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metadata 
configs:
  - config_name: default
    data_files:
      - split: test
        path: sample.csv
size_categories:
  - 100B<n<1T
tags:
  - Seismology
language:
  - en
pretty_name: CWA

CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research

Dataset Description

This dataset includes a larger number of seismic events, especially high-magnitude. A comprehensive set of events collected by the Central Weather Bureau in Taiwan. The CWA benchmark features over 40 attributes and ∼500,000 seismograms, providing valuable data labels for various seismology-related tasks. In the future, we will keep updating the dataset to ensure its relevance and utility.

The entire dataset are released in Seisbench, the dataset can be loaded properly by the following:

  1. Install Seisbench & HuggingfaceHub
$ git clone https://github.com/seisbench/seisbench.git

$ cd seisbench
$ pip install .
$ pip install --upgrade huggingface_hub
  1. Load the CWA dataset
import seisbench.data as sbd

basepath = <path to metadata.csv and chunks.hdf5>
cwa = sbd.CWA(download_kwargs={'basepath': basepath})
print(cwa.metadata.head())
  • Some parameters customizing the CWA dataset
    • (Bool) merge: Whether to load the merged version (CWASN + TSMIP + Noise), default=True.
    • (String) subset: Specify the seismographic network (CWASN, TSMIP, All), default="All".
    • (List) train_year: The range of years used for the training set, default=[2011, 2018].
    • (List) dev_year: The range of years used for the development set default=[2019].
    • (List) test_year: The range of years used for the testing set, default=[2020, 2021].
import seisbench.data as sbd

merge = False
subset = "CWASN"
train_year = [2012, 2017]
dev_year = [2018, 2020]
test_year = [2021, 2021]

basepath = <path to the directory of metadata.csv and chunks.hdf5>
cwa = sbd.CWA(subset=subset, 
              merge=merge, 
              download_kwargs={'basepath': basepath},
              train_year = train_year,
              dev_year = dev_year,
              test_year = test_year
)

print(cwa.metadata.head())

Overview of the CWA

Attribute CWASN TSMIP Noise
Events 5,849 12,306 x
Traces 1,237,272 98,665 806,914
p-picks 1,237,272 98,665 x
s-picks 821275 98,665 x
sampling rate 100 200 100
Multi-event 196965 0 x
Measurement cm/s2, cm/s cm/s2 cm/s2, cm/s
Time period 2012-2021 2011-2020 2012-2021

Number of trace

  • Categorized into 5 levels by five different pickers
  • Noise samples are also provided
  • Number of traces
    Level Meaning CWASN TSMIP
    0 The 3-component data are all zeros 45,244 9,620
    1 None of the pickers predicted correctly 174,720 3,046
    2 The seismic signal is interrupted 714 0
    3 1 or 2 pickers predicted correctly 192,889 3,487
    4 More than 3 pickers predicted correctly 823,705 82,512

File size

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Noise Total
1.7G 40 G 46 G 41 G 53 G 57 G 41 G 83 G 44 G 43 G 50 G 176 G 836 G

Station in CWA

image/jpeg

Event in CWA

image/jpeg

Acknowledgement

This work was supported by grants from the Central Weather Administration (CWA) of Taiwan and the National Science and Technology Council of Taiwan (NSTC 112-2636-E-011-002, NSTC 112-2628-E-011-008-MY3, and NSTC 113-2640-B-002-005). In addition, the authors acknowledge the support of the “Empower Vocational Education Research Center” at the National Taiwan University of Science and Technology through the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The authors would also like to express our gratitude to the National Center for High-performance Computing of the National Applied Research Laboratories (NARLabs) in Taiwan for providing computational and storage resources.

Citation 

@misc{tang2024CWA,
  title={The CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research},
  author={Kuan‐Wei Tang, Kuan‐Yu Chen, Da‐Yi Chen, Tai‐Lin Chin, and Ting‐Yu Hsu},
  howpublished={https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220230393/650394/The-CWA-Benchmark-A-Seismic-Dataset-from-Taiwan?redirectedFrom=fulltext}, 
  note={Early publication on Seismological Research Letters},
  year={2024},
}