Feasibility of Estimating Parameters and Enhancing Reflections of Dipping Moho From Teleseismic P Wave Coda Autocorrelation

Abstract

During the last decade, there have been an increasing number of applications of teleseismic P wave coda autocorrelation in lithospheric imaging, including studies on the mid-lithosphere discontinuity (Sun & Kennett, 2017), Moho (Ruigrok & Wapenaar, 2012), subducting slabs (Nishitsuji et al., 2016), basin structures (Plescia et al., 2021), and Antarctic ice sheet (Phạ m & Tkalčić, 2018), among others. Moreover, the coda autocorrelation method also contributes to the study of Mars (Knapmeyer-Endrun et al., 2021). Teleseismic P-wave coda autocorrelation can be traced back to the hypothesis of Claerbout (1968). The P-wave reflections of horizontally stratified acoustic media can be retrieved from the autocorrelation of the plane-wave transmission response. By directly extending the hypothesis to global-scale seismology, Ruigrok and Wapenaar (2012) proposed a global-phase seismic interferometry method (abbreviated as GloPSI). This method extracts P-wave reflections from the autocorrelation of global phases and coda. GLoPSI has been successfully applied to the field data from the Hi-CLIMB experiment, which revealed the P-wave reflectivity below the Himalayas and Tibet. The principle of GloPSI is identical to the original setting of Claerbout (1968). In theory, it can obtain good imaging results; however, there are numerous artifacts. This is mainly due to insufficient events available for GloPSI (epicentral distances >120°). As the individual autocorrelogram contains noise resulting from the source and raypath, sufficient autocorrelograms must be available to efficiently suppress the noise via stacking. Another factor may be that this method ignores move out correction before stacking, which can align the reflections extracted from different events.

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

“…For the velocity analysis, there are two other implicit variables: dip angle and azimuth of the interface. Using a grid search method, a set of dip parameters that maximize the PP reflected energy in the velocity spectrum can be obtained (Zhou et al., 2022). Here, we generalize the method to the autocorrelation of the radial component.…”

“…As the derivation of Tan et al (2018) and Zhou et al (2022), the arrival time formulae of PP and SS reflections share a similar format: where H is the depth of the target interface beneath the seismic station; m represents different wave modes, which could be P wave or S wave; ϕ is the dip angle of the target interface; n is the unit normal vector of the target interface; M R is the slowness vector of the reflected wave for different wave modes; t mm is the arrival time of the PP reflection (t pp ) or SS reflection (t ss ).…”

“…Based on the velocity analysis method (Zhou & Zhang, 2021), Zhou et al. (2022) proposed a method to estimate parameters and enhance PP reflections of dipping Moho from teleseismic P‐coda autocorrelation. The method is meaningful for imaging and interpretation in areas that contain dipping interfaces.…”