Mrinal K. Sen
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View article: Physics-guided unsupervised deep learning approach for the inversion of receiver functions
Physics-guided unsupervised deep learning approach for the inversion of receiver functions Open
SUMMARY The converted wave technique, namely Receiver function (RF), has been routinely employed for estimating 1-D velocity models of the Earth's crust and mantle structures. Physics-driven methods such as inversions are employed to RF da…
View article: Mapping Spatiotemporal Variations of Near‐Surface Seismic Velocity to Monitor Groundwater in Central Oklahoma
Mapping Spatiotemporal Variations of Near‐Surface Seismic Velocity to Monitor Groundwater in Central Oklahoma Open
Global climate changes have intensified drought events, necessitating accurate groundwater monitoring. Traditional methods often lack the spatial and temporal resolution to track groundwater variations. This study calculates instantaneous …
View article: A bayesian approach to inclusion based rock physics modeling with multiple statistical ensembles
A bayesian approach to inclusion based rock physics modeling with multiple statistical ensembles Open
A statistics-based approach to rock physics often includes the calculation of a series of simulations to fit data along with probability associated with the modeling to characterize uncertainty. We present a Bayesian approach to determine …
View article: Seismic inversion using hybrid quantum neural networks
Seismic inversion using hybrid quantum neural networks Open
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 limitat…
View article: Multi-ensemble Bayesian Analysis of Inclusion-based Rock-physics Models
Multi-ensemble Bayesian Analysis of Inclusion-based Rock-physics Models Open
A statistics-based approach to rock physics often includes the calculation of a series of simulations to fit data along with probability associated with the modeling to characterize uncertainty. We present a Bayesian approach to determine …
View article: Seismic Image Denoising With A Physics-Constrained Deep Image Prior
Seismic Image Denoising With A Physics-Constrained Deep Image Prior Open
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 convoluti…
View article: Enhancing RBF-FD Efficiency for Highly Non-Uniform Node Distributions via Adaptivity
Enhancing RBF-FD Efficiency for Highly Non-Uniform Node Distributions via Adaptivity Open
Radial basis function generated finite-difference (RBF-FD) methods have recently gained popularity due to their flexibility with irregular node distributions.However, the convergence theories in the literature, when applied to nonuniform n…
View article: Evolution Of Self-Help Groups In India: It's Need And Function.
Evolution Of Self-Help Groups In India: It's Need And Function. Open
Development of the self-help groups model of microfinance and social empowerment project. The paper relies on a qualitative research design and secondary literature to discuss the expansion, issues and effects of self-help genres in enhanc…
View article: Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion
Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion Open
Seismic inversion is an approach to obtain the physical properties of the Earth layers from the seismic data, which aids in reservoir characterization. In seismic inversion, spatially variable physical parameters, such as impedance (), wav…
View article: Elastic Full Waveform Inversion using a Physics guided deep convolutional autoencoder
Elastic Full Waveform Inversion using a Physics guided deep convolutional autoencoder Open
Elastic full waveform inversion can construct highresolution P-wave and S-wave velocity models in complex geological settings. However, several factors make the application of elastic FWI challenging. Elastic FWI is prone to the problem of…
View article: Elastic Full Waveform Inversion using a Physics guided deep convolutional autoencoder
Elastic Full Waveform Inversion using a Physics guided deep convolutional autoencoder Open
Elastic full waveform inversion can construct highresolution P-wave and S-wave velocity models in complex geological settings. However, several factors make the application of elastic FWI challenging. Elastic FWI is prone to the problem of…
View article: Correlations of inclusion-based rock-physics model inputs from Bayesian analysis
Correlations of inclusion-based rock-physics model inputs from Bayesian analysis Open
For any given rock-physics model, knowledge of correlations among its inputs helps to define geologically and physically meaningful and informed models for a given problem. These informed models can, in turn, reduce the uncertainty in forw…
