Short‐ and Long‐Term Variations in the Reykjanes Geothermal Reservoir From Seismic Noise Interferometry

Sánchez‐Pastor, Pilar; Obermann, Anne; Schimmel, Martin; Weemstra, Cornelis; Verdel, Arie; Jousset, Philippe

doi:10.1029/2019gl082352

Cited by 38 publications

(30 citation statements)

References 58 publications

(91 reference statements)

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“…Cross-correlations of ambient seismic noise form the basis of many applications in seismology from site effects studies (e.g., Aki, 1957;Roten et al, 2006;Bard et al, 2010;Denolle et al, 2013;Bowden et al, 2015) to ambient noise tomography (e.g., Shapiro et al, 2005;Yang et al, 2007;Nishida et al, 2009;Haned et al, 2016;de Ridder et al, 2014;Fang et al, 2015;Singer et al, 2017) and coda wave interferometry (e.g., Sens-Schönfelder and Wegler, 2006;Brenguier et al, 2008;Obermann et al, 2013;Sánchez-Pastor et al, 2019). Auto-correlations of the ambient noise are also increasingly used to study seismic interfaces as suggested by Claerbout (1968) (e.g., Taylor et al, 2016;Saygin et al, 2017;Romero and Schimmel, 2018) and to monitor subsurface properties (Viens et al, 2018;Clements and Denolle, 2018).…”

Section: Motivationmentioning

confidence: 99%

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

et al. 2020

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Abstract. We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

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Section: Motivationmentioning

confidence: 99%

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

et al. 2020

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“…Interferometric techniques may well play an important role in the future of geothermal exploration (e.g., [8]). In particular, this applies to tomographic travel time inversions: contrary to travel time tomography exploiting earthquake signals, the spatial sampling by the interferometric ray paths does not suffer from either a limited number of earthquakes or an irregular distribution of these earthquakes [3].…”

Section: Introductionmentioning

confidence: 99%

On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula

2021

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Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network.

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“…Since then, the detection of temporal changes in the codafrom repeating earthquakes or more recently from ambient noise correlations-has been successfully applied in various settings such as: CO 2 and geothermal fluid injection studies (Ugalde et al 2013;Hillers et al 2015;Obermann et al 2015;Taira et al 2018;Sánchez-Pastor et al 2019), volcanoes (e.g. Poupinet et al 1996;Grêt et al 2005;Sens-Schönfelder & Wegler 2006;Brenguier et al 2016;Donaldson et al 2017;Sánchez-Pastor et al 2018), fault zones (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, retrieving the spatial Coda kernels in elastic media 935 distribution of scattering (velocity, respectively) changes from a set of coda-wave decorrelation (phase shift, respectively) measurements amounts to solving an inverse problem. This was successfully applied to: numerical simulations (Planès 2013;Planès et al 2015;Kanu & Snieder 2015b;, locating precursors of volcanic eruptions (Obermann et al 2013a;Lesage et al 2014;Sánchez-Pastor et al 2018), assessing fault zone damages (Froment 2011;Obermann et al 2014), monitoring subsurface changes due to high-pressure fluid injections (Hillers et al 2015;Obermann et al 2015;Sánchez-Pastor et al 2019) and monitoring crack/fissure growth in concrete blocks (Larose et al 2010(Larose et al , 2015Zhang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Coda-wave decorrelation sensitivity kernels in 2-D elastic media: a numerical approach

Durán

Planès

Obermann

2020

Geophysical Journal International

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Summary Probabilistic sensitivity kernels based on the analytical solution of the diffusion and radiative transfer equations have been used to locate tiny changes detected in late arriving coda waves. These analytical kernels accurately describe the sensitivity of coda-waves towards velocity changes located at large distance from the sensors in the acoustic diffusive regime. They are also valid to describe the acoustic waveform distortions (decorrelations) induced by isotropically scattering perturbations. However, in elastic media, there is no analytical solution that describes the complex propagation of wave energy, including mode conversions, polarizations, etc. Here, we derive sensitivity kernels using numerical simulations of wave propagation in heterogeneous media in the acoustic and elastic regimes. We decompose the wavefield into P- and S-wave components at the perturbation location to construct separate P to P, S to S, P to S, and S to P scattering sensitivity kernels. This allows to describe the influence of P-wave scattering perturbations and S-wave scattering perturbations separately. We test our approach using acoustic and elastic numerical simulations where localized scattering perturbations are introduced. We validate the numerical sensitivity kernels by comparing them with analytical kernel predictions and with measurements of coda decorrelations on the synthetic data.

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Short‐ and Long‐Term Variations in the Reykjanes Geothermal Reservoir From Seismic Noise Interferometry

Cited by 38 publications

References 58 publications

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula

Coda-wave decorrelation sensitivity kernels in 2-D elastic media: a numerical approach

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