Data-Driven and Theory-Guided Pseudo-Spectral Seismic Imaging Using Deep Neural Network Architectures

TL;DR

This paper integrates pseudo-spectral Full Waveform Inversion with deep neural networks, demonstrating improved subsurface imaging accuracy and stability over classical methods through data-driven and theory-guided approaches.

Contribution

It introduces novel deep learning architectures for pseudo-spectral FWI, combining data-driven and physics-guided methods, and evaluates their performance on synthetic and Marmousi datasets.

Findings

DNNs outperform classical FWI in deep and over-thrust regions.

RNNs achieve higher accuracy and fault detection.

DNNs excel in velocity contrast recovery, RNNs in edge definition.

Abstract

Full Waveform Inversion (FWI) reconstructs high-resolution subsurface models via multi-variate optimization but faces challenges with solver selection and data availability. Deep Learning (DL) offers a promising alternative, bridging data-driven and physics-based methods. While FWI in DL has been explored in the time domain, the pseudo-spectral approach remains underutilized, despite its success in classical FWI. This thesis integrates pseudo-spectral FWI into DL, formulating both data-driven and theory-guided approaches using Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs). These methods were theoretically derived, tested on synthetic and Marmousi datasets, and compared with deterministic and time-domain approaches. Results show that data-driven pseudo-spectral DNNs outperform classical FWI in deeper and over-thrust regions due to their global approximation capability. Theory-guided RNNs yield greater accuracy, with lower error and better fault identification. While DNNs excel in velocity contrast recovery, RNNs provide superior edge definition and stability in shallow and deep sections. Beyond enhancing FWI performance, this research identifies broader applications of DL-based inversion and outlines future directions for these frameworks.