Overview

Introduction

  ADFWI is an open-source framework for high-resolution subsurface parameter estimation by minimizing discrepancies between observed and simulated waveform. Utilizing automatic differentiation (AD), ADFWI simplifies the derivation and implementation of full waveform inversion (FWI), enhancing the design and evaluation of methodologies. It supports wave propagation in various media, including isotropic acoustic, isotropic elastic, and both vertical transverse isotropy (VTI) and horizontal transverse isotropy (HTI) medias.

  In addition, ADFWI provides a comprehensive collection of Objective functions, regularization techniques, optimization algorithms, and deep neural networks. This rich set of tools facilitates researchers in conducting experiments and comparisons, enabling them to explore innovative approaches and refine their methodologies effectively.

ADFWI NNFWI

Features

  • Automatic Differentiation for Gradient Computation
    Eliminates manual adjoint-state derivation by leveraging automatic differentiation (AD) for efficient gradient computation.

  • Versatile Wave Propagation Models
    Supports isotropic acoustic, isotropic elastic, and anisotropic (VTI & HTI) wave simulations for broader subsurface imaging applications.

  • Modular and Customizable
    Easily integrates custom objective functions, regularization techniques, optimization algorithms, and deep neural networks for flexible method development.

  • Advanced Optimization and Regularization
    Includes L-BFGS, Adam, SGD, total variation, sparsity constraints, and physics-guided priors for stable and robust inversion.

  • Deep Learning Integration
    Supports deep priors, learned regularization, and uncertainty quantification, enhancing traditional FWI approaches.

  • Benchmark Datasets and Reproducibility
    Provides ready-to-use datasets (Marmousi2, Overthrust) for standardized evaluation and comparison.

  • Open-Source and Extensible
    Encourages community contributions, allowing researchers to expand functionalities and adapt ADFWI to novel FWI challenges.

Installation

To install ADFWI, please follow these steps:

  1. Ensure Prerequisites

  2. Create a Virtual Environment (Optional) It is recommended to create a virtual environment using conda:

    conda create --name adfwi-env python=3.8
conda activate adfwi-env

  3. Install Required Packages

  • Method 1: Clone the github Repository This method provides the latest version, which may be more suitable for your research:

    git clone https://github.com/liufeng2317/ADFWI.git
cd ADFWI

    Then, install the necessary packages:

    pip install -r requirements.txt

  • Method 2: Install via pip Alternatively, you can directly install ADFWI from PyPI:

      pip install ADFWI-Torch