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Multi-Dimensional Seismic Data Reconstruction via RBF Interpolation and K-NN AlgorithmNormal access

Authors: M. Zhang, Y. Liu and Y. Chen
Event name: 80th EAGE Conference and Exhibition 2018
Session: Student Poster: Geophysics Other, Energy, CO2
Publication date: 11 June 2018
DOI: 10.3997/2214-4609.201801665
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 3.46Mb )
Price: € 20

Accurate reconstruction of irregularly sampled data plays an important role in seismic data processing. We propose a novel approach to reconstruct multi-dimensional seismic data based on radial basis function (RBF) interpolation and k-nearest neighbours (k-NN) algorithm. RBF interpolation is nearly the most accurate and stable method for solving discrete data interpolation. The introduction of k-NN algorithm helps to narrow down the effective range of the input data for each unknown point. This reduces the computational cost of the RBF interpolation without compromising the accuracy. Application of the proposed method on field seismic data demonstrates superior performances on both SNR comparisons and visual observations compared with the typical rank reduction method, known as the multichannel singular spectrum analysis algorithm (MSSA).

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