Seismic random noise removal by delay-compensation time-frequency peak filtering. (27th April 2017)

• Record Type: Journal Article
• Authors: Yu, Pengjun; Li, Yue; Lin, Hongbo; Wu, Ning
• Is Part Of: Journal of geophysics and engineering. Volume 14:Number 3(2017:Jun.)
• Page Start: 691
• Page End: 697
• Publication Date: 2017-04-27
• Subjects: complex stratum -- time–space domain -- delay-compensation TFPF -- seismic data denoising; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; Engineering -- Periodicals; 622.1505
• DOI: 10.1088/1742-2140/aa6495
• Languages: English
• ISSNs: 1742-2132

Abstract

Abstract: Over the past decade, there has been an increasing awareness of time-frequency peak filtering (TFPF) due to its outstanding performance in suppressing non-stationary and strong seismic random noise. The traditional approach based on time-windowing achieves local linearity and meets the unbiased estimation. However, the traditional TFPF (including the improved algorithms with alterable window lengths) could hardly relieve the contradiction between removing noise and recovering the seismic signal, and this situation is more obvious in wave crests and troughs, even for alterable window lengths (WL). To improve the efficiency of the algorithm, the following TFPF in the time–space domain is applied, such as in the Radon domain and radial trace domain. The time–space transforms obtain a reduced-frequency input to reduce the TFPF error and stretch the desired signal along a certain direction, therefore the time–space development brings an improvement by both enhancing reflection events and attenuating noise. It still proves limited in application because the direction should be matched as a straight line or quadratic curve. As a result, waveform distortion and false seismic events may appear when processing the complex stratum record. The main emphasis in this article is placed on the time–space TFPF applicable expansion. The reconstructed signal in delay-compensation TFPF, which is generated according to the similarity among the reflection events, overcomes the limitation of the direction curve fitting. Moreover, the reconstructed signal just meets the TFPF linearity unbiased estimation and integrates signal reservation with noise attenuation. Experiments on both the synthetic model and field data indicate that delay-compensation TFPF has a better performance over the conventional filtering algorithms.