Seismic Image Processing by Wavelet Transform

SunjayGeophysics,BHU, Varanasi-221005,India

sunjay.sunjay@gmail.com

Wavelet (Mathematical Microscope) analysis of seismic data is made fashionable for thin bed precise subsurface imaging and interpretation. 3D seismic data interpretation for subsurface imaging of thin bed contourite systems is integral part of research work . Seismic expression of bottom current deposits from that of other related deepwater sediments (turbidites, hemipelagites, debrites, etc.), and to maximising the information that can be derived from seismic data. A wide variety of seismic facies are common in contourites, most of which are equally present in turbidite systems. Seismic facies associations that may be typical of contourites are still to be defined. Seismic characteristics also depend very closely on the methods of seismic acquisition and processing. Sediment waves and channels are very common both in contourite and turbidite systems, and not specifically diagnostic of either system. Slope deformation, sediment creep, and large-scale water-escape may cause a hummocky seismic facies that can be misinterpreted as sediment waves. The identification of hydrocarbon reservoirs from seismic data is a key issue in the oil industry. This is often difficult to achieve in deep-sea sediments, where similar morphologies can prove to be sand-bearing or mud bearing (e.g. the seismic mounds that characterize the basin-floor fan and slope fan. Contourites are widespread throughout the deep sea, ranging from those that build up individually distinct bodies (mounded drifts) to those that occur closely interbedded with other deep-water facies. Although seismic data should not be used to make a firm identification of contourites without supporting evidence, much progress has been made in determining the combination of seismic criteria that best represent contourite deposits. Texture Segmentation of a 3D Seismic Section with Wavelet Transform is employed for pattern recognition .Because of the segmentation, zones of different internal stratification are identified in the seismic section. This recognition is based on the comparison of the 3-D seismic data with the reference patterns extracted from the representative areas, characterized by different textures. In splicing 3-D seismic data, consistent processing is one of the key technologies because it has a great effect on imaging quality.Seismic geomorphology goal is to look for and recognize geologically or geomorphologically meaningful patterns in plan view as well as in section view. Seismic geomorphology, the extraction of geomorphic insights using predominantly 3D seismic data, is a rapidly evolving discipline that facilitates the study of the subsurface using plan view images. A variety of analytical techniques is employed to image and visualize depositional elements and other geologically significant features. Rock visualization stereoscopic volume rendering computer-based display and visualizationof 3D data began to take hold making true 3D interpretations possible. Methods evolved for generating horizontal and flatted slices, arbitrary traverses,wavelet attribute extractions and mapping, and rapid analysis of large complex data volumes.A geological feature must have an expression that is scientifically reasonable in multiple dimensions. Analyses of section view integrated with plan view images represents the integration of seismic stratigraphy with seismic geomorphology. Pattern recognition, involving the interpreter being able to recognize geologically significant features in plan view on 3D .the seismic data, is critical to the seismic geomorphological approach. In conjunction, it is also essential to cross reference plan view with section view images, thus integrating the geomorphology with the stratigraphy. Seismic geomorphology is a rapidly evolving discipline, benefiting from the rapidly accelerating widespread availability of 3D seismic data. Sequence stratigraphy has proven to be an extremely useful predictive tool in the search for hydrocarbons along the continental margins.

