Innovative Insights into Earth's Interior: Moho Topography Estimation using Conditional Generative Adversarial Networks from Observed Gravity Anomalies

Precise determination of Moho topography holds paramount importance in advancing our comprehension of Earth's structural characteristics, geodynamic phenomena, and the exploration of resources. This study introduces an innovative methodology employing conditional Generative Adversarial Networks (cGAN) to unveil Moho topographies from observed gravity anomalies. To address the scarcity of real Moho datasets for training the cGAN model, we meticulously generated a comprehensive set of quasi-realistic synthetic training data using the FFT filtering technique. The forward estimation of gravity anomalies, arising from synthetic Moho topographies, was assessed through spherical prism-based gravity modeling. These estimated anomalies served as input data for the training of the cGAN model. For evaluating the efficacy of our developed cGAN algorithm in deriving Moho architecture, we conducted a comparative analysis against a conventional inversion scheme. This assessment utilized various synthetic datasets and a real case study in Southern Peninsular India, renowned for its geological diversity and ancient continental tectonic blocks. The established Bott's inversion scheme was employed as a benchmark to validate the Moho surface estimation obtained through the Deep Learning approach. To mitigate the impact of diverse factors such as topography, bathymetry, sediments, crustal and mantle heterogeneities, observed gravity anomalies underwent meticulous corrections using spherical prism-based forward gravity modeling for real case studies. The gravity contribution exclusively associated with the pure Moho was subsequently inverted using both the cGAN and traditional Bott's inversion schemes. Crucial hyperparameters, including the mean Moho depth and density contrast between the crust and mantle, were determined by utilizing seismic constraints. Our results underscore the potential of the cGAN and spherical prism-based gravity modeling approach in accurately predicting Moho topography. This study provides valuable insights into high-resolution Earth's Moho architecture and contributes to advancing our understanding of geodynamic processes, facilitating resource exploration endeavours with reduced computational demands.