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

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 (= 2(18)) 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Kuwatani T,Nagata K,Okada M,Watanabe T,Ogawa Y,Komai T,Tsuchiya N

doi

10.1038/srep07077

subject

Has Abstract

pub_date

2014-11-17 00:00:00

pages

7077

issn

2045-2322

pii

srep07077

journal_volume

4

pub_type

杂志文章

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