Automatic Classification of seismic signals of the Chilean Llaima Volcano using Cartesian Genetic Programming based Artificial Neural Network
Automatic Classification of seismic signals of the Chilean Llaima Volcano using Cartesian Genetic Programming based Artificial Neural Network
- Author(s): G. Khattak ; M.S. Khan ; G.M. Khan ; F. Huenupan ; M. Curilem
- DOI: 10.1049/cp.2017.0165
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
8th International Conference of Pattern Recognition Systems (ICPRS 2017) — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): G. Khattak ; M.S. Khan ; G.M. Khan ; F. Huenupan ; M. Curilem Source: 8th International Conference of Pattern Recognition Systems (ICPRS 2017), 2017 page ()
- Conference: 8th International Conference of Pattern Recognition Systems (ICPRS 2017)
- DOI: 10.1049/cp.2017.0165
- ISBN: 978-1-78561-652-5
- Location: Madrid, Spain
- Conference date: 11-13 July 2017
- Format: PDF
Volcanoes are ruptures in the earth's crust unleashing the dormant forces lying buried deep beneath the crust. The present work is an endeavour towards the quest of automatic volcanic event classification. We propose a volcanic event classification system based on Cartesian Genetic Programming based Artificial Neural Network (CGPANN). CGPANN is a technique for generation of ANN networks without any constraints on network size and topology. Two types of volcanic events for the Chilean Llaima volcano, long period (LP), related to pressure in the volcanic ducts occurring at discrete periods, and volcano tectonic (VT), that is due to the rock fracture, are classified in the present work. The system shows over 80% correct classification for unseen events. The current work also attempts to explore the networks generated and features selected in order to gain an insight into the underlying processes.
Inspec keywords: topology; volcanology; Earth; neural nets; seismic waves; genetic algorithms; seismology; pattern classification; geophysics computing
Subjects: Volcanology; Geometry, differential geometry, and topology; Data handling techniques; Seismic waves; Combinatorial mathematics; Seismology; Optimisation techniques; Geophysics computing
Related content
content/conferences/10.1049/cp.2017.0165
pub_keyword,iet_inspecKeyword,pub_concept
6
6