Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Ventosa, Sergi; Schimmel, Martin; Stutzmann, É.

doi:10.1093/gji/ggx284

Cited by 57 publications

(41 citation statements)

References 85 publications

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“…To do so, instrument response Baker, 1979;Kasmarek & Robinson, 2004). We apply 1-bit normalization and autocorrelate 6-min segments (with 50% overlapping window) from a daily record and compute phase-weighted stacks (Ventosa et al, 2017). We apply 1-bit normalization and autocorrelate 6-min segments (with 50% overlapping window) from a daily record and compute phase-weighted stacks (Ventosa et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…To do so, instrument response removed data are demeaned, detrended, filtered between 0.01 and 8 Hz, and spectrally whitened. We apply 1-bit normalization and autocorrelate 6-min segments (with 50% overlapping window) from a daily record and compute phase-weighted stacks (Ventosa et al, 2017). Daily changes in seismic velocity (light gray lines) are found by grid search from −0.02 to 0.02 of dv/v that maximizes the correlation coefficient between the stretch-applied autocorrelograms and the reference autocorrelation record.…”

Section: Methodsmentioning

confidence: 99%

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Groundwater Variations From Autocorrelation and Receiver Functions

Kim

Lekić

2019

Geophysical Research Letters

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Using a 20-year continuous broadband record and two independent single-station techniques -ambient noise autocorrelation and receiver functions-we document a relationship between subsurface seismic response and groundwater levels (GWLs) in the Gulf Coast Aquifer System of southern Texas. We find that a surge of GWL following three consecutive hurricanes and documented at an adjacent monitoring well is accompanied with changes in receiver function power spectra and ambient noise autocorrelations. Using a simple physical model, we show that GWL changes should affect P-(V P ) more strongly than S-wave (V S ) velocities, consistent with our observations and previous ones based on inter-station correlations. Agreement between receiver function and ambient noise analyses shows that both can be used to reliably estimate temporal changes in subsurface properties on long timescales. Due to their sensitivity to V P , single-station techniques respond more strongly to GWL changes, making them useful for characterizing and monitoring aquifer systems.Plain Language Summary Even though groundwater in large aquifer systems is often relied upon as a water source, it is one of the least well-known components of the hydrologic cycle. Here, we document a relationship between ground vibrations recorded by a seismometer and the groundwater levels in the Gulf Coast Aquifer System of southern Texas. We find that a surge of groundwater level following three consecutive hurricanes is accompanied with changes in the characteristics of seismic vibrations set-up in the shallow subsurface. Characteristics of vibrations due to both ambient noise and from earthquake show similar signals. Using a simple physical model, we show how groundwater level changes the properties of the subsurface to explain the seismic observations. Our work illustrates how even a single seismometer can be used to monitor groundwater level changes, which is particularly useful in aquifer systems lacking monitoring wells, and can complement other geophysical measurements from gravity or uplift/subsidence sensed data from satellites.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Groundwater Variations From Autocorrelation and Receiver Functions

Kim

Lekić

2019

Geophysical Research Letters

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“…The seasonal variations of the secondary microseism during the whole 2014 are described through the study of surface-wave arrivals on the stacked CC traces for each month. According to the literature (Buffoni et al 2018;Kimman et al 2012;Schimmel et al 2017;Ventosa et al 2017), only the CC traces along Z component are shown from all the traces calculated along the Z, N and E components.…”

Section: Seasonal Changes Of the Secondary Microseism Investigated Bymentioning

confidence: 99%

Analysis of the primary and secondary microseisms in the wavefield of the ambient noise recorded in northern Poland

Lepore

Grad

2018

Acta Geophys.

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Seasonal changes of the primary and secondary microseisms were analysed in the wavefield of the ambient noise recorded during the entire 2014 at the ''13 BB star'' array located in northern Poland, composed of thirteen, symmetrically arranged, broadband seismic stations. To that, spectral analysis, seismic interferometry, surface scalar wind speed distribution, and beamforming were used. Spectral analysis allowed to observe that a splitting of the secondary microseism peak was present in winter and autumn, and that the primary microseism peak was visible in spring, summer and autumn. Using seismic interferometry, the long-term characteristics of the noise wavefield were recognized. The seasonal variations of the secondary microseism source were described by means of the analysis of the surface scalar wind speed for each month. The splitting of the secondary peak was attributed to the interaction of a strong wind blowing from the North Sea with a weak wind blowing from the Baltic Sea. The seasonal variations of the primary microseism peak were characterized through the frequency-domain beamforming. The peak was identified during spring, summer and autumn, when the generated wavefield was coming from the Baltic Sea. The velocity of the wavefield was evaluated within the 2.0-5.0 km/s range. The described mechanism of generation of the microseisms, based on the interaction of the nearby winds, was found to be consistent with the models reported in the literature.

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“…Zeng and Thurber (2016) present a GPU implementation of the time-frequency phase-weighted stack (tf-PWS; Schimmel et al, 2011), which is up to 20 times faster than equivalent CPU implementations using the fast Fourier transform (FFT). Ventosa et al (2017) reduce computational cost of tf-PWS by introducing the timescale phase-weighted stack (ts-PWS) that uses the wavelet transform for the time-frequency representation, and propose unbiased phase coherence and two-stage ts-PWS to improve convergence speed and quality of the extracted signals. The unbiased phase coherence estimator contributes to increase noncoherent noise attenuation because it equals to 0 if the signals are totally incoherent.…”

Section: Introductionmentioning

confidence: 99%

Towards the Processing of Large Data Volumes with Phase Cross‐Correlation

Ventosa

Schimmel

Stutzmann

2019

Seismological Research Letters

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Interstation correlation is the basic operation in seismic noise and coda-wave interferometry for signal extraction in imaging and monitoring applications. Conventional cross-correlations evaluate the similarity between two signals along lag time, and they are efficiently computed by the fast Fourier transform (FFT), valuable to manage the large data volumes that ambient noise applications demand. The phase cross-correlation (PCC) method contributes to increase convergence, a key issue in seismic ambient noise imaging and monitoring; however, it is much more computationally demanding. PCC evaluates similarity by subtracting the modulus of the sum and difference of the instantaneous phase of two signals. We introduce solutions to dramatically reduce the high-computational cost of PCC. We show that PCC can be rewritten as a complex cross-correlation and computed by the FFT when the moduli are raised to the power of 2, and we demonstrate PCC can improve waveform coherence and increase convergence compared with the default processing flow of 1-bit amplitude normalization and standard cross-correlation. Moreover, we develop a graphics processing unit implementation to accelerate computations when using powers other than 2 and particularly when using the power of 1. Finally, we extract Rayleigh-and body-wave signals from many years of data from seismic stations distributed worldwide using PCC without a significant increase in computational cost compared with conventional cross-correlation.

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Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Cited by 57 publications

References 85 publications

Groundwater Variations From Autocorrelation and Receiver Functions

Groundwater Variations From Autocorrelation and Receiver Functions

Analysis of the primary and secondary microseisms in the wavefield of the ambient noise recorded in northern Poland

Towards the Processing of Large Data Volumes with Phase Cross‐Correlation

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