MULTIVARIATE EMPIRICAL MODE DECOMPOSITION BASED MULTITRACE SEISMIC COHERENT NOISE REMOVAL
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摘要:
地震勘探采集到的地震信号中往往包含大量的相干噪声, 这些相干噪声常常使得资料质量变得很差，从而严重的妨碍了科研工作者进行正确的地震解释, 因此对相干噪声进行压制就显得十分必要。而传统的相干噪声压制方法在消除相干噪声同时往往会对有效信号造成一定程度的损害，或者根本无法有效压制干扰波。为了解决以上问题，从多道联合时频率分析角度出发，结合利用EMD数据驱动分解特性，提出了基于多元经验模态分解的多道地震相干噪声去除方法，能够在有效去除相干噪声同时，保证有效信号不受伤害。本文通过模型和实际资料的处理充分证明了基于多元经验模态分解的降噪方法的有效性和稳健性。
Abstract:Seismic signals collected by seismic surveys often contain a large amount of coherent noises, which often results in poor data quality and seriously impedes researchers from performing correct seismic interpretation. Therefore, suppression of coherent noises is quite significant and necessary. The traditional method to eliminate coherent noises, however, may often cause a certain degree of damage to the effective signals at the same time, or simply lack the capability to suppress the interference wave. In order to solve the above problems, this paper proposes a multi-trace seismic coherent noise removal method based on multivariate empirical mode decomposition combined with EMD data-driven decomposition from the perspective of multi-joint frequency analysis, which can effectively remove coherent noises and ensure effective signals not to be hurt. In this paper, the superiority and robustness of noise reduction method based on multivariate empirical mode decomposition are fully proved by the processing of model and actual data.
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