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.

本文回顾了算法粒子群优化 (PSO) 在地球物理数据的随机逆建模中的应用。总结了 PSO 的主要特点，分析了其在几个地球物理领域中最重要的贡献。目的是指出已用于模拟地球地下的 PSO 方法演进的基本步骤，然后对其益处和局限性进行严格评估。从现有的地球物理文献中选择了原创作品，以说明 PSO 成功应用于电磁（大地电磁和时域）数据、重力和磁数据、自势、直流和地震数据的解释。这些案例研究进行了批判性描述和比较。此外，提出了通过多目标 PSO 对多个地球物理数据集进行联合优化，以突出使用单个求解器的优势，该求解器部署帕累托最优性来处理不同的数据集，而不会产生冲突的解决方案。最后，我们提出了从头开始实施定制算法的最佳实践，以对任何类型的地球物理数据集执行随机逆向建模，以造福于 PSO 从业者或缺乏经验的研究人员。