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Spatial Estimation and Face-forward Prediction of Elution Amount of Arsenic in Mountain Tunneling Using Geostatistics
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- KUBO Taiki
- Department of Urban Management, Graduate School of Engineering, Kyoto University
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- OKUZAWA Koichi
- Technical Research Institute, Obayashi Corporation
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- KOIKE Katsuaki
- Department of Urban Management, Graduate School of Engineering, Kyoto University
Bibliographic Information
- Other Title
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- 地球統計学を用いた山岳トンネルでのヒ素溶出量の 空間分布推定と切羽前方予測
- チキュウ トウケイガク オ モチイタ サンガク トンネル デ ノ ヒソ ヨウシュツリョウ ノ クウカン ブンプ スイテイ ト セッパ ゼンポウ ヨソク
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Description
<p>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.</p>
Journal
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- Geoinformatics
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Geoinformatics 30 (2), 51-58, 2019-06-25
Japan Society of Geoinformatics