Inversão 1,5D e 2D de dados eletromagnéticos e elétricos usando métodos de otimização global e processamento em paralelo

Abstract

The use of optimization global techniques in geophysics to solve inverse problems, allowing to incorporate with easiness several kinds of constraints to create an stable mathematically solution. The 1D forward modeling of time domain electromagnetic data (TDEM) and 2D-DC resistivity is computationally inexpensive, allowing to use global optimization methods (GOMs) to solve 1.5D and 2D inverse problems, incorporating several stabilizers. Nonetheless, these meta-heuristic techniques are time-consuming, specially when a high number of parameters is involved. In this sense, the use of paralleling processing tools have become paramount to alleviate the computing time in inverse problems.

In this work was developed a newly particle swarm optimization technique with elitism and mutation (EMPSO) using a hybrid parallel MPI/OpenMP version named (PEMPSO). This algorithm, also supported with the use of Total Variation (TV) and Global Smoothness (GS) constraints allowed studying the resistivity distribution in subsurface, with both synthetic and real data. The inversion outcomes reflect good results for both constraints, having better sensitivity with the Total Variation (TV) constraint in detecting high and low resistivity contrasts. The inversion times, also demonstrated a speedup of 17x in the assessed models in relation with the sequential processing times.

Apart from this 2D electrical resistivity method, we also presented an inversion approach for 1,5D TDEM data, that similarly with the 2D case aforementioned, presented good outcomes in the inverted models using different global optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). These algorithms allowed mapping and localizing high and low resistivity contrasts for both synthetic and real data. Similarly with 2D case, an approach of parallel processing using pure MPI allowed to speedup the computational time that involve the use of these meta-heuristic algorithms. The major outcomes of 1,5D electromagnetic case show that: (i) both constraints offer good results to map the resistivity distribution, (ii) the WOA and PSO algorithms offer better computational performance, converging first than the GWO, (iii) WOA presented the better performance in the cost function value attained than PSO and GWO and (iv) a pure MPI parallelized version provided a 17x speedup in the time processing for synthetic models and up to 50x in the time computing of real data studied.