19 citations · 19 across the 7 of their papers we have counts for
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Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Hoang Anh Nguyen, Divakar Vashisth, Ali Tura
Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and…
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…
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…
Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
Divakar Vashisth, Tapan Mukerji
Petrophysical inversion is an important aspect of reservoir modeling. However due to the lack of a unique and straightforward relationship between seismic traces and rock propertie…