Earthquake Prediction Using the Fields Estimated by an Adaptive Algorithm

V. G. Gitis, A. B. Derendyaev, S. A. Pirogov, V. G. Spokoiny, E. F. Yurkov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We suggest a new approach to the estimation of the parameters of in-homogeneous spatio-Temporal marked point fields. It is based on the method of adaptive weights smoothing (AWS).We construct a generalized version of AWS algorithm for calculating spatial and spatio-Temporal fields of density, mean values and the correlation (fractal) dimension. The algorithm is used to evaluate the seismic process parameter fields from earthquake catalogues. We use these estimates to predict strong earthquakes. We compare the results of forecasting based on the generalized AWS algorithm and on the kernel smoothing procedure. The AWS-based forecasting was observed to surpass the forecasting using the kernel estimates.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450352253
DOIs
Publication statusPublished - 19 Jun 2017
Externally publishedYes
Event7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017 - Amantea, Italy
Duration: 19 Jun 201722 Jun 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F129475

Conference

Conference7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017
Country/TerritoryItaly
CityAmantea
Period19/06/1722/06/17

Keywords

  • Adaptive weights smoothing (aws)
  • Earthquake catalogue
  • Earthquake prediction
  • GIS GeoTime 3
  • Marked point fields

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