Inversion of DC Resistivity Data using Physics-Informed Neural Networks

TL;DR

This paper presents a physics-informed neural network approach for inverting DC resistivity data, effectively handling limited data, ensuring geologically consistent solutions, and quantifying uncertainty in near-surface characterization.

Contribution

It introduces a CNN-based PINN method for resistivity inversion that incorporates physics constraints and uncertainty estimation, advancing deep learning applications in geophysics.

Findings

PINNs produce median profiles comparable to existing methods.

The approach effectively estimates prediction uncertainty.

Synthetic and field data validations demonstrate robustness.

Abstract

The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant attention due to their capability to elucidate intricate non-linear relationships between geophysical data and model parameters. Nevertheless, these methods face challenges such as limited training data availability and the generation of geologically inconsistent solutions. These concerns can be mitigated through the integration of a physics-informed approach. Moreover, the quantification of prediction uncertainty is crucial yet often overlooked in deep learning-based inversion methodologies. In this study, we utilized Convolutional Neural Networks (CNNs) based Physics-Informed Neural Networks (PINNs) to invert both synthetic and field Schlumberger sounding data while also estimating prediction uncertainty via Monte Carlo dropout. For both synthetic and field case studies, the median profile estimated by PINNs is comparable to the results from existing literature, while also providing uncertainty estimates. Therefore, PINNs demonstrate significant potential for broader applications in near-surface characterization.