动态时间规整的断层增强方法

地震属性可在一定程度上反映断层的空间特征，但对噪声很敏感，对复杂断裂的检测效果不佳。常规断层增强算法首先根据属性体计算断层方向，然后增强断层方向的断层信息，压制非断层方向的其他信息，但计算精度依赖于断层方向的计算精度。为此，基于断层在平面上具有线性特征、在空间上具有平面特征的假设，提出基于动态时间规整（DTW）的断层增强方法。利用DTW方法计算断层图像的最优断层线，运用非线性平滑、正向积累和反向追踪等方法有效提高了最优断层线的求解精度，完全去除了断层属性图像中的非断层信息，使断层更连续、清晰，断层分布、延伸特征更明显，同时去除了地震属性图像中的一些非线性局部特征。将复杂断层分解为局部窗口内的简单断层，然后在局部窗口内计算最优断层线，最后综合局部最优断层线得到最终的复杂断层增强结果。模型试算和实际资料处理结果表明，所提方法能抑制断层图像中的非断层信息，断层经过增强处理后更连续、清晰，具有较好的鲁棒性和适用性。

A fault enhancing method based on dynamic time warping

Seismic attributes can reflect the spatial characteristics of faults to a certain extent, but they are very sensitive to noises, resulting in less effective detection of complex faults. The conventional fault enhancing algorithm first calculates the fault direction according to the fault attribute, then enhances the fault information along the fault direction and suppresses other information along the non-fault direction, but the calculation accuracy depends on the calculation accuracy of the fault direction. This paper proposes a fault enhancing method based on dynamic time warping (DTW) by assuming that faults are linear in plane and planar in space. This method uses DTW to calculate optimal fault lines, and uses nonlinear smoothing, forward and reverse tracking to effectively improve the accuracy of the optimal fault lines. So the non-fault information in the fault attribute is completely removed, the faults are more continuous and clearer, the fault distribution and extension features are more obvious, and at the same time some nonlinear local features in seismic images are removed too. Complex faults are divided into simple faults within local windows, and then the optimal fault lines are calculated within the local windows. Final enhanced complex faults are obtained by integrating the local optimal fault lines. Applications on model and field data have proved that the proposed method can suppress the non-fault information on fault images, and provide more continuous and clearer faults after enhancing processing, and it has good robustness and applicability.