Characterizing Aftershock Sequences of the Recent Strong Earthquakes in Central Italy

Kossobokov, Vladimir; Nekrasova, Anastasia

doi:10.1007/s00024-017-1624-9

Cited by 25 publications

(15 citation statements)

References 37 publications

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“…The other mode is located considerably closer in time and space to the origin; it corresponds to clustered events. This bimodality has been documented in multiple regions and on multiple scales (Gentili et al, , ; Gu et al, ; Kossobokov & Nekrasova, ; Moradpour et al, ; Peresan & Gentili, ; Ruhl et al, ; Schoenball et al, ; Trugman et al, ; Vasylkivska & Huerta, ; Zaliapin & Ben‐Zion, , , , ). The bimodality of observed seismicity facilitates cluster detection and declustering.…”

Section: Nearest‐neighbor Proximity For Earthquakesmentioning

confidence: 91%

“…The problem of detecting earthquake clusters (which is different from although closely related to declustering) was shown recently to be effectively addressed by a nearest‐neighbor analysis in space‐time‐magnitude domain (Zaliapin et al, ; Zaliapin and Ben‐Zion, , , , , ). This cluster identification methodology is effective in diverse settings, including tectonic seismicity (Gentili et al, , ; Gu et al, ; Kossobokov & Nekrasova, ; Moradpour et al, ; Peresan & Gentili, ; Reverso et al, ; Ruhl et al, ; Trugman et al, ; Zaliapin et al, ; Zaliapin & Ben‐Zion, , , ), induced earthquakes (Goebel et al, ; Martínez‐Garzón et al, ; Schoenball et al, ; Schoenball & Ellsworth, ; Teng & Baker, ; Vasylkivska & Huerta, ; Zaliapin & Ben‐Zion, ), synthetic seismicity in Epidemic Type Aftershock Sequence (ETAS) models (Gu et al, ; Zaliapin et al, ; Zaliapin & Ben‐Zion, ), and laboratory rock fracture experiments (Davidsen et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

Zaliapin

Ben‐Zion

2020

JGR Solid Earth

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We introduce an algorithm for declustering earthquake catalogs based on the nearest-neighbor analysis of seismicity. The algorithm discriminates between background and clustered events by random thinning that removes events according to a space-varying threshold. The threshold is estimated using randomized-reshuffled catalogs that are stationary, have independent space and time components, and preserve the space distribution of the original catalog. Analysis of catalog produced by the Epidemic Type Aftershock Sequence model demonstrates that the algorithm correctly classifies over 80% of background and clustered events, correctly reconstructs the stationary and space-dependent background intensity, and shows high stability with respect to random realizations (over 75% of events have the same estimated type in over 90% of random realizations). The declustering algorithm is applied to the global Northern California Earthquake Data Center catalog with magnitudes m ≥ 4 during 2000-2015; a Southern California catalog with m ≥ 2.5, 3.5 during 1981-2017; an area around the 1992 Landers rupture zone with m ≥ 0.0 during 1981-2015; and the Parkfield segment of San Andreas fault with m ≥ 1.0 during 1984-2014. The null hypotheses of stationarity and space-time independence are not rejected by several tests applied to the estimated background events of the global and Southern California catalogs with magnitude ranges Δm < 4. However, both hypotheses are rejected for catalogs with larger range of magnitudes Δm > 4. The deviations from the nulls are mainly due to local temporal fluctuations of seismicity and activity switching among subregions; they can be traced back to the original catalogs and represent genuine features of background seismicity.We analyze global seismicity, earthquakes in California, and a synthetic catalog produced by the ETAS model. Table 1 summarizes information about the examined data.

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Section: Nearest‐neighbor Proximity For Earthquakesmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

Zaliapin

Ben‐Zion

2020

JGR Solid Earth

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“…For every event j we identify its nearest‐neighbor ( parent )

i = \underset{k}{argmin} η_{kj}

that corresponds to the shortest proximity of equation :

η_{j} = min_{i} η_{ij}

. As illustrated in Figure , the distribution of the nearest‐neighbor proximities η j in observed seismicity catalogs is generally bimodal (e.g., Gu et al, ; Kossobokov & Nekrasova, ; Schoenball et al, ; Zaliapin et al, ; Zaliapin & Ben‐Zion, , , ). The long‐proximity mode (representing larger rescaled times and/or distances) roughly corresponds to background events, while the short‐proximity mode is associated with clustered earthquakes (i.e., foreshocks and aftershocks); we refer to Zaliapin and Ben‐Zion (, ) for detailed discussion and illustrations.…”

