This study explores wavefield reconstruction using machine learning methods for data compression and wavefield separation. We test various architectures to treat DAS data as two-dimensional arrays, such as the Implicit Neural Representation (INR) models and the SHallow REcurrent Decoder (SHRED) model.
This repository provides independent notebook examples of model training and inference performed in the manuscript. All codes are implemented in PyTorch.
The notebook of SHRED model training on the CI DAS data is available at notebooks/training_SHRED_KKFLS.ipynb. See below for instructions of getting the training data.
- Random Fourier Feature Network (RFFN, Tancik et al., 2020): notebooks/training_RFFN_KKFLS.ipynb
- Sinusoidal Representation Network (SIREN, Sitzmann et al., 2020): notebooks/training_SIREN_KKFLS.ipynb
The earthquake data from the Cook Inlet DAS experiment are available at https://dasway.ess.washington.edu/gci/index.html. Earthquakes and daily data reports are updated daily.
Due to the size of the data used in this study (~260 GB per cable), we cannot upload it directly in this repository. However, we prepared a Python script to download these data from our archival server. Please refer to the script download.py and list of events event_list.csv in the repository.
Ni, Y., Denolle, M. A., Shi, Q., Lipovsky, B. P., Pan, S., & Kutz, J. N. (2024). Wavefield Reconstruction of Distributed Acoustic Sensing: Lossy Compression, Wavefield Separation, and Edge Computing. Journal of Geophysical Research: Machine Learning and Computation, accepted.