A multimodal neural network-based microseismic event detection method is proposed to address the problem that the time series of effective microseismic signals has severe limitations.First,the multichannel time-domain mode with the target chan-nel as the axis symmetry is established using gather data correlation,and the S-domain modal characteristics are obtained by using time-frequency analysis for the target channel.Then,the neural network for microseismic event detection is designed by combining the time-domain mode and S-domain mode.Multimodal features are synthesized for training and learning to improve the accuracy of detection.Finally,method validation is performed through the analyses of synthetic low-SNR and small-amplitude data and actu-al oil-well microseismic events.The results showed that our method could detect low-SNR and weak microseismic signals effective-ly.Compared with SVM,CNN,and supervised machine learning,our method has improved anti-noise performance and accuracy.
microseismicevent detectionLaplace transformmulti-modal networktime-frequency spectrumgather data correla-tion
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