空间自适应方向全变分地震数据去噪模型

Model for denoising of spatial adaptive directional total variation seismic data

随着我国油气勘探工作的不断推进,地震勘探面临着重大挑战。受到复杂的勘探环境、采集方式、检波器灵敏度等因素的影响,野外采集的地震数据中往往混杂着大量随机噪声,导致后续地震数据处理的保真度、信噪比和分辨率降低,并且最终影响地质解释的精确性、可靠性。为了突破传统地震数据处理问题的局限性,提出了一种用于地震数据随机噪声压制的空间自适应方向全变分正则化模型。首先,针对地震反射同相轴具有空间变化的方向性和倾角计算抗噪性差的问题,提出了基于梯度结构张量的空变倾角逐点估计公式来获取同相轴的方向信息;然后,建立空间自适应方向全变分地震数据去噪模型,并采用优化最小化算法求解模型;最后,讨论了该模型的参数选取方法,将合成地震数据和实际地震数据的去噪结果与同类方法进行比较。实验结果表明,所提出的模型不但能较好地提高地震剖面的垂直分辨率和同相轴的横向连续性,而且在提高信噪比的同时能够保留更多的地质特征信息。

With the continuous advancement of oil and gas exploration in China,seismic exploration is facing great challenges.Affected by the complex exploration environment,acquisition method,detector sensitivity and other factors,the obtained seismic data are often mixed with a large amount of random noise,resulting in the decrease in fidelity,signal-to-noise ratio(SNR)and resolution of subsequent seismic data processing,and the accuracy and reliability of geological interpretation are ultimately affected.In order to break through the limitations of traditional seismic data processing problems,a spatially adaptive directional total variation(SADTV)regularization model for random noise suppression of seismic data is proposed.First,aiming at the problem that the seismic reflection events have the directivity of spatial variation and the poor noise resistance of dip angle calculation,a point by point estimation formula for the spatially varying dip angle based on the gradient structure tensor(GST)is proposed to obtain the direction information on events;Then,the denoising model of SADTV seismic data is established,and the Majorization-Minimization(MM)algorithm for solving the model is derived.Finally,the parameter selection method of the model is discussed,and the denoising results of synthetic and real seismic data are compared with those by similar methods.Experimental results show that the proposed model can not only improve the vertical resolution of the seismic profile and the lateral continuity of the seismic event,but also retain more geological feature information while improving the signal-to-noise ratio.