Research and Application of Deep Learning Selection Method for Magnetotelluric Power Spectrum Based on Rhoplus Correction

Abstract

Magnetotelluric (MT) is an electromagnetic exploration method using natural field sources, which are unstable, irregular, and vulnerable to interference from electromagnetic signals. The total power spectrum of the magnetotelluric signal is superimposed by a sub-power spectrum of segmented time series. To reduce the influence of electromagnetic interference, the pre-processing of magnetotelluric data will delete the severely interfered sub-power spectrum. The selection of power spectrum often requires human-computer interaction, which is time-consuming and laborious, heavily dependent on personal experience, and has great randomness. This article innovation is put forward based on the classification of Rhoplus correction and deep learning automatic power spectrum method, first of all, the MT power spectrum impedance estimation results are Rhoplus one dimensional inversion and obtain fitting, using neural network learning fitting differential characteristics and classification, and then for the power spectrum of MT again after low-quality quantum power spectrum to eliminate overlay, Impedance estimation is performed, and the above process is repeated to find the optimal power spectrum combination. Through the comparison and verification of measured MT data, the automatic power spectrum selection method based on Rhoplus correction and deep learning classification achieve similar results to manual editing.