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

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 Lp-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.