Antarctic ice-sheet structures retrieved from P-wave coda autocorrelation method and comparisons with two other single-station passive seismic methods

Abstract

Passive seismology is becoming increasingly popular for glacier/ice-sheet structure investigations in Polar regions. Single-station passive seismic methods including P-wave receiver functions (PRFs), horizontal-to-vertical spectral ratio (HVSR) and a recently proposed autocorrelation method have been used to retrieve glacier/ice-sheet structures. Despite their successful applications, analysis regarding their detection abilities in different glaciological environments has not been reported. In this study, we compare ice thicknesses and vp/vs ratios obtained from the three methods using data collected at GAMSEIS and POLENET/ANET seismic arrays in Antarctica. Ice thickness estimates derived from the three methods are found to be consistent. Comparisons conducted under various model setups, including those involving tiled layers and sedimentary layers, show that the effectiveness of the autocorrelation method is not superior to the PRF method for retrieving ice-sheet structures. The autocorrelation method however can complement other methods as it only requires a single component seismic record.

New Insight Into Antarctic Ice Sheet Properties Using an Improved Teleseismic P‐Wave Coda Autocorrelation Method

“…• A novel method presented to reveal the dip parameters of subglacial topography and estimate ice thickness and v p /v s ratios • Dip parameters of ice-rock interfaces below 65 over-ice seismic stations were well estimated in this study • In situ results prove the improvements of BedMachine, but the ice bed slope might have been underestimated in some regions receiver functions (Chaput et al, 2014;Hansen et al, 2010;Yan et al, 2017), H/V spectral ratio (i.e., the ratio between the Fourier amplitude spectra of the horizontal and the vertical component of microtremors; (Picotti et al, 2017;Yan et al, 2018)), and teleseismic P-wave coda autocorrelations (Phạ m & Tkalčić, 2017;Phạ m & Tkalčić, 2018;Yan et al, 2020). The results of passive seismological methods can also compensate for the data gaps of RES measurments.…”

“…(d) Comparison of v p / v s obtained in this study with that extracted from Phạm and Tkalčić (2018) and Yan et al. (2020).…”

Autocorrelation R2 on Mars

“…Finally, the daily autocorrelograms were stacked to retrieve R 2 surface waves using both linear stacking (LS) and time‐frequency domain phase‐weighted stacking (tf‐PWS) (G. Li et al., 2018; Schimmel & Gallart, 2007). In this study, we empirically chose the power of tf‐PWS as 2, which is a common selection for Earth data analysis (Hable et al., 2019; Haned et al., 2016; Schimmel et al., 2011; Yan et al., 2019; Zeng et al., 2017). Figure S6 in Supporting Information shows tests for different exponents in the tf‐PWS.…”

Constraining Floating Ice Shelf Structures by Spectral Response of Teleseismic P‐Wave Coda: Ross Ice Shelf, Antarctica

“…Because they are not the main focus of this paper, we show the temporal autocorrelation results for the stations RS13 and RS14 in Appendix A. According to Bedmap2 dataset, the ice cap beneath other five stations is less than 1,000 m thick (see Table S1), which is below the resolution power of the earthquake-based autocorrelation method, which performs well for ice thickness larger than around 1,000-1,500 m Yan et al, 2020). This lower threshold allows necessary separation between the reflection pulse and the central peak that is typical in any autocorrelogram.…”

“…In seismology, autocorrelation method is a type of waveform data analysis involving only single‐component recordings to study the crustal and mantle structure immediately beneath the receiver. This class of methods is of special importance in places with minimal access, where a seismic network is hardly feasible, such as in space missions to the Moon (e.g., Nishitsuji et al., 2016), Mars (e.g., Deng & Levander, 2020), or for experiments to the interior of Antarctica (e.g., Phạm & Tkalčić, 2018; Yan et al., 2020). It also serves as an effective tool to use older data from achieves with only vertical recordings (e.g., Kennett et al., 2015).…”