The rapid and accurate detection of geological hazards on urban roads is an urgent matter requiring prompt action. Current continuous towed seismic detection methods provide an effective and economical way for repeated detection along the same survey line. However, the conventional weighted stacking method used for multiple detection data encounters two key challenges. First, the presence of a low signal-to-noise ratio (SNR) in the detection data can adversely affect the final stack result. Second, the presence of strong energy noise makes it difficult to identify weak energy reflected and diffracted waves. To address these constraints, this paper proposes a novel multi-scale stacking method using a deep belief network (DBN) to eliminate the dependence on the real labels and improve the resolution of the continuous towed seismic stack section. We have also redesigned the algorithm for training the weight matrix by utilizing a limited amount of data and extracting the reflected and diffracted waves separately. The results of the numerical simulation demonstrate that the proposed multi-scale stacking method offers distinct advantages compared to the weighted stacking method: (1) It effectively addresses the effects of low SNR on the final stack results; (2) it allows for the extraction of wavefields with different characteristics and enables the analysis of near-surface anomalous structures from multiple angles; (3) it suppresses noise and extracts weak energy reflected and diffracted waves. The proposed multi-scale stacking method is useful for interpreting continuous towed seismic data and has real-world engineering applications.
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