Accurate estimation of Moho topography plays a crucial role in understanding Earth’s structure, geodynamic processes, and resource exploration. This study presents a novel approach that utilizes conditional Generative Adversarial Networks (cGAN) to reveal Moho topography based on observed gravity anomalies. Synthetic training datasets of Moho topography were generated using the FFT filtering method due to the scarcity of true datasets. Spherical prism-based forward gravity modeling was employed to evaluate the resulting gravity anomalies.

Bibliographic Info: Arka Roy, Rajat Kumar Sharma, Dharmadas Jash, B. Padma Rao, J. Amal Dev,J.K. Tomson. (2024).  
https://doi.org/10.1016/j.jseaes.2024.106093.

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