ABSTRACT A number of deblending methods and workflows have been reported in the past decades to eliminate the source interference noise recorded during a simultaneous shooting acquisition. It is common that denoising algorithms focusing on optimizing coherency and weighting down/ignoring outliers can be considered as deblending tools. Such algorithms are not only enforcing coherency but also handling outliers either explicitly or implicitly. In this paper, we present a novel approach based on detecting amplitude outliers and its application on deblending based on a local outlier factor that assigns an outlier‐ness (i.e. a degree of being an outlier) to each sample of the data. A local outlier factor algorithm quantifies outlier‐ness for an object in a data set based on the degree of isolation compared with its locally neighbouring objects. Assuming that the seismic pre‐stack data acquired by simultaneous shooting are composed of a set of non‐outliers and outliers, the local outlier factor algorithm evaluates the outlier‐ness of each object. Therefore, we can separate the data set into blending noise (i.e. outlier) and signal (i.e. non‐outlier) components. By applying a proper threshold, objects having high local outlier factors are labelled as outlier/blending noise, and the corresponding data sample could be replaced by zero or a statistically adequate value. Beginning with an explanation of parameter definitions and properties of local outlier factor, we investigate the feasibility of a local outlier factor application on seismic deblending by analysing the parameters of local outlier factor and suggesting specific deblending strategies. Field data examples recorded during simultaneous shooting acquisition show that the local outlier factor algorithm combined with a thresholding can detect and attenuate blending noise. Although the local outlier factor application on deblending shows a few shortcomings, it is consequently noted that the local outlier factor application in this paper obviously achieves benefits in terms of detecting and attenuating blending noise and paves the way for further geophysical applications.

中文翻译：

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局部异常因子作为工作流程的一部分，用于检测和衰减同时采集数据中的混合噪声

摘要 在过去的几十年中，已经报道了许多去混合方法和工作流程，以消除在同步拍摄采集过程中记录的源干扰噪声。通常，专注于优化一致性和加权/忽略异常值的去噪算法可以被视为去混合工具。此类算法不仅可以强制执行一致性，还可以显式或隐式地处理异常值。在本文中，我们提出了一种基于检测幅度异常值的新方法及其在基于局部异常值因子的去混合中的应用，该局部异常值因子为数据的每个样本分配异常值（即异常值的程度）。局部异常值因子算法基于与其局部相邻对象相比的隔离程度来量化数据集中对象的异常值。假设同时拍摄的地震叠前数据由一组非离群点和离群点组成，局部离群因子算法评估每个对象的离群点。因此，我们可以将数据集分成混合噪声（即异常值）和信号（即非异常值）分量。通过应用适当的阈值，具有高局部异常值因子的对象被标记为异常值/混合噪声，并且相应的数据样本可以用零或统计上足够的值代替。从解释局部异常因子的参数定义和属性开始，我们通过分析局部异常因子的参数并提出具体的去混合策略来研究局部异常因子应用于地震去混合的可行性。同时拍摄采集期间记录的现场数据示例表明，局部异常值因子算法与阈值相结合可以检测和衰减混合噪声。尽管局部异常因子在去混合中的应用存在一些缺点，但因此注意到本文中的局部异常因子应用在检测和衰减混合噪声方面明显取得了好处，并为进一步的地球物理应用铺平了道路。