Joint ambient noise autocorrelation and receiver function analysis of the Moho

Abstract

Summary In the field of seismic interferometry, cross-correlations are used to extract Green’s function from ambient noise data. By applying a single station variation of the method, using auto-correlations, we are in principle able to retrieve zero-offset reflections in a stratified Earth. These reflections are valuable as they do not require an active seismic source and, being zero-offset, are better constrained in space than passive earthquake based measurements. However, studies that target Moho signals with ambient noise auto-correlations often give ambiguous results with unclear Moho reflections. Using a modified processing scheme and phase-weighted stacking, we determine the Moho P wave reflection time from vertical auto-correlation traces for a test station with a known simple crustal structure (HYB in Hyderabad, India). However, in spite of the simplicity of the structure, the auto-correlation traces show several phases not related to direct reflections. Although we are able to match some of these additional phases in a qualitative way with synthetic modelling, their presence makes it hard to identify the reflection phases without prior knowledge. This prior knowledge can be provided by receiver functions. Receiver functions (arising from mode conversions) are sensitive to the same boundaries as auto-correlations, so should have a high degree of comparability and opportunity for combined analysis but in themselves are not able to independently resolve VP, VS, and Moho depth. Using the timing suggested by the receiver functions as a guide, we observe the Moho S wave reflection on the horizontal auto-correlation of the north component but not on the east component. The timing of the S reflection is consistent with the timing of the PpSs-PsPs receiver function multiple, which also depends only on the S velocity and Moho depth. Finally, we combine P receiver functions and auto-correlations from HYB in a depth-velocity stacking scheme that gives us independent estimates for VP, VS, and Moho depth. These are found to be in good agreement with several studies that also supplement receiver functions to obtain unique crustal parameters. By applying the auto-correlation method to a portion of the EASI transect crossing the Bohemian Massif in central Europe, we find approximate consistency with Moho depths determined from receiver functions and spatial coherence between stations, thereby demonstrating that the method is also applicable for temporary deployments. Although application of the auto-correlation method requires great care in phase identification, it has the potential to resolve both average crustal P and S velocities alongside Moho depth in conjunction with receiver functions.

Extracting high‐resolution P‐wave reflectivity of the shallow subsurface by seismic interferometry based on autocorrelation of blast mining signals

“…Our study also demonstrates that only one blast shot with sufficiently high signal-to-noise ratio (SNR) and frequency components can provide high-resolution autocorrelation reflections. In autocorrelation analysis, the source locations are crucial aspects for effectively retrieving reflections beneath the station from stationary phase zones (Aldawood et al, 2019(Aldawood et al, , 2022Mroczek & Tilmann, 2021;Oren & Nowack, 2017;Schuster, 2009;Ito & Shiomi, 2012), although such information is challenging to determine in our situations. To evaluate the stationary phase contribution in the second survey result, we assumed that the reflections from stacking 40 autocorrelations in the first survey are close to the stationary phase.…”

“…deep‐basin imaging or crustal thickness determinations). For instance, the depths of the Moho and crustal thickness are estimated from autocorrelation analysis using frequencies of 0.2–4 Hz (Becker & Knapmeyer‐Endrun, 2018; Gorbatov et al., 2013; Kennett et al., 2015; Mroczek & Tilmann, 2021; Oren & Nowack, 2017; Taylor et al., 2016; Tibuleac & von Seggern, 2012). In contrast, numerous studies have mapped shallower subsurface features such as basins using higher frequency ambient noise.…”

Drivers and collaborative governance of public health emergency response in the context of digital city

“…Different quadrants represent the different relationships between a phenomenon in a province and the neighboring provinces. The four quadrants of local autocorrelation analysis are shown in Figure 5 ( 30 , 31 ).…”

A new approach to construct 3-D crustal shear-wave velocity models: method description and application to the Central Alps

“…Our methodology can be readily extended to future joint inversion taking advantage that other seismological techniques (e.g. traveltime tomography, Ammon et al 2004;Kiselev et al 2008; ambient noise studies, Chen et al 2019;Guo et al 2015;Mroczek and Tilmann 2021;surface wave dispersion, Bodin et al 2012;Chen and Niu 2016;Julià et al 2000;Shen et al 2012) and gravity data (e.g. Basuyau and Tiberi 2011;Scarponi et al 2021;Tiberi et al 2003) are complementary in terms of resolution area and sensitivity to the only receiver functions investigations.…”