基于统计分布的Shearlet域连片数据一致性处理方法

估计相对精确的时空变一致性校正因子是提高连片数据处理质量的关键。目前常用的连片数据一致性处理方法如反褶积参数调整法、匹配滤波法等,虽然取得了一定的应用效果,但是存在缺乏时空变处理能力、易受噪声因素的干扰和稳定性差等缺陷,难以满足精细化连片数据一致性处理的要求。针对这一问题,提出一种基于统计分布的Shearlet域连片数据一致性处理方法。首先,将连片数据变换到Shearlet域,利用Alpha-trim均值滤波方法估计不同尺度和方向上的时变均值和均方差;其次,在数据品质相对较好的探区选取一定量的数据,在Shearlet域计算统计平均后的时变均值和均方差并作为目标模型;之后,提取待处理数据Shearlet域均值和均方差在时间、空间上的低频趋势,并利用提取的低频趋势与目标模型计算均值和均方差的时空变校正因子;最后,应用该时空变校正因子对待处理数据进行校正处理,得到连片数据一致性处理结果。模型试算和实际资料的验证结果表明,该方法能够较好地消除连片数据的振幅、频率在空间上的差异,在噪声环境下具有较好的稳定性,具有一定的实际应用价值。

Shearlet-domain consistency processing method based on statistical distribution for multi-survey data

A relatively accurate time-space-varying consistency correction factor is the key to improving the quality of multi-survey data processing. At present,the commonly used consistency processing methods for multi-survey data mainly include the deconvolution parameter adjustment method and the matched filtering method. Although these methods have achieved certain application results,they still fail to meet the needs of refined consistency processing of multi-survey data due to their lack of time-space-varying processing ability,susceptibility to noise disturbance,and poor stability. To solve this problem,this paper proposes a Shearlet-domain consistency processing method based on the statistical distribution for multi-survey data. Specifically,the multi-survey data are transformed into Shearlet-domain,and the time-varying mean and mean square deviation at different scales and in different directions are estimated by the Alpha-trim mean filtering method. Subsequently,a certain amount of data are selected in the exploration area with relatively high data quality,and the statistical time-varying mean and mean square deviation are calculated in Shearlet-domain to serve as the target model. Then,the temporal and spatial low-frequency trends of the mean and mean square deviation of the data to be processed are extracted in the Shearlet-domain,and the time-space-varying correction factors of the mean and the mean square deviation are calculated with the extracted low-frequency trends and the target model. Finally,the data to be processed are corrected with the time-space-varying correction factors to obtain the results of multi-survey data consistency processing. Model-based tentative calculation and field data processing are conducted to verify this method. The results show that the proposed method deserves practical application as it can effectively eliminate the differences among multi-survey data in amplitude,frequency,and space,and it has favorable stability in noisy environments.