Micro-Seismic Event Detection using statistical feature extraction and machine learning techniques

Abstract

In this paper, we propose a novel methodology for micro-seismic event detection using multiple statistical feature extraction and machine learning techniques. The method is based on amplitude thresholding and is grounded on the idea, that each relevant seismic event crosses a particular threshold value. We have created a unique dataset of movement, hammering and no-movement using off-the-shelve geophones and 16-bit analog to digital converter. The data is sampled at 10000Hz. Each recorded dataset is of duration 2 min. The data recorded is passed through amplitude thresholding method. Multiple statistical features like root mean square, standard deviation, variance, minimum value and maximum value are extracted from the dataset in both time and frequency domain. k-fold cross validation is done to obtain validation accuracy. We have explored various machine learning algorithms and the results are discussed here. Among all classifiers, Random Forest algorithm achieves the overall highest accuracy of 92.76%.