arXiv subjects

Mrinal K. Sen

Publications and source records attributed to Mrinal K. Sen.

4 recordsLinked to original sources

arXiv2025

Seismic inversion using hybrid quantum neural networks

Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine learning, have been developed to address some of these limitations. Motivated by the potential of quantum computing, we report on our attempt to map one such classical physics-informed algorithm to a quantum framework. The primary goal is to investigate the technical challenges of this mapping, given that quantum algorithms rely on computing principles fundamentally different from those in classical computing. Quantum computers operate using qubits, which exploit superposition and entanglement, offering the potential to solve classically intractable problems. While current quantum hardware is limited, hybrid quantum-classical algorithms-particularly in quantum machine learning (QML)-demonstrate potential for near-term applications and can be readily simulated. We apply QML to subsurface imaging through the development of a hybrid quantum physics-informed neural network (HQ-PINN) for post-stack and pre-stack seismic inversion. The HQ-PINN architecture adopts an encoder-decoder structure: a hybrid quantum neural network encoder estimates P- and S-impedances from seismic data, while the decoder reconstructs seismic responses using geophysical relationships. Training is guided by minimizing the misfit between the input and reconstructed seismic traces. We systematically assess the impact of quantum layer design, differentiation strategies, and simulator backends on inversion performance. We demonstrate the efficacy of our approach through the inversion of both synthetic and the Sleipner field datasets. The HQ-PINN framework consistently yields accurate results, showcasing quantum computing's promise for geosciences and paving the way for future quantum-enhanced geophysical workflows.

quant-ph↗

arXiv2024

Seismic Image Denoising With A Physics-Constrained Deep Image Prior

Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning method based on Deep Image Prior (DIP) which uses convolutional neural networks. Our approach optimizes the network weights to refine the migration velocity model, rather than the seismic image, effectively isolating meaningful image features from noise and artifacts. We apply this method to synthetic and real seismic data, demonstrating significant improvements over standard DIP techniques with minimal computational overhead.

physics.geo-ph↗

arXiv2022

Transdimensional 2D Full-Waveform Inversion and Uncertainty Estimation

Full-Waveform Inversion (FWI) has now become a widely accepted tool to obtain high-resolution velocity models from seismic data. Typically, the velocity model in its discrete form is represented on a rectangular grid, and we solve for the elastic properties at these grid points. FWI is mostly solved employing a local optimization method, where one obtains a velocity update by minimizing the misfit between the observed and the calculated seismograms. Note also that FWI is a highly non-linear problem which is known to be prone to non-uniqueness. The convergence to a globally optimum solution is not guaranteed; it depends on the choice of the starting model. Thus, a Bayesian formulation of the inverse problem with subsequent sampling of the posterior distribution is a preferred choice, since it enables uncertainty quantification. However, with the increase in the dimension of a model, sampling search space becomes computationally expensive. We employ a recently developed trans-dimensional sampling method called Reversible Jump Hamiltonian Monte Carlo (RJHMC), to the 2D full waveform inversion problem. We represent our velocity model using Voronoi cells, determined from the distribution of certain nuclei points in the model space. This method offers two advantages. First, it solves for a variable dimensional velocity updates by using a trans-dimensional reversible jump Markov Chain Monte Carlo (RJMCMC) step and thus tries to achieve an optimum number of nuclei to represent the model and minimize the misfit. A smaller number of parameters helps in an efficient sampling of the model search space. Second, it applies the gradient-based Hamiltonian Monte Carlo (HMC) step, which further improves the sampling by allowing the algorithm to take a large step guided by the gradient. This two-step algorithm proves to be a useful tool for model exploration and uncertainty quantification in FWI.

physics.geo-ph↗

arXiv2019

Finite Difference Based Wave Simulation in Fractured Porous Rocks

Biot's theory provides a framework for computing seismic wavefields in fluid saturated porous media. Here we implement a velocity-stress staggered grid 2D finite difference algorithm to model the wave-propagation in poroelastic media. The Biot's equation of motion are formulated using a finite difference algorithm with fourth order accuracy in space and second order accuracy in time. Seismic wave propagation in reservoir rocks is also strongly affected by fractures and faults. We next derive the equivalent media model for fractured porous rocks using the linear slip model and perform numerical simulations in the presence of fractured interfaces. As predicted by Biot's theory a slow compressional wave is observed in the particle velocity snapshots. In the layered model, at the boundary, the slow P-wave converts to a P-wave that travels faster than the slow P-wave. We finally conclude by commenting on the major details of our results.

physics.geo-ph↗