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.

中文翻译：

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复杂地表区域高密度采集地震记录的高精度自动初破拾取方法

摘要 随着高密度高效采集技术的应用越来越广泛，地表条件复杂的地区逐渐成为目标勘探区，海量低信噪比数据的首次采摘工作是一项艰巨的任务。大挑战。传统方法花费大量的人力和时间来交互拣选先发，大量的交互工作影响了拣选的准确性和效率。为了克服传统方法对低信噪比一次波抗噪能力弱的缺点，本文提出了一种高准确度的低信噪比一次波自动初波拾取方法。具有复杂表面的区域的密度采集。首先，该方法利用多方位空间插值技术确定初破时间窗；然后使用改进的聚类算法对初破进行初步挑选，然后按照以下顺序对初破进行多角度综合质量评价：“单次跟踪→传播→单次拍摄→多次拍摄”识别异常初破；最后通过构建的评价函数，利用蚁群算法修正异常初破，确定最优路径。多方位时间窗空间插值技术为准确选择初发时间提供基础；聚类算法可有效提高低信噪比一次波的拾取准确率；多角度综合质量评价，准确有效地排除异常初破；蚁群算法可以有效提高低信噪比异常初破的校正质量。通过实例分析，并与常用的赤池信息判据法进行比较，本文研究的自动初破采摘理论与技术具有较高的采摘精度和稳定处理低信噪比地震数据的能力，具有显着的对复杂地表区域高密度采集地震记录的影响，能够满足海量数据近地表建模和静力学计算的精度和效率要求。蚁群算法可以有效提高低信噪比异常初破的校正质量。通过实例分析，并与常用的赤池信息判据法进行比较，本文研究的自动初破采摘理论与技术具有较高的采摘精度和稳定处理低信噪比地震数据的能力，具有显着的对复杂地表区域高密度采集地震记录的影响，能够满足海量数据近地表建模和静力学计算的精度和效率要求。蚁群算法可以有效提高低信噪比异常初破的校正质量。通过实例分析，并与常用的赤池信息判据法进行比较，本文研究的自动初破采摘理论与技术具有较高的采摘精度和稳定处理低信噪比地震数据的能力，具有显着的对复杂地表区域高密度采集地震记录的影响，能够满足海量数据近地表建模和静力学计算的精度和效率要求。