Ambient noise multimode Rayleigh and Love wave tomography to determine the shear velocity structure above the Groningen gas field

Chmiel, Małgorzata; Mordret, Aurélien; Boué, A.; Boué, Pierre; Brenguier, Florent; Lecocq, Thomas; Courbis, Roméo; Hollis, Dan; Campman, Xander; Romijn, R.; VanderVeen, W.; Arndt, Nicholas; Beauprêtre, Sophie; Lynch, Richard A.; Gradon, Chloé

doi:10.1190/segam2019-3216087.1

Cited by 14 publications

(22 citation statements)

References 10 publications

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“…are located below 800 m in the faster layer of the Chalk Group formation, while smaller variations are observed in the shallower North Sea Group sediments (Fig. 9 Kruiver et al 2017;Chmiel et al 2019). The decrease of the amplitude below 1600 m is mostly due to the disappearance of the sensitivity of the first overtone at these depths and we cannot rule out large velocity changes deeper in the subsurface.…”

Section: Resultsmentioning

confidence: 77%

“…2b). The fundamental mode exhibit large amplitudes at frequencies lower than 0.5 Hz (Chmiel et al 2019) but at these frequencies, the wavelengths become large compared to the size of the array which impedes the measurement of the time shift and reduces the distance range on which the linear regression can be performed. As shown in Figure 4, the time-shifts data do not exhibit a linear trend for the whole range of distances.…”

Section: Resultsmentioning

confidence: 99%

“…1). The array was deployed for 30 days in 2017 from February 11 (day 42) to March 12 (day 71) and laid out as a regular square grid with an aperture of about 8 km and a nominal inter-station distance of 400 m. The original purpose of the array was to perform high-resolution ambient seismic noise tomography to characterize the near surface for seismic hazard assessment and ground motion prediction (Chmiel et al 2019). The gas reservoir is located at about 3 km depth in the Permian Bourne et al (2018) show that the gas production in this field led to a 15 MPa average reservoir pore-pressure depletion since 1995 which is associated 83 with seismicity rates exponentially increasing with time.…”

Section: Datamentioning

confidence: 99%

“…We follow Chmiel et al (2019)'s procedure for the correlation computation. We average the causal and acausal sides of the correlations, then the symmetrized correlations are further stacked in 50 m inter-station distance bins, thanks to a continuous distribution of inter-station distances between 400 m and 8 km, to enhance the signal to noise ratio, to mitigate the azimuthal variations of noise sources and to help to converge closer to the true Green's function (Boué et al 2013;Mordret et al 2014a;Nakata et al 2015).…”

Section: Datamentioning

confidence: 99%

“…Equation 10 holds if one assumes that either (1) Poissons ratio and density are fixed or (2) P-wave velocity and density are fixed. In each case, the sensitivity kernel has to be adapted (see Haney & Tsai 2017, for details) and we modified Haney & Tsai (2017)'s code to output the corresponding K computed from an average velocity model of the area (Chmiel et al 2019). This average model is in good agreement with local borehole measurements (Kruiver et al 2017) and predicts properly the average phase velocity dispersion curves for both the fundamental mode and the first overtone.…”

Section: Relative Phase Velocity Change Estimationmentioning

confidence: 99%

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Noise-based ballistic wave passive seismic monitoring – Part 2: surface waves

Mordret

Courbis

Brenguier

et al. 2020

Geophysical Journal International

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SUMMARY We develop a new method to monitor and locate seismic velocity changes in the subsurface using seismic noise interferometry. Contrary to most ambient noise monitoring techniques, we use the ballistic Rayleigh waves computed from 30 d records on a dense nodal array located above the Groningen gas field (the Netherlands), instead of their coda waves. We infer the daily relative phase velocity dispersion changes as a function of frequency and propagation distance with a cross-wavelet transform processing. Assuming a 1-D velocity change within the medium, the induced ballistic Rayleigh wave phase shift exhibits a linear trend as a function of the propagation distance. Measuring this trend for the fundamental mode and the first overtone of the Rayleigh waves for frequencies between 0.5 and 1.1 Hz enables us to invert for shear wave daily velocity changes in the first 1.5 km of the subsurface. The observed deep velocity changes (±1.5 per cent) are difficult to interpret given the environmental factors information available. Most of the observed shallow changes seem associated with effective pressure variations. We observe a reduction of shear wave velocity (–0.2 per cent) at the time of a large rain event accompanied by a strong decrease in atmospheric pressure loading, followed by a migration at depth of the velocity decrease. Combined with P-wave velocity changes observations from a companion paper, we interpret the changes as caused by the diffusion of effective pressure variations at depth. As a new method, noise-based ballistic wave passive monitoring could be used on several dynamic (hydro-)geological targets and in particular, it could be used to estimate hydrological parameters such as the hydraulic conductivity and diffusivity.

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Section: Resultsmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Relative Phase Velocity Change Estimationmentioning

confidence: 99%

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Noise-based ballistic wave passive seismic monitoring – Part 2: surface waves

Mordret

Courbis

Brenguier

et al. 2020

Geophysical Journal International

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Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

Pan

Wang

et al. 2020

JGR Solid Earth

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We utilize an array method called the frequency‐Bessel transformation method to extract the multimodal dispersion curves of Rayleigh waves from ambient seismic noise data recorded by the USArray Transportable Array. We observe as many as five overtones' dispersion curves of Rayleigh waves in the Midwestern United States, and four and three overtones' dispersion curves, respectively, in the U.S. Great Plain area and Northeastern United States. We employ a quasi‐Newton method to invert the multimodal dispersion curves for the shear velocity. We find that the sensitivity of overtones to deeper structures is higher than that of fundamental mode in the same frequency range. The utilization of overtones significantly improved the non‐uniqueness and convergence of the inversions, which make the final inversion results robust and reliable. Our inversion results for S wave velocity (Vs) model in the studied areas exhibit some differences compared with Shen and Ritzwoller's model (2016, https://doi.org/10.1002/2016JB012887). The Vs falls abruptly in the lower crust (21–33 km) in the U.S. Great Plain area and Northeastern U.S. areas. A high‐velocity zone is observable in depth of 50–60 km in the U.S. Great Plain area and Midwestern United States, and all three models show larger Vs values below 50 km than those of Shen and Ritzwoller (2016).

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Ambient Noise Surface Wave Reverse Time Migration for Fault Imaging

et al. 2020

JGR Solid Earth

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Faults are basic components of subsurface structures that represent discontinuities in physical properties. In addition to being the controlling factor of major tectonic earthquakes (e.g., Angelier, 1990; Martin et al., 2016; Sibson, 1983), subsurface faults also play an essential role in natural resources, including hydrocarbon accumulation and formation of oil and gas reservoirs (Aydin, 2000), aggregation of metallic ore deposits (e.g., Goldfarb et al., 2005; Spencer & Welty, 1986), and generation of geothermal reservoirs (Chen & Huang, 2015). Therefore, detailed characterization of subsurface faults is crucial for understanding tectonic activities and for exploring natural resources. Many geophysical approaches for fault delineation have been proposed in the past decades, such as gravity, magnetic, electromagnetic, and seismic methods (e.g.,

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Ambient noise multimode Rayleigh and Love wave tomography to determine the shear velocity structure above the Groningen gas field

Cited by 14 publications

References 10 publications

Noise-based ballistic wave passive seismic monitoring – Part 2: surface waves

Noise-based ballistic wave passive seismic monitoring – Part 2: surface waves

Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array

Ambient Noise Surface Wave Reverse Time Migration for Fault Imaging

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