Continuous monitoring of instrumental clock errors at 50 volcanoes in Japan based on seismic interferometry

Abstract

The correct absolute time of a seismogram is an important prerequisite for many seismological processing methods. Recently, seismic interferometry has been applied to continuously estimate instrumental clock errors in certain regions. However, previous studies have focused on estimating clock errors in only one target region for each study, and data processing methods that provide more stable results in different regions have not been explored. This study presents a new method to estimate clock errors that are more stable in various regions based on seismic interferometry with windowed cross-correlation and least absolute deviation regression. The applicability of this method was validated by its application to 50 active volcanoes in Japan. We found clock errors at six stations at five volcanoes between January 2020 and June 2021. The maximum clock errors ranged between 0.2 and 7 s. This value range is significantly larger than the amplitude of the background fluctuation in the estimated values of clock errors, which is less than 0.05 s for most volcanoes. A comparison of different approaches for the estimation of clock error shows that our proposed method that uses windowed cross-correlation and least absolute deviation regression can reduce the effect of waveform changes in seismic ambient noise cross-correlation functions, probably because of noise source fluctuations. Managing the time stamps of data using the method proposed in this study will contribute to data quality assurance and ensure the reliability of analysis using time information in various study regions.

Monitoring velocity change over 20 years at Parkfield

“…et al (2007) pointed out that clock drift causes the artifacts in the travel time estimation obtained with the stacked CFs. Various methods have been proposed to correct the time shift (e.g., Sens-Schönfelder, 2008;Gouédard et al, 2014;Hable et al, 2018;Hirose & Ueda, 2023). In our dataset, we found the time shift in daily CFs associated with the EADB around 2017 in the plot of CCFs (see the CCFs for EADB-SCYB in Supplementary dataset S2), although it seems to be corrected within the same year.…”

Monitoring Velocity Change Over 20 Years at Parkfield

“…(2007) pointed out that clock drift causes the artifacts in the travel time estimation obtained with the stacked CFs. Various methods have been proposed to correct the time shift (e.g., Gouédard et al., 2014; Hable et al., 2018; Hirose & Ueda, 2023; Sens‐Schönfelder, 2008). In our data set, we found the time shift in daily CFs associated with the EADB around 2017 in the plot of CCFs (see the CCFs for EADB‐SCYB in Data Set S2), although it seems to be corrected within the same year.…”