Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Classification of Seismic Body Wave Phases Using Supervised Learning
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

The task of accurately distinguishing between arrivals of different types of seismic waves is a common and important task within the field of seismology. For data generated by seismic stations operated by SNSN this task generally requires manual effort. In this thesis, two automatic classification models which distinguish between two types of body waves, P- and S-waves, are implemented and compared, with the aim of reducing the need for manual input. The algorithms are logistic regression and feed-forward artificial neural network. The applied methods use labelled historical data from seismological events in Sweden to train a set of classifiers, with a unique classifier associated with each seismic station. When evaluated on test data, the logistic regression classifiers achieve a mean accuracy of approximately 96% over all stations compared to approximately 98% for the neural network classifiers. The results suggest that both implemented classifiers represent a good option for automatic body wave classification in Sweden.

sted, utgiver, år, opplag, sider
2019. , s. 59
Serie
IT ; 19036
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-396766OAI: oai:DiVA.org:uu-396766DiVA, id: diva2:1368915
Utdanningsprogram
Master Programme in Computational Science
Veileder
Examiner
Tilgjengelig fra: 2019-11-08 Laget: 2019-11-08 Sist oppdatert: 2019-11-08bibliografisk kontrollert

Open Access i DiVA

fulltext(2499 kB)12 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 2499 kBChecksum SHA-512
cf3da101edc6bdc4a18e0b826f7b3e7d918a2efcd30ec6e9e847d12e70a930c7329708f9cc2e0a5167337c9dace4068690098a856d7ebee07ca804b8e77d5a38
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 12 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 304 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf