Abstract: Abstract: Environmental interference noise is the main challenge for on-site monitoring of debris flow infrasound， which greatly limits the accuracy of debris flow infrasound signal identification. In view of the performance of deep learning in acoustic signal recognition， this paper proposes a debris flow infrasound signal recognition method based on improved AlexNet network， which effectively improves the accuracy and convergence speed of debris flow infrasound signal recognition. Firstly， the original infrasound data set is preprocessed such as data expansion， filtering and noise reduction， and wavelet transform is used to generate a time-frequency spectrum image. Then the obtained time-frequency spectrum image is used as input， and an improved AlexNet network model is built by reducing the convolution kernel， introducing a batch normalization layer and selecting the Adam optimization algorithm. Experimental results show that the improved AlexNet network model has a recognition accuracy of 91.48%， achieves intelligent identification of debris flow infrasound signals and provides efficient and reliable technical support for debris flow infrasound monitoring and early warning.

Key words: Key words: debris flow, infrasound, deep learning, monitoring and early warning, signal recognition