Algorithm for Surface Wave Suppression on 2D Seismic Data Using the Slant Karhunen–Loeve Transform in a Time-Frequency Domain

Abstract

—Surface waves are the main source of coherent noise in land seismic survey, and their suppression is one of the main stages of common depth point data processing designed to improve the quality of tracking primary reflections on time sections. In practice, noise reduction is carried out using procedures from modern software based on numerical modeling of waveforms. However, they are too resource-intensive and have a large number of subjectively customizable parameters. The known algorithms have a common drawback: either the energy of reflected waves is distorted in an interference zone with a noise wave or the noise suppression quality is unsatisfactory. The current research is aimed at improving the filtering algorithm in a time-frequency domain using the slant Karhunen–Loeve transform in order to overcome these limitations, to increase the accuracy and rate of its software implementation, and also to test it when processing profile field data from land-based 2D seismic surveys. The algorithm is modified by developing a new method for determining static corrections for surface wave hodograph rectification in a time-frequency domain and by the application of preprocessing in which the reflected wave signal is removed preliminarily. These and other modifications ensure faster calculations and improve the quality of surface wave interference suppression. In addition, the slant Karhunen–Loeve transform is accelerated by parallelizing calculations across logical processor cores. In this paper, the algorithm is described in detail, its significant advantage over the standard methods of bandpass filtering and F–K filtering is shown, and the results of processing the field data obtained by the SWANA procedure (Geovation 2.0) and by the slant Karhunen–Loeve transform. The result obtained by the slant Karhunen–Loeve transform is superior to the SWANA procedure in terms of the surface wave filtering quality and has only four adjustable parameters (SWANA has 20 parameters)