1,799 research outputs found
Seismic Image Analysis Using Local Spectra
This report considers a problem in seismic imaging, as presented by researchers from Calgary Scientific Inc. The essence of the problem was to understand how the S-transform could be used to create better seismic images, that would be useful in identifying possible hydrocarbon reservoirs in the earth.
The important first step was to understand what aspect of the imaging problem we were being asked to study. However, since we would not be working directly with raw seismic data, traditional seismic techniques would not be required. Rather, we would be working with a two dimensional image, either a migrated image, a common mid-point (CMP) stack, or a common depth point (CDP) stack. In all cases, the images display the subsurface of the earth with geological structures evident in various layers.
For a given image the local spectrum is computed at each point. The various peaks in the spectrum are used to classify each pixel in the original seismic image resulting in an enhanced and hopefully more useful seismic pseudosection. Thus, the objective of this project was to improve the identification of layers and other geological structures apparent in the two dimensional image (a seismic section, or CDP gather) by classifying and coloring image pixels into groups based on their local spectral attributes
Seismic Data Compression using Wave Atom Transform
Seismic data compression SDC is crucially confronted in the oil Industry with large data volumes and Incomplete data measurements In this research we present a comprehensive method of exploiting wave packets to perform seismic data compression Wave atoms are the modern addition to the collection of mathematical transforms for harmonic computational analysis Wave atoms are variant of 2D wavelet packets that keep an isotropic aspect ratio Wave atoms have a spiky frequency localization that cannot be attained using a filter bank based on wavelet packets and offer a significantly sparser expansion for oscillatory functions than wavelets curvelets and Gabor atom
High-dimensional wave atoms and compression of seismic datasets
Wave atoms are a low-redundancy alternative to curvelets, suitable for high-dimensional seismic data processing. This abstract extends the wave atom orthobasis construction to 3D, 4D, and 5D Cartesian arrays, and parallelizes it in a shared-memory environment. An implementation of the algorithm for NVIDIA CUDA capable graphics processing units (GPU) is also developed to accelerate computation for 2D and 3D data. The new transforms are benchmarked against the Fourier transform for compression of data generated from synthetic 2D and 3D acoustic models.National Science Foundation (U.S.); Alfred P. Sloan Foundatio
High-dimensional wave atoms and compression of seismic data sets
Wave atoms are a low-redundancy alternative to curvelets, suitable for high-dimensional seismic data processing. This abstract extends the wave atom orthobasis construction to 3D, 4D, and 5D Cartesian arrays, and parallelizes it in a sharedmemory environment. An implementation of the algorithm for NVIDIA CUDA capable graphics processing units (GPU) is also developed to accelerate computation for 2D and 3D data. The new transforms are benchmarked against the Fourier transform for compression of data generated from synthetic 2D and 3D acoustic models.TOTAL (Firm)National Science Foundation (U.S.)Alfred P. Sloan Foundatio
Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments
The localized nature of curvelet functions, together with their frequency and
dip characteristics, makes the curvelet transform an excellent choice for
processing seismic data. In this work, a denoising method is proposed based on
a combination of the curvelet transform and a whitening filter along with
procedure for noise variance estimation. The whitening filter is added to get
the best performance of the curvelet transform under coherent and incoherent
correlated noise cases, and furthermore, it simplifies the noise estimation
method and makes it easy to use the standard threshold methodology without
digging into the curvelet domain. The proposed method is tested on
pseudo-synthetic data by adding noise to real noise-less data set of the
Netherlands offshore F3 block and on the field data set from east Texas, USA,
containing ground roll noise. Our experimental results show that the proposed
algorithm can achieve the best results under all types of noises (incoherent or
uncorrelated or random, and coherent noise)
Compression approaches for the regularized solutions of linear systems from large-scale inverse problems
We introduce and compare new compression approaches to obtain regularized solutions of large linear systems which are commonly encountered in large scale inverse problems. We first describe how to approximate matrix vector operations with a large matrix through a sparser matrix with fewer nonzero elements, by borrowing from ideas used in wavelet image compression. Next, we describe and compare approaches based on the use of the low rank singular value decomposition (SVD), which can result in further size reductions. We describe how to obtain the approximate low rank SVD of the original matrix using the sparser wavelet compressed matrix. Some analytical results concerning the various methods are presented and the results of the proposed techniques are illustrated using both synthetic data and a very large linear system from a seismic tomography application, where we obtain significant compression gains with our methods, while still resolving the main features of the solutions.European Research Council (Advanced Grant 226837)United States. Defense Advanced Research Projects Agency (Contract N66001-13-1-4050)National Science Foundation (U.S.) (Contracts 1320652 and 0748488
Wavelets, its Application and Technique in signal and image processing
Wavelets are functions that satisfy certain mathematical requirement and used in representing data or functions. Wavelets allow complex information such as data compression, signal recognition, signal and image processing, music and computer graphics etc. The wavelet decomposition analysis is used most often in wavelet signal processing. It is used in signal compression as well as in signal identification, although in the latter case, reconstruction of the original is not always required. The decomposition separates a signal into components at various scales corresponding to successive octave frequencies. Each component can be processed individually by a different algorithm. In this work we first try to introduce wavelet and then some of its applications and technique in signal and image processing. Here we also approach a new filtering techniqu
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