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Constraining Crustal Properties Using Receiver Functions and the Autocorrelation of Earthquake‐Generated Body Waves
Author(s) -
Delph Jonathan R.,
Levander Alan,
Niu Fenglin
Publication year - 2019
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2019jb017929
Subject(s) - coda , geology , seismology , discontinuity (linguistics) , receiver function , deconvolution , autocorrelation , seismic wave , geodesy , lithosphere , mathematical analysis , physics , mathematics , optics , statistics , tectonics
Abstract Passive seismic methods for imaging the discontinuity structure of Earth have primarily focused on differences in vertically and radially polarized energy in the coda of earthquake‐generated body waves (e.g., receiver functions). To convert the timing of scattered wave arrivals to depth, three parameters must be known or inferred: depth or layer thickness ( H ), P ‐wave velocity ( V P ), and S ‐wave velocity ( V S ). A common way to solve for these parameters is through H ‐ κ stacking analysis, in which layer thickness and the ratio between V P and V S ( κ ) is calculated while holding one of the velocity parameters constant. However, this assumption biases estimates of layer properties and leads to uncertainties that are not appropriately quantified. As these results are commonly used as starting models for more complex seismic or geodynamic analyses, these assumptions can propagate much further than the initial study. In this study, we introduce independent observations from body‐wave autocorrelations that can help constrain this underdetermined problem. P ‐wave autocorrelation allows for the recovery of the Moho‐reflected P ‐wave phase from teleseismic earthquakes, which is removed during deconvolution in the calculation of receiver functions. As the Moho‐reflected P ‐wave is independent of V S , this constraint allows us to create a system of equations that better quantifies the thickness, V P , and V S of a layer and produces a more appropriate estimation of associated uncertainties. We apply this to 88 seismic stations that are spatially distributed throughout the United States to obtain a model of crustal variability that is unbiased by a priori assumptions of velocity structure.

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