Yeonguk Jo

Characterizing fracture networks in deep (hundreds-of-meters) crystalline rock is a key requirement for assessing the suitability of a high-level radioactive waste repository site. Hydraulically conductive fracture networks may act as preferential groundwater pathways and therefore need to be identified for long-term safety assessment. In line with the geophysical exploration objective to investigate and visualize connected fracture networks in deep rock, this study proposes an integrated interpretation strategy combining borehole-based multi geophysical data systems. This work is part of a basic research project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) entitled “Development of Core Technologies for Characterization and Modeling of Fractured Rock in the Assessment of Site Suitability for High-Level Radioactive Waste Disposal”.This study aims to identify and quantitatively characterize major fracture-network intervals around the borehole by integrating acoustic televiewer (ATV) borehole imaging logs with borehole radar data. ATV provides high-resolution structural information for fractures penetrating the borehole wall, including dip, dip direction, and aperture (where resolvable). However, because ATV observation is confined to the borehole wall image, it has limited capability to evaluate the continuity and spatial distribution of the fractures around the borehole. In contrast, while quantitative characterization of detailed fracture geometry (e.g., orientation and aperture) from borehole radar data alone is difficult, it can image relatively large and continuous fractures within approximately 10-15 m of the borehole that may be hydraulically conductive. Borehole radar method can also detect fractures that do not directly intersect the borehole. Using these complementary strengths, we propose the joint interpretation strategy to image major fracture-network candidates around borehole and to infer their dip, dip direction, aperture, and relative continuity. In addition, we demonstrate the workflow through a case study with field data.The case study was conducted using ATV and borehole radar datasets acquired from a KIGAM borehole down to a depth of 100 m. First, based on the ATV log, fracture characteristics (e.g., dip, dip direction, and aperture) were analyzed, and major fracture-network intervals with clustered fractures were identified. Then, near-borehole (early-time) reflection events in the borehole radar data were analyzed to evaluate their relationship with the major fracture-network intervals derived from the ATV log. In particular, distinct reflectors observed at relatively later times (i.e., beyond the near-borehole) in radar data were matched to the major fracture-network intervals derived from ATV to estimate the relative continuity of them around the borehole. The proposed borehole-based integrated ATV and radar interpretation strategy enables characterization of major fracture networks and is expected to provide a practical approach for screening and visualizing deep fracture networks associated with the stability of repository sites.

We introduce a comprehensive strategy to monitor subsurface fault behavior and associated geophysical environment (e.g., micro-seismicity, stress, groundwater), using a deep borehole based monitoring system. This study provides an in-depth overview of the TELLUS (The Earth Login Leverage for Underground Signal) project, with its individual monitoring system tracking subsurface fault movements with high precision by deploying borehole seismometers, and gathering important data on various geophysical properties.This paper details site selection process and characterizations, the operational framework on monitoring system installations, and the potential of deep borehole monitoring approach in advancing subsurface-related geophysical studies. We conducted extensive review of the distributions of major fault systems in the south-eastern part of South Korea. Subsequently, we strategically selected and arranged candidates for monitoring system installations. Total of six TELLUS deep borehole monitoring systems were installed in the vicinity of the major faults (Yangsan and Ulsan fault).In the TELLUS observatories, preliminary monitoring data is being collected in real time, and this is establishing a foundation for a more precise understanding of the behavior of subsurface faults and the related geophysical environment. It would be expected that the comprehensive analysis of these datasets will further elucidate the intricate subsurface geophysics. This enhanced understanding promises to contribute substantially to our seismic risk assessment capabilities and to the broader field of geoscience research, offering new insights into earthquake prediction and geophysical phenomena.

