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Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET)

Citation

Mitra, Supriyo (2022), Local earthquake coda waveform from the Jammu And Kashmir Seismological NETwork (JAKSNET), Dryad, Dataset, https://doi.org/10.5061/dryad.n2z34tmzz

Abstract

This dataset contains local earthquake coda waveform and pre-signal noise waveforms from the Jammu And Kashmir Seismological NETwork (JAKSNET), a joint endeavor between the Indian Institute of Science Education and Research Kolkata (IISER-K), Shri Mata Vaishno Devi University (SMVD) and the University of Cambridge, UK. The network was initiated in July 2013 and comprised 24 broadband seismograph systems deployed across the J&K Himalaya. A total of 696 vertical component coda waveforms, from 121 small-to-moderate local earthquakes of magnitude between 3.0 and 5.5, within the epicentral distance of 200 km are provided in this database. Pre-signal representative noise from 22 stations, which recorded these earthquakes, have been provided for computing signal-to-noise ratio for the coda signal. The waveform data is sampled at 100 samples per second (sps),  are corrected for instrument response, and filtered in the frequency band of 0.02 to 30 Hz. Coda waveforms start from twice the S-wave arrival time and are of 90 s duration. This data has been used to compute the seismic coda-wave attenuation of the Jammu and Kashmir Himalaya. The manuscript is submitted for review in JGR Solid Earth and this dataset complements the manuscript. 

Methods

The data was recorded by the JAKSNET experiment between 2013 and 2018. The waveform data is sampled at 100 samples per second (sps), are corrected for instrument response and filtered in the frequency band of 0.02 to 30 Hz. Coda waveforms start from twice the S-wave arrival time and are of 90 s duration. The representative noise is of 10 s duration before the P-wave arrival of the earthquake. 

Funding

Ministry of Earth Sciences, Award: MoES/P.O.(Seismo)/1(315)/2017

Royal Society, Award: ICA\R1\180234 - International Collaboration Awards 2018