RISCluster is a package that implements deep embedded clustering (DEC) and Gaussian mixture model (GMM) clustering of seismic data recorded on the Ross Ice Shelf, Antarctica from 2014-2017. This package is an accompaniment to the paper published in the Journal of Geophysical Research: Solid Earth.
Figure 1. 34-station passive seismic array deployed on the Ross Ice Shelf, Antarctica from 2014-2017.
Pre-requisites: Anaconda or Miniconda
The following steps will set up a Conda environment and install RISProcess, and have been tested on MacOS 11.1 and Red Hat Enterprise Linux 7.9. If you have a CUDA-enabled machine (i.e., not MacOS), you can install the CUDA version of RISCluster. Unfortunately, PyTorch GPU & RAPIDS libraries are not implemented for MacOS, so you will need to install the CPU version if you use a Mac, or if your Linux machine is not CUDA-capable. This package has not been tested on Windows.
- Open a terminal and navigate to the directory you would like to download the RISCluster_CUDA.yml environment file.
- Save RISCluster_CUDA.yml to your computer by running the following:
wget --no-check-certificate --content-disposition https://raw.githubusercontent.com/NeptuneProjects/RISCluster/master/RISCluster_CUDA.yml - In terminal, run:
conda env create -f RISCluster_CUDA.yml - Once the environment is set up and the package is installed, activate your
environment by running
conda activate RISCluster_CUDAin terminal.
- Open a terminal and navigate to the directory you would like to download the RISCluster_CPU.yml environment file.
- Save RISCluster_CPU.yml to your computer by running the following:
a. Mac:curl -LJO https://raw.githubusercontent.com/NeptuneProjects/RISCluster/master/RISCluster_CPU.yml
b. Linux:wget --no-check-certificate --content-disposition https://raw.githubusercontent.com/NeptuneProjects/RISCluster/master/RISCluster_CPU.yml - In terminal, run:
conda env create -f RISCluster_CPU.yml - Once the environment is set up and the package is installed, activate your
environment by running
conda activate RISCluster_CPUin terminal.
Please refer to the RISWorkflow repository for detailed instructions on how to implement the workflow.
William F. Jenkins II, Peter Gerstoft, Michael J. Bianco, Peter D. Bromirski; Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica. Journal of Geophysical Research: Solid Earth, 30 August 2021; doi: https://doi.org/10.1029/2021JB021716
Dylan Snover, Christopher W. Johnson, Michael J. Bianco, Peter Gerstoft; Deep Clustering to Identify Sources of Urban Seismic Noise in Long Beach, California. Seismological Research Letters 2020; doi: https://doi.org/10.1785/0220200164
Junyuan Xie, Ross Girshick, Ali Farhadi; Unsupervised Deep Embedding for Clustering Analysis. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, 2016; https://arxiv.org/abs/1511.06335v2
Project assembled by William Jenkins
wjenkins [@] ucsd [dot] edu
Scripps Institution of Oceanography
University of California San Diego
La Jolla, California, USA