This study applied the long short-term memory (LSTM) algorithm to detect the depths of borehole breakouts in image logs to overcome the limitations of the extreme gradient boosting (XGBoost) model. Using 99,084 data obtained from the preprocessed image log of a 1 km deep borehole in Gyeongju-si, we developed both XGBoost and LSTM models. While the XGBoost model considers only single-depth data, the LSTM model analyzes the occurrence of borehole breakouts at the central depth using data from seven consecutive depths. The LSTM model showed an 88% reduction in false negatives and 29% improvement in recall on the test data compared to the XGBoost model. It also outperformed the XGBoost model that using the seven-depth data. These results suggest that the LSTM model can systematically analyze sequential depth data to enhance the efficiency of experts in stress analysis. Key words : borehole breakout, image log, long short-term memory (LSTM), extreme gradient boosting (XGBoost), confusion matrix 요 약 본 연구는 이미지로그의 압축파쇄대 심도를 검출하기 위해 장단기기억(long short-term memory, LSTM)을 적용하여 XGBoost(extreme gradient boosting) 모델의 한계를 보완하고자 한다. 경주시 심도 1 km 시추공의 이미지로그를 전처리하여 얻어진 99,084개 자료로 XGBoost와 LSTM 모델을 개발하였다. 단일 심도만을 고려하는 XGBoost 모델과 달리, LSTM 모델은 연속된 7개 심도자료로 중앙심도의 압축파쇄대 여부를 분석한다. XGBoost 대비 LSTM 모델은 테스트자료에서 false negative가 88% 감소하였고 재현율이 29% 향상되었다. 또한 7개 심도자료를 활용한 XGBoost 모 델과 비교 시에도 우수한 성능을 보였다. 이는 LSTM이 연속된 심도자료를 체계적으로 분석하여 전문가의 응력분석 효율성을 향상시킬 수 있음을 시사한다.

This study developed a classification model for the detection of borehole breakout depths in image logs using machine learning algorithms. Approximately 1 km of image log data acquired in Gyeongju was preprocessed to make it suitable for applying machine learning algorithms. This included removing missing values and data within casing intervals and aligning the resolution among image log data-that is, vertical and horizontal resolutions were set at 0.01 m and 2.5°, respectively. Decision tree-based algorithms, random forest, and XGBoost were then applied to the preprocessed 99,090 data entries, which was divided into training (80%) and testing (20%) groups. The trained models were evaluated based on the false negative (FN) of the confusion matrix. The XGBoost model showed a superior predictive performance with 87 FNs when compared to the random forest model's 162 FNs. The developed model can enable experts to perform detailed reviews of detected depths via the model.

Among many parameters needed to characterize subsurface rock mass related to underground storage and disposal projects, an important one is the heat production rate of the bedrock itself.The rock produces heat from the decay of radioactive isotopes consisting of minerals and its gamma-ray emissions, of which the magnitude is dominated by contents of major radioactive isotopes, e.g., uranium, thorium, and potassium. The heat production rate is generally calculated from the rock density and the radiogenic isotope contents, which can be measured from the spectrometry of drilled cores or rock fragments. However, such approaches are rarely available in deep boreholes because recovering rock samples from several hundred meters to a few kilometers is quite difficult.A recent geophysical logging technique for deep boreholes is available where the uranium, thorium, and potassium contents are measured from the gamma-ray spectrum. However, this technique requires the density to be measured separately, and the measurement depth of the equipment is still limited. As an alternative method, a natural gamma-ray logging tool was adopted to estimate briefly the heat production from the total gamma activity, which is relatively easy to measure. This study introduces the development of the proposed method for evaluating the heat production of a granitic rock mass with domestic commercial borehole logging tools, as well as its application and verifications in deep boreholes.

Extended leak-off test (XLOT) is one of the in-situ tests, routinely conducted to evaluate integrity of the cased and cemented wellbores during deep borehole drilling, as well as in situ hydraulic properties at a casing shoe depth.We introduce results of the XLOT conducted in a large diameter borehole, which is drilled for installation of deep borehole based geophysical monitoring system to monitor micro-earthquakes and fault behavior of major linearments in the subsurface. The borehole was planned to secure a final diameter of 200 mm (or more) at a depth of ~1 km deep, with 12" diameter wellbore to intermediate depths, and 7-7/8" (~200 mm) to the bottom hole depths.We drilled first the 12" diameter borehole to approximately 504 m deep and installed API standard 8-5/8" casing, then cemented the annulus between the casing and bedrock. Then we carried out the XLOT, for several purposes such as confirming casing and cementing integrity, as well as estimating in-situ stress and hydraulic conductivity at the casing shoe depth. To that end, we drilled 4 m length interval to directly inject water and pressurize into the rock mass using the upper API casings. During the XLOT, we recorded flow rates and interval pressures in real time. Based on the logs, we tried to analyze hydraulic conductivity of the test interval, and compare the results with previously reported hydraulic properties measured in other ways.