Implementation of Genetic Algorithm for Rapid Inversion in Seismic Wave Impedance Analysis

When developing programs, MATLAB programming language can be employed to implement genetic algorithms for achieving rapid inversion. This approach is primarily applied to wave impedance inversion in seismology, delivering accurate results within shortened timeframes. The genetic algorithm operates as an optimization technique that simulates natural genetic processes to continuously refine solutions toward optimal objectives. In seismic research, rapid inversion represents a critical task that enables better understanding of subsurface structures and properties, providing substantial support for earthquake prediction and disaster prevention. From an implementation perspective, the MATLAB code typically involves several key components: population initialization using random or heuristic methods, fitness evaluation through forward modeling calculations, selection operations employing roulette wheel or tournament selection, crossover operations using single-point or multi-point recombination, and mutation operations with controlled probability rates. The algorithm iteratively improves solution quality by maintaining population diversity while converging toward global optima. For seismic wave impedance inversion specifically, the fitness function would incorporate seismic waveform matching criteria, possibly using root mean square error calculations between observed and synthetic seismograms. The chromosome encoding might represent impedance values across different subsurface layers, with genetic operators designed to preserve geological consistency throughout the optimization process.