scholarly journals Improving quality of empirical Greens functions, obtained by cross-correlation of high-frequency ambient seismic noise

2019 ◽  
Author(s):  
Nikita Afonin ◽  
Elena Kozlovskaya ◽  
Jouni Nevalainen ◽  
Janne Narkilahti
Keyword(s):  
High Frequency ◽  
Seismic Noise ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Ambient Seismic Noise ◽  
Signal To Noise ◽  
Low Level ◽  
Seismic Methods ◽  
High Level

Abstract. Studying the uppermost structure of the subsurface is a necessary part for solving many practical problems (exploration of minerals, groundwater studies, geoengineering, etc.). Practical application of active seismic methods is not always possible because of different reasons, such as logistical difficulties, high cost of work, high level of seismic and acoustic noise, etc. That is why developing and improving of passive seismic methods for these purposes is one of the important problems in applied geophysics. In our study, we describe the way of improving quality of Empirical Green’s Functions (EGFs), evaluated from high-frequency ambient seismic noise, by using of advanced technique of cross-correlation functions stacking in the time domain (in this paper we use term “high-frequency” for the frequencies higher than 1 Hz). In compare to existing techniques, based on weight-stacking, our proposed technique makes it possible to more significantly increase the signal-to-noise ratio and, therefore quality of the EGF. The technique is based on both iterative and global optimization algorithms, where the optimized parameter is a signal-to-noise ratio of an EGF, retrieved for each iteration. The technique has been tested with the field data acquired in an area with high level of industrial noise (Pyhäsalmi Mine, Finland) and in an area with low level of anthropogenic noise (Kuusamo Greenstone Belt, Finland). The results show that the our proposed technique can be used for extraction of EGFs from high-frequency seismic noise in practical problems of mapping of the shallow subsurface in areas with high and low level of high-frequency seismic noise.

scholarly journals Improving the quality of empirical Green's functions, obtained by cross-correlation of high-frequency ambient seismic noise

Solid Earth ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 1621-1634 ◽  
Author(s):  
Nikita Afonin ◽  
Elena Kozlovskaya ◽  
Jouni Nevalainen ◽  
Janne Narkilahti
Keyword(s):  
High Frequency ◽  
Seismic Noise ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Ambient Seismic Noise ◽  
Signal To Noise ◽  
Seismic Methods ◽  
Noise Ratio ◽  
High Level

Abstract. Studying the uppermost structure of the subsurface is a necessary part of solving many practical problems (exploration of minerals, groundwater studies, geoengineering, etc.). The practical application of active seismic methods for these purposes is not always possible for different reasons, such as logistical difficulties, high cost of work, and a high level of seismic and acoustic noise. That is why developing and improving passive seismic methods is one of the important problems in applied geophysics. In our study, we describe a way of improving the quality of empirical Green's functions (EGFs), evaluated from high-frequency ambient seismic noise, by using the advanced technique of cross-correlation function stacking in the time domain (in this paper we use term “high-frequency” for frequencies higher than 1 Hz). The technique is based on the global optimization algorithm, in which the optimized objective function is a signal-to-noise ratio of an EGF, retrieved at each iteration. In comparison to existing techniques, based, for example, on weight stacking of cross-correlation functions, our technique makes it possible to significantly increase the signal-to-noise ratio and, therefore, the quality of the EGFs. The technique has been tested with the field data acquired in an area with a high level of industrial noise (Pyhäsalmi Mine, Finland) and in an area with a low level of anthropogenic noise (Kuusamo Greenstone Belt, Finland). The results show that the proposed technique can be used for the extraction of EGFs from high-frequency seismic noise in practical problems of mapping of the shallow subsurface, both in areas with high and low levels of high-frequency seismic noise.

scholarly journals On cross-correlations, averages and noise in electron microscopy

2019 ◽  
Vol 75 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Michael Radermacher ◽  
Teresa Ruiz
Keyword(s):  
High Resolution ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
3D Structure ◽  
Signal To Noise ◽  
High Resolution Structure ◽  
Multiple Copies ◽  
High Level ◽  
2D Images ◽  
Resolution Structure

Biological samples are radiation-sensitive and require imaging under low-dose conditions to minimize damage. As a result, images contain a high level of noise and exhibit signal-to-noise ratios that are typically significantly smaller than 1. Averaging techniques, either implicit or explicit, are used to overcome the limitations imposed by the high level of noise. Averaging of 2D images showing the same molecule in the same orientation results in highly significant projections. A high-resolution structure can be obtained by combining the information from many single-particle images to determine a 3D structure. Similarly, averaging of multiple copies of macromolecular assembly subvolumes extracted from tomographic reconstructions can lead to a virtually noise-free high-resolution structure. Cross-correlation methods are often used in the alignment and classification steps of averaging processes for both 2D images and 3D volumes. However, the high noise level can bias alignment and certain classification results. While other approaches may be implicitly affected, sensitivity to noise is most apparent in multireference alignments, 3D reference-based projection alignments and projection-based volume alignments. Here, the influence of the image signal-to-noise ratio on the value of the cross-correlation coefficient is analyzed and a method for compensating for this effect is provided.

