First-Arrival Picking Method for Active Source Data with Ocean Bottom Seismometers Based on Spatial Waveform Variation Characteristics

The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer (OBS) refraction data. Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors, especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval. In this paper, a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented. First, the distribution law of theoretical error is examined; adjacent traces exhibit variation characteristics in their waveforms. Second, a label cross-correlation superposition method for extracting high-frequency signals is presented to enhance the first-arrival picking precision. Results from synthetic and field data verify that the proposed approach is robust, successfully overcomes the limitations of low sampling frequency, and achieves precise outcomes that are comparable with those of high-sampling-frequency data.