During the filtering process of explosion-induced vibration signals in shallow underground layers, the fixed step size of the D-LMS algorithm is not flexible enough for time-varying signal processing, which can easily lead to the amplification of gradient noise. Moreover, it relies on prior information about the effective signal or noise as the desired signal, which is usually unknown in shallow underground vibration testing. To address these issues and meet the needs of shallow underground explosion-induced vibration detection, an improved D-LMS filtering algorithm was developed based on normalization principles. This improved algorithm was compared with traditional algorithms in terms of convergence speed and filtering accuracy through simulations. The results showed that this improved algorithm achieved approximately 2.3 dB higher filtering accuracy and doubled convergence speed compared to the D-LMS algorithm in adaptive denoising of vibration testing. It was deployed on a ZYNQ programmable logic device, where modules for delay, step size, coefficient update, filtering, and error calculation were designed and encapsulated into an IP core. This core was integrated into the acquisition system for field tests of shallow underground vibrations. Experimental results demonstrated that the filtered signals were significantly better than unfiltered ones, confirming the effectiveness of the adaptive filtering module. This achieved real-time on-chip adaptive denoising of vibration signals, providing crucial support for the reconstruction of shallow underground vibration fields.