Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems

García Pallero, José Luis; Zulima Fernández-Muñiz, María; Cernea, Ana; Álvarez Machancoses, Óscar; Pedruelo González, Luis Mariano; Bonvalot, Sylvain y Fernández Martínez, Juan Luis (2018). Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems. "Entropy", v. 20 (n. 2); pp. 96-110. ISSN 1099-4300.


Título: Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems
  • García Pallero, José Luis
  • Zulima Fernández-Muñiz, María
  • Cernea, Ana
  • Álvarez Machancoses, Óscar
  • Pedruelo González, Luis Mariano
  • Bonvalot, Sylvain
  • Fernández Martínez, Juan Luis
Tipo de Documento: Artículo
Título de Revista/Publicación: Entropy
Fecha: Enero 2018
Volumen: 20
Palabras Clave Informales: Inverse problems; nonlinear inversion; noise and regularization; model reduction; Uncertainty analysis; particle swarm optimization (PSO)
Escuela: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Departamento: Ingeniería Cartográfica y Topografía
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty.

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Depositado por: Memoria Investigacion
Depositado el: 22 Feb 2018 12:31
Ultima Modificación: 22 Feb 2018 12:31
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