3-D Inversion Based on the Particle Swarm Optimization-Quasi-Newton Hybrid Algorithm for SOTEM

The short offset transient electromagnetic method (SOTEM) has become one of the main methods for coal hydrogeology investigations and mineral explorations due to its performance advantages of high precision, large depth abilities, and high efficiency. At the present time, due to the computational complexity and memory requirements for 3-D SOTEM inversion algorithms, SOTEM data inversion is mainly based on 1-D, which has made it difficult to meet fine detection requirements under complex geological conditions. In this study, a particle swarm optimization-quasi-Newton hybrid algorithm (PSO-QN) was proposed, with high precision and fast 3-D inversions of SOTEM data achieved. In addition, a flight strategy was introduced in order to ensure the global optimization of individual particles, and a weighting strategy was adopted to avoid falling into local minimums. The 3-D inversions of the SOTEM data were tested based on a variety of geoelectric models. The results showed that the dependence of the PSO-QN hybrid algorithm on the number of particle swarms had dropped to two-thirds. Furthermore, the calculation time was significantly reduced to one-third. The inversion results of the field data revealed that the proposed inversion method possessed a strong anti-noise capacity, high speed, good stability, and high resolution.