model space to approximate the shapes and the values of the anomalous bodies and the geophysical structure of the geological background. This approach results in discrete inverse problems with a huge uncertainty space, and the common way of stabilizing the inversion consists in introducing a reference model (through prior infor­ mation) to define the set of correctness of geophysical models. We present a different way of dealing with the high underdetermined character of this kind of problems, consisting in solving the inverse problem using a low dimensional parameterization that provides an approximate solution of the anomaly via Particle Swarm Opti­ mization (PSO). This methodology has been designed for anomaly detection in geological set-ups that correspond with this kind of problem. We show its application to synthetic and real cases in gravimetric inversion per­ forming at the same time uncertainty analysis of the solution. We have compared two different parameterizations for the geophysical anomalies (polygons and ellipses), showing that we have obtained similar results. This methodology outperforms the common least squares method with regularization.

More information

Item ID: 62889
DC Identifier: http://oa.upm.es/62889/
OAI Identifier: oai:oa.upm.es:62889
DOI: 10.1016/j.cageo.2020.104492
Official URL: https://www.sciencedirect.com/science/article/pii/S0098300419306612?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 09 Feb 2021 08:51
Last Modified: 09 Feb 2021 08:51
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