From removing to using ghost reflections

Berkhout, Guus; Blacquière, Gerrit

doi:10.1190/segam2015-5887995.1

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Many methods have been proposed to solve the ghost problems, either during different stages of the migration process or by using advanced acquisition systems. The free‐surface reflections are usually separated, suppressed or removed from the recorded data before the imaging process (Berkhout & Blacquière, 2015; Lu, 2021; Verschuur, 1991; Weglein, 2016). In an ideal case of RTM, seismic data free of surface reflection are extrapolated from receivers, and purely down‐going waves are directly extrapolated from the source.…”

Section: Introductionmentioning

confidence: 99%

The effect of free‐surface reflections on depth images for acquisition with buried sources and receivers: Investigation through the point spread function#

Han

Xie

2023

Geophysical Prospecting

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In reverse time migration, if sources or receivers are buried at certain depths, the interference between the primary wave and the free‐surface reflection causes losing spectral contents in seismic data. If they are not properly compensated, errors will be carried to the target during the reverse time migration and cause a distorted image by so‐called ghost image. By invoking the point spread function, we investigate how distorted seismic data are mapped to the subsurface. Quantitative relations between the free‐surface parameters (i.e. source/receiver depth, free‐surface incident angle and near‐surface velocity) and wavenumber‐domain illumination can be created, which map missing frequency contents in seismic data to missing wavenumber contents in the point spread function. After transformed back to the space domain, we can investigate its effect on the image distortion. Numerical calculations expand the above approach to handle complex overburden structures and more realistic acquisition geometries. The proposed method provides us with a useful tool to investigate deghosting related problems, for example optimizing parameters of acquisition geometry, evaluating capabilities of existing acquisition systems and data processing technologies in suppressing the free‐surface reflection effect in the depth image, or developing new deghosting approaches.

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Section: Introductionmentioning

confidence: 99%

The effect of free‐surface reflections on depth images for acquisition with buried sources and receivers: Investigation through the point spread function#

Han

Xie

2023

Geophysical Prospecting

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“…Despite the evidence that noise does not conform to the white, Gaussian noise (WGN) assumption (see next section for definitions), the majority of published approaches still use WGN to test the robustness of event imaging and detection algorithms with respect to noise [e.g., Grion et al, 2015;Berkhout and Blacquière, 2015;Shao et al, 2015;Trojanowski and Eisner, 2015]. Pearce and Barley [1977] included the effect of noise on synthetic seismograms by convolving a sample of recorded noise with broadband white noise creating coloured, Gaussian noise as opposed to the simple WGN approach.…”

Section: Introductionmentioning

confidence: 99%

Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site

Birnie

Chambers²,

Angus

et al. 2016

Geophys. J. Int.

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Noise is a persistent feature in seismic data and so poses challenges in extracting increased accuracy in seismic images and physical interpretation of the subsurface. In this paper, we analyse passive seismic data from the Aquistore carbon capture and storage pilot project permanent seismic array to characterise, classify and model seismic noise. We perform noise analysis for a three month subset of passive seismic data from the array and provide conclusive evidence that the noise field is not white, stationary, or Gaussian;characteristics commonly yet erroneously assumed in most conventional noise models. We introduce a novel noise modelling method that provides a significantly more accurate characterisation of real seismic noise compared to conventional methods, which is quantified using the Mann-Whitney-White statistical test. This method is based on a statistical covariance modelling approach created through the modelling of individual noise signals. The identification of individual noise signals, broadly classified as stationary, pseudo-stationary and non-stationary, provides a basis on which to build an appropriate spatial and temporal noise field model. Furthermore, we have developed a workflow to incorporate realistic noise models within synthetic seismic datasets providing an opportunity to test and analyse detection and imaging algorithms under realistic noise conditions.

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