A thin bed is a stratigraphic unit that has a thickness much less than the dominant wavelength of the seismic wavelet that illuminates the bed. It is generally accepted that if is the dominant wavelength of the illuminating seismic wavelet, then a thin bed is a bed with a thickness that is one-fourth of or less. It is important to note that the definition of a thin bed depends on the length of the investigative wavelength. A bed that is thin relative to a low-frequency (long-wavelength) wavelet may not be thin when a higher frequency (shorter wavelength) wavelet is considered. Seismic reflection amplitude can be related to net pay and can provide information about the presence or absence of hydrocarbons in a reservoir interval. Information extracted from reflection amplitudes, however, becomes ambiguous when there are two or more closely spaced reflectors, because reflections from these interfaces interfere constructively or destructively, depending on the time delays between successive reflection events and the shape of the illuminating wavelet. The variation in the shape of a reflection wavelet created by closely spaced reflecting interfaces is called tuning effect. Application capability of the wavelet transform depends on the selection of the wavelet functions from which a basis function can be constructed for signal decomposition. There are two types of wavelet functions: orthogonal and non-orthogonal wavelet funclions, thus the algorithms of the wavelet transform vary. The commonly used wavelet functions are orthonormal and compactly supported, but do not have a finite impulse response and linear phase. These features are, however, undesirable for applications in exploration seismology, especially when further subsequent proccssing is required in complex domain. Compactly supported non-orthogonal wavelets do not have phase distortion problem and provide a better choice for seismic data processing. Reflection and refraction events, coherent noises such as ground roll, air-wave, and ringing in seismic data have characteristic features and can be distinguished in the time-frequency space. The Morlet wavelet, a well-known example of non-orthogonal wavelets, is tested in this study for effectively suppressing coherent noise. It was also demonstrated that the reconstructed signals after the weighted wavelet transform show significant improvements in the S/N ratio.A wavelet-based method for analysis of singularities improves analysis and information gathering from seismic trace data. A wavelet transform is applied to seismic trace data. The Holder exponent is calculated for every time point of the wavelet transform for each seismic trace. Holder exponents are then plotted versus time. These graphs are utilized in place of seismic traces themselves in creating two and three dimensional images. The graphs produced using Holder exponents greatly improve interpretation of stratigraphic boundaries and other geological information to be readily identified. This provides for better, more accurate stratigraphic analysis. In addition, the nature of the Holder exponents of the seismic trace are consistent with Holder exponents calculated from acoustic impedance of the various strata.

Conclusion : The study of contourites is important in at least two main respects: (1) Decoding the very subtle signatures of the deep oceanic currents that deposited the contourite sediments can yield vital evidence about long-term variability in paleocirculation patterns and hence in past climates. (2) A better understanding of contourite economic significance is particularly crucial as exploration moves into progressively deeper water.Singularity detection of the thin bed seismic signals with wavelet transform: The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities, after wavelet transform is performed, are only affected by two factors (their original locations and the seismic wavelet length) irrespective of the shape the wavelet. The wavelet length can be determined according to the wavelet transform results to detect thin bed with resolution of Wavelength/Thirty Two .The singularities have been recovered with improved resolution of the seismic section by real data processing. Thus, the wavelet transform provides better resolution of the thin beds which, in turn, gives better picture of the seismic stratigraphy. Three-dimensional seismic data sets provide a vital contribution to geological understanding. One key geological aspect that can be gleaned from such 3D seismic data sets, is the pattern of geological faulting

Wavelet analysis , known as a mathematical microscope, has scope to cope with non stationary signal to delve deep into geophysical seismic signal processing and interpretation for oil & gas exploration & production : Petrophysical imaging for oil & gas reservoir ,Advanced Seismic Stratigraphy: A Sequence-Wavelet Analysis Exploration-Exploitation,high resolution subsurface imaging. Non-Stationary statistical Geophysical Seismic Signal Processing (GSSP) is of paramount importance for imaging underground geological structures and is being used all over the world to search for petroleum deposits and to probe the deeper portions of the earth. For extraction of informations from signals with the help of time-frequency representation & transformations for rectification of uncertainty principle limitation by wavelet 1 st,2nd & 3rd generation.First generation wavelet analysis for high resolution; Second generation wavelet transform (SGWT)/lifting scheme for super resolution; third generation wavelet - a Complex Finite Ridgelet Transform (CFRIT),to achieve the forensic dissection, morphological features from micro/nano scalar of surface topographic data. The goal of seismic processing is to convert terabytes of survey data into a 3D volume description of the earth's subsurface structure. Because the velocity field is initially unknown, we generally start by assuming a rather simple velocity model. Then the migration process gives us a better image of the earth's subsurface that allows us to refine the velocity field. This iterative process finally converges toward our best approximation to the exact earth reflectivity model. At the end of the processing, the 3D volume of data is far cleaner and easier to understand. Some attributes can be extracted to help geologists interpret the results.

Figure : Thin bed seismic resolutionFigure : Thin bedded contourites

Geosciencias 2013 ,April 01-05, Havana,Cuba

For image processing applications we need wavelets that are two-dimensional. This problem reduces down to designing 2D filters. Image Denoising Using Wavelets the Discrete Wavele Transform(DWT) of the image is calculated then thresholding the wavelet coefficients. The threshold may be universal or subband adaptive, then computed the Inverse DWT to get the denoised estimate.Edges correspond to the singularities in the image and are related to the local maxima of wavelet coefficients.