Test of P-wave receiver functions for a seismic velocity and gravity model across the Baikal Rift Zone. Issue 1 (25th August 2022)

• Record Type: Journal Article
• Authors: Zhou, Zhipeng; Thybo, Hans; Tang, Chi-Chia; Artemieva, Irina; Kusky, Timothy
• Is Part Of: Geophysical journal international. Volume 232:Issue 1(2023)
• Page Start: 176
• Page End: 189
• Publication Date: 2022-08-25
• Subjects: Composition and structure of the continental crust -- Gravity anomalies and Earth structure -- Crustal imaging -- Crustal structure; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac335
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: The seismic receiver function (RF) technique is widely used as an economic method to image earth's deep interior in a large number of seismic experiments. P -wave receiver functions (RFs) constrain crustal thickness and average Vp / Vs in the crust by analysis of the Ps phase and multiples (reflected/converted waves) from the Moho. Regional studies often show significant differences between the Moho depth constrained by RF and by reflection/refraction methods. We compare the results from RF and controlled source seismology for the Baikal Rift Zone by calculating 1480 synthetic RFs for a seismic refraction/reflection velocity model and processing them with two common RF techniques [H–κ and Common Conversion Point (CCP) stacking]. We compare the resulting synthetic RF structure with the velocity model, a density model (derived from gravity and the velocity model), and with observed RFs. Our results demonstrate that the use of different frequency filters, the presence of complex phases from sediments and gradual changes in the properties of crustal layers can lead to erroneous interpretation of RFs and incorrect geological interpretations. We suggest that the interpretation of RFs should be combined with other geophysical methods, in particular in complex tectonic regions and that the long-wavelength Bouguer gravity anomaly signal may provide effective calibration for the determination of the correct Moho depth from RF results. We propose and validate a new automated, efficient method for this calibration.