CAJ | 학술논문

非局部平均滤波方法的去噪性能优异，但其在地震资料处理中的应用刚刚起步。该方法利用数据具有的结构冗余，以包含局部结构的小窗口或邻域为单元，利用局部结构相似性进行加权运算，增强有效信号，压制随机噪音。针对叠前地震资料数据量大、噪音背景强、局部结构简单；原始非局部平均算法对每一点滤波，需要对数据体内所有点计算权系数后进行加权计算，计算量大，对强噪音背景适用性差等不足，对原始非局部平均算法进行了改进，主要包括：基于速度谱的搜索窗口分割；基于梯度域奇异值分解的局部结构相似集选择方法；基于相似集大小的自适应滤波参数选择方法。试验结果表明，该方法改进后对于叠前地震数据的随机噪声具有较好的压制作用。
비국부평균려파방법적거조성능우이，단기재지진자료처리중적응용강강기보。해방법이용수거구유적결구용여，이포함국부결구적소창구혹린역위단원，이용국부결구상사성진행가권운산，증강유효신호，압제수궤조음。침대첩전지진자료수거량대、조음배경강、국부결구간단；원시비국부평균산법대매일점려파，수요대수거체내소유점계산권계수후진행가권계산，계산량대，대강조음배경괄용성차등불족，대원시비국부평균산법진행료개진，주요포괄：기우속도보적수색창구분할；기우제도역기이치분해적국부결구상사집선택방법；기우상사집대소적자괄응려파삼수선택방법。시험결과표명，해방법개진후대우첩전지진수거적수궤조성구유교호적압제작용。
The nonlocal means method has good denoising performance, but its application is newly developing in seismic data processing. The method, using the structural redundancy of data, taking the small window with local structure and neighborhood as unit, conducts weighted arithmetic by using local structural similarity to enhance effective signals and to depress random noises. Aiming at huge amount of pre-stack seismic data, strong background noise and simple local structure, the original nonlocal means method filters each point, conducts weighted calculation after calculating the weight coefficient of all points within data. Because of short points such as huge computation volume and poor adaptability to strong noise background, the original nonlocal means method has been improved. Three modifications have been proposed for the nonlocal means algorithm. Firstly, the scan windows are divided with velocity spectrum; then, pre-selection of similar set is based on singular value decomposition in gradient domain; lastly, selection of self-adaptive filtering parameter is based on the scale of similar set. De-noising results for the test data demonstrate that the method can effectively depress the random noise of seismic data.