ABSTRACT To improve the resolution of seismic data, it is important to accurately estimate the near‐surface quality factor, Q, which provides a measure of seismic wave attenuation. In view of the unique advantages provided by uphole surveys when investigating near‐surface structures, they are widely employed to estimate the near‐surface Q factor. However, the Q factor estimated using the traditional spectral ratio method is not always precise and provides larger oscillations in the estimated Q factors due to errors associated with first‐break picking and velocity estimation. Following on the traditional logarithmic spectral ratio method, a new method called the logarithmic spectral ratio integral method was proposed to estimate the layer Q factor using uphole survey data. It calculates first the weighted integral of the logarithmic spectral ratio in an effective frequency interval between non‐adjacent traces, then makes a linear regression between the inter‐trace travel moveout and the weighted integral of logarithmic spectral ratio under the constraint of velocity stratification. The result of model analysis shows that under an ideal condition (without first‐break picking errors), the layer Q values estimated by the logarithmic spectral ratio integral method are fairly consistent with the true layer‐specific Q values in the model. In addition, the Q values estimated from field‐measured data and data from forward modelling with 10% random noise added, both have smaller mean relative errors than the results using traditional spectral ratio method and the double‐linear regression method. A case study is employed and the results show that the layer Q factor estimated using the new method correlates well with the velocity stratification and is thus applicable for use with various uphole survey observation systems. Furthermore, all results indicate that the logarithmic spectral ratio integral method delivers a more precise and stable estimation of the layered Q than the other methods, and the anti‐noise characteristics are stronger.

中文翻译：

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基于井上测量数据的分层速度约束下近地表Q因子估计

摘要 为了提高地震数据的分辨率，准确估计近地表质量因子 Q 非常重要，它提供了地震波衰减的度量。鉴于井上测量在调查近地表结构时提供的独特优势，它们被广泛用于估计近地表 Q 因子。然而，使用传统谱比方法估计的 Q 因子并不总是精确的，并且由于与初碎采摘和速度估计相关的误差，在估计的 Q 因子中提供了更大的振荡。继传统的对数谱比法的基础上，提出了一种新的方法，称为对数谱比积分法，利用井上测量数据估计层Q因子。它首先计算非相邻道间有效频率区间内的对数谱比的加权积分，然后在速度分层约束下对道间行程时差与对数谱比的加权积分进行线性回归。模型分析结果表明，在理想条件下（无初起采摘误差），对数谱比积分法估计的层Q值与模型中真实的层特定Q值相当一致。此外，从现场实测数据和加入 10% 随机噪声的正向建模数据估计的 Q 值，均比使用传统谱比法和双线性回归法的结果具有更小的平均相对误差。采用案例研究，结果表明，使用新方法估计的层 Q 因子与速度分层有很好的相关性，因此适用于各种井上勘测观测系统。此外，所有结果表明，对数谱比积分方法比其他方法提供了更精确和稳定的分层 Q 估计，并且抗噪声特性更强。