Abstract: 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 channel 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 actual oil-well microseismic events. The results showed that our method could detect low-SNR and weak microseismic signals effectively. Compared with SVM, CNN, and supervised machine learning, our method has improved anti-noise performance and accuracy.