EGU22-12489
https://doi.org/10.5194/egusphere-egu22-12489
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

“Fully-automated” clustering method for stress inversions (CluStress)

Lukács Kuslits1, Lili Czirok1,2,3, and István Bozsó1
Lukács Kuslits et al.
  • 1Institute of Earth Physics and Space Science (ELKH EPSS), Sopron, Hungary (kuslits.lukacs@epss.hu)
  • 2University of Pécs, Faculty of Sciences, Institute of Geography and Earth Sciences, Department of Geology and Meteorology, Pécs, Hungary
  • 3University of Sopron, Faculty of Forestry, Roth Gyula Forestry and Wildlife Management Doctoral School, Sopron, Hungary

As it is well-known, stress fields are responsible for earthquake formation. In order to analyse stress relations in a study area using focal mechanisms’ (FMS) inversions, it is vital to consider three fundamental criteria:

(1)       The investigated area is characterized by a homogeneous stress field.

(2)       The earthquakes occur with variable directions on pre-existing faults.

(3)       The deviation of the fault slip vector from the shear stress vector is minimal (Wallace-Bott hypothesis).

The authors have attempted to develop a “fully-automated” algorithm to carry out the classification of the earthquakes as a prerequisite of stress estimations. This algorithm does not call for the setting of hyper-parameters, thus subjectivity can be reduced significantly and the running time can also decrease. Nevertheless, there is an optional hyper-parameter that is eligible to filter outliers, isolated points (earthquakes) in the input dataset.

In this presentation, they show the operation of this algorithm in case of synthetic datasets consisting of different groups of FMS and a real seismic dataset. The latter come from a survey area in the earthquake-prone Vrancea-zone (Romania). This is a relatively small region (around 30*70 km) in the external part of SE-Carpathians where the distribution of the seismic events is quite dense and heterogeneous.

It shall be noted that though the initial results are promising, further developments are still necessary. The source codes are soon to be uploaded to a public GitHub repository which will be available for the whole scientific community.

How to cite: Kuslits, L., Czirok, L., and Bozsó, I.: “Fully-automated” clustering method for stress inversions (CluStress), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12489, https://doi.org/10.5194/egusphere-egu22-12489, 2022.

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