Journal Article

Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models

Jin, Aibing, Mahtab, Shakil, Basnet, Prabhat Man Singh

2025

Overview

Microseismic (MS) information is often utilised in deep underground engineering projects for the early warning of short-term rockburst hazards. Due to the complex nature of rockburst occurrence, predicting short-term rockburst is always challenging. Recently, machine learning (ML) methods are often employing in different geotechnical engineering applications. Parametric and non-parametric ML methods are two different kinds of approaches, each with distinct characteristics. However, the current applications in short-term rockburst prediction are focused on non-parametric methods. Therefore, this paper proposes and studies the feasibility of a parametric model over the non-parametric model, adopting two fundamental parametric and non-parametric ML models, including logistic regression and support vector machine, to predict short-term rockburst using MS information based on two types of normally and non-normally distributed datasets. After modelling, precision, recall, F1 score, and receiving operating curve are considered to evaluate the model’s strength in predicting tasks. The results indicate that the parametric model, which obtained an average F1 score and AUC score of 0.72 and 0.91 on a normally distributed dataset achieved more remarkable output in evaluating short-term rockburst risk. Limited data availability is always a challenge in short-term rockburst prediction. In such cases, parametric models can accurately classify the rockburst risk levels due to their characteristics of assuming the predefined function, simplifying the learning processes independent of the data size. However, normally distributed data is beneficial for them that allows a perfect fit. The presented work effectively identifies the rockburst risk in deep underground excavation projects regardless of data size.

• Publisher: Springer Netherlands,Springer Nature B.V
• Subject: Civil Engineering / Datasets / Decision trees / Earth and Environmental Science / Earth Sciences / Environmental Management / Excavation / Failure analysis / Feasibility studies / Geophysics/Geodesy / Geotechnical engineering / Geotechnical Engineering & Applied Earth Sciences / Hydrogeology / Learning algorithms / Machine learning / Microseisms / Mining / Natural Hazards / Non-parametric methods / Original Paper / Parametric methods / Predictions / Regression analysis / Regression models / Risk levels / Rockbursts / Support vector machines