High accurate automated first break picking method for seismic records from high density acquisition in the areas with complex surface16 Dec 2019 23:46
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 (S/N) 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 S/N primary wave, this paper proposes a high accurate automated first break picking method for low S/N primary wave from high density acquisition in the areas with complex surface. Firstly, this method determines first break time window using multi‐azimuth spatial interpolation technology; then 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 determine 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 S/N 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 S/N abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion(AIC) method, the automated first break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low S/N seismic data, and has a significant effect on seismic records from high density acquisition in the areas with complex surface, and can meet the requirements of accuracy and efficiency for massive data near‐surface modeling and statics calculation.
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With the wide application of the high-density high-efficiency acquisition technology in the complex zones of oil fields, the first break picking of massive low S/N ratio data is facing great challenges. Conventional first break automatic picking methods require considerable man-machines interactive modification due to their poor anti-noise performance. Such considerable interaction affects the accuracy and efficiency of picking. First break picking takes up about one-third of the entire processing cycle, which restricts the oil and gas exploration and development process severely. In order to overcome this shortcoming, this paper proposes a fast first break auto-picking method for low S/N data from high efficiency acquisition. The paper has the following highlights: (1) Use multi-azimuth time window spatial interpolation technology to conveniently and quickly determine primary wave time window range, which provides the base for accurately picking first-break time. (2) The improved clustering algorithm can effectively separate signal and noise, and improve the accuracy of first break picking in single trace, and at the same time, use the waveforms and energy of adjacent traces to correct the abnormal first break with big variation to the right position. (3) As for the discrimination to abnormal primary waves, the principle of 'single trace → single spread → single shot →multiple shots' is proposed to judge first breaks by the multiangle comprehensive method such as the energy of primary wave, waveform, relationship between adjacent traces, nearsurface velocity and the spatial position relationship between shot points and receiver, which can effectively identify abnormal first breaks. (4) Ant colony algorithm can ensure that the picked first breaks belong to the same layer, and at the same time, it can recognize the abnormal first break with small cross-strata, which reduces human intervention and obviously improves the efficiency of first break picking of seismic data.