Development of Deep Learning-Based Detection Model for Borehole Breakout Depths in Image Logs

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 (long short-term memory, LSTM) XGBoost(extreme gradient boosting) . 1 km 99,084 XGBoost LSTM . XGBoost , LSTM 7 .XGBoost LSTM false negative 88% 29% . 7 XGBoost . LSTM .