LAUSR

ABSTRACT As the application of high‐density high‐efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first‐break picking work of massive low signal‐to‐noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti‐noise to low signal‐to‐noise ratio primary wave, this paper proposes a high accurate automated first‐break picking method for low signal‐to‐noise ratio primary wave from high‐density acquisition in areas with a complex surface. Firstly, this method determines first‐break time window using multi‐azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi‐angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi‐azimuth time window spatial interpolation technology provides the base for accurately picking first‐break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal‐to‐noise ratio primary waves; the multi‐angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal‐to‐noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first‐break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal‐to‐noise ratio seismic data, has a significant effect on seismic records from high‐density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near‐surface modelling and statics calculation.