Phase-matched filters: Application to the study of Rayleigh waves

Herrin, Eugene; Goforth, Tom

doi:10.1785/bssa0670051259

Cited by 181 publications

(17 citation statements)

References 1 publication

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“…Thus, for each station pair, nine cross‐correlation functions are calculated (Z‐Z, Z‐E, Z‐E, N‐Z, N‐N, N‐E, E‐Z, E‐N, E‐E). We then measure phase velocity dispersion curves from the symmetrical component of the Z‐Z cross‐correlation functions for all station pairs by an automatic frequency‐time analysis (e.g., Dziewonski et al., 1969; Herrin & Goforth, 1977; Levshin & Ritzwoller, 2001). With those interstation dispersion curves, we adopt the Fast‐Marching Method (Kennett et al., 1988; Rawlinson & Sambridge, 2004a, 2004b) to perform the tomographic inversion at periods between 6 and 40 s. The tomographic results are shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Sedimentary and Crustal Structure of the Western United States From Joint Inversion of Multiple Passive Seismic Datasets

Bidgoli

Chen

et al. 2022

JGR Solid Earth

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The modern tectonic framework of the western US is dominated by a long-lived convergent margin, associated with the subduction of the Farallon plate beneath North America, and its transition, since the Miocene, to a transform boundary, made up of the San Andreas fault and its system of right-lateral strike-slip faults (Atwater, 1970;Atwater & Stock, 1998;Severinghaus & Atwater, 1990). Within this geologically complex framework, a few large and a number of small basins developed, covering much of California and Nevada in sediments. For example, the large Great Valley forearc basin in California was created coevally with the basements of the Coast Ranges and the Sierra Nevada during the subduction (

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Section: Methodsmentioning

confidence: 99%

Sedimentary and Crustal Structure of the Western United States From Joint Inversion of Multiple Passive Seismic Datasets

Bidgoli

Chen

et al. 2022

JGR Solid Earth

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“…A phase-matched filter, like the one Herrin and Goforth used to detect Rayleigh waves [17], ignores the amplitude term of H( f ):…”

Section: Phase-matched Filtermentioning

confidence: 99%

“…In contrast, a phase-matched filter does the same thing, but uses only the phase information of the template signal. In seismic signal processing, a phase-matched filter is often used to detect weak surface waves [17,18] because the phase of the dispersion of a given path is easier to be predicted than the amplitude of the dispersion. Recently, Geroski and Dowling [19] reported an MFP-style localization algorithm termed phase-only matched autoproduct processing (POMAP) for passive source localization in the deep ocean using array signals.…”

Section: Introductionmentioning

confidence: 99%

“…This study explores the application of a phase-matched filter [17] on single-hydrophone ranging and aims to provide an imperfect matching scheme that gives full consideration to robustness, computational efficiency, and also convenience for applications. The pursuit of the conventional ranging scheme MIR, which is based on a matched filter, is a search for (the most similar) replica of the received channel impulse response in both amplitude terms and phase terms.…”

Section: Introductionmentioning

confidence: 99%

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Single Hydrophone Passive Source Range Estimation Using Phase-Matched Filter

Liang

Zhou

Yang

2022

JMSE

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Algorithms working in mode space instead of directly matching the received complex sound pressure were developed to improve computational efficiency and robustness, but these algorithms may be inconvenient to apply in practice because manual operations are often inevitable when performing modal filtering. Based on a phase-matched filter, an imperfect matching scheme named the modal phase based matched impulse response (MP-MIR) is proposed to estimate the source range rapidly and conveniently with a single hydrophone. The field to be matched is still the received complex sound pressure. The replica field is a sum of several “phase” modes, which can be efficiently and conveniently synthesized merely with the horizontal wavenumbers of normal modes and the source–receiver range. The effectiveness of the proposed MP-MIR was demonstrated in localizing 84 emissions along a weakly range-dependent track at ranges of 2.54–20 km in the South China Sea. Although it was found, from cross-correlation coefficients, that the received signals showed strong variation even between adjacent emissions, MP-MIR outperformed the classical matched impulse response (MIR) with a lower standard deviation in most cases, demonstrating good robustness and potential for practical applications.

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“…Surface wave dispersion property (phase or group velocities at different frequencies) can be measured in different ways depending on different acquisition systems. In the case of a single station or a sparse receiver array, as is often the case in seismology, the dispersion property can be measured by using the frequency-time analysis (FTAN) method [10,22,21,17,38,36,20,2]. In FTAN one constructs a frequency-time domain envelope image for each seismic trace by using a set of narrow bandpass Gaussian filters, and measures the group velocity using the arrival time of the maximum envelope at each frequency.…”

Section: Introductionmentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang,

Zheng,

Curtis

2022

Preprint

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Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.

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Phase-matched filters: Application to the study of Rayleigh waves

Cited by 181 publications

References 1 publication

Sedimentary and Crustal Structure of the Western United States From Joint Inversion of Multiple Passive Seismic Datasets

Sedimentary and Crustal Structure of the Western United States From Joint Inversion of Multiple Passive Seismic Datasets

Single Hydrophone Passive Source Range Estimation Using Phase-Matched Filter

Surface wave dispersion inversion using an energy likelihood function

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