An advanced median filter for improving the signal-to-noise ratio of seismological datasets

Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed using the recently proposed structure-oriented space-varying median filter (SOSVMF) by filtering out the unwanted noise from noise -corrupted signals. However, the SOSVMF approach has limitations when the raw data is corrupted by various sources of noise which is more common in practice. To conquer the difficulties in improving the S/N in the face of diverse types of strong noise, we introduce MATamf, an open-source MATLAB code package based on a novel advanced median filter (AMF). The proposed AMF workflow takes advantage of multiple denoising operators, i.e., the bandpass (BP) filter, SOSVMF, dip filter in the frequency-wavenumber (FK) domain, Curvelet, and local orthogonalization (LO), all in the same framework to simultaneously attenuate all types of noise and improve S/N. To demonstrate the usefulness of the MATamf package and its advantage over conventional denoising methods, we introduce the principles behind the proposed AMF framework and present results from a variety of seismological problems, including seismic reflection, distributed acoustic sensing (DAS), as well as global seismological tasks of receiver function and SS-precursors imaging. All results show that the proposed AMF workflow can be conveniently utilized to improve the S/N of a wide spectrum of seismological applications.