Multiple regression analysis for estimating earthquake magnitude as a function of fault length and recurrence interval

Takashi Kumamoto, Kozo Oonishi, Yoko Futagami, Mark W. Stirling

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Multiple regressions are developed using world earthquake data and active fault data, and the regressions are then evaluated with Akaike’s Information Criterion (IEEE Trans Autom Control, 19(6):716–723). The AIC method enables selection of the regression formula with the best fit while taking into consideration the number of parameters. By using parameters relevant to earthquakes and active faults in the regression analyses, we develop a new empirical equation for magnitude estimation as Mw = 1.13logLs + 0.16logR + 4.62.

Original languageEnglish
Title of host publicationEarthquakes, Tsunamis and Nuclear Risks: Prediction and Assessment Beyond the Fukushima Accident
PublisherSpringer Japan
Pages43-53
Number of pages11
ISBN (Print)9784431558224, 9784431558200
DOIs
Publication statusPublished - Jan 1 2016

Keywords

  • Fault length
  • Magnitude
  • Multiple regression analysis
  • Recurrence interval

ASJC Scopus subject areas

  • Engineering(all)
  • Environmental Science(all)
  • Earth and Planetary Sciences(all)
  • Mathematics(all)

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    Kumamoto, T., Oonishi, K., Futagami, Y., & Stirling, M. W. (2016). Multiple regression analysis for estimating earthquake magnitude as a function of fault length and recurrence interval. In Earthquakes, Tsunamis and Nuclear Risks: Prediction and Assessment Beyond the Fukushima Accident (pp. 43-53). Springer Japan. https://doi.org/10.1007/978-4-431-55822-4_3