DAS-ANI: Distributed Acoustic Sensing Preprocessing & Ambient Noise Interferometry Tools

Overview

This repository provides a modular, configuration-driven framework for Distributed Acoustic Sensing (DAS) preprocessing and Ambient Noise Interferometry (ANI) workflows.

The primary goals are:

• Preprocessing large-scale DAS datasets

• Efficient computation of noise cross-correlations (NCFs)

• Temporal stacking of NCFs (daily, 7d, 15d, 30d)

• Dispersion imaging (f–v panels) and dispersion curve picking

• Exploration of data compression strategies (e.g., sketching, low-rank methods) and their impact on virtual source gathers and dispersion results

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Installation

Option 1: Install as editable package (pip)

# Create virtual environment "das_ani"
python -m venv das_ani

# Activate venv
source das_ani/bin/activate

# 3. Upgrade pip (recommend)
pip install --upgrade pip

# 4. Install as editable package
pip install -e .

Option 2: Conda environment

# Create environment 
conda env create -f environment.yml

# Activate 
conda activate das_ani-env

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Downloading DAS Data from Google Cloud

Large DAS datasets (e.g., preprocessed windows or NCF products) are typically hosted on Google Cloud Storage (GCS).

Requirements

• gsutil installed
• Authenticated GCP account with read permission

Official documentation: https://cloud.google.com/storage/docs/gsutil

Example

gsutil -m cp -n -r gs://path/to/data .

# or
gsutil -m \ -o "GSUtil:parallel_process_count=1" \ -o "GSUtil:parallel_thread_count=16" \ cp -r "gs://path/to/data" .

Flag explanation

1. gsutil Google Cloud Storage command-line tool.
2. -m (multi-threading) Enables parallel transfers for faster downloads.
3. cp Copy command (similar to Unix cp), works cloud ↔ local.
4. -n (no-clobber) Skip files that already exist locally.
5. -r (recursive) Copy entire folders.
6. gs://path/to/data Source path inside a Google Cloud Storage bucket.
7. . Destination = current directory.

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Repository Structure

.
├── .gitignore
├── LICENSE
├── README.md
├── environment.yml
├── pyproject.toml
├── Makefile
├── configs/                     # YAML configuration files
│   ├── cc.yaml                  # Cross-correlation parameters
│   └── disp.yaml                # Dispersion + picking parameters
│
├── geometry/
│   └── .gitkeep                 # Geometry-related inputs (offsets, maps)
│
├── data/
│   ├── preprocessed/            # Preprocessed DAS time windows (.npz)
│   ├── ncf_raw/                 # Raw noise cross-correlations (.npy)
│   └── ncf_stacks/              # Stacked NCFs (.npy)
│       ├── daily/
│       ├── 7d/
│       ├── 15d/
│       └── 30d/
│
├── notebooks/
│   ├── geometry.ipynb        # Fiber geometry visualization
│   ├── processing.ipynb      # End-to-end DAS processing demo
│   ├── dispersion.ipynb      # Dispersion imaging & picking
│   └── kernel.ipynb          # Tensor Sketch / kernel demos
│
└── src/
    ├── utils.py                 # I/O, config helpers, diagnostics
    ├── ani.py                   # ANI preprocessing + correlation kernels
    ├── cc.py                    # Cross-correlation workflow
    ├── stack.py                 # NCF stacking (daily, multi-day)
    ├── disp.py                  # Dispersion imaging + picking algorithms
    ├── disp_pick.py             # Dispersion workflow (main script)
    └── plot.py                  # Plotting utilities

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Workflow Overview

All scripts are config-driven via YAML files in configs/. You generally do not need to modify Python code for parameter changes.

1. Cross-correlation (NCF generation)

make cc_only

Produces:

data/ncf_raw/*.npy

Raw NCFs can be large. It is safe to delete them after stacking if storage is limited.

2. Stacking (daily, 7d, 15d, 30d)

make stack_only

Produces:

data/ncf_stacks/<window>/*.npy

Stack windows are controlled in configs/cc.yaml.

3. Dispersion imaging & picking

make disp_only

Produces:

results/dispersion/<window>/VS_xxx/

Each virtual source folder contains:

• f–v panel
• frequency & velocity axes
• picked dispersion curve
• metadata (.json) To process multiple windows:

make disp_only DISP_WINDOWS="daily 7d 15d 30d" NJOBS=12

4. Full pipeline

make all

Runs:

cc → stack → dispersion

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License

This project is licensed under the MIT License. See the LICENSE file for full text.