Deep learning for joint geophysical inversion of seismic and MT data sets

Using more than one geophysical technique provides a more reliable way to delineate the subsurface structure than a single geophysical method. In this paper, we present a novel way for the joint inversion of seismic and MT datasets using artificial neural networks. Different rock models are taken to compute the joint relation between the porosity, shale content, velocity and resistivity of the strata. The velocity and resistivity models thus generated were used to compute seismic traces and apparent resistivity curves, the dataset used for training and testing the neural network. Such a method of performing joint inversion is advantageous when we want to check the reliability of our Machine Learning model in regions of low velocity or when a particular physical property does not vary across the layers while another does since, unlike other optimisation schemes used for the geophysical inversion, we do not need to rerun the model for every initial model. The efficacy of artificial neural networks (ANN) to perform the join inversion is tested, and in the process, a particular type of ANN architecture is developed.