Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity

Kamer, Yavor; Ouillon, Guy; Sornette, Didier

doi:https://doi.org/10.5194/nhess-20-3611-2020

Articles | Volume 20, issue 12

https://doi.org/10.5194/nhess-20-3611-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-20-3611-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 12

Research article

|

23 Dec 2020

Research article |

| 23 Dec 2020

Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity

Yavor Kamer, Guy Ouillon, and Didier Sornette

Data sets

Condensation of earthquake location distributions: Optimal spatial information encoding Y. Kamer https://doi.org/10.5281/zenodo.4356218

KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity [Data set] Y. Kamer, E. Kissling, G. Ouillon, and D. Sornette https://doi.org/10.5281/zenodo.4355424

Model code and software

Agglomorative Clustering for Fault Network Reconstruction Y. Kamer https://doi.org/10.5281/zenodo.4274628

Video abstract

Fault network reconstruction using agglomerative clustering: applications to south Californian seismicity Y. Kamer https://doi.org/10.5446/49879

Short summary

Earthquakes cluster in space highlighting fault structures in the crust. We introduce a method to identify such patterns. The method follows a bottom-up approach that starts from many small clusters and, by repeated mergings, produces a larger, less complex structure. We test the resulting fault network model by investigating its ability to forecast the location of earthquakes that were not used in the study. We envision that our method can contribute to future studies relying on fault patterns.