This study introduces a new attribute to identify seismic erratic noise, i.e. outlier, in the context of unsupervised anomaly detection and is defined as local outlier probabilities. The local outlier probabilities calculate scores of degrees of isolation, i.e. outlier-ness, for each object in a data set, which represents how far an object is deviated from its surrounding objects. Since the local outlier probabilities combines a density-based outlier detection method with a statistically oriented scheme, its scoring system provides regularized outlier-ness, which is an outlier probability, to be used for making a binary decision to do inclusion or exclusion of an object; such a decision only requires a simple and straightforward threshold on a probability. Based on the binary decision that flags outliers versus non-outliers, local outlier probabilities-denoising workflows are developed by combining multiple steps to complete an application of the local outlier probabilities to attenuate seismic erratic noise. Higher stability and improved robustness in the detection and rejection of seismic erratic noise have been achieved by implementing moving windows and decision tree-based processes. To avoid loss of useful signal energy, signal enhancement applications are additionally suggested. Numerical experiments on synthetic data investigate the applicability of the proposed algorithms to seismic erratic noise attenuation. Field data examples demonstrate the feasibility of a local outlier probabilities-denoising application as an effective tool in seismic denoising portfolio.

中文翻译：

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使用无监督异常检测的地震不稳定噪声衰减

本研究在无监督异常检测的背景下引入了一种新属性来识别地震不稳定噪声，即异常值，并被定义为局部异常值概率。局部异常值概率计算数据集中每个对象的隔离度分数，即异常值，这表示对象与其周围对象的偏离程度。由于局部异常值概率将基于密度的异常值检测方法与面向统计的方案相结合，因此其评分系统提供了正则化的异常值，即异常值概率，用于做出包含或排除对象的二元决策; 这样的决定只需要一个简单而直接的概率阈值。基于标记异常值与非异常值的二元决策，局部异常值概率去噪工作流程是通过组合多个步骤来开发的，以完成局部异常值概率的应用，以衰减地震不稳定噪声。通过实施移动窗口和基于决策树的过程，在检测和抑制地震不稳定噪声方面实现了更高的稳定性和更强的鲁棒性。为了避免有用信号能量的损失，还建议使用信号增强应用。合成数据的数值实验研究了所提出的算法对地震不稳定噪声衰减的适用性。现场数据示例证明了局部异常概率去噪应用作为地震去噪组合中的有效工具的可行性。