https://github.com/blainerothrock/seisml

A pytorch framework for building deep learning models with seismic data
https://github.com/blainerothrock/seisml

detection paper seismic-data

Last synced: 2 months ago
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A pytorch framework for building deep learning models with seismic data

• Host: GitHub
• URL: https://github.com/blainerothrock/seisml
• Owner: blainerothrock
• Created: 2020-04-22T16:40:02.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-22T13:51:14.000Z (almost 2 years ago)
• Last Synced: 2025-03-18T17:59:57.225Z (2 months ago)
• Topics: detection, paper, seismic-data
• Language: Jupyter Notebook
• Homepage:
• Size: 19.6 MB
• Stars: 10
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# SeisML

![Test](https://github.com/blainerothrock/seisml/workflows/Test/badge.svg)

[![codecov](https://codecov.io/gh/blainerothrock/seisml/branch/master/graph/badge.svg)](https://codecov.io/gh/blainerothrock/seisml)

a deep learning experimentation framework for seismic data.

Created to fullful of research practicum for Masters in Artificial Intelligence @ 

Northwestern University. Advised by [Suzan van der Lee](https://www.earth.northwestern.edu/our-people/faculty/van-der-lee-suzan.html).

The main goal of this project is to explore a project structure that allowed for rapid experimentation, with hyperparameter reporting for use by seismology researchers.

The codebase includes a reproduction and generalization of the paper [Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm](https://pubs.geoscienceworld.org/ssa/srl/article-abstract/91/2A/901/579921/Automating-the-Detection-of-Dynamically-Triggered?redirectedFrom=fulltext). The goal was to extend the research the research to dynamically triggered tremors. In addition, explored new experiments using the dataset introduced [Generalized Seismic Phase Detection with Deep Learning](https://pubs.geoscienceworld.org/ssa/bssa/article/108/5A/2894/546740/Generalized-Seismic-Phase-Detection-with-Deep?casa_token=XTBzGohrUBoAAAAA:Y2OHyF4u4VknJXTjPxs6lPjA5rW-2yIkjEVSSvJDSP4GoSzxZu7-GeN9F7nE0pI663-EqpU) and unsupervised deep clustering with data from SEIS, a seismic instrument deployed as part of the InSight Mars lander, in an attempt to automate Martian seismic event classification.

Developing a baseline phase detection experiment using work and data from the paper Generalized Seismic Phase Detection with Deep Learning. • Experimented with unsupervised deep clustering with data from SEIS, a seismic instrument deployed as part of the InSight Mars lander, in an attempt to automate Martian seismic event classification.

## Active Experiments

* [Triggered Earthquake Detection](experiments/triggered_earthquake/README.md)

* [Triggered Tremor Detection](experiments/triggered_tremor/README.md)

## Supported Models

* [Deep Convolutional Network](seisml/networks/convnet.py)

* [Dialated Convolutional Network for Deep Clustering](seisml/networks/dilated_convolutional.py)

## Datasets

* [Triggered Earthquake](experiments/triggered_earthquake)

* [Triggered Tremor](experiments/triggered_tremor)

## Repo Structure

* `experiments/`

    - the directory that contains code specific to a experiment utilizing seisml components.

    - This includes, model training code, inference and hyperparameter configuration 

* `playground/`

    - A place for experiments in progress, example code, data exploration, etc.

* `seisml`

    - The root directory for the framework (python package)

    * `core`

        - contains universal components

        - `transforms`

            - model after transforms in `torchvision`, used for preprocessing steps before feeding data into a model

    * `datasets`

        - build in Pytorch datasets for use in experiments

    * `metrics`

        - calculations modeling

    * `networks`

        - custom Pytorch models

    * `utility`

        - universal helper methods

* `tests`

    - Pytest unit tests for seiml code. These test run in continuous integration and utilize limited resources

* `tests-non-ci`

    - Pytest test that are more specific to experiement debugging. Not intended for continuous integration because of the amount of resources used.

* `environment.yml`

    - conda environment file for CI and use

    

    

## Installation

* clone the repository and `cd` to root

* create a new Anaconda environment

```shell script

conda create env -f environment.yml

``` 

* run a experiments following the `README.md` found in the specific experiment directory. 

## Inspiration 

The inspiration and starting codebase for this model is from the Seismological Research Letters paper 

*[Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm](https://pubs.geoscienceworld.org/ssa/srl/article-abstract/91/2A/901/579921/Automating-the-Detection-of-Dynamically-Triggered)*

([original codebase](https://github.com/interactiveaudiolab/earthquakes)). 

* Steps to reproducing this paper can be found [here](experiments/triggered_earthquake/README.md).