Quantitative logging data clustering with hidden Markov model to assist log unit classification

Suguru Yabe*, Yohei Hamada, Rina Fukuchi, Shunichi Nomura, Norio Shigematsu, Tsutomu Kiguchi, Kenta Ueki

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Revealing subsurface structures is a fundamental task in geophysical and geological studies. Logging data are usually acquired through drilling projects, which constrain the subsurface structure, and together with the description of drill core samples, are used to distinguish geological units. Clustering is useful for interpreting logging data and making log unit classification and is usually performed by manual inspection of the data. However, the validity of clustering results with such subjective criteria may be questionable. This study proposed the application of a statistical clustering method, the hidden Markov model, to conduct unsupervised clustering of logging data. As logging data are aligned along the drilled hole, they and the geological structure hidden behind such sequential datasets can be regarded as observables and hidden states in the hidden Markov model. When log unit classification is manually conducted, depth dependency of logging data is usually focused. Therefore, we included depth information as observables to explicitly represent depth dependency of logging data. The model was applied to the following geological settings: the accretionary prism at the Nankai Trough, the onshore fault zone at the Kii Peninsula (southwest Japan), and the forearc basin at the Japan Trench. The optimum number of clusters were searched using a quantitative index. The clustering results using the hidden Markov model were consistent with previously reported classifications or lithological descriptions; however, our method allowed a more detailed division of logging data, which is useful to interpret geological structures, such as a fault or a fault zone. Therefore, the use of the hidden Markov model enabled us to clarify assumptions quantitatively and conduct clustering consistently for the entire depth range, even for different geological sites. The proposed method is expected to have wider applicability and extensibility for other types of data, including geochemical and structural geological data. Graphical Abstract: [Figure not available: see fulltext.]

Original languageEnglish
Article number93
JournalEarth, Planets and Space
Issue number1
Publication statusPublished - 2022 Dec


  • Clustering
  • Hidden Markov model
  • Logging data
  • Unit classification

ASJC Scopus subject areas

  • Geology
  • Space and Planetary Science


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