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Classification of reflected signals from surface sediments can improve our understanding of the properties of these sediments. In this paper, we propose a method for classifying reflection signals using deep learning techniques. The method uses a pulse compression algorithm to convert reflection signals into reflection compressed data, and then uses a one-Dimensional Convolutional Neural Network - Double Long Short-Term Memory (1DCNN-DLSTM) network to classify these data. The advantage of this method is that the pulse compression algorithm can improve the resolution of the stratigraphic reflection signal, thus better capturing the details of the signal. Meanwhile, 1DCNN can effectively extract the spatial features of reflection compression signals and capture the differences between different sediment types. DLSTM, on the other hand, can capture the temporal dynamic features of the signals, which is very advantageous for modeling temporal information. By fusing these two network structures, it is possible to categorize deep-sea surface sediments in a more comprehensive way. To verify the feasibility of the method, we conducted experiments using reflection data from surface sediments on the South China Sea continental slope. The experimental results show that the method is feasible in classifying the reflection signals from deep-sea surface sediments. We obtain high classification accuracy by training and testing different types of reflection compression data. This indicates that the method can effectively distinguish different types of deep-sea surface sediments, which helps us to better understand the deep-sea environment and related geological processes.
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