Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning

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 earthqu..., 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., 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.

地震学数据可为边坡失稳灾害评估提供及时的信息支持，其中落震波形识别颇具挑战——不同事件与台站间的波形差异显著。落震波形并不像天然地震那样具备典型体波，因此科研人员已投入大量精力探索用于自动落震波形检测的特征函数参数。近年来随着深度学习技术的发展，相关算法已可实现从输入数据到目标函数的自动映射。本研究基于多任务学习与迁移学习构建了RockNet模型，该模型包含单台站检测模块与关联模块：前者用于区分落震与天然地震波形；后者则通过整合多台站记录与单台站检测模块的特征表示，判定落震与地震事件的局地发生情况。RockNet在落震与地震波形判别任务中分别取得了0.990与0.981的宏F1分数……。原始地震波形（.sac格式文件）由检波器与DATA-CUBE采集（设备说明详见https://digos.eu/wp-content/uploads/2020/11/2020-10-21-Broschure.pdf），并通过CubeTools工具包（https://digos.eu/seismology/）的`cub2mseed`命令转换为`mseed`格式，转换流程说明详见https://digos.eu/CUBE/DATA-CUBE-Download-Data-2017-06.pdf。

.tfrecord格式文件通过托管于GitHub的脚本生成，并在Zenodo平台获取了永久标识符。请克隆GitHub上的RockNet项目（https://github.com/tso1257771/RockNet），并将下载得到的数据集放置于克隆后的项目目录中。

* SAC软件（Seismic Analysis Code，http://ds.iris.edu/ds/nodes/dmc/software/downloads/sac/102-0/）用于处理与可视化.sac格式文件。
* ObsPy库（https://docs.obspy.org/）用于在Python编程接口中处理与操作.sac格式文件。
* h5py库（https://docs.h5py.org/en/stable/）用于将地震数据与头部信息（即元数据，包含台站与标注信息）存储为HDF5（https://hdfgroup.org/）格式，以提升数据的通用性。
* 联合使用ObsPy与TensorFlow库（https://www.tensorflow.org/），可将.sac格式文件转换为适用于TensorFlow应用的`TFRecord`格式（https://www.tensorflow.org/tutorials/load_data/tfrecord），以适配TensorFlow相关应用。