Autocorrelation of the Seismic Wavefield at Newberry Volcano: Reflections From the Magmatic and Geothermal Systems

Heath, Benjamin; Hooft, E. E.; Toomey, D. R.

doi:10.1002/2017gl076706

Cited by 21 publications

(28 citation statements)

References 38 publications

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“…These interferometry methods are commonly used for measuring surface wave velocities (e.g., Lin et al, ; Shapiro et al, ) and show promise for elucidating discontinuity structure using body waves (Draganov et al, ; Lin et al, ; Poli, Campillo, et al, ; Poli, Pedersen, et al, ). More recently, attempts to estimate Earth's discontinuity structure through the cross correlation of seismic signals at individual stations, or autocorrelation, have had varied degrees of success (Gorbatov et al, , Heath et al, ; Kennett, ; Kennett et al, ; Oren & Nowack, ; Sun & Kennett, ; Tibuleac & von Seggern, ). Though it has been theoretically proven that the autocorrelation of an upgoing wave at a station is equivalent to the reflectivity response of the underlying media (Claerbout, ; Frasier, ; Gorbatov et al, ), most studies prefer to estimate discontinuity structure by deconvolving different seismic components during the coda of direct arrivals from earthquakes to highlight body‐wave conversions (e.g., receiver functions; Langston, ).…”

Section: Introductionmentioning

confidence: 99%

Constraining Crustal Properties Using Receiver Functions and the Autocorrelation of Earthquake‐Generated Body Waves

2019

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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 (VP), and S‐wave velocity (VS). A common way to solve for these parameters is through H‐κ stacking analysis, in which layer thickness and the ratio between VP and VS (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 VS, this constraint allows us to create a system of equations that better quantifies the thickness, VP, and VS 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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Section: Introductionmentioning

confidence: 99%

Constraining Crustal Properties Using Receiver Functions and the Autocorrelation of Earthquake‐Generated Body Waves

2019

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“…Lag time is equivalent to a two-way traveltime. The constructive and destructive interference of noise sources results in a phase shift of the autocorrelation compared to the zero-offset reflection (Heath et al 2018). This phase shift converges to π /2 with increasing frequency (Heath et al 2018).…”

Section: Autocorrelation Methodsmentioning

confidence: 99%

“…Pha . m & Tkalčić 2017;Saygin et al 2017;Heath et al 2018) but the Moho remains ill defined. Although the receiver function method (see e.g.…”

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confidence: 99%

Joint ambient noise autocorrelation and receiver function analysis of the Moho

Mroczek

Tilmann

2021

Geophysical Journal International

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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.

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“…Despite the success of many studies on the processing and/or forward modeling of autocorrelograms (e.g., Becker & Knapmeyer-Endrun, 2018;Clayton, 2018;Daneshvar et al, 1995;Gorbatov et al, 2013;Heath et al, 2018;Ito & Shiomi, 2012;Kennett et al, 2015;Kennett & Sippl, 2018;Nishitsuji et al, 2016;Oren & Nowack, 2017;Pham & Tkalčić, 2017, 2018Romero & Schimmel, 2018;Ruigrok & Wapenaar, 2012;Saygin et al, 2017;Sun & Kennett, 2016Sun et al, 2018;Taylor et al, 2016;Tibuleac & von Seggern, 2012), to our best knowledge, there are no published studies on the inversion of autocorrelograms for mapping major discontinuities in the crust and upper mantle. Here, we investigate the inversion of autocorrelograms for crustal imaging.…”

Section: Introductionmentioning

confidence: 99%

Crustal Imaging With Bayesian Inversion of Teleseismic P Wave Coda Autocorrelation

Qashqai

Saygin

2019

JGR Solid Earth

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The autocorrelation of the seismic transmission response of a layered medium (autocorrelogram), in the presence of a free surface, corresponds to the reflection response. Despite many studies on the imaging of local structures through retrieval and forward modeling of stacked autocorrelograms, there is limited work on the inversion of these data. In this study, we demonstrate that the probabilistic inversion of autocorrelograms is efficient and can be used as an alternative imaging tool when other approaches are not applicable. Here, we calculate autocorrelograms of teleseismic P wave coda recorded on more than 1,200 permanent and temporary seismic stations across Australia and utilize a Bayesian framework to invert these data for crustal imaging. The results show patterns of structures consistent with those seen in previous crustal models constructed from receiver function, seismic reflection, and refraction methods. The new approach can therefore image large‐scale crustal structures comparable to those from other seismological methods.

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Autocorrelation of the Seismic Wavefield at Newberry Volcano: Reflections From the Magmatic and Geothermal Systems

Cited by 21 publications

References 38 publications

Constraining Crustal Properties Using Receiver Functions and the Autocorrelation of Earthquake‐Generated Body Waves

Constraining Crustal Properties Using Receiver Functions and the Autocorrelation of Earthquake‐Generated Body Waves

Joint ambient noise autocorrelation and receiver function analysis of the Moho

Crustal Imaging With Bayesian Inversion of Teleseismic P Wave Coda Autocorrelation

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