International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 2, pages: 587-593, DOI:10.18517/ijaseit.9.2.7269

Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

Ali Ridho Barakbah, Tri Harsono, Amang Sudarsono

Abstract

Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.

Keywords:

seismicity prediction analysis; earthquake prediction; automatic clustering; semantic interpretation.

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