Correlational Analysis of MWD Data for Rock Mass Characterization and Risk Assessment

Summary The study demonstrates how the Measurement-While-Drilling (MWD) data is used for real-time rock mass characterization, highlighting the importance of preprocessing MWD data to address its complex nature. The correlation analysis method is central to understanding the relationships within MWD datasets, aiding in feature selection and reducing dimensionality. The research showcases the ability of the correlational analysis method to maintain and boost the data's informational value, making it suitable for advanced applications like machine learning. The methodology integrates Spearman's correlation coefficient to measure variable associations, emphasizing the exploration of relational dynamics over point-value analysis. The research demonstrates that models trained on data averaged over specific depth or time windows—via a 'sliding window' technique—outperform those trained on per borehole averaged data. This indicates that localized averaging captures essential information that enhances model performance. The research advocates for a comprehensive preprocessing regime as a precursor to effective data analysis and robust outputs from machine learning models.