改进光流算法在地震图像纹理分析的应用研究

Application research of an improved optical flow algorithm in seismic image texture analysis

为在消除噪声的同时有效保护地震图像线性结构,提出一种改进光流算法与纹理平滑滤波相结合的新方法.首先,利用高斯金字塔多尺度描述方法解决大流速计算问题,提高精度;其次通过设置迭代结果残差的均方根的门限值,减少迭代次数,缩短处理时间;最后,根据地震图像剖面纹理复杂度,结合纹理属性分析,选用不同的模板进行纹理平滑滤波,提高信噪比.经与传统的均值滤波和目前较先进的改进Sobel滤波器以及利用标准化全梯度进行地震图像边界探测的方法相比,本文提出的算法能够有效的保存地震数据的边缘信息,增强地震图像同相轴的连续性,提高信噪比7~10 dB,缩短处理时间2~3分钟.实验结果表明:本文构建的高斯金字塔多尺度描述与光流算法结合,同时结合纹理平滑滤波构成的综合改进算法,在提高信噪比的同时,较好地保持了原图像的纹理结构和能量,并减少了处理时间,提高了地震资料解释的效率,是目前地震图像纹理分析领域处理效果较好的方法之一.

To effectively protect the linear structure of seismic image while eliminating noise, a new method which combines improved optical flow algorithm with texture smoothing filter was proposed. First, the multiple-scale description method of Gauss pyramid was used to solve the problem of large flow velocity calculation and improve its accuracy. Secondly, by setting the threshold value of the mean square root for the residual error of the iteration results, the number of iterations was reduced and the processing time was shortened. Finally, according to the texture complexity of seismic profile image and combined with texture attribute analysis, different templates were selected for texture smoothing filtering to improve the signal-to-noise ratio (SNR). Compared with the conventional median filter and some advanced algorithms such as the improved Sobel filter and the Normalized Full Gradient method for seismic image boundary detection, the proposed algorithm could effectively preserve the edge information of seismic data, and enhance the continuity of seismic image in-phase axis. In particular, the SNR was increased by 7~10 dB, and the processing time was shortened by 2~3 minutes. Experimental results show that by combining the Gauss pyramid multiple-scale description with the optical flow algorithm and the texture smoothing filtering, the texture structure and energy of the original image could be well preserved by the integrated improved algorithm proposed in this paper. Meanwhile, the SNR was enhanced and the processing time was reduced. Therefore, the efficiency of seismic data interpretation was improved, indicating that the proposed algorithm is a better processing method in the field of seismic image texture analysis.