Curiosity regarding what we cannot see has always driven research. Science has helped us to uncover many of those hidden secrets. In particular, geophysics has helped us to image the inside of the Earth. By sending a seismic signal into the Earth and recording the signal that comes back, geophysicists can characterize the layers of the subsurface. Nowadays, geophysics is used for many purposes, for example, the localization of fossil fuels, the characterization of the subsurface for the construction of wind farms and the evaluation of reservoirs for geothermal energy. In order to decrease the risk and cost involved in these activities, we need images of the subsurface that are as accurate as possible.
These images can only be obtained if we fully understand the propagation of the seismic signal in the subsurface. A long-standing problem in geophysical imaging is the presence of internal multiple reflections. When imaging the subsurface, we assume that the signal only reflects once when there is a contrast in velocity and/or density (for example, when changing from sand to rock). However, in reality, the signal can reflect many times inside the subsurface before being recorded at the surface. When treating the arrivals that have reflected many times as arrivals that have only reflected once, we incorrectly image the subsurface and create ghost reflectors that do not exist. This problem is particularly strong in geological settings that have a complex structure with many strong velocity and/or density contrasts above an area of interest. This may happen, for example, when there is a reservoir of oil below a thick stratified salt layer. In such cases, the image of the area of interest is unreliable due to the presence of many ghost reflectors. Therefore, we have to use knowledge of wave propagation to predict and attenuate the internal multiples in the data prior to imaging.
In this thesis, I further develop the data-driven and wave-equation-based Marchenko method to make it suitable for the attenuation of internal multiples in seismic field data. In addition, I evaluate the performance of suitable methods by applying them to field datasets recorded in different geological settings. I start this evaluation by demonstrating that what we call the conventional Marchenko method is perhaps not the most suitable Marchenko method for the application to field data. I develop an alternative Marchenko method instead: the adaptive double-focusing method. I show that this method indeed produces improved results compared to the conventional Marchenko method when applying it to a line of 2D data of the Santos Basin, Brazil.
Since the 2D results show promise, I continue with the extension to 3D applications. I first identify the key acquisition parameters that affect the result of our Marchenko method on 3D synthetic data and conclude that the limited crossline aperture and the coarse sail line spacing have the strongest effect on the quality of the result. Based on this evaluation, I interpolate the sail line spacing on 3D field data acquired in the Santos Basin and use the adaptive double-focusing method to predict and subtract internal multiples. I conclude that 3D Marchenko internal multiple attenuation seems to be sufficiently robust for the application to narrow azimuth streamer data in a deep marine setting, provided that there is sufficient aperture in the crossline direction and that the sail lines are interpolated. In addition, the adaptive double-focusing method is suitable for the attenuation of internal multiples generated by a complex overburden and for simultaneously redatuming to a level below this overburden.
Next, I modify the adaptive double-focusing method to obtain an adaptive double dereverberation method that is suitable when only aiming to attenuate internal multiples generated in an overburden without redatuming. Moreover, this method does not require a velocity model. I apply this method to a 2D line of data acquired in the very shallow Arabian Gulf. Also, I assess how to meet the data requirements for the Marchenko method in shallow water environments (e.g., the removal of surface-related multiples, the deconvolution of the source signature) and demonstrate that the state-of-the-art Robust Estimation of Primaries by Sparse Inversion (R-EPSI) method is capable of producing the correct input data for the Marchenko method in such settings.
Subsequently, I discuss the role of the adaptive filter in the application of the Marchenko method to field data. I argue that developments in seismic data processing allow us to predict internal multiples with more accuracy, such that only a conservative adaptive filter is needed to correct for the unavoidable minor amplitude and phase discrepancies between the internal multiples in the data and the predicted internal multiples. I demonstrate this by using a conservative adaptive filter to subtract internal multiples that were predicted by applying an adaptive Marchenko multiple elimination method to a 2D line of field data acquired in the Norwegian North Sea.
Finally, based on the results presented in this thesis, I conclude that the Marchenko method is an effective, data-driven and robust method for the prediction of internal multiples in marine seismic data. Different Marchenko methods are suitable for different purposes. There are two key elements for the successful application of a Marchenko method to field data: 1) the acquisition geometry needs to be sufficiently dense and 2) a careful processing workflow needs to be constructed that accounts for the specifics of the geological setting at hand, with significant emphasis on amplitude and phase preservation.