Logistic regression and neural network classification of seismic records

• Approach for labelling seismic records (blasts, events, rockbursts) automatically.
• Three models reviewed: current approach, logistic regression and neural networks.
• Current approach presented accuracies lower than 90%.
• Logistic and neural network models presented accuracies higher than 95%.
• Logistic and neural network models outperformed the currently used approach.

The identification of seismic records in seismically active mines is examined by considering logistic regression and neural network classification techniques. An efficient methodology is presented for applying these approaches to the classification of seismic records. The proposed procedure is applied to mining seismicity from two mines in Ontario, Canada, and compared based on an analysis of the receiver operating characteristic curve as well as a number of performance metrics related to the contingency matrix. The logistic and neural network models presented excellent performance for identifying blasts, seismic events and reported events in the training and testing datasets for both mining seismicity catalogues. Operated under their respective optimal decision threshold values, the logistic and neural network models, accuracy was higher than 95% for classification of seismic records. In general, the logistic regression and neural network methods had close overall classification accuracies. The ability of the models to reproduce the frequency-magnitude distribution of the testing dataset was used as a signature of classification quality. The logistic and neural network models reproduced the reference distribution in a satisfactory manner. The advantages and limitations pertaining to the two classifiers are discussed.