1887

Abstract

Summary

Field conditions and economic factors always lead to irregular data along the spatial axis. However, many subsequent processing steps are based on the prerequisite of regular data distribution. Data interpolation has become a key technique in seismic data processing workflow. The iterative interpolation using prediction filter or prediction-error filter is often used to reconstruct seismic data, but these methods are difficult to guarantee both accuracy of adaptive interpolation for nonstationary data and fast computational speed. In this paper, we develop a new approach for seismic data interpolation using streaming prediction filter (SPF) in the f−x domain. Aiming at the nonstationary property of seismic data, we employ an f−x streaming manner to calculate the space-varying filter under the assumption of similar neighboring filter coefficients. The steaming strategy also avoids high computational cost of estimating adaptive prediction filter by directly solving inverse problem. The proposed method allows us to better control the balance between interpolating precision and computational cost. In comparison with traditional Fourier projection onto convex sets (POCS), the numerical examples demonstrate that the f−x SPF can recover missing data more effectively and efficiently even in the case of complex conditions, such as curved or conflicting events.

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/content/papers/10.3997/2214-4609.201901009
2019-06-03
2024-03-29
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