A. Kritski

Adaptive wavelets for analyzing dispersive seismic waves

Our primary objective is to develop an efficient and accurate method for analyzing time series with a multiscale character. Our motivation stems from the studies of the physical properties of marine sediment (stiffness and density) derived from seismic acoustic records of surface/interface waves along the water-seabed boundary. These studies depend on the dispersive characteristics of water-sediment surface waves. To obtain a reliable retrieval of the shear-wave velocities, we need a very accurate time-frequency record of the surface waves. Such a time-frequency analysis is best carried out by a wavelet-transform of the seismic records. We have employed the wavelet crosscorrelation technique for estimating the shear-wave propagational parameters as a function of depth and horizontal distance. For achieving a greatly improved resolution in time-frequency space, we have developed a new set of adaptive wavelets, which are driven by the data. This approach is based on a Karhunen-Loeve (KL) decomposition of the seismograms. This KL decomposition allows us to obtain a set of wavelet functions that are naturally adapted to the scales of the surface-wave modes. We demonstrate the superiority of these adaptive wavelets over standard wavelets in their ability to simultaneously discriminate the different surface-wave modes. The results can also be useful for imaging and statistical data analysis in exploration geophysics and in other disciplines in the environmental sciences.

Marchenko Imaging of Volve Field, North Sea

Marchenko redatuming estimates the full response (including internal multiples) from a virtual source inside of an medium, using only reflection measurements at the Earth’s surface and a smooth estimate of the velocity model. As such, it forms a new way to obtain full field propagators to form images of target zones in the subsurface by means of Marchenko imaging, without necessarily have to create detailed models of overburden structure. One of the main obstacles to the application of such novel techniques to field datasets is the set of requirements of the reflection response: it should be wideband, acquired with wide aperture, densely sampled arrays of co-located sources and receivers, and should have undergone removal of direct waves, source and receiver ghosts and free-surface multiples. We use a wave-equation approach to jointly redatum, demultiple, and source designature to transform data recorded using ocean-bottom acquisition systems into a suitable proxy of the reflection response required by the Marchenko scheme. We briefly review the Marchenko redatuming scheme, and present the first encouraging field results of 2D target-oriented imaging of an ocean-bottom cable dataset, acquired over the Volve field. We further discuss the ‘challenge of convergence’ of the Marchenko redatuming scheme for real data.

Vector-acoustic Reverse-time Migration of Volve OBC Dataset without Up/Down Decomposed Wavefields

Wavefield separation based on the combination of pressure and particle velocity data is generally used to extract the up- and down-going components from multi-component seabed or towed marine seismic recordings prior to imaging. By carefully combining vector-acoustic (VA) data in the extrapolation of shot gathers in reverse-time migration (RTM) we show that wavefield separation (deghosting) can be performed ‘on-the-fly’ at no extra cost. We call such a strategy VARTM and we successfully apply it to a North Sea OBC field dataset, acquired in the Volve field. We also discuss additional advantages of VARTM over standard RTM of up-going only waves such as improved handling of directivity information contained in the acquired vector-acoustic data for clearer shallow sections and imaging of the down-going component of the recorded field (mirror VARTM) without the need for an additional finite difference modelling.

Imaging Strategies Using Marchenko Focusing Functions

The development of Marchenko methods for seismic data has enabled the creation of an array of new imaging, redatuming, and multiple or primary estimation methods. Most of these methods make use of the subsurface up- and down-going Green’s functions produced by the Marchenko scheme. However, equally important outputs are the so-called focusing functions. Here, we establish a novel interpretation of these focusing functions and use it to develop new methods to image the data. One of our methods can be used to image reflectors individually, which demonstrate on two experiments: a synthetic model and a field dataset from the North Sea. Our methods are computationally cheaper than standard Marchenko imaging and is shown to provide more continuous images with fewer artifacts than standard RTM. In addition they show the first individually imaged reflector using a Marchenko method on field data.

Retrieving Reservoir-only Reflection and Transmission Responses from Target-enclosing Extended Images

The Marchenko redatuming approach reconstructs wavefields at depth that contain not only primary reflections, but also multiply-scattered waves. While such fields in principle contain additional subsurface information, conventional imaging approaches cannot tap into the information encoded in internal multiples in a trivial manner. We discuss a new approach that uses the full information contained in Marchenko-redatumed fields, whose output are local reflection and transmission responses that fully enclose a target volume at depth, without contributions from over- or under-burden structures. To obtain the Target-Enclosing Extended Images (TEEIs) we solve a multi-dimensional deconvolution (MDD) problem that can be severely ill-posed, so we offer stable estimates to the MDD problem that rely on the physics of the Marchenko scheme. We validate our method on ocean-bottom field data from the North Sea. In our field data example, we show that the TEEIs can be used for reservoir-targeted imaging using reflection and, for the first time, local transmission responses, shown to be the direct by-product of using internal multiples in the redatuming scheme. Finally, we present local, TEEI-derived reflection and transmission images of the target volume at depth that are structurally consistent with a benchmark image from conventional migration of surface data.

Multi-dimensional Free-surface Multiple Elimination and Source Deblending of Volve OBC Data

The wave-equation approach to signature deconvolution and free-surface related multiple elimination of multi-component ocean-bottom data of Amundsen (2001) has recently been linked to seismic interferometry by multi-dimensional deconvolution (MDD). When applied to simultaneous-source data this method can also unravel and reorganise blended data into sequential source responses. We have generated two blended versions of the Volve OBC dataset and compared the ability of MDD to deblend different types of simultaneous-source acquisitions together with suppressing free-surface multiples. Reverse-time migration of the deblended responses produces seismic images of similar quality to those from truly sequential source data.

Shear wave velocity inversion from the seismic surface waves using data adaptive wavelets

We study the dynamic and physical properties (shear velocity, seismic attenuation, stiffness and density) of marine sediments from seismic-acoustic records of surface/interface waves along the water-seabed boundary. Accurate models of the marine sediments properties in the shallow subsurface are critical in many disciplines. For example, in oil and gas exploration knowledge of the sedimentary acoustic properties helps to improve the shear wave static corrections, it provides shear strength modulus in the geotechnical engineering, helps to estimate acoustic loss for sonar operation in the shallow water. Our method relies on using dispersive characteristics of the seismic surface waves which can be further inverted for the shear velocities as function of depth and distance. We use wavelet analysis to separate surface wave modes and extract their group and phase velocities. To improve the accuracy in imaging of the surface wave modes we have developed and applied a new tool - adaptive wavelet which parameters are defined directly from the data. We demonstrate how these wavelets can be employed to provide more accurate shear waves velocities estimates.