Application of Deep Learning and Optical Character Recognition Technology to Automate Classification and Database of Borehole Log for Ground Stability Investigation of Abandoned Mines

초록

Boring logs are essential for the evaluation of ground stability in abandoned mine areas, representing geomaterial and subsurface structure information. However, because boring logs are maintained in various analog formats, extracting useful information from them is prone to human error and time-consuming. Therefore, this study develops an algorithm to efficiently manage and analyze boring log data for abandoned mine ground investigation provided in PDF format. For this purpose, the EfficientNet deep learning model was employed to classify the boring logs into five types with a high classification accuracy of 1.00. Then, optical character recognition (OCR) and PDF text extraction techniques were utilized to extract text data from each type of boring log. The OCR technique resulted in many cases of misrecognition of the text data of the boring logs, but the PDF text extraction technique extracted the text with very high accuracy. Subsequently, the structure of the database was established, and the text data of the boring logs were reorganized according to the established schema and written as structured data in the form of a spreadsheet. The results of this study suggest an effective approach for managing boring logs as part of the transition to digital mining, and it is expected that the structured boring log data from legacy data can be readily utilized for machine learning analysis.

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