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Advancements on the use of the Non Local Means algorithm for seismic data processing

机译:用于地震数据处理的非本地手段算法的推进

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Among the techniques and algorithms usually applied to seismic data for random noise attenuation, Non Local Means (NLM) filtering is a promising option. It is based on a weighted mean in which the weights depend on the measure of similarity between patches surrounding each sample. This methodology allows the preservation of features and structures while incoherent signal is filtered out The application of such methodology can be n-dimensional but its high computational cost disadvantages a 3D implementation. In previous work we presented a revised version of the NLM algorithm, improved from both the computational and signal to noise enhancement points of view. In the present paper we focus on the application of this revised NLM on real data time-slices, and investigate more in detail the 3D implementation of the method.
机译:在通常应用于随机噪声衰减的地震数据的技术和算法中,非本地方法(NLM)滤波是一个有前途的选项。它基于加权均值,其中重量取决于每个样品周围的斑块之间的相似性的量度。该方法允许保存特征和结构,同时滤除不连贯的信号,这种方法的应用可以是n维的,但其高计算成本缺点是3D实现。在以前的工作中,我们介绍了NLM算法的修订版,从计算和信号改进到噪声增强点。在本文中,我们专注于在实际数据时片上的应用程序的应用,并详细研究方法的3D实现。

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