High-Dimensional Multiscale Trapezoidal Side Window Filtering and Its Application for Seismic Data Denoising

Abstract

In the classic local window filtering seismic data denoising methods, the target sample is usually placed at the center of the given fixed-scale filter kernel. When the target sample is located on the structure edges, the filter kernel will cross the structure edges and leads to blurry structure edges. Multiscale adaptive right-angle side window filtering (MRSF) has better edge preservation capability. However, its 2-D filter kernel and eight right-angle side windows cannot better adapt to complex data, limiting its denoising capability. We extend the 2-D multiscale filter kernel in MRSF with the 3-D multiscale filter kernel. Meanwhile, we extend the eight 2-D right-angle side windows in MRSF with multiple 2-D and 3-D trapezoidal side windows. Finally, we propose the high-dimensional multiscale adaptive trapezoidal side window filtering (HMTSF). Synthetic and field 3-D seismic data examples demonstrate the good denoising capability of HMTSF.