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master's thesis
Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža
Zagreb: University of Zagreb, Faculty of Science, 2024. urn:nbn:hr:217:618108
Wild, Dorian
University of Zagreb
Faculty of Science
Department of Geophysics

Institutional repository: Repository of the Faculty of Science

Cite this document

Wild, D. (2024). Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:618108

Wild, Dorian. "Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:618108

Wild, Dorian. "Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:618108

Wild, D. (2024). 'Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža', Master's thesis, University of Zagreb, Faculty of Science, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:618108

Wild D. Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2024 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:217:618108

D. Wild, "Detekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:217:618108

Metadata
TitleDetekcija potresa u sjeverozapadnim Dinaridima uz upotrebu neuronskih mreža
Title (english)Neural network based earthquake detection in the northwest Dinarides
AuthorDorian Wild
MentorJosip Stipčević (mentor)
Committee memberJosip Stipčević (predsjednik povjerenstva)
Committee memberIva Dasović (član povjerenstva)
Committee memberZoran Pasarić (član povjerenstva)
Committee memberIvana Herceg Bulić (član povjerenstva)
Committee memberIgor Mandić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Geophysics)
Zagreb
Defense date and country2024-02-27, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Geophysics
Seismology and Physics of the Earth's Interior
Abstract
Dolaskom doba nazvanim Big Data, dogodilo se eksponencijalno povećanje količine seizmičkih podataka zbog povećanja broja seizmičkih postaja i napretka tehnologije, zbog čega tradicionalne metode analize i ručne detekcije faza postaju neodržive. Ususret tom vremenu, testira se novi pristup koji koristi neuronske mreže za automatizaciju detekcije i karakterizaciju seizmičkih događaja, s ciljem povećanja točnosti i učinkovitosti u odnosu na postojeće automatizirane sustave. U ovom radu analiziran je sveobuhvatni skup podataka kojeg čini testni katalog potresa u razdoblju 2009. - 2018. godine u sjeverozapadnim Dinaridima primjenom modela PhaseNet za detekciju seizmičkih faza i GaMMA Associatora za izradu sintetičkih kataloga te procedura koje značajno poboljšavaju detekciju potresa. Korišteno je pet varijanti modela PhaseNet treniranih na pet različitih baza podataka: INSTANCE, STEAD, ETHZ, SCEDC i GEOFON sa svrhom detekcije P- i S-faza, te algoritam na temelju strojnog učenja zvan GaMMA Associator sa svrhom asocijacije i izrade sintektičkog kataloga. Najprije se varijante modela PhaseNet primjenjuju na uzorak iz stvarnog kataloga u svrhu određivanja najefikasnije varijante modela PhaseNet, te se potom ta varijanta primjenjuje na cijeli skup podataka. Kombinacija modela PhaseNet s tehnikom učenja prijenosom i GaMMA Associatora omogućava stvaranje kataloga potresa koji sadrži preko 92 % identičnih događaja iz usporednog stvarnog kataloga, te također 10 puta više novih događaja. Rezultati naglašavaju potencijal strojnog učenja za revoluciju u praćenju potresa pružanjem bržih, većih i pravovremenijih sposobnosti detekcije. Ovo istraživanje ne samo da doprinosi napretku metoda detekcije potresa, već otvara i puteve za buduća istraživanja u seizmologiji i povezanim područjima.
Abstract (english)
With the arrival of the era called Big Data, there has been an exponential increase in the volume of seismic data due to the expansion of seismic stations and the advancement of technology, due to which traditional methods of analysis and manual phase detection are becoming unsustainable. Therefore, a new approach that uses neural networks to automate the detection and characterization of seismic events is being tested, with the goal of increasing accuracy and efficiency compared to existing automated systems. In this thesis, the PhaseNet models and GaMMA Associator were applied in the analysis of a comprehensive data set of the test earthquake catalogue for the period of 2009 – 2018 in the northwest Dinarides to significantly improve earthquake detection and create a synthetic earthquake catalogue. Five variants of the PhaseNet model trained on five different databases were used: INSTANCE, STEAD, ETHZ, SCEDC and GEOFON to detect P and S phases, and GaMMA Associator, an algorithm based on machine learning for association and creation of a synthetic catalog. The models were first applied to a sample from the real catalog to determine the most efficient variant of PhaseNet, and then that variant is applied to the entire data set. The combination of PhaseNet and GaMMA Associator generates an earthquake catalog that contains over 92% identical events from the comparative real catalog, and 10 times more new events. The findings underscore the potential of machine learning to revolutionize earthquake monitoring by providing more reliable and timely detection capabilities. This research not only contributes to the advancement of seismic detection methods but also opens avenues for future investigations in seismology and related fields.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:618108
Study programmeTitle: graduate academic research programme: Master of Physics – geophysics, modules: Seismology and solid Earth physics, Meteorology and physical oceanography; specializations in: Seismology and solid Earth physics, Meteorology and physical oceanography
Course: Seismology and solid Earth physics
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni magistar fizike – geofizike (sveučilišni magistar fizike – geofizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-03-19 10:32:34