Physically Guided Deep Unsupervised Inversion for 1D Magnetotelluric Models

2024-10-20

arXiv.org

AI Summary

Magnetotelluric (MT) inversion for geothermal and white hydrogen exploration suffers from heavy reliance on manual parameter tuning in conventional methods and requires large volumes of labeled training data in supervised learning approaches. Method: This paper proposes a physics-guided, deep unsupervised inversion framework. Its core innovation is the development of the first differentiable MT forward operator, seamlessly integrated into the TensorFlow automatic differentiation framework, enabling end-to-end, observation-driven 1D resistivity structure inversion without ground-truth model labels. Contribution/Results: By unifying differentiable physical modeling with unsupervised loss optimization, the method significantly improves inversion accuracy and robustness on both synthetic and field datasets. It outperforms traditional iterative algorithms and supervised learning methods in computational efficiency and generalization capability, establishing a novel paradigm for few-shot geophysical inversion.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The global demand for unconventional energy sources such as geothermal energy and white hydrogen requires new exploration techniques for precise subsurface structure characterization and potential reservoir identification. The Magnetotelluric (MT) method is crucial for these tasks, providing critical information on the distribution of subsurface electrical resistivity at depths ranging from hundreds to thousands of meters. However, traditional iterative algorithm-based inversion methods require the adjustment of multiple parameters, demanding time-consuming and exhaustive tuning processes to achieve proper cost function minimization. Although recent advances have incorporated deep learning algorithms for MT inversion, primarily based on supervised learning, paul{and} needs large labeled datasets for training. This work utilizes TensorFlow operations to create a differentiable forward MT operator, leveraging its automatic differentiation capability. Moreover, instead of solving for the subsurface model directly, as classical algorithms perform, this paper presents a new deep unsupervised inversion algorithm guided by physics to estimate 1D MT models. Instead of using datasets with the observed data and their respective model as labels during training, our method employs a differentiable modeling operator that physically guides the cost function minimization, making the proposed method solely dependent on observed data. Therefore, the optimization paul{algorithm} updates the network weights to minimize the data misfit. We test the proposed method with field and synthetic data at different acquisition frequencies, demonstrating that the resistivity models obtained are more accurate than those calculated using other techniques.

Problem

• Geothermal Energy Analysis
• Magnetotelluric (MT) Technique
• Deep Learning Application

Innovation

• Deep Unsupervised Learning
• Physics-Informed Algorithms
• TensorFlow Optimization