TY - JOUR
T1 - Inferring fracture forming processes by characterizing fracture network patterns with persistent homology
AU - Suzuki, A.
AU - Miyazawa, M.
AU - Okamoto, A.
AU - Shimizu, H.
AU - Obayashi, I.
AU - Hiraoka, Y.
AU - Tsuji, T.
AU - Kang, P. K.
AU - Ito, T.
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers JP17H04976 , JP16K17638 (Japan); JST ACT-X Grant Number JPMJAX190H (Japan); JST CREST Mathematics Grant number 15656429 (Japan); and the Structural Materials for Innovation, Strategic Innovation Promotion Program D72 (Japan), which are gratefully acknowledged.
Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers JP17H04976, JP16K17638 (Japan); JST ACT-X Grant Number JPMJAX190H (Japan); JST CREST Mathematics Grant number 15656429 (Japan); and the Structural Materials for Innovation, Strategic Innovation Promotion Program D72 (Japan), which are gratefully acknowledged.
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/10
Y1 - 2020/10
N2 - Persistent homology is a mathematical method to quantify topological features of shapes, such as connectivity. This study applied persistent homology to analyze fracture network patterns in rocks. We show that persistent homology can detect paths connecting from one boundary to the other boundary constituting fractures, which is useful for understanding relationships between fracture patterns and flow phenomena. In addition, complex fracture network patterns so-called mesh textures in serpentine were analyzed by persistent homology. In previous studies, fracture network patterns for different flow conditions were generated by a hydraulic–chemical–mechanical simulation and classified based on additional data and on expert's experience and knowledge. In this study, image analysis based on persistent homology alone was able to characterize fracture patterns. Similarities and differences of fracture network patterns between natural serpentinite and simulation were quantified and discussed. The data-driven approach combining with the persistent homology analysis helps to infer fracture forming processes in rocks. The results of persistent homology analysis provide critical topological information that cannot be obtained by geometric analysis of image data only.
AB - Persistent homology is a mathematical method to quantify topological features of shapes, such as connectivity. This study applied persistent homology to analyze fracture network patterns in rocks. We show that persistent homology can detect paths connecting from one boundary to the other boundary constituting fractures, which is useful for understanding relationships between fracture patterns and flow phenomena. In addition, complex fracture network patterns so-called mesh textures in serpentine were analyzed by persistent homology. In previous studies, fracture network patterns for different flow conditions were generated by a hydraulic–chemical–mechanical simulation and classified based on additional data and on expert's experience and knowledge. In this study, image analysis based on persistent homology alone was able to characterize fracture patterns. Similarities and differences of fracture network patterns between natural serpentinite and simulation were quantified and discussed. The data-driven approach combining with the persistent homology analysis helps to infer fracture forming processes in rocks. The results of persistent homology analysis provide critical topological information that cannot be obtained by geometric analysis of image data only.
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U2 - 10.1016/j.cageo.2020.104550
DO - 10.1016/j.cageo.2020.104550
M3 - Article
AN - SCOPUS:85088922823
VL - 143
JO - Computers and Geosciences
JF - Computers and Geosciences
SN - 0098-3004
M1 - 104550
ER -