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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 - Sci Rep (2014)

Bottom Line: 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.The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan.

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.

No MeSH data available.


Related in: MedlinePlus

(a) Sampling locations of 2011 Tohoku tsunami deposits38. The locations of the tsunami samples are indicated by red circles. This figure was generated using MATLAB. (b) Scatter diagrams of whole-rock compositions. The horizontal axis is Si, and the vertical axes are the elements named at the top of each diagram. Red circles indicate the tsunami deposits, and blue squares indicate the non-tsunami sediments. The unit wt% indicates weight percent oxides.
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f1: (a) Sampling locations of 2011 Tohoku tsunami deposits38. The locations of the tsunami samples are indicated by red circles. This figure was generated using MATLAB. (b) Scatter diagrams of whole-rock compositions. The horizontal axis is Si, and the vertical axes are the elements named at the top of each diagram. Red circles indicate the tsunami deposits, and blue squares indicate the non-tsunami sediments. The unit wt% indicates weight percent oxides.

Mentions: We used 129 samples of the deposits from the March 11, 2011, Tohoku tsunami. Samples were taken along the coastline from Kuji City, Iwate Prefecture, via Miyagi Prefecture, to Minami-Soma City, Fukushima Prefecture, from April to August 201138 (Fig. 1a). Many tsunami deposits were sampled from a depth of 0.5–5 cm underground; these deposits consisted primarily of mud and sand, ranging from silt to coarse sand. In the stricken region, the tsunami deposits were sampled only from well-preserved locations, such as inundation points and basements in surviving buildings, because much of the sediment was disturbed by the removal of rubble. Although we cannot deny the possibility of a slight modification of the geochemical pattern by diagenesis and/or leaching by rainfall, Chague-Goff and co-workers reported that the geochemical signature was still retained in most sediment samples seven months after the 2011 Tohoku tsunami14. The details of the sampling and analytical methods will be discussed in the Methods section.


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 - Sci Rep (2014)

(a) Sampling locations of 2011 Tohoku tsunami deposits38. The locations of the tsunami samples are indicated by red circles. This figure was generated using MATLAB. (b) Scatter diagrams of whole-rock compositions. The horizontal axis is Si, and the vertical axes are the elements named at the top of each diagram. Red circles indicate the tsunami deposits, and blue squares indicate the non-tsunami sediments. The unit wt% indicates weight percent oxides.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4233330&req=5

f1: (a) Sampling locations of 2011 Tohoku tsunami deposits38. The locations of the tsunami samples are indicated by red circles. This figure was generated using MATLAB. (b) Scatter diagrams of whole-rock compositions. The horizontal axis is Si, and the vertical axes are the elements named at the top of each diagram. Red circles indicate the tsunami deposits, and blue squares indicate the non-tsunami sediments. The unit wt% indicates weight percent oxides.
Mentions: We used 129 samples of the deposits from the March 11, 2011, Tohoku tsunami. Samples were taken along the coastline from Kuji City, Iwate Prefecture, via Miyagi Prefecture, to Minami-Soma City, Fukushima Prefecture, from April to August 201138 (Fig. 1a). Many tsunami deposits were sampled from a depth of 0.5–5 cm underground; these deposits consisted primarily of mud and sand, ranging from silt to coarse sand. In the stricken region, the tsunami deposits were sampled only from well-preserved locations, such as inundation points and basements in surviving buildings, because much of the sediment was disturbed by the removal of rubble. Although we cannot deny the possibility of a slight modification of the geochemical pattern by diagenesis and/or leaching by rainfall, Chague-Goff and co-workers reported that the geochemical signature was still retained in most sediment samples seven months after the 2011 Tohoku tsunami14. The details of the sampling and analytical methods will be discussed in the Methods section.

Bottom Line: 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.The proposed methodology has the potential to aid in the identification of past tsunamis by using other tsunami proxies.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan.

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.

No MeSH data available.


Related in: MedlinePlus