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A method to remove depositional background data based on the Modifed Kernel Hebbian Algorithm

Dou Yutan

Acta Geophysica

2020

Vol. 68, no. 3

701--710

The seismic sedimentology is an emerging inter-discipline originating from the seismic stratigraphy and sequence stratigraphy. However, implementation of the seismic sedimentological research is found with high difculties, due to infuences imposed by structural and depositional background data (including strong refections). In this paper, seismic records are regarded as a combination of the refection from the depositional background and lithological data volumes, and moreover, the seismogram of the depositional background data is characterized by the low frequency and stable phase. Subsequently, the Kernel Hebbian Algorithm (KHA) has been modifed to remove the infuence of the depositional background data. The seismic trace data are used as the training set, and an innovative attempt has been made to incorporate the Ricker wavelet kernel function. Finally, a depositional background data volume extraction methodology with respect to input of higherdimension seismic data has been developed, on the basis of the Modifed KHA (MKHA), so as to obtain the lithological data volume. Utilizing the unsupervised online learning capabilities of the MKHA, iterative calculation of Kernel PCA can greatly reduce the computational complexity and can be adapted to big data problems. This paper introduces the Ricker wavelet kernel function to transform the original seismic data into the feature space through the inner-product operation, extract the non-linear features, and solve the problem that the seismic data of the original sample space is linearly inseparable. The seismic sedimentological analysis based on the lithological data volume that is able to refect hidden sand bodies can achieve elaborate carving of the reservoir. The proposed method has been tested in Working Block A of the East-1 district in the Sulige gas feld, the Ordos Basin, China. The case study demonstrates that the presented method is capable of efciently removing the depositional background data, and making great contributions to improving accuracy of the seismic sedimentological analysis of the efective reservoir, with the help of higher-dimension seismic data.