RockNet

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

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
• Make prediction on hourly SAC files

Installation

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

Clone this repository:

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

Create a new environment

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

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