scholarly journals Triangular Vertex Transform Based Watermarking on Contourlet Coefficients for Improved Robustness

2021 ◽  
Vol 12 (3) ◽  
pp. 5434-5442
Author(s):  
Reena Thomas Et. al.
Keyword(s):  
High Frequency ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Standard Test ◽  
Embedding Capacity ◽  
Signal To Noise ◽  
Watermark Embedding ◽  
Hybrid Watermarking ◽  
Watermarking Scheme

A hybrid watermarking scheme based on Triangular Vertex Transform (TVT) and Contourlet coefficients for high robustness is implemented. During watermark embedding, the cover image is first decomposed using Contourlet Transform to obtain high frequency and low frequency coefficients. The lower frequency coefficients are applied with TVT. Then, the W coefficients obtained from TVT are again subdivided. The watermark bit is then embedded on the subdivided coefficients to obtain the watermarked image. Reverse operation is followed in the extraction phase. The performance of this algorithm is evaluated using embedding capacity, Normalized cross correlation (Ncc) and Peak Signal to Noise Ratio (PSNR) using standard test images. These evaluation results disclose the domination of proposed scheme over traditional schemes

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Weak Transient Electromagnetic Radiation Signal Detection Method Considering the New Watershed Image Segmentation Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2054009
Author(s):  
Wenjun Huo ◽  
Peng Chu ◽  
Kai Wang ◽  
Liangting Fu ◽  
Zhigang Niu ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Cross Correlation ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Detection Methods ◽  
Signal To Noise ◽  
Long Distance ◽  
Transient Electromagnetic ◽  
Noise Ratio

In order to study the detection methods of weak transient electromagnetic radiation signals, a detection algorithm integrating generalized cross-correlation and chaotic sequence prediction is proposed in this paper. Based on the dual-antenna test and cross-correlation information estimation method, the detection of aperiodic weak discharge signals under low signal-to-noise ratio is transformed into the estimation of periodic delay parameters, and the noise is reduced at the same time. The feasibility of this method is verified by simulation and experimental analysis. The results show that under the condition of low signal-to-noise ratio, the integrated method can effectively suppress the influence of 10 noise disturbances. It has a high detection probability for weak transient electromagnetic radiation signals, and needs fewer pulse accumulation times, which improves the detection efficiency and is more suitable for long-distance detection of weak electromagnetic radiation sources.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

Morphological traits of the development of the skin and wool cover in sheep of Prikatunsky meat and wool type

2021 ◽  
pp. 60-68
Author(s):  
I. I. Dmitrik ◽  
G. V. Zavgorodnyaya ◽  
M. I. Pavlova ◽  
N. A. Podkorytov
Keyword(s):  
Physical Properties ◽  
Morphological Traits ◽  
Age Groups ◽  
Body Side ◽  
Low Level ◽  
Research Material ◽  
High Level ◽  
Significant Factors

A large number of works are devoted to the development of the skin and hair cover of sheep, depending on their breed affiliation, age, feeding conditions and housing. The authors point out that along with other conditions the quality of wool and wool clip is greatly influenced by the conditions of the feeding of animals. A high level of feeding increases the wool clip and improves the quality of the wool and vice versa a low level reduces, causes thinning and worsens other physical properties of the wool. As is known, one of the significant factors that determine the increase in wool clip is the size of the animal and, consequently, the total area of the skin. The purpose of the research was to investigate the morphological traits of the development of the skin and wool cover in sheep of Prikatunsky meat and wool type. The research material was wool samples from four topographic areas of the animal’s body (side/thigh/back/belly) and skin (side) of different sex and age groups, selected from the animals of the studied groups. In the course of research, the quality of wool and indicators of the histostructure of the skin of Prikatunsky meat and wool type of sheep have been investigated. In terms of thick-haired of wool, replacement rams and gimbers of Prikatunsky meat and wool type of sheep were superior by 4,82 pcs. per mm² or 19,0 % and 4,41 pcs. per mm² or 15,8 % of adult animals, respectively. Balance secondary follicles/primary follicles in young animals were higher by 10,3 and 17,3 % compared to breeding rams and ewes. The wool of the replacement young animals was thinner by 7,28 and 4,78 microns and they were more thick-haired. The obtained data will be used in the mating campaign when mating program rams in order to improve the sheep of Prikatunsky meat and wool type.

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