Abstract：Conventional detection methods for microseismic events are almost based on calculating the features of signals.The accuracy of events detection depends on the parameters of algorithms,so it is greatly affected by the changes in the features of signals and signal-to-noise ratio.This paper proposes a method for microseismic event detection based on a convolutional neural network(CNN).To train and test the CNN,a sample set is constructed on the microseismic data monitored by multiple stations in an oil well that was hydraulically fractured.The data consist of effective event signals and ineffective background noises and their classifications.Then the CNN is trained and tested by the sample data set,and an optimal CNN model is obtained with best accuracy of event detection.To test the performance of the CNN model,synthesized microseismic signals with different signal-to-noise ratios,and actual microseismic signals from several oil and gas wells are fed into the CNN model.The processing results demonstrate that the CNN model can automatically and effectively detect microseismic events.It has good abilities for noise suppressing and generalization.
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