GravPSO2D: A Matlab package for 2D gravity inversion in sedimentary basins using the Particle Swarm Optimization algorithm

Abstract

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A Review of Geophysical Modeling Based on Particle Swarm Optimization

“…At the beginning, the particles in the search space are by default randomly distributed and bounded between a minimum and maximum value. This range is constant during the optimization but can vary from each layer (or group of layers or cells) to another (Godio and Santilano 2018 ; Pallero et al 2021 ). The decision of the lower and upper boundaries is problem dependent and should be coherent with the desired coverage of the search space of solutions.…”

“…This criterion has the advantage of showing the equivalence region within a tolerance of the RMSE. Moreover, the swarm can be inspected at the last iteration by calculating the mean (or median) and standard deviation of each model parameter to estimate a model solution based on these statistical quantities (Fernández Martínez et al 2010b ; Godio and Santilano 2018 ; Pallero et al 2018 , 2021 ). Finally, to assess the uncertainty of the final outcome, it is recommended to analyze the a posteriori probability density ( ppd ) function of each model parameter.…”

“…Finally, to assess the uncertainty of the final outcome, it is recommended to analyze the a posteriori probability density ( ppd ) function of each model parameter. Different approaches were explored considering the ppd of the swarm either for a single PSO run or for several PSO trials (several runs with equal settings) (Santilano et al 2018 ; Pallero et al 2018 , 2021 ). It may happen that the most occurred value of a parameter in the ppd curve is different from the value it assumed in the lowest-RMSE model.…”

“…The main applications of PSO to the geophysical inverse problem include the interpretation of: vertical electrical sounding (VES) (Fernández-Álvarez et al 2006 ; Fernández Martínez et al 2010a ; Pekşen et al 2014 ; Cheng et al 2015 ; Pace et al 2019b ); gravity data (Yuan et al 2009 ; Pallero et al 2015 , 2017 , 2021 ; Darisma et al 2017 ; Jamasb et al 2019 ; Essa and Munschy 2019 ; Anderson et al 2020 ; Essa and Géraud 2020 ; Essa et al 2021 ); magnetic data (Liu et al 2018 ; Essa and Elhussein 2018 , 2020 ); multi-transient electromagnetic data (Olalekan and Di 2017 ); time-domain EM data (Cheng et al 2015 , 2019 ; Santilano et al 2018 ; Pace et al 2019c ; Li et al 2019 ; Amato et al 2021 ); MT data (Shaw and Srivastava 2007 ; Pace et al 2017 , 2019a , c ; Godio and Santilano 2018 ; Santilano et al 2018 ) and radio-MT data (Karcıoğlu and Gürer 2019 ); self-potential data (Santos 2010 ; Pekşen et al 2011 ; Göktürkler and Balkaya 2012 ; Essa 2019 , 2020 ) and induced polarization (Vinciguerra et al 2019 ); Rayleigh wave dispersion curve (Song et al 2012 ) and full waveform inversion (Aleardi 2019 ). …”

“…gravity data (Yuan et al 2009 ; Pallero et al 2015 , 2017 , 2021 ; Darisma et al 2017 ; Jamasb et al 2019 ; Essa and Munschy 2019 ; Anderson et al 2020 ; Essa and Géraud 2020 ; Essa et al 2021 );…”