Multi‐dimensional non‐linear full waveform inversion of gas cloud reflection data using a genetic algorithm and a blended acquisition approach

Abstract

In this paper, we present a multi-dimensional non-linear full waveform inversion approach using an optimized Genetic Algorithm (GA), which includes a blended acquisition approach to invert the highly non-linear gas cloud reflection response for its effective medium parameters. This non-linear inversion process is vital to construct full waveform transmission operators (including the codas) from the gas cloud reflection response via an effective medium representation. However, extending this approach to a 2D non-linear full waveform inversion is not simple or straightforward, as multi-parameter inversion may become inefficient. Furthermore, to simulate the actual seismic wavefield in the subsurface we use the finite difference method as the forward modeling process and thus it becomes an expensive inversion procedure for the GA. We demonstrate that multi-dimensional non-linear full waveform inversion to obtain effective gas cloud medium parameters from the gas reflection response is viable using a GA. Several modifications to the conventional GA are suggested to overcome its limitations and therefore to gain faster convergence of the inversion process.