Segmentation of fault networks determined from spatial clustering of earthquakes

Ouillon, Guy; Sornette, Didier

doi:10.1029/2010jb007752

Cited by 34 publications

(42 citation statements)

References 59 publications

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“…In other words, there is not enough information to identify all seismological and seismotectonic characteristics of a region based on data-mining approaches among observed data. This pessimistic observation must be balanced by the fact that the recorded seismic events reveal some of the most correlated part of seismological parameters, like strain field in the region (Ouillon and Sornette, 2011). The located earthquakes in a region usually occur on a subset of the fault network, and earthquake clusters reveal the underlying existence of such tectonic structures.…”

Section: Area Source Determination By Fuzzymentioning

confidence: 99%

“…As Ouillon and Sornette (2011) mentioned, in statistical seismology it is assumed that earthquakes do not have any interaction. As a result, it is a common task to decluster earthquake catalogs before performing hazard estimation.…”

Section: Earthquake Catalogmentioning

confidence: 99%

“…Such interpretations imply that earthquake clusters should have an impact with the minimum volume. Ouillon and Sornette (2011) proposed another index for determining the optimum number of clusters in the context of cross-validation tests. To calculate this index, the catalog is divided into training and testing sets.…”

Section: Gg Clustering Algorithmmentioning

confidence: 99%

“…It is assumed that a fault segment or fault line produces earthquakes around it, thus the clusters of events should have minimum volume and maximum density in their ideal form. Ouillon and Sornette (2011) provided two viewpoints of seismology and structural geology about the locations of earthquakes. Both viewpoints indicate that earthquakes occur near Euclidean surfaces that are geometrical representation of active faults.…”

Section: Gg Clustering Algorithmmentioning

confidence: 99%

“…In this regard, clustering analysis is an efficient tool to decrease the dimensionality of information and to extract hidden information and patterns among huge amount of observations (Berkhin, 2002). The subject of using data-mining techniques in identification of fault segments and seismic sources has attracted the attention of many researchers in the last decade (Woo, 1996;Burton et al, 2008;Ouillon et al, 2008;Weatherill and Burton, 2009;Ouillon and Sornette, 2011;Wang et al, 2013;Burton and Hall, 2014).…”

Section: Introductionmentioning

confidence: 98%

See 4 more Smart Citations

Delineation of Seismic Sources in Probabilistic Seismic‐Hazard Analysis Using Fuzzy Cluster Analysis and Monte Carlo Simulation

Ansari¹,

Firuzi²,

Etemadsaeed³

2015

Bulletin of the Seismological Society of America

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Determination of seismic sources is the first step in probabilistic seismichazard analysis (PSHA); however, this step, especially in low seismic regions, is often controversial. In conventional PSHA procedure, determination of seismic sources is merely based on the subjective judgments of experts, and in many cases, there are great differences among proposed seismic models in a specific region. As a result, one important source of uncertainty in PSHA is due to determination of seismic sources. In this article, by combination of fuzzy clustering analysis and Monte Carlo simulation, an objective method for determination and probabilistic modeling of seismic sources is presented. By clustering spatial locations of earthquakes, it is possible to specify the extent of each seismic source in an objective way. A cluster quality index is used to identify the optimum number of clusters. The density and spread of events in each cluster determines the geometrical shape of seismic sources. Moreover, in this article a method is proposed to construct spatial probability density functions (PDFs) of earthquake locations based on the results of fuzzy clustering analysis. The spatial PDF of earthquakes can be used for the generation of synthetic events in Monte Carlo simulation. The Azarbaijan region, with its varied seismotectonics and generally high seismicity, is used as an important area of seismicity in which to develop and demonstrate the application and capability of fuzzy clustering analysis in specifying seismic sources. The PSHA is performed for the city of Tabriz, and a comprehensive comparison is made between the results of conventional PSHA, ordinary Monte Carlo hazard analysis, and the proposed method. The results indicate there is an objective relationship between observed seismicity and seismotectonic evidences in the region. Moreover, the distribution of synthetic events is highly correlated with the observed seismicity, seismotectonic, and geological information of the region.

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Section: Area Source Determination By Fuzzymentioning

confidence: 99%

Section: Earthquake Catalogmentioning

confidence: 99%

Section: Gg Clustering Algorithmmentioning

confidence: 99%

Section: Gg Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Delineation of Seismic Sources in Probabilistic Seismic‐Hazard Analysis Using Fuzzy Cluster Analysis and Monte Carlo Simulation

Ansari¹,

Firuzi²,

Etemadsaeed³

2015

Bulletin of the Seismological Society of America

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Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

Wang

Ouillon²,

Woessner

et al. 2013

JGR Solid Earth

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[1] We introduce the anisotropic clustering of location uncertainty distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, we apply six different validation procedures in order to select the corresponding best fault network. Two of the validation steps (cross validation and Bayesian information criterion (BIC) process the fit residuals, while the four others look for solutions that provide the best agreement with independently observed focal mechanisms. Tests on synthetic catalogs allow us to qualify the performance of the fitting method and of the various validation procedures. The ACLUD method is able to provide solutions that are close to the expected ones, especially for the BIC and focal mechanism-based techniques. The clustering method complemented by the validation step based on focal mechanisms provides good solutions even in the presence of a significant spatial background seismicity rate. Our new fault reconstruction method is then applied to the Landers area in Southern California and compared with previous clustering methods. The results stress the importance of taking into account undersampled subfault structures as well as of the spatially inhomogeneous location uncertainties.

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Systematic survey of high‐resolutionbvalue imaging along Californian faults: Inference on asperities

2014

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Understanding and forecasting earthquake occurrences is presumably linked to understanding the stress distribution in the Earth's crust. This cannot be measured instrumentally with useful coverage. However, the size distribution of earthquakes, quantified by the Gutenberg‐Richter b value, is possibly a proxy to differential stress conditions and could therewith act as a crude stress‐meter wherever seismicity is observed. In this study, we improve the methodology of b value imaging for application to a high‐resolution 3‐D analysis of a complex fault network. In particular, we develop a distance‐dependent sampling algorithm and introduce a linearity measure to restrict our output to those regions where the magnitude distribution strictly follows a power law. We assess the catalog completeness along the fault traces using the Bayesian Magnitude of Completeness method and systematically image b values for 243 major fault segments in California. We identify and report b value structures, revisiting previously published features, e.g., the Parkfield asperity, and documenting additional anomalies, e.g., along the San Andreas and Northridge faults. Combining local b values with local earthquake productivity rates, we derive probability maps for the annual potential of one or more M6 events as indicated by the microseismicity of the last three decades. We present a physical concept of how different stressing conditions along a fault surface may lead to b value variation and explain nonlinear frequency‐magnitude distributions. Detailed spatial b value information and its physical interpretation can advance our understanding of earthquake occurrence and ideally lead to improved forecasting ability.

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Segmentation of fault networks determined from spatial clustering of earthquakes

Cited by 34 publications

References 59 publications

Delineation of Seismic Sources in Probabilistic Seismic‐Hazard Analysis Using Fuzzy Cluster Analysis and Monte Carlo Simulation

Delineation of Seismic Sources in Probabilistic Seismic‐Hazard Analysis Using Fuzzy Cluster Analysis and Monte Carlo Simulation

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

Systematic survey of high‐resolutionbvalue imaging along Californian faults: Inference on asperities

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