CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation

Li, Zhengbo; Zhou, Jie; Wu, Gaoxiong; Wang, Jiannan; Zhang, Gongheng; Dong, Sheng; Pan, L.; Yang, Zhentao; Gao, Limin; Ma, Qingbo; Ren, Hengxin; Chen, Xiaofei

doi:10.1785/0220210042

Cited by 30 publications

(22 citation statements)

References 37 publications

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“…The MFJ method was applied in this study for dispersion extraction, and the related python package can be obtained from Li et al. (2021).…”

Section: Methodology: the Tds And The Frequency‐bessel Transform (F‐j...mentioning

confidence: 99%

“…In addition, replacing the Bessel function with the Hankel function in Equation 4 has been proven to yield a higher signal-to-noise ratio dispersion spectrum (modified F-J (MFJ) method, Forbriger, 2003a;Xi et al, 2021). The MFJ method was applied in this study for dispersion extraction, and the related python package can be obtained from Li et al (2021).…”

Section: The Frequency-bessel Transform (F-j) Methodsmentioning

confidence: 99%

“…The reference model of Shen and Ritzwoller (2016) was also provided by the IRIS (http://ds.iris.edu/ds/ products/emc-us2016/). The package for the F-J method (CC-FJpy, https://doi.org/10.1785/0220210042, Li et al, 2021) and the theoretical dispersion spectrum (https://doi.org/10.5281/zenodo.4574722, Li, 2021) are now available for non-commercial use.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Constraints on Crustal P Wave Structure With Leaking Mode Dispersion Curves

Shi

Chen

2021

Geophysical Research Letters

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Rayleigh and Love wave dispersion curves are known as normal modes and are utilized for inversion. These two waves arrive after the S wave and account for the main energy in all waveforms. However, weaker signals arriving between the P and S waves also have dispersive properties, which are controlled by so-called leaking modes.Studies of leaking mode can be traced back to Somville (1930), who identified and named the PL phase. The PL phase is a train of long-period (>10 s) dispersed waveforms that arrive shortly after the P wave. Oliver and Major (1960) measured PL phase dispersion curves and linked the PL phase with the leaking mode of the crust-mantle waveguide. In addition to the PL phase, some other phases are also considered to have the same wave behavior as the PL phase, which can be explained by the leaking mode (e.g., Pg phase, Shaw & Orcutt, 1984; W phase, Furumura & Kennett, 2018). The leaking mode describes waveguides (leaking waves) whose radiation energy leaks into half-space and phase velocities are higher than the maximum shear velocity (Phinney, 1961). Subsequently, studies have mainly focused on how to calculate leaking modes and simulate leaking waves through leaking modes (e.g., Gilbert, 1964;Haddon, 1984Haddon, , 1986Watson, 1972). However, due to the complexity of leaking modes, limited research has been conducted on imaging underground structures by using the dispersion of leaking waves, and most of the extracted dispersion is below 0.1 Hz for lithospheric research (e.g.,

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“…The MFJ method was applied in this study for dispersion extraction, and the related python package can be obtained from Li et al. (2021).…”

Section: Methodology: the Tds And The Frequency‐bessel Transform (F‐j...mentioning

confidence: 99%

Section: The Frequency-bessel Transform (F-j) Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

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Constraints on Crustal P Wave Structure With Leaking Mode Dispersion Curves

Shi

Chen

2021

Geophysical Research Letters

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“…In this study, data from the LASSO array (Figure 1a), which was deployed in Oklahoma for approximately one month (Dougherty et al., 2016, 2019), were applied to calculate the NCF between each station pair. More than 1,800 vertical (Z)‐component records from nodal seismometers spaced approximately 400 m apart were used to calculate the cross‐correlation functions with the package CC‐FJpy (Li, Zhou et al., 2021). All continuous records were decimated to a 50 Hz sampling frequency and cut into 1‐hr long segments.…”

Section: Retrieval Of Leaking Modesmentioning

confidence: 99%

Multiple Leaking Mode Dispersion Observations and Applications From Ambient Noise Cross‐Correlation in Oklahoma

Shi

Ren

et al. 2022

Geophysical Research Letters

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With the advent of the ambient noise cross-correlation technique (Campillo & Paul, 2003;Shapiro & Campillo, 2004), surface wave imaging was freed from its dependence on the occurrence of earthquakes; as a consequence, surface wave imaging has undergone substantial development in the past two decades (e.g., Shapiro et al., 2005;Yao et al., 2006). By calculating the cross-correlation function, body wave impulse responses (which are much weaker than surface waves) can also be retrieved from earthquake coda waves and continuous ambient noise data (e.g.,

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“…Based on this property of I(ω, k), they developed the F-J method to extract dispersion curves from the I(ω, k) image. In this study, to extract dispersion curves from NCFs, we apply the F-J method and a relevant python package CC-FJpy as described by Li et al (2021).…”

Section: Methodsmentioning

confidence: 99%

A crustal LVZ in Iceland revealed by ambient noise multimodal surface wave tomography

Zhang

Zhang²,

Feng³

et al. 2022

Front. Earth Sci.

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The crustal low-velocity zone (LVZ), an important anomaly found in some regional structures of Iceland, is still absent in the Icelandic average velocity structure due to limitations of tomography methods. Using stations from the HOTSPOT experiment and other supplemental stations throughout Iceland, we apply the frequency-Bessel transform method (F-J method) to extract the first two mode dispersion curves from ambient noise data. We obtain an average S-wave velocity (Vs) model of Iceland down to 120 km depth, where two LVZs at depths of 12–22 km and below 55 km are found. The shallow LVZ, whose rationalities are justified using theoretical dispersion curves of certain models to recover themselves, may improve the understanding of the Icelandic average crust. Furthermore, our model shows better representativeness by comparing travel time residuals of the primary wave between observed and synthetic data predicted using different average velocity models. Based on the variations of the Vs gradient, the Icelandic crust with an average thickness of 32 km is divided into the upper crust (0–10 km), middle crust (10–22 km), and lower crust (22–32 km). The asthenosphere starts from the deeper LVZ at 55 km depth, potentially indicating the relatively concentrated melt in this depth range. In this study, crustal LVZs are revealed both in a volcanic active zone and a non-volcanic zone, which may also suggest the LVZ in the average model has more complex origins than the high-temperature zone beneath the central volcanoes. The prevalent thick-cold crustal model of Iceland, considered to rule out the existence of a broad region of partial melt in the crust, also strengthens the possibility of diverse origins. The variations in petrology may also contribute to the crustal LVZ in the average model.

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CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation

Cited by 30 publications

References 37 publications

Constraints on Crustal P Wave Structure With Leaking Mode Dispersion Curves

Constraints on Crustal P Wave Structure With Leaking Mode Dispersion Curves

Multiple Leaking Mode Dispersion Observations and Applications From Ambient Noise Cross‐Correlation in Oklahoma

A crustal LVZ in Iceland revealed by ambient noise multimodal surface wave tomography

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