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Review of physics-informed machine-learning inversion of geophysical data
We review five types of physics-informed machine-learning (PIML) algorithms for inversion
and modeling of geophysical data. Such algorithms use the combination of a data-driven …
and modeling of geophysical data. Such algorithms use the combination of a data-driven …
Overcoming the spectral bias problem of physics-informed neural networks in solving the frequency-domain acoustic wave equation
X Chai, W Cao, J Li, H Long… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Physics-informed neural networks (PINNs) have recently been utilized to tackle wave
equation-based forward and inverse problems. However, they encounter challenges in …
equation-based forward and inverse problems. However, they encounter challenges in …
Bayesian neural network and Bayesian physics-informed neural network via variational inference for seismic petrophysical inversion
Deep-learning methods are being successfully applied to seismic inversion and reservoir
characterization problems; however, the uncertainty quantification process has not been …
characterization problems; however, the uncertainty quantification process has not been …
Petrophysical parameter estimation using a rock-physics model and collaborative sparse representation
The estimation of petrophysical parameters is key in the identification of underground
reservoirs. Current petrophysical parameter estimation methods are typically constrained by …
reservoirs. Current petrophysical parameter estimation methods are typically constrained by …
Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion
Physics-informed neural networks have shown significant potential in solving partial
differential equations (PDEs) across diverse scientific fields. However, their performance …
differential equations (PDEs) across diverse scientific fields. However, their performance …
[HTML][HTML] Machine Learning-Based Prediction of Well Logs Guided by Rock Physics and Its Interpretation
J Zhang, G Liu, Z Wei, S Li, Y Zayier, Y Cheng - Sensors, 2025 - mdpi.com
The refinement of acquired well logs has traditionally relied on predefined rock physics
models, albeit with their inherent limitations and assumptions. As an alternative, effective yet …
models, albeit with their inherent limitations and assumptions. As an alternative, effective yet …
Pre-stack seismic geostatistical inversion based on mixed prior information of random media
The parameter information of subsurface media can be categorized into large-scale and
small-scale information, and both of them are important for reservoir prediction and …
small-scale information, and both of them are important for reservoir prediction and …
A two-branch neural network for gas-bearing prediction using latent space adaptation for data augmentation-An application for deep carbonate reservoirs
Deep learning has been utilized for gas-bearing prediction in recent years due to its
powerful nonlinear fitting capacity; however, the scarcity of log labels severely restricts its …
powerful nonlinear fitting capacity; however, the scarcity of log labels severely restricts its …
An explainable operator approximation framework under the guideline of Green's function
Traditional numerical methods, such as the finite element method and finite volume method,
adress partial differential equations (PDEs) by discretizing them into algebraic equations …
adress partial differential equations (PDEs) by discretizing them into algebraic equations …
Seismic poststack impedance inversion using geophysics-informed deep-learning neural network
Traditionally, seismic post-stack impedance inversion is implemented using linear
optimization algorithms. Recently, deep learning neural networks have been successfully …
optimization algorithms. Recently, deep learning neural networks have been successfully …