论文详情
当单点高密度采集数据一致性处理不完全时,均匀加权的室内组合会导致地震数据高频信息的损失。从波束形成理论的角度引入余弦窗、汉明窗、布莱克曼窗,通过调节权重矢量减小组合对数据的影响,考虑到窗函数作为权重矢量对数据的适应性较弱,进一步研究了基于主成分分析(principal component analysis,PCA)的鲁棒自适应最小方差信号无畸变响应(minimum variance distortionless response,MVDR)加权组合。Marmousi2模型正演模拟数据测试结果表明:相比于均匀加权组合,基于PCA的鲁棒自适应MVDR加权组合实现了信号保真与信噪比的有效统一,对深层地震数据信号的保护作用更为明显,信噪比也得到进一步提高。研究成果丰富了单点高密度地震数据的室内组合技术,拓宽了理论研究思路。
Processing inconsistency of single-point high-density seismic data could result in the loss of high frequencies of the high-density seismic data due to the uniformly weighted indoor array.We use beamforming theory to develop two beamforming algorithms and apply them to indoor array processing of high-density seismic data.To decrease the effect of the array on the data,we first adjust the weight vector using Cosine,Hamming,and Blackman windows.Second,we investigate the use of the robust adaptive Minimum Variance Distortionless Response (MVDR) weighted array method based on Principal Component Analysis (PCA),since window function as the weight vector has weak adaptability to the data.The test results of Marmousi2 model data show that the robust adaptive MVDR weighted array method based on PCA results in signal fidelity,improves the SNR,and protects the deep seismic data signal more effectively than the uniformly weighted indoor array method.Our results showed that the proposed method is effective at processing indoor array single-point high-density seismic data.
国家油气专项项目(2016ZX05007-006)资助。