论文详情
鉴于微测井资料在近地表结构调查中的独特优势,其常用于估算近地表品质因子Q值,且以相邻道记录的谱比法应用居多。因受旅行时间拾取误差或速度估算误差等影响,Q估计结果波动剧烈、稳定性较差、估算精度较低。为此,借鉴微测井分层速度回归分析思想,提出了一种双线性回归品质因子Q估计算法。该方法利用谱比法原理,首先求取微测井各道记录相对于第一道的非相邻道谱比对数值,然后在速度分层约束的基础上采用两次线性回归求得不同层段品质因子Q值。模型试算结果表明,与常用相邻道谱比法相比,基于非相邻道的双线性回归Q估计方法明显降低了时间拾取误差和噪声干扰的影响,Q估计最大相对误差不超过25%,平均相对误差约为10%,提高了近地表Q估计的稳定性和估算精度。实测微测井数据试算结果表明,该方法可以获得与近地表速度分层相一致的较为稳定的、地质意义明确的近地表Q估计值,而且适用于不同微测井观测系统的测井数据。
At present,uphole survey data is always used to estimate the near-surface quality factor Q by the adjacent trace spectral ratio method because of its unique advantages in the near-surface structure investigation.The estimated Q values generally show sharp jumps,poor stability and low accuracy due to the influence of travel time pickup error or velocity estimation error.Therefore,a new method for estimating quality factor Q by dual linear regression is provided from a velocity regression analysis based on the uphole survey data.We first calculate the non-adjacent trace spectral ratio of the first trace of the uphole survey data using the theory of spectral ratio method;then we estimate the quality factor Q for different layers by dual linear regression under the constraint of layered results from a velocity regression analysis.Model tests show that the method called dual linear regression based on the non-adjacent trace spectral ratio method can greatly reduce the influence of travel time pickup error and noise interference compared to the conventional adjacent trace spectral ratio method and improve the stability and accuracy of Q value estimations.The maximum relative error of the estimation Q is less than 25%,and the average relative error is about 10%.The estimation results based on real uphole survey data further show that the method can obtain more stable near-surface Q values,which are consistent with near-surface velocity layering and have clear geological significance.This method has good adaptability for logging data different uphole survey systems.
河南理工大学博士基金项目(B2009-85)资助。