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http://dx.doi.org/10.9720/kseg.2017.4.367

A Study on the Automation Algorithm to Identify the Geological Lineament using Spatial Statistical Analysis  

Kwon, O-Il (Geotechnical Engineering Research Institute, Korea Institute of Civil Engineering and Building Technology)
Kim, Woo-Seok (Geotechnical Engineering Research Institute, Korea Institute of Civil Engineering and Building Technology)
Kim, Jin-Hwan (Multi Disaster Countermeasures Organization, Korea Institute of Civil Engineering and Building Technology)
Kim, Gyo-Won (School of Earth System Science, College of Natural Sciences, Kyungpook National University)
Publication Information
The Journal of Engineering Geology / v.27, no.4, 2017 , pp. 367-376 More about this Journal
Abstract
Recently, tunneling under the seabed is becoming increasingly common in many countries. In Korea, there are proposals to tunnel from the mainland to Jeju Island. Safe construction requires geologic structures such as faults to be characterized during the design and construction phase; however, unlike on land, such structures are difficult to survey seabed. This study aims to develop an algorithm that uses geostatistics to automatically derive large-scale geological structures on the seabed. The most important considerations in this method are the optimal size of the moving window, the optimal type of spatial statistics, and determination of the optimal percentile standard. Finally, the optimal analysis algorithm was developed using the R program, which comprehensibly presents variations in spatial statistics. The program allows the type and percentile standard of spatial statistics to be specified by the user, thus enabling an analysis of the geological structure according to variations in spatial statistics. The geotechnical defense-training algorithm shows that a large, linear geological lineament is best visualized using a $3{\times}3$ moving window and a 10% upper standard based on the moving variance value and fractile. In particular, setting the fractile criterion to the upper 0.5% almost entirely eliminates the error values from the contour image.
Keywords
geological structure; subsea tunnel; spatial statistics; automation method;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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