dorsal/arxiv
View SchemaAutomatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering
| Authors | Guy Ouillon, Caroline Ducorbier, Didier Sornette |
|---|---|
| Categories | |
| ArXiv ID | physics/0703084 |
| URL | https://arxiv.org/abs/physics/0703084 |
| DOI | 10.1029/2007JB005032 |
Abstract
We propose a new pattern recognition method that is able to reconstruct the 3D structure of the active part of a fault network using the spatial location of earthquakes. The method is a generalization of the so-called dynamic clustering method, that originally partitions a set of datapoints into clusters, using a global minimization criterion over the spatial inertia of those clusters. The new method improves on it by taking into account the full spatial inertia tensor of each cluster, in order to partition the dataset into fault-like, anisotropic clusters. Given a catalog of seismic events, the output is the optimal set of plane segments that fits the spatial structure of the data. Each plane segment is fully characterized by its location, size and orientation. The main tunable parameter is the accuracy of the earthquake localizations, which fixes the resolution, i.e. the residual variance of the fit. The resolution determines the number of fault segments needed to describe the earthquake catalog, the better the resolution, the finer the structure of the reconstructed fault segments. The algorithm reconstructs successfully the fault segments of synthetic earthquake catalogs. Applied to the real catalog constituted of a subset of the aftershocks sequence of the 28th June 1992 Landers earthquake in Southern California, the reconstructed plane segments fully agree with faults already known on geological maps, or with blind faults that appear quite obvious on longer-term catalogs. Future improvements of the method are discussed, as well as its potential use in the multi-scale study of the inner structure of fault zones.
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"abstract": "We propose a new pattern recognition method that is able to reconstruct the\n3D structure of the active part of a fault network using the spatial location\nof earthquakes. The method is a generalization of the so-called dynamic\nclustering method, that originally partitions a set of datapoints into\nclusters, using a global minimization criterion over the spatial inertia of\nthose clusters. The new method improves on it by taking into account the full\nspatial inertia tensor of each cluster, in order to partition the dataset into\nfault-like, anisotropic clusters. Given a catalog of seismic events, the output\nis the optimal set of plane segments that fits the spatial structure of the\ndata. Each plane segment is fully characterized by its location, size and\norientation. The main tunable parameter is the accuracy of the earthquake\nlocalizations, which fixes the resolution, i.e. the residual variance of the\nfit. The resolution determines the number of fault segments needed to describe\nthe earthquake catalog, the better the resolution, the finer the structure of\nthe reconstructed fault segments. The algorithm reconstructs successfully the\nfault segments of synthetic earthquake catalogs. Applied to the real catalog\nconstituted of a subset of the aftershocks sequence of the 28th June 1992\nLanders earthquake in Southern California, the reconstructed plane segments\nfully agree with faults already known on geological maps, or with blind faults\nthat appear quite obvious on longer-term catalogs. Future improvements of the\nmethod are discussed, as well as its potential use in the multi-scale study of\nthe inner structure of fault zones.",
"arxiv_id": "physics/0703084",
"authors": [
"Guy Ouillon",
"Caroline Ducorbier",
"Didier Sornette"
],
"categories": [
"physics.geo-ph",
"physics.data-an"
],
"doi": "10.1029/2007JB005032",
"title": "Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering",
"url": "https://arxiv.org/abs/physics/0703084"
},
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