Jihoe Kwon

Data generated in the field of geoscience has unique properties that can be characterized by high complexity, sparsity, and site-specific variability. Owing to the unique characteristics, applying general artificial intelligence frameworks and achieving model generalization in geoscience still remains a challenging problem. In this work, we introduce the KIGAM GeoAI Platform, an integrated AI environment designed to bridge the gap between advancing AI technology and practical geoscience research. The platform supports the entire research workflow through a user-friendly, web-based interface, systematically covering essential stages: data uploading, preprocessing, model development, testing, validation, and the final deployment of analytical applications. By providing a centralized online environment for collaborative research, the platform aims to reduce technical entry barriers for geoscientists who may not be AI experts, while establishing a robust foundation for data-driven cooperation. We plan to continue improving and scaling this platform to ensure it remains a stable, accessible, and high-performance tool for both domestic and international geoscience communities. Through these efforts, the KIGAM GeoAI Platform is expected to accelerate digital transformation and foster a more integrated global research ecosystem in the field of geoscience.

Urban geological hazards have increased rapidly in metropolitan areas, with ground subsidence incidents rising five-fold from 69 cases in 2014 to 338 cases in 2018. This study presents a comprehensive quality analysis of borehole data across Seoul Metropolitan Area to enhance urban geotechnical information reliability for underground space safety. A total of 14,016 unique boreholes from 67,054 original records were systematically processed using data from Korea Public Data Portal (data.go.kr) and digital elevation model from the National Geographic Information Institute (ngii. go.kr). Data preprocessing revealed significant quality issues including layer thickness calculation errors and duplicate records, which were systematically corrected. Geotechnical analysis showed Seoul’s subsurface comprises primarily sedimentary layers (26.5%), landfill layers (19.5%), and weathered rock layers (17.5%). Altitude-based analysis confirmed distinct characteristics. High-altitude areas dominated by weathered soil and rock formations, while low-altitude areas predominantly composed of sedimentary deposits, reflecting Seoul’s complex topography from mountainous terrain to alluvial plains. Correlation analysis between Korean geotechnical classifications and Unified Soil Classification System (USCS) standards demonstrated quantitative relationships. Elevation accuracy assessment revealed 75.5% of boreholes exhibited good accuracy within 10 m difference, while 8.5% showed large errors exceeding 50 m. This comprehensive analysis provides essential baseline data for 3D geological modeling, hazard assessment, and underground development planning, supporting smart city-based integrated geospatial platforms for sustainable urban development.

Despite its effectiveness in determining breakage function parameters (BFPs) for quantifying breakage characteristics in mineral grinding processes, the back-calculation method has limitations owing to the uncertainty regarding the distribution of the error function. In this work, using Korean uranium and molybdenum ores, we show that the limitation can be overcome by searching over a wide range of initial values based on the conjugate gradient method. We also visualized the distribution of the sum of squares of the error in the two-dimensional parameter space. The results showed that the error function was strictly convex, and the main problem in the back-calculation of the breakage functions was the flat surface of the objective function rather than the occurrence of local minima. Based on our results, we inferred that the flat surface problem could be significantly mitigated by searching over a wide range of initial values. Back-calculation using a wide range of initial values yields BFPs similar to those obtained from single-sized-feed breakage tests (SSFBTs) up to four-dimensional parameter spaces. Therefore, by searching over a wide range of initial values, the feasibility of the back-calculation approach can be significantly improved with a minimum number of SSFBTs.

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.