A Convolutional Neural Network-Based Estimation of Depth to the Bottom of Magnetic Sources from Aeromagnetic Data and Its Applications in Southern Peninsular India

The magnetic field from Earth's crust helps us understand its thermal structure by finding the depth to the bottom of magnetic sources, an essential indicator of the crustal thermal properties. This study aims to estimate the depth to the bottom of magnetic sources precisely using the magnetic field. Traditional methods, like the spectral peak and centroid techniques, are commonly used to estimate the depth to the bottom of magnetic sources. However, these methods typically require prior knowledge about the magnetization source, derived from empirical relationships of wave-vectors in the spectral domain, which is challenging to obtain over large regions. We devised an innovative deep-learning approach utilizing a convolutional neural network to directly estimate the depth to the bottom of the magnetic sources, eliminating the need for prior knowledge of the fractal magnetization source. Synthetic fractal magnetizations were constructed to train the model, and the performance of the convolutional neural network was compared to the modified centroid approach. Our convolutional neural network methodology was confirmed by utilizing a diverse range of realistic synthetic fractal magnetization, incorporating various window widths and depths to the bottom of the magnetization source. The model is applied to the high-resolution aeromagnetic data of the southern Indian shield to understand the crustal-scale thermal structure.