분포형 광섬유 센싱 자료의 준실시간 모니터링을 위한 기계학습 기반 미소지진 신호 탐지 연구

초록

Distributed Acoustic Sensing (DAS) systems are a promising technology for microseismic monitoring due to their high spatial resolution and broad detection coverage. However, real-time monitoring generates large volumes of data and therefore require efficient data management, signal detection, and classification. This study aimed to develop efficient data processing procedures and optimal machine learning models for near-real-time microseismic monitoring. Using DAS data from the Utah FORGE geothermal project, we systematically applied traditional data processing procedures step-by-step and performed signal detection based on the VGG-19 and YOLO v11 models to compare and analyze their performance. A quantitative performance evaluation was conducted using confusion matrices, accompanied by processing-time analysis for each data processing procedure, and model-selection considerations for monitoring applications were examined. Additionally, signal-detection models were applied to data processed with machine-learning-based denoising, and the results were compared to evaluate model performance. This study confirms that developing a practical near-real-time monitoring system requires an optimal combination of lightweight machine learning models and traditional data processing procedures, with comprehensive consideration of model performance, processing time, and data-preparation efficiency.

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