Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits

Tatsu Kuwatani, Kenji Nagata, Masato Okada, Takahiro Watanabe, Yasumasa Ogawa, Takeshi Komai, Noriyoshi Tsuchiya

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Geochemical discrimination has recently been recognised as a potentially useful proxy for identifying tsunami deposits in addition to classical proxies such as sedimentological and micropalaeontological evidence. However, difficulties remain because it is unclear which elements best discriminate between tsunami and non-tsunami deposits. Herein, we propose a mathematical methodology for the geochemical discrimination of tsunami deposits using machine-learning techniques. The proposed method can determine the appropriate combinations of elements and the precise discrimination plane that best discerns tsunami deposits from non-tsunami deposits in high-dimensional compositional space through the use of data sets of bulk composition that have been categorised as tsunami or non-tsunami sediments. We applied this method to the 2011 Tohoku tsunami and to background marine sedimentary rocks. After an exhaustive search of all 262,144 (=218) combinations of the 18 analysed elements, we observed several tens of combinations with discrimination rates higher than 99.0%. The analytical results show that elements such as Ca and several heavy-metal elements are important for discriminating tsunami deposits from marine sedimentary rocks. These elements are considered to reflect the formation mechanism and origin of the tsunami deposits. The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies.

Original languageEnglish
Article number7077
JournalScientific reports
Volume4
DOIs
Publication statusPublished - 2014 Nov 17

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits'. Together they form a unique fingerprint.

  • Cite this