Sensing prior constraints in deep neural networks for solving exploration geophysical problems

Abstract

One of the key objectives in geophysics is to characterize the subsurface through the process of analyzing and interpreting geophysical field data that are typically acquired at the surface. Data-driven deep learning methods have enormous potential for accelerating and simplifying the process but also face many challenges, including poor generalizability, weak interpretability, and physical inconsistency. We present three strategies for imposing domain knowledge constraints on deep neural networks (DNNs) to help address these challenges. The first strategy is to integrate constraints into data by generating synthetic training datasets through geological and geophysical forward modeling and properly encoding prior knowledge as part of the input fed into the DNNs. The second strategy is to design nontrainable custom layers of physical operators and preconditioners in the DNN architecture to modify or shape feature maps calculated within the network to make them consistent with the prior knowledge. The final strategy is to implement prior geological information and geophysical laws as regularization terms in loss functions for training the DNNs. We discuss the implementation of these strategies in detail and demonstrate their effectiveness by applying them to geophysical data processing, imaging, interpretation, and subsurface model building.

Intelligent reconstruction for spatially irregular seismic data by combining compressed sensing with deep learning

“…In general, prior constraints are applied to DNNS to improve the generalizability, interpretability and physical consistency of the model, including three general strategies: imposing constraints on data, fusing constraints into network architecture, and integrating constraints into loss functions (Wu et al, 2023). As an objective function related to network parameters, Eq.…”

“…During the research process, we can see that it has been difficult to achieve the goal of AI seismic exploration with a single route or paradigm. Wu et al (Wu et al, 2023) concluded that domain knowledge constraints can be applied to deep neural networks to improve those with weak generalization ability, low interpretability and poor physical consistency, such as physics-driven intelligent seismic processing (Pham and Li, 2022), impedance inversion (Yuan et al, 2022), porosity prediction (Sang et al, 2023), designing priorconstraint network architectures for seismic waveform inversion (Sun et al, 2020) and exploring the physics-informed neural network (PINN) for solving geophysical forward modeling (Song and Wang, 2023). From these studies, we conclude that a more reasonable direction to deal with reconstruction problems can be the combination of data-driven model and mechanism model.…”

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

“…They demonstrated the effectiveness of domain adaptation by applying it to seismic imaging problems. Wu et al [33] proposed to integrate domain knowledge to impose prior constraints for geophysical problems, which can improve the generalizability and interpretability of DNN models.…”

Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach

“…However, starting in the second decade of the 2000s, a new era of HPC has developed thanks to the arrival of the new graphic processing units (GPU), its high parallelization capacity, and easy access outside the business or academic field due to its low cost. The most commonly used techniques in geophysics have focused mainly on electromagnetic and seismic methods and can be generally classified as parallelization of numerical methods [55][56][57][58][59][60][61], based on neural networks and deep learning [62][63][64][65], and more recently, based on quantum computing [66].…”