Particle swarm optimization for simultaneous analysis of Magnetotelluric (MT) and Time Domain EM (TDEM) data

We present an innovative, simultaneous 1D optimization of electromagnetic data. The proposed 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 the proposed scheme, the static shift of the MT apparent resistivity curve is considered an additional parameter S to be optimized. We tested the suggested method on synthetic data and then applied it to the data from an electromagnetic 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.