Particle swarm optimization for simultaneous analysis of magnetotelluric and time-domain electromagnetic data

Abstract

We have developed an innovative, simultaneous 1D optimization of electromagnetic (EM) data. Our scheme is suitable for the simultaneous analysis of magnetotelluric (MT) and time-domain EM (TDEM) data based on the probabilistic and evolutionary particle swarm optimization (PSO) algorithm. The simultaneous optimization also identifies and removes the static shift from the MT data. In our scheme, the static shift of the MT apparent resistivity curve is considered as an additional parameter [Formula: see text] to be optimized. We tested the suggested method on the synthetic data and then applied it to the data from an EM geophysical study carried out in the geothermal area of Larderello-Travale (Tuscany, Italy). Apart from the novelty of using the PSO algorithm to estimate the model parameters by joint analysis, the simultaneous optimization of the static shift parameter addresses a major problem in MT, i.e., how to define and remove the galvanic effects on MT curves according to independent information, such as that provided by TDEM data. The procedure is expected to strongly influence the application of MT, particularly in geothermal exploration, which commonly relies extensively on EM methods.