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Report No.

Machine learning for activity evaluation of crush zones using chemical composition; Introduction of examples

Shimada, Koji   ; Tateishi, Ryo*

We need a method to assess the activity of crush zones alternative to the application of overlying sediments, because the zones encountered in underground tunnels and boring have unknown extensions to the ground surface. The method to be developed is one in which the result is objective and independent of the person, which helps professional judgment. In addition, implementation, dissemination and verification must be executable by a general geological engineer. In light of these goals, the whole-rock chemical composition of the fault gouge along a principal slip zone of the crush zone is attractive. Is there any difference in the chemical composition of fault rocks between active and inactive faults? We thought that the utilization of multivariate analysis could be a solution. Therefore, we collected literature values of the chemical composition of fault gouges for faults with known activities, and began searching for a primary equation that distinguishes active from non-active faults by multivariate analysis in 2018. The results of studies on granitic rocks show that there are multiple discriminants that separate active and non-active faults with a discrimination rate of 100%. In the presentation, we would like to introduce the current status of initiatives, including past case studies.



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