Intelligent Microseismic Events Recognition in Fiber-Optic Microseismic Monitoring System Compared With Electronic One

Fiber-optic seismic sensor has become an emerging effective tool for microseismic monitoring, which is of importance for oil and gas production improvement. When processing the fiber-optic data, the recognition of microseismic events is the first crucial step, which is rarely reported due to the lack of such data. In this paper, based on the features of central data distribution, skewness, kurtosis, energy entropy, etc., the classification accuracy of microseismic (MS) events obtained by fiber-optic MS monitoring system and electronic one is compared using machine learning algorithm (e. g. SVM and KNN) for the first time. The results show that fiber-optic data has higher classification accuracy than electronic data when using the feature of central data distribution owing to the high signal-to-noise ratio of fiber-optic data. However, when choosing the features of energy entropy, zero-crossing rate and the energy proportion of specific frequency band, electronic data has higher classification accuracy than fiber-optic data benefiting from the longer events duration and the lower frequency components of electronic data. When using skewness and kurtosis, the classification performance in fiber-optic data is almost consistent with the electronic one. Moreover, the results indicate that the characteristics of MS signal itself have a greater impact on the discrimination ability of MS events than the applied machine learning classification algorithm.