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Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits
Kuwatani, T.; Nagata, K.; Okada, M.; Watanabe, T.; Ogawa, Y.; Komai, T.; Tsuchiya, N. (2014). Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits. NPG Scientific Reports 4(7077): 6 pp. hdl.handle.net/10.1038/srep07077
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322, more
Peer reviewed article  

Available in  Authors 

Keywords
    Geochemistry; Geology; Marine

Authors  Top 
  • Kuwatani, T.
  • Nagata, K.
  • Okada, M.
  • Watanabe, T.
  • Ogawa, Y.
  • Komai, T.
  • Tsuchiya, N.

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.

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