Markets and Competition

Okubo, Shohei

doi:10.4324/9781003362487-3

Cited by 1 publication

(3 citation statements)

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“…The calculation of a dv/v time series from an ambient seismic noise data set of this size (1.6 TB) is a computational challenge due to the extremely large number of cross-correlations that are required to be computed. To overcome this computationally prohibitive obstacle, which prevents the use of existing seismic processing tools, we developed a Julia-based software SeisMonitoring.jl (Okubo, 2023c) which can be used in highperformance computing environments. Julia is a powerful language to perform computationally intensive processes (Bezanson et al, 2017) such as computing large numbers of cross-correlations in parallel.…”

Section: Methodsmentioning

confidence: 99%

“…We maintain the Julia-based software SeisMonitoring.jl (Okubo, 2023c) by the continuous integration testing on GitHub to be executed in different machine environments. The input files to conduct the ambient noise processing and the Jupyter notebooks for the post-processing are documented in the GitHub repository Seis-Monitoring_Paper (Okubo, 2024) to reproduce the results from scratch.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…The advantage of using ambient seismic noise is that one does not have to wait for earthquakes to occur, which enables continuous measurements of dv/v. The dv/v is sensitive to the groundwater level (GWL) (Clements & Denolle, 2018, 2023Illien et al, 2021Illien et al, , 2022Mao et al, 2022;Nishida et al, 2020;Rivet et al, 2015;Sens-Schönfelder & Wegler, 2006) and the thermoelastic deformation induced by the air temperature (Colombero et al, 2018;Gassenmeier et al, 2016;Richter et al, 2014), or even both (Feng et al, 2021;Lecocq et al, 2017;Wang et al, 2017). When interpreting dv/v measurements, it is thus crucial to identify the environmental factors as well as other mechanisms that cause variations in dv/v, such as a drop in velocity associated with coseismic earthquake rupture, logarithmic healing following an earthquake, and the other potential tectonic factors (Clements & Denolle, 2023;Feng et al, 2021;Taira et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Velocity Change Over 20 Years at Parkfield

Okubo,

Delbridge,

Denolle

2024

JGR Solid Earth

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We monitored the time history of the velocity change (dv/v) from 2002 to 2022 to investigate temporal changes in the physical state near the Parkfield Region of the San Andreas Fault throughout the interseismic period. Following the coseismic decrease in dv/v caused by the 2003 San Simeon (SS) and the 2004 Parkfield earthquakes, the dv/v heals logarithmically and shows a net long‐term increase in which the current dv/v level is equivalent to, or exceeding, the value before the 2003 SS earthquake. We investigated this long‐term trend by fitting the model accounting for the environmental and coseismic effects to the channel‐weighted dv/v time series. We confirmed with the metrics of Akaike information criterion and Bayesian information criterion that the additional term of either a linear trend term, or a residual healing term for the case where the healing had not been completed before the SS earthquake occurred, robustly improved the fit to the data. We eventually evaluated the sensitivity of the dv/v time history to the GNSS‐derived strain field around the fault. The cumulative dilatational strain spatially averaged around the seismic stations shows a slight extension, which is opposite to what would be expected for an increase in dv/v. However, the cumulative rotated axial strain shows compression in a range near the maximum contractional horizontal strain (azimuth of N35°W to N45°E), suggesting that the closing of pre‐existing microcracks aligned perpendicular to the axial contractional strains would be a candidate to cause the long‐term increase observed in the multiple station pairs.

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Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%