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Seismic Velocity Model Building with Deep Convolutional Neural Networks SEP-190 (2023)


Thesis (32.3 MB)

Table of contents

  • Chapter 1: Introduction
  • Chapter 2: Wave-equation and deep learning theory
  • Chapter 3: Earth model building with supervised learning
  • Chapter 4: Tiber WATS Velocity Model Building
  • Appendix A: Elastic wave equation as a linear system
  • Appendix B: Acoustic wave equation as a linear system
  • Bibliography


This thesis focuses on recovering wave velocity estimates from seismic data with limited recording offsets and band-limited frequency content using Deep Learning (DL), specifically using deep convolutional neural networks (CNN). A combined strategy combines a supervised learning framework to recover the low-wavenumber components of velocity models with the traditional Full Waveform Inversion (FWI) to recover high-wavenumbers. We demonstrate the methodology on synthetic industry benchmark problems and a field data application with an open-source Gulf of Mexico seismic dataset.

Reproducibility and source codes

This thesis has been tested for reproducibility. The source codes and training data are made available at these GitHub repositories:


Defense presentation

Stuart Farris
Publication Date
August, 2023