@article{fdi:010090592,
  title = {{P}osterior analysis of particle swarm optimization results applied to gravity inversion in sedimentary basins},
  author = {{P}allero, {J}. {L}. {G}. and {F}ernández-{M}uñiz, {M}. {Z}. and {F}ernández-{M}artínez, {J}. {L}. and {B}onvalot, {S}ylvain},
  editor = {},
  language = {{ENG}},
  abstract = {{A}s is well known, it is impossible to model reality with its true level of detail. {A}dditionally, it is impossible to make an infinite number of observations, which are always contaminated by noise. {T}hese circumstances imply that, in an inverse problem, the misfit of the best estimated model will always be less than that of the true one. {T}herefore, it is not possible to reconstruct the model that actually generated the collected observations. {T}he best way to express the solution of an inverse problem is as a collection of models that explain the observations at a certain misfit level according to a defined cost function. {O}ne of the main advantages of global search methods over local ones is that, in addition to not depending on an initial model, they provide a set of generated models with which statistics can be made. {I}n this paper we present a technique for analyzing the results of any global search method, particularized to the particle swarm optimization algorithm applied to the solution of a two-dimensional gravity inverse problem in sedimentary basins. {S}tarting with the set of generated models, we build the equivalence region of a predefined tolerance which contains the best estimated model, i.e., which involves the estimated global minimum of the cost function. {T}he presented algorithm improves the efficiency of the equivalence region detection compared to our previous works.},
  keywords = {global optimization methods ; particle swarm optimization ; inverse ; problems ; uncertainty analysis ; gravity inversion},
  booktitle = {},
  journal = {{AIMS} {M}athematics},
  volume = {9},
  numero = {6},
  pages = {13927--13943},
  year = {2024},
  DOI = {10.3934/math.2024677},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090592},
}