Section: Methodsmentioning

confidence: 99%

Comparative Study of Earthquake Clustering in Relation to Hydraulic Activities at Geothermal Fields in California

et al. 2018

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Earthquake clustering properties are investigated in relation to fluid balance H(t) (the difference of fluid injection and production rates) using about nine years of data from The Geysers (both the entire field and a local subset), Coso, and Salton Sea geothermal fields in California. Individual earthquake clusters are identified and classified using the nearest-neighbor approach of Zaliapin and Ben-Zion (2013a, https://doi. org/10.1785/0120150211, 2013b, https://doi.org/10.1093/gji/ggw300). These are used to calculate nine complementary cluster statistics as time series with a step of about one month. Three alternative techniques (moving window correlation, analysis of variance, and regression) are employed to assess the relations between (possibly nonstationary) time series of cluster statistics and H(t). A total of 108 pairwise relations between cluster statistics and H(t) are analyzed to clarify effects of fluid activities on seismicity in different places. The seismic clustering response to the fluid balance differs among the examined fields. The Geysers and Salton Sea areas display the highest and lowest clustering responses, respectively. The proportion of clusters consisting of a single event with no offspring (singles) is correlated significantly with H(t) at all examined data sets, with a lower proportion of singles during periods of high fluid balance. This may reflect increased susceptibility to earthquake triggering in time intervals with high injection rates. The background seismicity rates significantly increase with H(t) at the Geysers and Coso, while an opposite relation holds at the Salton Sea. This could be related to the high structural and tectonic complexity at the Salton Sea compared to the other two geothermal fields.Motivated by these challenges, Zaliapin and Ben-Zion (2013a, 2013b) systematically examined properties of earthquake clusters in Southern California with a novel nearest-neighbor approach (see section 3.1). This method allows to robustly identify and analyze seismicity clusters covering a broad range of sizes, energies, and spatio-temporal extents in the presence of varying levels of completeness and uncertainties. Their results highlight the existence of three different basic types of clusters: burst-like clusters (reminiscent of classical

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“…In this study, we assume that the relationship between the magnitude and frequency of aftershocks can be expressed using the Gutenberg-Richter (GR) law [13]. Further, we observe that the relationship between the time elapsed after the main shock and the occurrence of the aftershocks agree with the modified Omori's (MO) law [14][15][16]. Consequently, Reasenberg and Jones proposed a model that can describe the rate of occurrence of aftershocks with a magnitude greater than or equal to M at a time t immediately after a main shock of magnitude Mm, and this can be expressed as follows [17]: ( , , ) = 10…”

Section: Aftershock Definitionmentioning

confidence: 99%

Engineering Applications Using Probabilistic Aftershock Hazard Analyses: Aftershock Hazard Map and Load Combination of Aftershocks and Tsunamis

et al. 2017

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Abstract:After the Tohoku earthquake in 2011, we observed that aftershocks tended to occur in a wide region after such a large earthquake. These aftershocks resulted in secondary damage or delayed rescue and recovery activities. In addition, it has been reported that there are regions where the intensity of the vibrations owing to the aftershocks was much stronger than those associated with the main shock. Therefore, it is necessary to consider the seismic risk associated with aftershocks. We used the data regarding aftershocks that was obtained from the Tohoku earthquake and various other historically large earthquakes. We investigated the spatial and temporal distribution of the aftershocks using the Gutenberg-Richter law and the modified Omori law. Subsequently, we previously proposed a probabilistic aftershock occurrence model that is expected to be useful to develop plans for recovery activities after future large earthquakes. In this study, the probabilistic aftershock hazard analysis is used to create aftershock hazard maps. We propose a hazard map focusing on the probability of aftershocks on the scale of the main shock for use with a recovery activity plan. Following the lessons learned from the 2011 Tohoku earthquake, we focus on the simultaneous occurrence of tsunamis and aftershocks just after a great subduction earthquake. The probabilistic aftershock hazard analysis is used to derive load combination equations of the load and resistance factor design. This design is intended to simultaneously consider tsunamis and aftershocks for tsunami-resistant designs of tsunami evacuation buildings.

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Characterizing Aftershock Sequences of the Recent Strong Earthquakes in Central Italy

Cited by 25 publications

References 37 publications

Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

Comparative Study of Earthquake Clustering in Relation to Hydraulic Activities at Geothermal Fields in California

Engineering Applications Using Probabilistic Aftershock Hazard Analyses: Aftershock Hazard Map and Load Combination of Aftershocks and Tsunamis

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