| Thin section identification is the basis of various geological work such as research on sedimentation,
diagenesis, and reservoir of carbonate rocks. Carbonate rocks have strong heterogeneity, various structural components
and particle types. The artificial thin section identification is subjective, difficult, time-consuming and labor-intensive,
and not easy to be widely popularized. In the big data and artificial intelligence (AI) background, it is promising to
increase the efficiency by applying AI identification technology. This study summarized the research status and analyzed
the existed problems in AI identification of carbonate thin sections. The main contents of AI identification of carbonate
thin sections include: (1) Preparation of thin sections and image processing. Dyeing thin sections with no-cover glass are
the basis of later recognition. The blue casting thin sections are significant for pore recognition. Photos should be captured
under different optical property including PPL and XPL with different rotation degree. Image pre-processing and
segmentation can help to increase the later identification. The establishment of carbonate thin section database is the basis
of AI identification. (2) Based on the prior knowledge of carbonate professionals, the structural components, mineral
components and pore types of the image are classified, label classification is established, and manual labeling is carried
out by carbonate professionals. It is established that the classification chart of major component labels in carbonate thin
sections. The establishment of label database can contribute to further machine learning. (3) The convolution neural
network and deep learning are introduced into the labeled thin section images, which can learn and discriminate the
morphology and internal structure of various components. The knowledge graph of the thin section image labels is
established by combination of machine learning and manual correction, which can classify rock types, recognize
sedimentary structures and grain types. (4) It is performed that intelligence recognition of structural components, mineral
components and pore types and contents. The denomination specification for AI identification of carbonate thin sections is
established. Automatically denomination would be achieved. There are still problems including label sample amount,
indeterminate semantic object segmentation, diagenesis, etc, which need further research. The future development
directions of AI carbonate identification include the identification of core-outcrop-microscopic image, geochemical
image (CT, SEM, FL, etc.), interpretation of logging and geophysical data. |
