Publications des scientifiques de l'IRD

Pallero J. L. G., Fernandez-Muniz M. Z., Cernea A., Alvarez-Machancoses O., Pedruelo-Gonzalez L. M., Bonvalot Sylvain, Fernandez-Martinez J. L. (2018). Particle swarm optimization and uncertainty assessment in inverse problems. Entropy, 20 (2), p. 96. ISSN 1099-4300.

Titre du document
Particle swarm optimization and uncertainty assessment in inverse problems
Année de publication
2018
Type de document
Article référencé dans le Web of Science WOS:000426793900019
Auteurs
Pallero J. L. G., Fernandez-Muniz M. Z., Cernea A., Alvarez-Machancoses O., Pedruelo-Gonzalez L. M., Bonvalot Sylvain, Fernandez-Martinez J. L.
Source
Entropy, 2018, 20 (2), p. 96 ISSN 1099-4300
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Géologie et formations superficielles [064] ; Géophysique interne [066]
Localisation
Fonds IRD [F B010072449]
Identifiant IRD
fdi:010072449
Contact