Poldw: a Python code to denoise 3C seismic data with a new threshold-free polarization technique#xD;

GEOPHYSICS Pub Date : 2024-07-14 DOI:10.1190/geo2023-0684.1
D. Velis, Julián L. Gómez
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Abstract

We present a Python code that implements a novel threshold-free polarization strategy for removing random noise from three-component (3C) linearly polarized seismic data. The code, which we refer to as poldw (polarization denoising through windowing), uses closed-form formulas along sliding windows that span the data to determine the optimal rotation angles that allow the transfer of most of the signal energy to a given component. The denoised 3C data is obtained after canceling out the other two components, which are assumed to contain predominantly noise, and rotating back. The method is simple and efficient because it only requires setting the sliding window length. Synthetic and microseismic field data examples show the method’s effectiveness, which significantly improves the signal-to-noise ratio without the need for threshold-based polarization filters. Even so, these filters can be pipelined in the rotation-based strategy for additional noise removal if necessary. When the dataset contains non-linearly polarized data or significant non-random noise, the method is likely to fail. For robustness against non-Gaussian noise and outliers, poldw allows for the use of alternative norms like the L1- or L p-norms instead of the energy. In addition to the code, we provide a Jupyter notebook to illustrate the method step by step and reproduce the results of the field data example.
Poldw:利用新型无阈值极化技术对 3C 地震数据进行去噪的 Python 代码#xD;
我们介绍的 Python 代码实现了一种新颖的无阈值极化策略,用于去除三分量(3C)线性极化地震数据中的随机噪声。我们将该代码称为 poldw(通过开窗进行极化去噪),它使用闭式公式,沿着横跨数据的滑动窗口确定最佳旋转角度,从而将大部分信号能量转移到给定分量上。去噪后的 3C 数据是在取消了假定主要包含噪声的其他两个分量并回转后得到的。该方法简单高效,因为只需设置滑动窗口长度。合成数据和微地震现场数据实例显示了该方法的有效性,它无需使用基于阈值的极化滤波器就能显著提高信噪比。即便如此,这些滤波器仍可在必要时通过基于旋转的策略进行流水线处理,以去除额外的噪声。当数据集包含非线性偏振数据或大量非随机噪声时,该方法很可能会失败。为了提高对非高斯噪声和异常值的稳健性,poldw 允许使用 L1- 或 L p-norms 等替代规范来代替能量规范。除代码外,我们还提供了一个 Jupyter 笔记本,以逐步说明该方法,并重现实地数据示例的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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