spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

An accurate and efficient solution method using spectral collocation method with domain decomposition is proposed for computing optical waveguides with discontinuous refractive index profiles. The use of domain decomposition divides the usual single domain into a few subdomains at the interfaces of discontinuous refractive index profiles. Each subdomain can be expanded by a suitable set of orth...

optimal attributes are useful in interpretation of seismic data. two proposed methods are presented in this paper for finding optimal attributes. regularized discriminate analysis(rda) is based on 2 parameters ë, ? which called regularization parameter. the other method is principal component analysi s(pca).in this paper gas chimney detection is defined as the subject of study for ranking relev...

In India, the frequency of occurrence of earthquakes is more, especially in Himalaya, northeast India and Gujarat region. Therefore it becomes essential to estimate the seismic inputs for such earthquakes to reduce the structural damage. From engineering point of view, the most sought-after data is the strong motion accelerograms (SMA), recorded in the places where earthquake has occurred. The ...

Recently, it was suggested that spectrum estimation can be accomplished by applying wavelet denoising methodology to wavelet packet coefficients derived from the logarithm of a spectrum estimate. The particular algorithm we consider consists of computing the logarithm of the multitaper spectrum estimator, applying an orthonormal transform derived from a wavelet packet tree to the log multitaper...

Abstract A long-standing challenge in dynamical oceanography is to distinguish nonlinearly intermingled regimes of oceanic flows. Conventional approaches focus on time-scale or space-scale decomposition. Here, we pursue a dynamics-based decomposition, where mean flow introduced extend the classic theory wavy and vortical modes. Mainly based relative magnitudes vorticity modified horizontal dive...

introduction various factors like climatic conditions, vegetation, soil properties, topography, time, plant residue quality and crop management strategies affect the decomposition rate of organic carbon (oc) and its residence time in soil. plant residue management concerns nutrients recycling, carbon recycling in ecosystems and the increasing co2 concentration in the atmosphere. plant residue d...

reduced differental transform method (rdtm), which isone of the useful and effective numerical method, is applied to solve nonlinear time-dependent foam drainage equation (fde) with different initial conditions. we compare our method with the famous adomian decomposition and laplace decomposition methods. the obtained resultsdemonstrated that rdtm is a powerful tool for solving nonlinear par...

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can...

Spectral decomposition is of fundamental importance in many applications. Generally speaking, spectral decomposition provides a canonical representation of a linear operator over a vector space in terms of its eigenvalues and eigenfunctions. The canonical form often facilitates discussions which, otherwise, would be complicated and involved. This paper generalizes the classical results of eigen...