PDF(1595 KB)
Research on multiple elimination methods in inverse data space based on randomized singular value decomposition
WANG Tiexing, WANG Deli, HU Bin, SUN Jing, SU Xiaobo
Global Geology ›› 2017, Vol. 20 ›› Issue (1) : 59-63.
PDF(1595 KB)
PDF(1595 KB)
Research on multiple elimination methods in inverse data space based on randomized singular value decomposition
Based on surfaced-related multiple elimination (SRME), this research has derived the methods on multiples elimination in the inverse data space. Inverse data processing means moving seismic data from forward data space (FDS) to inverse data space (IDS). The surface-related multiples and primaries can then be separated in the IDS, since surface-related multiples will form a focus region in the IDS. Muting the multiples energy can achieve the purpose of multiples elimination and avoid the damage to primaries energy during the process of adaptive subtraction. Randomized singular value decomposition (RSVD) is used to enhance calculation speed and improve the accuracy in the conversion of FDS to IDS. The synthetic shot record of the salt dome model shows that the relationship between primaries and multiples is simple and clear, and RSVD can easily eliminate multiples and save primaries energy. Compared with conventional multiples elimination methods and ordinary methods of multiples elimination in the inverse data space, this technique has an advantage of high calculation speed and reliable outcomes.
multiple elimination / SRME / inverse data space / feedback model / RSVD
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