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rocknet

Rockfall and earthquake detection and association via multitask learning and transfer learning

https://github.com/tso1257771/rocknet

Science Score: 49.0%

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    DOI references
    Found 3 DOI reference(s) in README
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    Links to: zenodo.org
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    Low similarity (10.4%) to scientific vocabulary

Keywords

deep-learning detection-model earthquakes multitask-learning rockfall seismology transfer-learning
Last synced: 6 months ago · JSON representation

Repository

Rockfall and earthquake detection and association via multitask learning and transfer learning

Basic Info
  • Host: GitHub
  • Owner: tso1257771
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 74.8 MB
Statistics
  • Stars: 12
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
deep-learning detection-model earthquakes multitask-learning rockfall seismology transfer-learning
Created over 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md

DOI

RockNet

Rockfall and earthquake detection and association via multitask learning and transfer learning.
Our preprint article can be found here.

2020-03-28T13:41:20 00

Complete dataset

Please also download the complete data hosted on Dryad (https://doi.org/10.5061/dryad.tx95x6b2f), follow the instructions and place the files to specified directories in this repository.

Summary

Installation

To run this repository, we suggest Anaconda and pip for environment managements.

Clone this repository:

bash git clone https://github.com/tso1257771/RockNet.git cd RockNet

Create a new environment

bash conda create -n rocknet python==3.7.3 anaconda conda activate rocknet pip install --upgrade pip pip install -r ./requirements.txt --ignore-installed

Make prediction on hourly SAC files

In this repository, we provide two hourly three-component seismograms as examples for making predictions on continuous data.
The data seismograms were collected in the Luhu tribe, Miaoli county, Taiwan.

Enter the directory ./Luhu_pred_ex
bash cd ./Luhu_pred_ex 1. Run script Luhu_pred_ex/P01_net_STMF.py to generate the output functions (also in SAC format) in Luhu_pred_ex/net_pred from the provided SAC files Luhu_pred_ex/sac
python P01_net_STMF.py 2. Plot some prediction results
python P02_plot.py

Owner

  • Login: tso1257771
  • Kind: user

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Dependencies

requirements.txt pypi
  • h5py ==3.1.0
  • matplotlib ==3.3.2
  • numpy ==1.19.5
  • obspy ==1.2.2
  • pandas ==1.3.5
  • scipy ==1.5.2
  • tensorflow ==2.5.3
  • tensorflow_addons ==0.11.2