A Review of Geophysical Modeling Based on Particle Swarm Optimization

Abstract

This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

“…Finally, the median model was selected as the final estimated solution as it is less impacted by the outliers. There is a trade-off between computation time and space as implied by the number of iterations and search agents (Engelbrecht, 2007;Sen and Stoffa, 2013;Pace et al, 2021). GWO and MOGWO were executed for 50 iterations with a varying number of search agents greater than 20 to tune the algorithms and achieve global convergence for the synthetic and the field examples presented below.…”

“…Global optimization techniques inspired by swarm intelligence have found increasing applications in geophysical inverse problems due to their ability to find global solutions by using simple tuning parameters, flexible applicability and easy implementation. Some of the popular swarm intelligencebased global optimization techniques include, but are not limited to, particle swarm Optimization (PSO) (Kennedy and Eberhart, 1995;Shaw and Srivastava, 2007;Naudet et al, 2008;Song et al, 2012;Pace et al, 2021), ant colony optimization (Yuan et al, 2009;Srivastava et al, 2014), grey wolf algorithm (GWO) (Mirjalili et al, 2014;Song et al, 2015;Agarwal et al, 2018;Vashisth and Shekar, 2019;Sharma et al, 2021) and whale optimization algorithm (Mirjalili and Lewis, 2016;Vashisth et al, 2018;. A couple of recently proposed swarm intelligence-based global optimization techniques include African vultures optimization (Abdollahzadeh et al, 2021a) and artificial gorilla troops optimization (Abdollahzadeh et al, 2021b) that mimic foraging and navigation behaviour of African vultures and social intelligence of gorilla troops, respectively.…”

Magnetic data interpretation for 2D dikes by the metaheuristic bat algorithm: sustainable development cases

“…On the other side, metaheuristic algorithms were developed to interpret the geomagnetic data, which rely on searching for global optimum solution that is more accurate and efficient than graphical and numerical methods 46 . Metaheuristic algorithms such as simulated annealing technique (SA) 47 , 48 , genetic algorithm (GA) 49 , particle swarm optimization (PSO) 50 , 51 , neural networks approach (NN) 22 , 52 , differential evolution algorithm (DE) 53 , and ant-colony optimization algorithm (ACO) 54 . These algorithms are popular among researchers because they are more adaptable and capable of dealing with a wide range of problems than traditional optimization techniques.…”

Three-Dimensional Magnetotelluric Characterization of the Travale Geothermal Field (Italy)

“…Some of these codes and tools have been made available to the electromagnetic academic community (e.g., [8,15]). Moreover, the rapid improvement of computational resources has encouraged the widespread adoption of computationally demanding methods such as derivative-based 3D inversion algorithms and global optimization techniques [16][17][18][19]. Currently, 3D MT inversion is of pivotal importance to providing new insights into the distribution of the electrical resistivities encountered in geothermal systems [6,[20][21][22][23].…”