Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
Data files
Jan 04, 2023 version files 5.56 GB
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README.md
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RockNet_data.zip
Abstract
Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.
Methods
The raw seismic waveforms (.sac files) were recorded by the Geophones and DATA-CUBE (https://digos.eu/wp-content/uploads/2020/11/2020-10-21-Broschure.pdf) and converted to `mseed` format with `cub2mseed` command (https://digos.eu/CUBE/DATA-CUBE-Download-Data-2017-06.pdf) of the CubeTools utility package (https://digos.eu/seismology/).
The .tfrecord files are generated using the scripts host on Github and a permanent identifier to Zenodo.
Usage notes
Please clone the RockNet project on Github (https://github.com/tso1257771/RockNet) and put the downloaded dataset under the cloned directory.
*The SAC software (Seismic Analysis Code, http://ds.iris.edu/ds/nodes/dmc/software/downloads/sac/102-0/) is used to process and visualize SAC files.
*The ObsPy (https://docs.obspy.org/) package is used to process and manipulate SAC files in the python interface.
*The h5py package (https://docs.h5py.org/en/stable/) is used to store seismic data and header information (i.e., metadata, including station and labeled information) in HDF5 (https://hdfgroup.org/) format for broader usages.
*The ObsPy and TensorFlow packages (https://www.tensorflow.org/) are collaboratively used to convert the SAC files into the `TFRecord` format (https://www.tensorflow.org/tutorials/load_data/tfrecord) for TensorFlow applications.