%0 Conference Proceedings
%T Identifying Asperity Patterns Via Machine Learning Algorithms
%+ Ionian University [Corfu]
%A Arvanitakis, Kostantinos
%A Avlonitis, Markos
%Z Part 2: Classification – Pattern Recognition (CLASPR)
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Thessaloniki, Greece
%Y Lazaros Iliadis
%Y Ilias Maglogiannis
%3 Artificial Intelligence Applications and Innovations
%V AICT-475
%P 87-93
%8 2016-09-16
%D 2016
%R 10.1007/978-3-319-44944-9_8
%K Asperity
%K Density
%K b-value
%K Seismicity
%K Machine learning
%Z Computer Science [cs]Conference papers
%X An asperity’s location is very crucial in the spatiotemporal analysis of an area’s seismicity. In literature, b-value and seismic density have been proven as useful indicators for asperity location. In this paper, machine learning techniques are used to locate areas with high probability of asperity existence using as feature vector information extracted solely by earthquake catalogs. Many machine learning algorithms are tested to identify those with the best results. This method is tested for data from the wider region of Hokkaido, Japan where in an earlier study asperities have been detected.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-01557625/document
%2 https://inria.hal.science/hal-01557625/file/430537_1_En_8_Chapter.pdf
%L hal-01557625
%U https://inria.hal.science/hal-01557625
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-475