Earthquake detection on fiber-optic data using machine learning

Feb 10, 2022 1 min read

Earthquake detection via fiber optic cables using deep learning

Author: Fantine Huot

Getting started

Update the submodules

After cloning the repository, run the following commands to initialize and
update the submodules.

git submodule init
git submodule update

Requirements

You can run the project from an interactive bash session within the provided
Docker container:

docker run --gpus all -it fantine/ml_framework:latest bash

If you do not have root permissions to run Docker, Singularity might be a good alternative for you. Refer to
containers/README.md for more details.

Folder structure

bin: Scripts to run machine learning jobs.
catalog: Earthquake and background noise database.
config: Configuration files.
containers: Details on how to use containers for this project.
das_reader: Legacy code for reading SEGY files.
docs: Documentation.
hptuning: Hyperparameter tuning for machine learning.
log: Directory for log files.
ml_framework: Machine learning framework.
preprocessing: Data preprocessing steps.
processing_utils: Processing utility functions.
tfrecords: Utility functions for converting input files to TFRecords.

Set the datapath for the project

Set the DATAPATH variable inside config/datapath.sh to the data or scratch directory
to which you want write data files.

Create and run a machine learning model

This repository provides a parameterized, modular framework for creating and
running ML models.

Detection Machine Learning Deep Learning

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