Intuitionistic fuzzy set similarity measure for precise lithology identification of volcanic Rocks in the Liaohe Basin
Year: 2025
Type: article
Abstract: The petrophysical characterization of igneous lithofacies in the eastern Liaohe Basin faces persistent challenges due to highly heterogeneous lithostratigraphic architectures and the scarcity of high-resolution borehole image logs essential for precise geological interpretation. Amid the big-data era, machine-learning algorithms have emerged as a promising avenue, offering novel perspectives. To enhance the accuracy of lithology recognition, this study proposes a lithology recognition algorithm grounded in the intuitionistic fuzzy set similarity measure. By leveraging 800 sets of traditional logging curve data, including natural gamma, density logging, corrected neutron porosity, acoustic time-difference, and deep lateral resistivity, the fundamental logging-response characteristics of various volcanic rocks are systematically compiled. Using 62.5% of the data as training samples, a lithology prediction model based on the intuitionistic fuzzy set similarity measure is constructed. Subsequently, 37.5% of the data are employed for prediction, and the model’s accuracy is verified. The results demonstrate that the average coincidence rate of this model for lithology prediction reaches 83.9%, enabling effective discrimination among the main volcanic rock types in the study area. This methodology not only provides a novel mathematical framework for lithology discrimination in volcanic terrains but also advances logging interpretation technology in complex geological settings through its interpretable IFS-based quantification.
DOI: 10.1093/jge/gxaf110
Authors: Yan Wang, Zhuwen Wang, Dan Mou, Lichun Zhang, Jiacheng Li, Lin Dong
Institutions: Beihua University, Jilin University
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,550
OpenAlex ID: W4414161089