Far-field Source Signature Reconstruction Using Direct Arrival Data

Abstract

For accurate source designature it is fundamental to have precise knowledge of the seismic far-field source signature. While near-field hydrophone data can often be used to provide good quality signatures, such recordings are not always available. This paper describes a technique to extract the far-field signature from the inversion of direct arrivals present in the seismic streamer data. The method has been applied to a deep water survey offshore Gabon, and has been found to produce comparable results to those using data recorded from near-field hydrophones mounted above the air guns on the seismic source array. The method presents advantages for the reprocessing of legacy surveys to achieve a more broadband seismic result.

Reprocessing 2-D airgun seismic reflection data SALTFLU (salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) in the Balearic promontory and the Algerian basin

“…4e). Using a single deterministic operator yields better sub-seabed imaging and low frequency recovery superior to probabilistic methods (e.g., surface consistent, spiking and predictive deconvolution) and modelled source methods (Sargent et al 2011;Davison and Poole 2015;Maunde et al 2017). Converting the seismic data to zero-phase tends to improve temporal resolution and lowers interference between closely spaced reflectors, allowing for better delineation of the stratigraphy, and is a necessary pre-condition for Kirchhoff-type migrations (Sheriff and Geldart 1995).…”

“…The data bandwidth is principally widened by performing a source designature, whereby the primary airgun impulse and the bubble pulse are collapsed into a sharp, zero-phase wavelet (Sheriff and Geldart 1995;Amundsen and Zhou 2013;ten Kroode et al 2013;Baldock et al 2013;O'Driscoll et al 2013). Deterministic deconvolution, where the operator is designed using an estimated source wavelet, can yield geological imaging superior to traditional statistical deconvolution methods, particularly for recovery of low frequencies and the preservation of amplitude information (Yilmaz 2001;Sargent et al 2011;Scholtz et al 2015;Davison and Poole 2015). Deghosting, instead, aims to deconvolve both the source-and receiver-side ghosts from the wavefield, further sharpening the wavelet and removing the frequency 'notches' associated with the ghost effect (e.g., Chuan et al 2014;Davison and Poole 2015;Tyagi et al 2016;Willis et al 2018).…”