Tutorial

This tutorial provides an in-depth introduction to the fundamental principles behind ADFWI and practical examples to illustrate its key components. By following this tutorial, users will gain a deeper understanding of the theoretical foundations and implementation details of the framework.

• Automatic Differentiation
  • Computational Graph (CG)
  • Automatic Differentiation (AD)
  • PyTorch Framework

• Wave Equations
  • 1. Acoustic Wave Equation
  • 2. Elastic Wave Equation

• Objective Functions
  • 1. Waveform-based
  • 2. Waveform-attributes based
  • 3. Data-alignment based
  • 4. Hybrid Objective Functions

• Optimization Methods
  • 1. First-Order Optimization Methods
  • 2. Adaptive Gradient Optimization
  • 3. Second-Order Optimization Methods

• Regularization Methods
  • 1. Tikhonov Regularization
  • 2. Total Variation (TV) Regularization

• Deep Reparameterization
  • Deep Image Prior
  • Network Structure
  • Uncertainty Estimation