Abstract: The "23.7" heavy rainfall event in Beijing triggered multiple geological disasters of flush flood and debris flows, resulting in 33 deaths, 18 missing persons, and significant economic losses, which has drawn widespread social attention. Currently, geological disaster monitoring and early warning systems struggle to achieve precise warning in complex environments, making the development of refined monitoring and early warning technologies a hot and challenging topic in the research of mountain disaster and engineering disaster prevention and control. Through field investigations and the analysis of continuous records from nearby seismic stations, this study determined that the debris flow at Che'erying Village at the foot of the Western Hills in Beijing occurred at 03:36 on July 31, 2023 (UTC+0, time), with the flood peak height of approximately 3.5 m. The seismic records triggered by this disaster event exhibited a spindle shape, lasting for about 100 minutes. This study employed three methods—the long-term and short-term window ratio, the statistical Benford's Law, and frequency-based feature detection, to identify the waveform of the Che'erying flash flood and debris flow event recorded at the LQS station. The results indicate that the traditional long-term and short-term window ratio method struggled to establish an effective threshold to distinguish between background noise and the prolonged process of the flash flood and debris flow, failing to identify this event. The identification method based on Benford's Law could not detect the disaster event without a noise threshold; however, when a statistical threshold based on background noise was set, it successfully detected and identified the disaster event. The centroid frequency-based detection method, requiring no prior information, effectively utilizes the rich time-frequency amplitude variations of high-energy events to accurately identify the small-scale flash flood and debris flow event at a distance of 1.5 kilometers. This method enhances the efficiency of geological disaster identification and detection based on seismic signals and holds promise for providing more effective technology, particularly with the dense seismic network in the Beijing area in the future.