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[SSS10-P02] Relation between the Noto Peninsula earthquake swarm and earth tides considering background earthquake probability based on nonstationary ETAS model
Keywords:Earth tide, Nonstationary ETAS model, Background earthquake probability, Noto Peninsula, Earthquake swarm
1. Introduction
Earthquake swarm activity and crustal deformation (expansion/uplift) continue at the tip of the Noto Peninsula. The existence of crustal fluid has been pointed out as a cause of swarm activity [Nakajima, 2022]. Regarding the Matsushiro earthquake swarm activity, which is famous as an earthquake swarm, it has been pointed out that it is related to tides [e.g., Iwata & Nakanishi, 1998]. In this study, we investigated the relationship between the Noto Peninsula earthquake swarm and tides. Here, we used data weighted by the background earthquake probability based on the nonstationary ETAS model [Kumazawa & Ogata, 2003] as declustering data.
2. Data
The earthquake swarm consists of four clusters in the north, south, east, and west (regions N, S, E, and W). Furthermore, in region S, the seismicity pattern differs at a depth of 14 km. Therefore, in this study, we consider the five regions N, Ss, Sd, E, and W. We extracted earthquakes from the earthquake catalog unified by Japan Meteorological Agency (January 1, 2018 to December 31, 2022, flag KkA, M 1.3 or more, depth less than 30 km).
It is necessary to set location, origin time, and fault parameters in order to estimate the theoretical below-ground tidal response. Catalog information was used for location and origin time, and fault parameters were assumed to be southeast-dipping faults in consideration of major earthquake mechanism solutions and hypocenter distributions: strike, dip, and rake were set at 45°, 45°, and 90°, respectively, for all events.
When an earthquake occurs, other earthquakes are likely to occur in the surrounding area due to stress redistribution. It is necessary to exclude (decluster) such induced earthquakes in advance for investigating the tidal correlation of seismic activity. In general, declustering is a process of extracting only independent events by excluding earthquakes that satisfy the spatio-temporal distance criterion. However, there is also a concern that important information may be artificially excluded from the catalog because the processing result depends on the set criteria. Therefore, in this study, we estimated the tidal sensitivity using all data weighted by the background earthquake probability based on the nonstationary ETAS model [Kumazawa & Ogata, 2013].
3. Method
Theoretical tide was calculated using TidalStrain.2 [Hirose et al., 2019; https://mri-2.mri-jma.go.jp/owncloud/s/tjqx7HfK8bD3KQf]. We analyzed volumetric strain (ΔV) at the hypocenter, and shear stress (Δτ), normal stress (Δσ), and the Coulomb failure function (ΔCFF) on the assumed fault plane as tidal indices. In the ΔCFF calculation, we assumed values of 0.1, 0.2, …, and 0.9 for the apparent friction coefficient μ′ (hereafter ΔCFF(0.1), ΔCFF(0.2), …, and ΔCFF(0.9), respectively). In the case of ΔV and Δσ, we defined expansion/dilatation as positive and contraction/compression as negative; consequently, positive Δσ values promote fault slip. We also defined Δτ and ΔCFF as positive when they promote fault slip. We assigned phase angles of –180° and 180° to the minimum values before and after an event, respectively, and 0° to the maximum value that occurred between these two minima. The phase angle at the earthquake occurrence time was estimated by linear interpolation in the time interval between –180º and 0° or between 0° and 180°.
The possible occurrence of earthquakes at certain tidal phase angles is often evaluated using the p-value, as calculated by Schuster [1897], and whether or not an earthquake depends on the tidal value (amplitude) can be evaluated using the alpha-value [Houston, 2015].
4. Result
Although the results are preliminary because the analysis is still in progress, a relationship with the tide is indicated in region Sd. Further analysis results will be reported at the meeting.
Earthquake swarm activity and crustal deformation (expansion/uplift) continue at the tip of the Noto Peninsula. The existence of crustal fluid has been pointed out as a cause of swarm activity [Nakajima, 2022]. Regarding the Matsushiro earthquake swarm activity, which is famous as an earthquake swarm, it has been pointed out that it is related to tides [e.g., Iwata & Nakanishi, 1998]. In this study, we investigated the relationship between the Noto Peninsula earthquake swarm and tides. Here, we used data weighted by the background earthquake probability based on the nonstationary ETAS model [Kumazawa & Ogata, 2003] as declustering data.
2. Data
The earthquake swarm consists of four clusters in the north, south, east, and west (regions N, S, E, and W). Furthermore, in region S, the seismicity pattern differs at a depth of 14 km. Therefore, in this study, we consider the five regions N, Ss, Sd, E, and W. We extracted earthquakes from the earthquake catalog unified by Japan Meteorological Agency (January 1, 2018 to December 31, 2022, flag KkA, M 1.3 or more, depth less than 30 km).
It is necessary to set location, origin time, and fault parameters in order to estimate the theoretical below-ground tidal response. Catalog information was used for location and origin time, and fault parameters were assumed to be southeast-dipping faults in consideration of major earthquake mechanism solutions and hypocenter distributions: strike, dip, and rake were set at 45°, 45°, and 90°, respectively, for all events.
When an earthquake occurs, other earthquakes are likely to occur in the surrounding area due to stress redistribution. It is necessary to exclude (decluster) such induced earthquakes in advance for investigating the tidal correlation of seismic activity. In general, declustering is a process of extracting only independent events by excluding earthquakes that satisfy the spatio-temporal distance criterion. However, there is also a concern that important information may be artificially excluded from the catalog because the processing result depends on the set criteria. Therefore, in this study, we estimated the tidal sensitivity using all data weighted by the background earthquake probability based on the nonstationary ETAS model [Kumazawa & Ogata, 2013].
3. Method
Theoretical tide was calculated using TidalStrain.2 [Hirose et al., 2019; https://mri-2.mri-jma.go.jp/owncloud/s/tjqx7HfK8bD3KQf]. We analyzed volumetric strain (ΔV) at the hypocenter, and shear stress (Δτ), normal stress (Δσ), and the Coulomb failure function (ΔCFF) on the assumed fault plane as tidal indices. In the ΔCFF calculation, we assumed values of 0.1, 0.2, …, and 0.9 for the apparent friction coefficient μ′ (hereafter ΔCFF(0.1), ΔCFF(0.2), …, and ΔCFF(0.9), respectively). In the case of ΔV and Δσ, we defined expansion/dilatation as positive and contraction/compression as negative; consequently, positive Δσ values promote fault slip. We also defined Δτ and ΔCFF as positive when they promote fault slip. We assigned phase angles of –180° and 180° to the minimum values before and after an event, respectively, and 0° to the maximum value that occurred between these two minima. The phase angle at the earthquake occurrence time was estimated by linear interpolation in the time interval between –180º and 0° or between 0° and 180°.
The possible occurrence of earthquakes at certain tidal phase angles is often evaluated using the p-value, as calculated by Schuster [1897], and whether or not an earthquake depends on the tidal value (amplitude) can be evaluated using the alpha-value [Houston, 2015].
4. Result
Although the results are preliminary because the analysis is still in progress, a relationship with the tide is indicated in region Sd. Further analysis results will be reported at the meeting.