基于正则化条件的地震数据局部信噪比估计方法

信噪比是衡量地震数据质量的重要指标之一,在地震数据处理和解释中有着重要的作用.目前已有的地震数据信噪比估计方法往往得到的是整个数据的全局信噪比,这种方法只能说明地震数据总体质量的好坏,无法直观细致地刻画地震信号的局部质量.本文提出一种基于正则化条件的局部信噪比估计方法.该方法的基本原理是使用正则化共轭梯度法求解局部信噪比最优解,正则化算子的参数将控制地震信号各点数据局部信噪比的平滑性.其中应用一种基于"过滤波"的级联信号估计方法来计算有效信号,该方法利用有效信号和噪声的相关性特征计算局部信噪比中的有效信号.局部信噪比估计方法利用了信号中每个数据点及其邻域各点的局部信息,避免了使用单个数据点而可能出现的信噪比不合理值,而且局部处理能够减少全局噪声对信噪比估算的影响,该方法可以更准确地表征地震资料信噪分布特征.另外,局部信噪比对去噪方法的评估也具有重要意义.理论模型测试和实际资料处理结果表明,局部信噪比估计方法能够准确反映任一给定地震信号剖面的局部信噪比特征,为非平稳地震数据质量评估提供了直观的评判标准.

Local SNR estimation method based on regularization for seismic data

SNR is an important standard to measure the quality of seismic data and it plays an important role in seismic data processing and interpretation. Traditional SNR estimation just works for the whole seismic profile which cannot indicate the local characteristics of data. In this paper, we propose a local SNR estimation method based on regularization. Local SNR method uses conjugate-gradient iterative inversion with regularization to find the optimal solution of local SNR. The parameters in regularization operator can control the smoothness of the local SNR at different data location. To obtain the signal, we propose the cascading estimation method based on "over-filtering". Local SNR estimation measures signal characteristics not instantaneously at each signal point but locally in the neighborhood of each point. Meanwhile, it can reduce the influence of global noise to local SNR. Local SNR estimation produces a visual and accurate SNR image. Moreover, it can also evaluate the denoising result. Synthetic model and real data tests show that local SNR estimation method is able to accurately indicate the local SNR characteristics and provides a visual judgment for the assessment of seismic data.