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RISCluster

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.


Installation

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.

CUDA-enabled RISCluster (Linux)

  1. Open a terminal and navigate to the directory you would like to download the RISCluster_CUDA.yml environment file.
  2. 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
  3. In terminal, run: conda env create -f RISCluster_CUDA.yml
  4. Once the environment is set up and the package is installed, activate your environment by running conda activate RISCluster_CUDA in terminal.

CPU-based RISCluster (Mac or Linux)

  1. Open a terminal and navigate to the directory you would like to download the RISCluster_CPU.yml environment file.
  2. 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
  3. In terminal, run: conda env create -f RISCluster_CPU.yml
  4. Once the environment is set up and the package is installed, activate your environment by running conda activate RISCluster_CPU in terminal.

Usage

Please refer to the RISWorkflow repository for detailed instructions on how to implement the workflow.


References

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


Author

Project assembled by William Jenkins
wjenkins [@] ucsd [dot] edu
Scripps Institution of Oceanography
University of California San Diego
La Jolla, California, USA

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Deep clustering for seismic signals (icequakes and earthquakes)

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