19 citations · 19 across the 7 of their papers we have counts for
8 papers
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…
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…
Pre-stack and post-stack seismic inversion using quantum computing
Divakar Vashisth, Rodney Lessard, Tapan Mukerji
Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within qua…
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…