摘要
Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.
特征值相干属性是一种高效、成熟的裂缝预测技术。但即使在连续、稳定的目的层段,部分裂缝隐没于相近高灰度值区域而难以识别;在破碎带发育区域,由于成像能量难以完美聚焦,中、小尺度的裂缝区域呈现相近低灰度值的云雾状模糊而无法区分。本文提出一种以直方图均衡化处理为核心技术的新型裂缝增强方法。该方法能够强化相干图像中不连续性信息与背景的差异性,突出裂缝的线性结构:采用相干图像阈值逐级调节的方式,实现对不同尺度裂缝的预测。该方法同时提升了显性、隐性裂缝识别能力和精度。
基金
sponsored by the National Science&Technology Major Special Project(Grant No.2011ZX05025-001-04)
