ABSTRACT

Near-surface scattered waves (NSWs) are the main noise in seismic data in areas with a complex near surface and can be divided into surface-to-surface scattered waves and body-to-surface scattered waves. We have developed a method for NSW enhancement that uses modified source-receiver interferometry. The method consists of two parts. First, deconvolutional intersource interferometry is used to cancel the common raypath of seismic waves from a near-surface scatterer to the common receiver and the receiver function. Second, convolutional interreceiver interferometry is used to compensate the common raypath of seismic waves from the common source to the near-surface scatterer and the source function. For an isotropic point scatterer near the earth’s surface in modified source-receiver interferometry, a body-to-surface scattered wave can be reconstructed by constructive interference not only among three body-to-surface scattered waves but also among a body-to-surface scattered wave and two surface-to-surface scattered waves; a surface-to-surface scattered wave can be reconstructed by constructive interference not only among three surface-to-surface scattered waves but also among a surface-to-surface scattered wave and two body-to-surface scattered waves. According to stationary phase analysis based on the superposition principle, we have developed a so-called dual-wheel driving configuration of modified source-receiver interferometry for enhancing NSWs in the data of conventional seismic exploration. The main advantages of the scheme are that (1) it can be used to enhance NSWs without the need for any a priori knowledge of topography and near-surface velocity, (2) it can be used to reconstruct NSWs from real sources to real receivers, including 3D near-surface side-scattered waves, and (3) it can be applied to conventional seismic data with finite-frequency bandwidth, spatially limited and sparse arrays, different source and receiver functions, and static correction. Numerically simulated data and field seismic data are used to demonstrate the feasibility and effectiveness of the scheme.

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