On the Use of Data Noise as a Site‐Specific Weight Parameter in a Hierarchical Bayesian Moment Tensor Inversion: The Case Study of The Geysers and Long Valley Caldera Earthquakes

Mustać, Marija; Tkalčić, Hrvoje

doi:10.1785/0120160379

Cited by 9 publications

(22 citation statements)

References 36 publications

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“…The 24 April 2007 and 11 January 2014 earthquakes had moment magnitudes of 4.46 and 4.53, estimated by the BSL, and were both smaller than the M W = 4.56 earthquake from 20 October 2006 presented in our previous paper (Mustać & Tkalčić, 2017). The 18 February 2004 earthquake had a slightly higher magnitude of M W = 4.58 but was recorded on fewer stations, while the most recent, 14 December 2016 event, was the largest (M W = 5.01).…”

Section: Inversions With Field Datacontrasting

confidence: 50%

“…Guilhem et al (2014) used local waveforms and retrieved focal mechanisms similar to the Berkeley Seismological Laboratory (BSL) regional solutions for a number of earthquakes. We presented results for the 20 October 2006 M W = 4.6 event (gray star in Figure 1b) in a previous paper and found a predominantly non-DC mechanism indicating crack opening and possible fluid extraction (Mustać & Tkalčić, 2017). We presented results for the 20 October 2006 M W = 4.6 event (gray star in Figure 1b) in a previous paper and found a predominantly non-DC mechanism indicating crack opening and possible fluid extraction (Mustać & Tkalčić, 2017).…”

Section: Introductionmentioning

confidence: 66%

“…In our previous applications (Mustać andTkalčić, 2016, 2017), the C D matrix was empirically estimated using ambient noise autocorrelation traces. The diagonal matrix accounts for noise amplitudes but not its correlations.…”

Section: Methodsmentioning

confidence: 99%

“…We choose a day without local earthquakes of magnitudes above 2, but containing a M W 6.5 teleseismic earthquake in the Northern California earthquake data center (NCEDC) catalogue. We treat it as a free parameter in the inversion, either as a common noise parameter for all stations or an individual noise variance at each station (Mustać & Tkalčić, 2017). The seismograms are processed in the same way as we process the data: the instrument responses are removed and they are band-pass filtered between 20 and 50 s. We compute autocorrelations for each hour, with a maximum lag of 220 s. Autocorrelations of vertical components are consistent among stations, with a period of ∼20 s ( Figure 2).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, we wish to explore how the noise model applied in the study affects the solutions. We choose the locations of stations that were previously used to obtain the mechanism of the 20 October 2006 event in Mustać and Tkalčić (2017): BKS, GASB, HATC, MHC, and RAMR for the first group and BRIB, JRSC, HAST, SUTB, and WENL for the second group. To explore the dependence of the DC, CLVD, and ISO component percentages on the azimuthal coverage, we perform a Jackknife test with two groups of five stations.…”

Section: Synthetic Testsmentioning

confidence: 99%

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The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

2018

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Moment tensor (MT) inversion studies of events in The Geysers geothermal field mostly focused on microseismicity and found a large number of earthquakes with significant non‐double‐couple (non‐DC) seismic radiation. Here we concentrate on the largest events in the area in recent years using a hierarchical Bayesian MT inversion. Initially, we show that the non‐DC components of the MT can be reliably retrieved using regional waveform data from a small number of stations. Subsequently, we present results for a number of events and show that accounting for noise correlations can lead to retrieval of a lower isotropic (ISO) component and significantly different focal mechanisms. We compute the Bayesian evidence to compare solutions obtained with different assumptions of the noise covariance matrix. Although a diagonal covariance matrix produces a better waveform fit, inversions that account for noise correlations via an empirically estimated noise covariance matrix account for interdependences of data errors and are preferred from a Bayesian point of view. This implies that improper treatment of data noise in waveform inversions can result in fitting the noise and misinterpreting the non‐DC components. Finally, one of the analyzed events is characterized as predominantly DC, while the others still have significant non‐DC components, probably as a result of crack opening, which is a reasonable hypothesis for The Geysers geothermal field geological setting.

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Section: Inversions With Field Datacontrasting

confidence: 50%

Section: Introductionmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Synthetic Testsmentioning

confidence: 99%

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The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

2018

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Point‐Source Inversion of Small and Moderate Earthquakes From P‐wave Polarities and P/S Amplitude Ratios Within a Hierarchical Bayesian Framework: Implications for the Geysers Earthquakes

Shang

Tkalčić

2020

JGR Solid Earth

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Toward Improving Point‐Source Moment‐Tensor Inference by Incorporating 1D Earth Model's Uncertainty: Implications for the Long Valley Caldera Earthquakes

Phạm

Tkalčić

2021

JGR Solid Earth

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Moment tensor (MT), a 3 × 3 matrix, is a convenient mathematical representation of underground seismic sources, including faulting or volume sources, by a system of body forces under point-source approximation (Aki & Richards, 2002). Each element, ij E m , where  , 1, 2, 3 E i j , of the MT represents opposite force-couple acting along coordinate with the arm resting along coordinate E j . The inversion of seismic moment-tensor (MT) from seismological observation is a classical inverse problem in seismology. Reliable MT solutions play an essential role in forensic seismology as they are important evidence in determining the nature of source types, which distinguish between tectonic, volcanic, or explosive sources (e.g.,

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On the Use of Data Noise as a Site‐Specific Weight Parameter in a Hierarchical Bayesian Moment Tensor Inversion: The Case Study of The Geysers and Long Valley Caldera Earthquakes

Cited by 9 publications

References 36 publications

The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

The Variability and Interpretation of Earthquake Source Mechanisms in The Geysers Geothermal Field From a Bayesian Standpoint Based on the Choice of a Noise Model

Point‐Source Inversion of Small and Moderate Earthquakes From P‐wave Polarities and P/S Amplitude Ratios Within a Hierarchical Bayesian Framework: Implications for the Geysers Earthquakes

Toward Improving Point‐Source Moment‐Tensor Inference by Incorporating 1D Earth Model's Uncertainty: Implications for the Long Valley Caldera Earthquakes

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