A DAS Signal Events Recognition Method Based on 1DCNNs-gMLP
Wanchang Jiang, Sun Zhenxiao · 2024
CNN is commonly used for DAS signal events recognition, such as 1D-CNNs, 2D-CNN and 1DCNNs-BiLSTM. However, these methods do not make full use of spatiotemporal information to identify DAS signal events, and even have the problem of identifying events confusion. This paper proposes a new DAS signal events recognition method. This method combines 1D-CNNs with gMLP, so that each part can give full play to its own advantages and fully exploit the spatiotemporal information of DAS signal. Firstly, the signal time features on each spatial sampling node are extracted by multiple sets of parallel 1D-CNN, which are recorded as 1D-CNNs. Secondly, the spatial gating unit of gMLP is used to realize the spatial information interaction between the feature vectors of different sampling nodes, so as to mine the spatial relationship between the signals on each sampling node. Finally, the temporal features extracted by 1D-CNNs and the spatial features of gMLP mining are superimposed to form spatiotemporal information, and the fully connected layer is used to realize DAS signal events recognition. The public data set is used for experimental verification. The recognition accuracy of 1DCNNs-gMLP method for training 3 epochs can reach 98.5%, and the highest one is 99.71%. Our accuracy is 7.1%, 4.7% higher than that of 2D-CNN and 1D-CNNs respectively. Compared with the 1DCNNs-BiLSTM that extracts and utilizes detailed time information and overall spatial relationship, the accuracy is improved by 1.4%. The 1DCNNs-BiLSTM has the problem of identification confusion for background noise, digging and watering events. 1DCNNs-gMLP reduces the confusion of these three types of events, and background noise events confusion is decreased by 0.8%; digging events decreased by 1.99%; watering events decreased by 6.16%.