Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat, India

Artificial Neural Networks (ANNs) are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of problems. However, to build an artificial neural network that is prudent enough to rely on, vast quantities of relevant data have to be fed. In this study, we analysed the scope of artificial neural networks in geothermal reservoir architecture. In particular, we attempted to solve joint inversion problem through Feedforward Neural Network (FNN) technique. In order to identify geothermal sweet spots in the subsurface, an extensive geophysical studies were conducted in Gandhar area of Gujarat, India. The data were acquired along six profile lines for gravity, magnetics and magnetotellurics. Initially low velocity zone was identified using refraction seismic technique in order to set a common datum level for other potential data. The depth of low velocity zone in Gandhar was identified at 11 m. The FNN backpropagation method was applied to gain the global minima of the data space and model space as desired. The input dataset fed to the inversion algorithm in the form of gravity, magnetic susceptibility and resistivity helped to predict the suitable model after network training in multiple steps. The joint inversion of data is conducive to understanding the subsurface geological and lithological features along with probable geothermal sweet spots. The results of this study show the geothermal sweet spots at depth ranging from 200 m to 300 m. The results from our study can be used for targeted zones for geothermal water exploitation.