Machine-Learning Inversion of Resistivity Profiles From Multifrequency Electromagnetic Measurements on Undulating Terrain Surfaces

This article first presents machine-learning (ML) inversion of resistivity profiles from multifrequency electromagnetic measurements on undulating terrain surfaces based on synthetic data training by the mixed spectral element method (MSEM). The inversion method combines several advanced technologies with various merits. A semiregular mesh generation method is designed and developed for adaption to complex undulating terrain and multifrequency measured data, and the proposed meshing technology is also suitable for modeling different training models under the same undulating terrain. By simulating the application scenarios of measurements, the apparent resistivity data at eight frequencies from 1 to 2048 Hz are simulated with the 2.5-D MSEM to ensure the accuracy and efficiency of the simulation of undulating terrains. Fast simulation of stochastic models for training datasets is achieved by twisting and extruding the initial model obtained by Bostick inversion. Since the unknown weight matrices are solved only once in the training process, the extreme learning machine (ELM) is used for ML inversion to reduce the training cost and obtain high-precision inversion results. Then it is applied to reconstruct a metallogenic model to verify the method's validity and accuracy and to reconstruct the resistivity profile of underground ore bodies with actual measurements. The results show that the proposed method can be effectively used to detect metal ores at a depth of less than 3000 m underground.