ONE DIMENSIONAL MAGNETOTELLURIC DATA INVERSION MODELING USING CONVOLUTIONAL NEURAL NETWORK - Iqbal Khatami, Muhammad
The magnetotelluric method is a geophysical method commonly used to map subsurface resistivity. The subsurface’s true resistivity is generated by inversion of the magnetotelluric data. This study tries to invert one-dimensional magnetotelluric data using one of the machine learning methods, the convolutional neural network, which is heavily inspired by the human nervous system. This method has been tested on synthetic data with curves of type A, H, K, and Q contaminated with noise. The inversion results show that the convolutional neural network model could approach the actual resistivity values and patterns with a fairly small error and exceptionally fast computation time without initial model guess and iteration.