Probing the<i>in situ</i>Elastic Nonlinearity of Rocks with Earth Tides and Seismic Noise

Sens‐Schönfelder, Christoph; Eulenfeld, Tom

doi:10.1103/physrevlett.122.138501

Cited by 44 publications

(57 citation statements)

References 40 publications

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“…The strategy is to use many measurements from shorter noise correlation stacks rather than measuring a single dispersion curve on the stack of all correlation traces. This idea has been applied successfully in Sens‐Schönfelder and Eulenfeld () for relative velocity measurements. For many monthly stacked correlation traces (Figure b), we calculate the discrete velocity‐frequency estimates from the zero crossings.…”

Section: Noise Cross Correlations and Rayleigh Wave Traveltime Measurmentioning

confidence: 99%

Magmatic and Sedimentary Structure beneath the Klyuchevskoy Volcanic Group, Kamchatka, From Ambient Noise Tomography

et al. 2020

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The Klyuchevskoy Volcanic Group is a cluster of the world's most active subduction volcanoes, situated on the Kamchatka Peninsula, Russia. The volcanoes lie in an unusual off‐arc position within the Central Kamchatka Depression (CKD), a large sedimentary basin whose origin is not fully understood. Many gaps also remain in the knowledge of the crustal magmatic plumbing system of these volcanoes. We conducted an ambient noise surface wave tomography, to image the 3‐D shear wave velocity structure of the Klyuchevskoy Volcanic Group and CKD within the surrounding region. Vertical component cross correlations of the continuous seismic noise are used to measure interstation Rayleigh wave group and phase traveltimes. We perform a two‐step surface wave tomography to model the 3‐D Vsv velocity structure. For each inversion stage we use a transdimensional Bayesian Monte Carlo approach, with coupled uncertainty propagation. This ensures that our model provides a reliable 3‐D velocity image of the upper 15 km of the crust, as well as a robust assessment of the uncertainty in the observed structure. Beneath the active volcanoes, we image small slow velocity anomalies at depths of 2–5 km but find no evidence for magma storage regions deeper than 5 km—noting the 15 km depth limit of the model. We also map two clearly defined sedimentary layers within the CKD, revealing an extensive 8 km deep sedimentary accumulation. This volume of sediments is consistent with the possibility that the CKD was formed as an Eocene‐Pliocene fore‐arc regime, rather than by recent (<2 Ma) back‐arc extension.

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Section: Noise Cross Correlations and Rayleigh Wave Traveltime Measurmentioning

confidence: 99%

Magmatic and Sedimentary Structure beneath the Klyuchevskoy Volcanic Group, Kamchatka, From Ambient Noise Tomography

et al. 2020

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“…After the reduction of the effect of precipitation with the tentative hyperparameters, the resultant temporal change shows sudden drops of seismic wave velocity associated with the 2016 Kumamoto earthquake (Figure 6). Since the drop related to the Kumamoto earthquake reaches 0.1%, we modeled it by an exponential decay (Gassenmeier et al, 2016; Hobiger et al, 2016; Sens‐Schönfelder & Eulenfeld, 2019) as

e_{t} = A_{t} e^{\frac{t - t_{0}}{τ_{e}}},

where A t is amplitude of the drop, t 0 is the origin time of the Kumamoto earthquake, and τ e is the decay time. We omitted a term of non‐recovering coseismic velocity drops (Hobiger et al, 2016) as the term could not be detected, as shown later (see Figure 10).…”

Section: Maximum Likelihood Methods For Determining the Hyperparametersmentioning

confidence: 99%

“…After the reduction of the effect of precipitation with the tentative hyperparameters, the resultant temporal change shows sudden drops of seismic wave velocity associated with the 2016 Kumamoto earthquake ( Figure 6). Since the drop related to the Kumamoto earthquake reaches 0.1%, we modeled it by an exponential decay (Gassenmeier et al, 2016;Hobiger et al, 2016;Sens-Schönfelder & Eulenfeld, 2019) as…”

