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- PublicationApplied Diffraction Imaging: Conventional vs. Machine Learning Approach(University College Dublin. School of Earth Sciences, 2021)
;0000-0002-0894-1249Diffractions are oft overlooked in favour of specular reflections for seismic imaging. Diffractions, however, are formed by objects and discontinuities which are comparable or smaller than the wavelength. Therefore, if the diffractions can be imaged, these objects and discontinuities can be directly imaged. Said features are geologically noteworthy as structural and stratigraphic features of interest which may directly affect hydrocarbon migration, flow, and trapping. While straightforward in theory, in application separating the diffractions from the wavefield is a complex and intricate task. This thesis aims to discuss what makes separation so problematic and attempts to address various methods for allaying some of these issues. Additionally, this thesis intends to tackle the real benefits diffraction imaging can add to a conventional seismic image. To achieve these goals, existing separation techniques are analysed in pre-migration and post-migration domains. These analytical methods leave a volume which contains both diffractions and noise and require additional inputs such as a dip field. Ergo, any errors present in the dip field are incorporated into the diffraction image, diminishing its quality. In this thesis, adaptations to existing methods have been proposed as well as a novel method in the pre-migration domain. These new methods incorporate common geophysical techniques to obtain a cleaner separation. The novel method, histogram-based separation, is an analytical curve-fitting technique to identify diffractions without the presence of noise. Deep learning has also been integrated with the methods. Firstly, with pre-migration separation deep learning has been used to automatically identify and separate diffractions, obtaining a cleaner separation in a faster time. Another neural network is also trained which can apply a post-migration-based diffraction imaging scheme directly on stacked, migrated, seismic data, something hitherto impossible. These methods have been applied to seismic and GPR data highlighting the benefits of diffraction imaging. 66
- PublicationDiffraction imaging of sedimentary basins: An example from the Porcupine Basiniffraction imaging is the technique of separating diffraction energy from the source wavefield and processing it independently. As diffractions are formed from objects and discontinuities, or diffractors, which are small in comparison to the wavelength, if the diffraction energy is imaged, so too are the diffractors. These diffractors take many forms such as faults, fractures, and pinch-out points, and are therefore geologically significant. Diffraction imaging has been applied here to the Porcupine Basin; a hyperextended basin located 200km to the southwest of Ireland with a rich geological history. The basin has seen interest both academically and industrially as a study on hyperextension and a potential source of hydrocarbons. The data is characterised by two distinct, basin-wide, fractured carbonates nestled between faulted sandstones and mudstones. Additionally, there are both mass-transport deposits and fans present throughout the data, which pose a further challenge for diffraction imaging. Here, we propose the usage of diffraction imaging to better image structures both within the carbonate, such as fractures, and below.
- PublicationAn Outlook on Seismic Diffraction Imaging Using Pattern RecognitionA seismic image is formed by interactions of the seismic wavefield with geological interfaces, in the form of reflections, diffractions, and other coherent noise. While in conventional processing workflows reflections are favoured over diffractions, this is only beneficial in areas with uniform stratigraphy. Diffractions form as interactions of the wavefield with discontinuities and therefore can be used to image them. However, to image diffractions, they must first be separated from the seismic wavefield. Here we propose a pattern recognition approach for separation, employing image segmentation. We then compare this to two existing diffraction imaging methods, plane-wave destruction and f-k filtering. Image segmentation can be used to divide the image into pixels which share certain criteria. Here, we have separated the image first by amplitude using a histogram-based segmentation method, followed by edge detection with a Sobel operator to locate the hyperbola. The image segmentation method successfully locates diffraction hyperbola which can then be separated and migrated for diffraction imaging. When compared with plane-wave destruction and f-k filtering, the image segmentation method proves beneficial as it allows for identification of the hyperbolae without noise. However, the method can fail to identify hyperbolae in noisier environments and when hyperbolae overlap.
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