论文详情
陆上地震数据的噪声主要包括来自复杂近地表的噪声、外源激发的波场、不能用于地震波成像的其它噪声，通常包括线性与非线性相干噪声、非相干噪声及随机噪声。噪声压制的基本思想是对实测数据中包含的信号或相干噪声建立预测模型，然后对信号或相干噪声进行预测，最后压制数据中相干噪声和随机的非相干噪声。全波形反演和最小二乘逆时偏移逐渐成为高精度地震波成像的代表性方法技术，它们对噪声压制方法提出了更高的要求。因此，对当前地震数据去噪理论、方法与技术进行了分析对比，首先，提出了勘探地震数据的概念模型，即具有线性或非线性结构的信号或相干噪声叠加上满足一定概率分布的随机噪声；然后，分析针对该概念模型的各种方法技术，对于线性信号或相干噪声，采用的预测方法包括自回归模型预测器、线性Radon变换方法、K-L变换方法、Hankel矩阵方法，对于非线性（双曲）信号或相干噪声，采用的预测方法包括Radon变换方法和多项式拟合方法；最后，指出对数据中的非线性信号进行最佳建模是地震数据去噪的基础。上述方法的对比分析结果加深了数据处理人员对目前主流去噪软件模块理论基础的认识，从而进一步提升实际地震数据的处理效果。
Noises in land seismic data, which can be classified into linear and nonlinear coherent noises, incoherent noises, and random noises, may come from near-surface formations, externally sourced wavefield, and other sources. The basic idea of noise suppression is to establish a model to predict signals or coherent noises and then remove coherent, random, and incoherent noises from seismic data. Some high-precision imaging methods, such as full waveform inversion and least-squares reverse time migration, should be accomplished using seismic data with high signal to noise ratios. This paper presents an overview of denoising theories and techniques, and develops a conceptual model of seismic data to show that signals or coherent noises with linear and/or nonlinear structures float in random noises that satisfy a certain probability distribution. Based on the conceptual model, various denoising methods are discussed, including AR model predictor, linear Radon transform, K-L transform, and Hankel matrix for the prediction of linear signals or coherent noises and Radon transform and polynomial fitting for the prediction of nonlinear (hyperbolic) signals or coherent noises. The fundamental point of denoising is optimal signal modeling, which is the basic idea for most denoising methods in commercial processing systems. The comparative analysis in this paper provides further insights on denoising theories to improve the effect of data processing.