The data publication contains a dataset for fast assessment of earthquakes based on seismic waveforms. The dataset encompasses Northern Chile. Due to the large scale of the dataset, it is intended for use in machine learning. A similar dataset for chile has been published as Münchmeyer et al. (2020). A similar dataset for Japan can be obtained using the scripts at https://github.com/yetinam/TEAM
The datasets are provided as a hdf5-file (Folk et al. 2011), a hierachical file format. Source code for reading and processing the data is available at https://github.com/yetinam/TEAM. The hdf5-file contains the two groups “metadata” and “data” that are described below. These groups are the hdf5-analog of folders in a file system.
TEAM, the Transformer Earthquake Alerting Model is a deep learning model for real time estimation of peak ground acceleration (TEAM), earthquake magnitude and earthquake location (TEAM-LM). This software package contains the joint implementation of both TEAM and the derivative TEAM-ML, as well as the scripts for training and evaluating these models. In addition, it contains scripts to download an early warning datasets for Japan and implementations of baseline approaches for the estimation of earthquake magnitude and peak ground acceleration. TEAM is implemented in Python.
TEAM and TEAM-ML have a variety of configuration parameters that are documented in the README. These configurations need to be provided in JSON format. In addition, multiple example configuration files are provided in the subdirectories pga_configs and magloc_configs. Please note that this implementation is intended for research purpose only. Production use is discouraged.
The data publication contains a dataset for fast assessment of earthquakes and early warning based on seismic waveforms. The dataset encompasses Italy and surrounding refions. Due to the large scale of the dataset, it is intended for use in machine learning. A similar dataset for Japan, with the same specifications as the one provided in this data publications, can be obtained using the scripts at https://github.com/yetinam/TEAM