# PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation

arXiv: https://arxiv.org/abs/2411.12636 · HTML: https://mlsanity.com/paper/2411.12636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [Seismic Signal Denoising and Decomposition Using Deep Neural Networks](https://mlsanity.com/paper/1811.02695/) (2018)
- [CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection](https://mlsanity.com/paper/1810.01965/) (2018)
- [QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1](https://mlsanity.com/paper/2403.18116/) (2024)
- [Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records](https://mlsanity.com/paper/2405.18451/) (2024)
- [Data Budgeting for Machine Learning](https://mlsanity.com/paper/2210.00987/) (2022)

## Abstract (arXiv)

Seismic data is often sparse and unevenly distributed due to the high costs and logistical challenges associated with deploying physical seismometers, limiting the application of Machine Learning (ML) in earthquake analysis. While simulation methods exist, no tool allows the generation of large datasets containing simulated measurements of the ground motion. To address this gap, we introduce PyAWD, a Python library designed to generate high-resolution synthetic datasets simulating spatio-temporal acoustic wave propagation in both two-dimensional and three-dimensional heterogeneous media. By allowing fine control over parameters such as the wave speed, external forces, spatial and temporal discretization, and media composition, PyAWD enables the creation of ML-scale datasets that capture the complexity of seismic wave behavior. We illustrate the library's potential with an epicenter retrieval task, showcasing its suitability for designing complex, accurate seismic problems that require advanced ML approaches in the absence or lack of dense real-world data. We also show the usefulness of our tool to tackle the problem of data budgeting in the framework of epicenter retrieval.