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Reservoir Imaging Using Ambient Noise Correlation From a Dense Seismic Network

Abstract : In September 2014, a dense temporary seismic network (EstOF) including 288 single-component geophones was deployed during 1 month in the Outre-Forêt region of the Upper Rhine Graben (France), where two deep geothermal projects (Soultz-sous-Forêts and Rittershoffen) are currently in operation. We apply ambient seismic noise correlation to estimate the empirical Green's function of the medium between the 41,200 station pairs in the network. The noise correlation functions obtained are comparable to those from previous studies based on the sparse long-term networks settled in the area mostly to monitor the induced seismic activity. However, the dense spatial coverage of the EstOF network improves our ability to identify the main phases of the Green's function. Both the fundamental mode and the first overtone of the Rayleigh waves are identified between most station pairs. P waves are also evidenced. We analyze the statistical distribution of the Rayleigh wave group velocity between station pairs as a function of the period (between 0.8 and 5 s), the station pair orientation, the distance over wavelength ratio and the signal-to-noise ratio. From these observations, we build a high-resolution three-dimensional S wave velocity model of the upper crust (down to 3 km deep) around the regional deep geothermal reservoirs. This model is consistent with local geological structures but also evidences nonlithological variations, particularly at depth in the basement. These variations are interpreted as large-scale temperature anomalies related to deep hydrothermal circulation.
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Submitted on : Wednesday, June 29, 2022 - 7:44:32 AM
Last modification on : Wednesday, June 29, 2022 - 7:44:34 AM

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M. Lehujeur, J. Vergne, J. Schmittbuhl, D. Zigone, A. Le Chenadec. Reservoir Imaging Using Ambient Noise Correlation From a Dense Seismic Network. Journal of Geophysical Research: Solid Earth, 2018, 123, pp.6671-6686. ⟨10.1029/2018JB015440⟩. ⟨insu-03707723⟩

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