View article: Transdimensional 2D Full-Waveform Inversion and Uncertainty Estimation
Transdimensional 2D Full-Waveform Inversion and Uncertainty Estimation Open
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 …
View article: Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands
Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands Open
Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that fra…
View article: Unsupervised Clustering of Continuous Ambient Noise Data to Get Higher Signal Quality in Seismic Surveys
Unsupervised Clustering of Continuous Ambient Noise Data to Get Higher Signal Quality in Seismic Surveys Open
Seismic interferometry has been shown to extract body wave arrivals from ambient noise seismic data. However, surface waves dominate ambient noise data, so cross-correlating and stacking all available data may not succeed in extracting bod…
View article: A fast least-squares migration with ultra-wide-band phase-space beam summation method
A fast least-squares migration with ultra-wide-band phase-space beam summation method Open
Least-squares migration (LSM) problems are often formulated as iterative schemes. At each iteration, traditional LSM methods require solving the wave equation several times to compute the gradient and single scattering (Born) predicted dat…
View article: Bayesian Analysis to Determine Relative Significance of Inputs of a Rock-Physics Model
Bayesian Analysis to Determine Relative Significance of Inputs of a Rock-Physics Model Open
Rock-physics models relate rock properties to elastic properties through non-unique relationships and often in the presence of seismic data that contain significant noise. A set of inputs define the rock-physics model, and any errors in th…
View article: Multifrequency beam-based migration in inhomogeneous media using windowed Fourier transform frames
Multifrequency beam-based migration in inhomogeneous media using windowed Fourier transform frames Open
SUMMARY We consider the problem of inhomogeneous subsurface imaging using beam waves. The formulation is based on the ultra-wide-band phase-space beam summation (UWB-PS-BS) method, which is structured upon windowed Fourier transform (WFT) …
View article: Adaptive radial basis function generated finite-difference (RBF-FD) on non-uniform nodes using p-refinement.
Adaptive radial basis function generated finite-difference (RBF-FD) on non-uniform nodes using p-refinement. Open
Radial basis functions-generated finite difference methods (RBF-FDs) have been gaining popularity recently. In particular, the RBF-FD based on polyharmonic splines (PHS) augmented with multivariate polynomials (PHS+poly) has been found sig…
View article: A hybrid Galerkin finite element method for seismic wave propagation in fractured media
A hybrid Galerkin finite element method for seismic wave propagation in fractured media Open
SUMMARY The discontinuous Galerkin finite element method (DGM) is a promising algorithm for modelling wave propagation in fractured media. It allows for discontinuities in the displacement field to simulate fractures or faults in a model. …
View article: Accelerating Least Squares Imaging Using Deep Learning Techniques
Accelerating Least Squares Imaging Using Deep Learning Techniques Open
Wave equation techniques have been an integral part of geophysical imaging workflows to investigate the Earth's subsurface. Least-squares reverse time migration (LSRTM) is a linearized inversion problem that iteratively minimizes a misfit …
View article: Finite Difference Based Wave Simulation in Fractured Porous Rocks
Finite Difference Based Wave Simulation in Fractured Porous Rocks Open
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 …
View article: Least-squares path-summation diffraction imaging using sparsity constraints
Least-squares path-summation diffraction imaging using sparsity constraints Open
Diffraction imaging aims to emphasize small-scale subsurface heterogeneities, such as faults, pinch-outs, fracture swarms, channels, etc. and can help seismic reservoir characterization. The key step in diffraction imaging workflows is bas…
View article: Lithospheric Removal Beneath the Eastern Flank of the Rio Grande Rift From Receiver Function Velocity Analysis
Lithospheric Removal Beneath the Eastern Flank of the Rio Grande Rift From Receiver Function Velocity Analysis Open
We develop and apply a technique for receiver functions that is analogous to “velocity analysis” in seismic reflection processing, in which a velocity model is found directly from the data. In the case of receiver functions, which represen…
View article: Reviews
Reviews Open
Numerical Simulation in Applied Geophysics, by Juan Enrique Santos and Patricia Mercedes Gauzellino, ISBN 978-3-319-48456-3, 2016, Birkhauser, 309 p.