Particle Swarm Optimization (PSO) is an inverse modeling optimization technique inspired by the collective behavior of social organisms, such as bird flocking or fish schooling. It has been widely applied in solving complex optimization problems across different fields. The algorithm maintains a set of candidate solutions and iteratively improves upon them. At each iteration, the particles evaluate their fitness based on an objective function and update their position and velocity based on their own experience and the experience of their neighbors. When applied to the elastic impedance equation, PSO can help determine the most suitable values for Vp and Vs that best fit the observed seismic data.
