5 papers
Inversion of Magnetotelluric Data using Bayesian Neural Networks
Dhruv Poddar, Rohan Sharma, Divakar Vashisth
Magnetotelluric (MT) inversion is a key technique in geophysics for imaging deep subsurface resistivity structures. However, the inherent ill-posedness and non-uniqueness of invers…
Seismic inversion using hybrid quantum neural networks
Divakar Vashisth, Rohan Sharma, Tejas Ganesh Iyer +2
Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine lea…
Inversion of DC Resistivity Data using Physics-Informed Neural Networks
Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar +1
The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant att…
Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization
Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar +1
Joint inversion of geophysical datasets is instrumental in subsurface characterization and has garnered significant popularity, leveraging information from multiple geophysical met…
DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity Dispersion Curves using Convolutional Neural Networks
Rohan Sharma, Divakar Vashisth, Bharath Shekar
Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse proble…