Due to the spherical divergence and specifically absorption in the earth, amplitude of a propagating seism wave varies as a function of time. This stretches the wavelet in time and reduces the time (or vertical) resolution of the seismic sections. To overcome the problem one has to apply so called spatial migration or Deconvolution on data. Usually the least square Wiener Deconvolution is used to boost up the attenuated frequency components. Unfortunately, the Wiener based Deconvolution methods assume that the source generated seismic wavelet is stationary (i.e. its frequency content remains unchanged within the record). A method of Deconvolution in the Gabor domain is applied in this paper that considers the seismic data as a non-stationary phenomenon.The Gabor transform (Equation 1) is a windowed or short time Fourier transform, where the window used to isolate the frequency content of input record in time, is a Gaussian type. According to the uncertainty principle, the Gabor transform has the least uncertainty among other windowed Fourier transforms.Please clik on PDF icon to viwe the complet abstract.

Deconvolution is considered as a successful tool in seismic exploration for increasing the temporal resolution of the data. Gabor Deconvolution is proposed to treat the non-stationarity issue of the problem by breaking it into several stationary sub-problems via a Gaussian window, solving them independently, and then, recombining/projecting the sub-solutions into an approximate solution to the original nonstationary problem. The projected Gabor Deconvolution has recently been proposed by the second author as an improvement over Gabor Deconvolution. In the projected Gabor Deconvolution, the sub-problems are projected to a unified problem in time domain, and then, the resulting problem is solved. This modification brings useful advantages over the Gabor Deconvolution including an improved convergence property, more efficiency for sparse Deconvolution, more flexibility for incorporating prior information in the presence of noise, and more reflectivity structure via a least-squares method. In this paper, we propose a method for sparse and non-sparse Deconvolution of non-stationary seismic signals in the presence of Gaussian and spike-like random noises. Numerical tests using simulated and field data are presented to show high performance of the proposed method for generating accurate and stable reflectivity models from nonstationary seismograms....

We consider Gabor systems generated by a Gaussian function and prove certain classical results of Paley and Wiener on nonharmonic Fourier series of complex exponentials for the Gabor expansion. In particular, we prove a version of Plancherel-Po ́ lya theorem for entire functions with finite order of growth and use the Hadamard factorization theorem to study regularity, exactness and deficiency of Gabor systems.

In order to increase the signal/noise ratio, one of the important challenges in seismic data processing is suppression of random noises. In this paper f-x Deconvolution or Deconvolution in frequency-space domain is employed for reduction of random noises from seismic section. This is based on data transformation from time-space domain to frequency-space domain. Seismic events are correlatable along x-direction from trace to trace but random noises are not. In fact f-x Deconvolution is able to predict coherent events from trace to trace in space direction. In this paper a computer code for f-x Deconvolution has been written. This program applied on shot records and zero offset section with different levels of random noises. Consequently, the ability of f-x Deconvolution in reduction of random noises has been proved.

A single reference image is used for detecting the face area in gray scale images. The algorithm uses Gabor transform as a powerful tool for feature extraction. Zero-crossing of Gabor transform is used for constructing the binary feature vectors and Hamming distance is used for measuring the similarity between feature vectors. Our developed method uses a pyramidal structure for finding the face images with different sizes. The algorithm has considerable robustness to the cluttered backgrounds and non-uniform illumination. More than 11,000 face images have been tested. The results show a very high detection rate for non occluded face images with low facial expression. The algorithm can also be used for correcting the head rotation angle in input images. The algorithm can also be used for correcting the head rotation angle in input images. The proposed method is suitable for the cases that contain one face image.

Background and the purpose of the study: Affinity-based target Deconvolution is an emerging method for the identification of interactions between drugs/drug candidates and cellular proteins, and helps to predict potential activities and side effects of a given compound. In the present study, we hypothesized that a part of safranal pharmacological effects, one of the major constituent of Crocus sativus L., relies on its physical interaction with target proteins.Methods: Affinity chromatography solid support was prepared by covalent attachment of safranal to agarose beads.After passing tissue lysate through the column, safranal-bound proteins were isolated and separated on SDS-PAGE or two-dimensional gel electrophoresis. Proteins were identified using MALDI-TOF/TOF mass spectrometry and Mascot software.Results and major conclusion: Data showed that safranal physically binds to beta actin, cytochrome b-c1 complex sub-unit 1, trifunctional enzyme sub-unit beta and ATP synthase sub-unit alpha and beta. These interactions may explain part of safranal’s pharmacological effects. However, phenotypic and/or biological relevance of these interactions remains to be elucidated by future pharmacological studies.

Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the worthful spatial characteristics. Moreover, some works that include the spatial features in the anomaly detection process extract features from each hyperspectral band that is a two dimensional image while the original structure of hyperspectral cube contains three dimensions. Ignoring the nature of hyperspectral image leads to lose the contained spectral-spatial correlations in the hyperspectral cube. To deal with this difficulty, in this work, the fused spectral and spatial features obtained by applying 3D Gabor filters are proposed for hyperspectral anomaly detection. Exploiting the 3D structure of hyperspectral cube by capturing multiple scales, orientations and its spectral-dependent characteristics provides an appropriate spectral-spatial feature space for anomalous targets detection. The extracted features are applied to the regularized RX detector to provide the detection map. The experiments show the superior performance of the proposed Gabor 3D based detector in terms of detection accuracy and computation time