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

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.

Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia

“…Dealing with earthquake data analysis, Suliswati et al [9,27] calculated the earthquake density value of each cluster in Indonesia using the automatic clustering method. Barakbah et al [10,27] analyzed cluster-oriented seismicity predictions of earthquakes in Indonesia and semantic interpretation of predicted results projected into each region. Shodiq et al [11][12][13][14] performed earthquake predictions using the Automatic Clustering and spatial analysis of magnitude distribution to cluster the earthquake data based on the location of the source point and Neural Networks methods.…”

Spatio-Temporal Associative Mining for Earthquake Data Distribution in Indonesia

“…Related to earthquake analysis, there are Amin Endah Suliswati et al [10] which calculated earthquake density values using the automatic clustering method, Ali Ridho Barakbah et al [11] which analyzed cluster-oriented seismicity predictions of earthquakes in Indonesia and semantic interpretation of predicted results projected into each region, Mohammad Nur Shodiq et al [12] performed earthquake predictions using the Automatic Clustering and Neural Networks methods, Ken-ichi Fukui et al [13] found the damage patterns in Fuel Cell and Earthquake Occurrence Patterns by Co-Occurring Cluster Mining, Evaldas Luksys et al [14] proposed tools for enabling scientists analyze and interpret large-scale datasets about earthquakes in a way which will complement current analytical tools and thinking thus, complement current effort on understanding the event itself.…”

Planning and Designing The Development of Software Library for Deep Learning

“…We propose a software library to realize the demands of intelligent tools that benefit researchers and practitioners in academia and industry on the implementation of deep learning algorithms practically into the codes that easily modify or add the mathematical formula in the model training to improve the model's performance in executing the tasks. The software library that we will develop specializes in implementing deep learning algorithms, and it will be part of Analytical Library Intelligent-computing (ALI) 1 as a submodule that complements the other machine learning algorithm modules including Automatic Clustering [11], Hierarchical K-Means, K Nearest Neighbors, and Neural Networks [12].…”