Geoinformatics
Online ISSN : 1347-541X
Print ISSN : 0388-502X
ISSN-L : 0388-502X
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Cover (GEOINFORMATICS 2019 Vol30. No.2)
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  • Taiki KUBO, Koichi OKUZAWA, Katsuaki KOIKE
    Type: research-article
    2019 Volume 30 Issue 2 Pages 51-58
    Published: June 25, 2019
    Released: June 25, 2019
    JOURNALS RESTRICTED ACCESS

    We aim to develop a method for face-forward prediction of naturally-occurring heavy metals concentrations using geostatistics, by selecting elution amount of arsenic (As) and a tunnel excavated in the Shimanto Group as a case study. The As elution amount data are revealed to be spatially correlated and the correlation structure is well fitted to the spherical model with small nugget effect, which enables to clarify general trend of the As elution amount over the tunnel interval by ordinary kriging (OK). OK’s accuracy of face-forward prediction is relatively high within 3 m ahead of face, but the accuracy decreases largely where the As elution amount changes abruptly, even though applying sequential Gaussian simulation that can reduce the smoothing effect of OK. To increase the prediction accuracy, effectiveness of normalized drilling velocity ratio (NDVR) is found as a supplementary factor, because NDVR increases in shale parts as the As elution amount does. In addition, a SEM-EDS observation of shale and sandstone samples clarifies that As is generally contained in pyrite common to the samples, but pyrite in shale has Framboidal texture, i.e. aggregation of small spherical particles. Amount and texture of pyrite may be additional significant geologic factors on the prediction accuracy.

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