Section: A Model For the Drops Associated With 2016 Kumamoto Earthquakementioning

confidence: 99%

Time‐Lapse Monitoring of Seismic Velocity Associated With 2011 Shinmoe‐Dake Eruption Using Seismic Interferometry: An Extended Kalman Filter Approach

et al. 2020

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Seismic interferometry is a powerful tool to monitor the seismic velocity change associated with volcanic eruptions. For the monitoring, changes in seismic velocity with environmental origins (such as precipitation) are problematic. In order to model the environmental effects, we propose a new technique based on a state-space model. An extended Kalman filter estimates seismic velocity changes as state variables, with a first-order approximation of the stretching method. We apply this technique to three-component seismic records in order to detect the seismic velocity change associated with the Shinmoe-dake eruptions in 2011 and 2018. First, ambient noise cross correlations were calculated from May 2010 to April 2018. We also modeled seismic velocity changes resulting from precipitation and the 2016 Kumamoto earthquake, with exponential type responses. Most of the results show no significant changes associated with the eruptions, although gradual inflation of the magma reservoir preceded the 2011 eruption by 1 year. The observed low sensitivity to static stress changes suggests that the fraction of geofluid and crack density at about 1-km depth is small, and the crack shapes could be circular. Only one station pair west of the crater shows the significant drop associated with the eruption in 2011. The gradual drop of seismic velocity up to 0.05% preceded the eruption by 1 month. When the gradual drop began, volcanic tremors were activated at about 2-km depth. These observations suggest that the drop could be caused by damage accumulation due to vertical magma migration beneath the summit.

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“…In the limit of a sufficiently random wave field, the cross‐correlation function converges toward the Green's function between the two receivers (Shapiro & Campillo, 2004; Snieder, 2004; Weaver & Lobkis, 2002). In this interreceiver setting, the Green's function or the cross‐correlation function for arbitrary time‐invariant noise sources can be used to monitor the wave velocity between and near the receivers (e.g., Brenguier et al., 2008; Hillers et al., 2015; Hobiger et al., 2016; Richter et al., 2014; Sens‐Schönfelder & Eulenfeld, 2019; Sens‐Schönfelder & Wegler, 2006; Wegler & Sens‐Schönfelder, 2007). In the reverse, seismic interferometry can be applied to use earthquakes as virtual receivers at depth.…”

Section: Introductionmentioning

confidence: 99%

Toward Source Region Tomography With Intersource Interferometry: Shear Wave Velocity From 2018 West Bohemia Swarm Earthquakes

Eulenfeld

2020

JGR Solid Earth

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The concept of seismic interferometry embraces the construction of waves traveling between receivers or sources with cross-correlation techniques. In the present study cross correlations of coda waves are used to measure traveltimes of shear waves between earthquake locations for five event clusters of the 2018 West Bohemia earthquake swarm. With the help of a high-quality earthquake catalog, I was able to determine the shear wave velocity in the region of the five clusters separately. The shear wave velocities range between 3.5 and 4.2 km/s. The resolution of this novel method is given by the extent of the clusters and better than for a comparable classical tomography. It is suggested to use the method in a tomographic inversion and map the shear wave velocity in the source region with unprecedented resolution. Furthermore, the influence of focal mechanisms and the attenuation properties on the polarity and location of the maxima in the cross-correlation functions is discussed. The intracluster ratio of P wave to S wave velocity is approximately fixed at 1.68.

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Probing thein situElastic Nonlinearity of Rocks with Earth Tides and Seismic Noise

Cited by 44 publications

References 40 publications

Magmatic and Sedimentary Structure beneath the Klyuchevskoy Volcanic Group, Kamchatka, From Ambient Noise Tomography

Magmatic and Sedimentary Structure beneath the Klyuchevskoy Volcanic Group, Kamchatka, From Ambient Noise Tomography

Time‐Lapse Monitoring of Seismic Velocity Associated With 2011 Shinmoe‐Dake Eruption Using Seismic Interferometry: An Extended Kalman Filter Approach

Toward Source Region Tomography With Intersource Interferometry: Shear Wave Velocity From 2018 West Bohemia Swarm Earthquakes

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