scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

Effects of averaging and sampling on the statistics of reflection coefficients

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 50-58 ◽  
Author(s):  
K. Hsu ◽  
R. Burridge
Keyword(s):  
Reflection Coefficient ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Exploration ◽  
Reflection Coefficients ◽  
Continuous Formation ◽  
Sampling Intervals ◽  
Frequency Components ◽  
Analytical Formulas

The reflection coefficients derived from sonic and density logs are frequently used in seismic exploration. Even though they measure the in‐situ formation slowness and density, sonic and density tools do not measure the exact, continuous formation properties but locally averaged properties sampled at discrete depth points. Furthermore, the logs are frequently reinterpolated to form a Goupillaud medium for many applications such as synthetic seismogram computation. Both the logging tools and the Goupillaud interpolation introduce averaging and sampling effects into the reflection coefficients and significantly alter the autocorrelation of the reflection coefficient sequence. Analytical formulas are derived to show how the autocorrelation is altered and to calculate how the autocorrelation depends on the averaging and sampling intervals. Essentially, these effects impose sincsquared envelopes on the power spectrum of the reflection coefficient sequence and alias high‐frequency components to low‐frequency components in the spectral domain. These findings are verified using synthetic and real examples.

Source-independent seismic envelope inversion based on the direct envelope Fréchet derivative

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R581-R595 ◽  
Author(s):  
Pan Zhang ◽  
Ru-Shan Wu ◽  
Liguo Han
Keyword(s):  
Objective Function ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Fréchet Derivative ◽  
Nonlinear Inversion ◽  
Low Velocity ◽  
Frechet Derivative ◽  
Frequency Information ◽  
Salt Domes ◽  
Full Waveform

Seismic envelope inversion (EI) uses low-frequency envelope data to recover long-wavelength components of the subsurface media. Conventional EI uses the same waveform Fréchet derivative as conventional full-waveform inversion. Due to linearization of the sensitivity operator (Born approximation), neither of these methods can yield good inversion results for media with strong preturbations, such as salt domes, when the source lacks low-frequency information. Because seismic envelope data contain large amount of ultra-low-frequency information and the direct envelope Fréchet derivative maps envelope data perturbation directly to velocity perturbation, the direct envelope inversion (DEI) method (based on the direct envelope Fréchet derivative) can handle such strong nonlinear inversion problems. However, this method is sensitive to source wavelet errors. We developed a source-independent DEI method. To achieve the source-independent objective function, we derive a convolution expression for the envelope data. We derive the gradient of the new objective function by using the direct envelope Fréchet derivative. Numerical tests conducted on a 2D salt model indicate that our method can achieve good reconstruction of salt bodies (strong velocity perturbations) and recover low-velocity background structures (weak velocity perturbations), despite using an inaccurate source wavelet.

Lipreading with Auditory Low-frequency Information Contextual Constraints

1996 ◽  
Vol 25 (2) ◽  
pp. 127-132
Author(s):  
Jerker Rönnberg ◽  
Stefan Samuelsson ◽  
Björn Lyxell ◽  
Stig Arlinger
Keyword(s):  
Low Frequency ◽  
Frequency Information

Removing Spurious Low-Frequency Variability in Drifter Velocities

2013 ◽  
Vol 30 (2) ◽  
pp. 353-360 ◽  
Author(s):  
Rick Lumpkin ◽  
Semyon A. Grodsky ◽  
Luca Centurioni ◽  
Marie-Helene Rio ◽  
James A. Carton ◽  
...  
Keyword(s):  
Low Frequency ◽  
Original Data ◽  
Strain Sensors ◽  
Wind Forcing ◽  
Stokes Drift ◽  
Interannual Variations ◽  
Transmission Frequency ◽  
Low Frequency Variability ◽  
Frequency Variations ◽  
Drifting Buoys

Abstract Satellite-tracked drifting buoys of the Global Drifter Program have drogues, centered at 15-m depth, to minimize direct wind forcing and Stokes drift. Drogue presence has historically been determined from submergence or tether strain records. However, recent studies have revealed that a significant fraction of drifters believed to be drogued have actually lost their drogues, a problem that peaked in the mid-2000s before the majority of drifters in the global array switched from submergence to tether strain sensors. In this study, a methodology is applied to the data to automatically reanalyze drogue presence based on anomalous downwind ageostrophic motion. Results indicate that the downwind slip of undrogued drifters is approximately 50% higher than previously believed. The reanalyzed results no longer exhibit the dramatic and spurious interannual variations seen in the original data. These results, along with information from submergence/tether strain and transmission frequency variations, are now being used to conduct a systematic manual reevaluation of drogue presence for each drifter in the post-1992 dataset.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

scholarly journals Both ongoing alpha and visually induced gamma oscillations show reliable diversity in their across-site phase-relations

2015 ◽  
Vol 113 (5) ◽  
pp. 1556-1563 ◽  
Author(s):  
Freek van Ede ◽  
Stan van Pelt ◽  
Pascal Fries ◽  
Eric Maris
Keyword(s):  
Phase Relation ◽  
Visual Stimulation ◽  
Low Frequency ◽  
Gamma Oscillations ◽  
Phase Relations ◽  
Neural Oscillations ◽  
High Signal ◽  
Noninvasive Methods ◽  
Healthy Human ◽  
Systems Neuroscience

Neural oscillations have emerged as one of the major electrophysiological phenomena investigated in cognitive and systems neuroscience. These oscillations are typically studied with regard to their amplitude, phase, and/or phase coupling. Here we demonstrate the existence of another property that is intrinsic to neural oscillations but has hitherto remained largely unexplored in cognitive and systems neuroscience. This pertains to the notion that these oscillations show reliable diversity in their phase-relations between neighboring recording sites (phase-relation diversity). In contrast to most previous work, we demonstrate that this diversity is restricted neither to low-frequency oscillations nor to periods outside of sensory stimulation. On the basis of magnetoencephalographic (MEG) recordings in humans, we show that this diversity is prominent not only for ongoing alpha oscillations (8–12 Hz) but also for gamma oscillations (50–70 Hz) that are induced by sustained visual stimulation. We further show that this diversity provides a dimension within electrophysiological data that, provided a sufficiently high signal-to-noise ratio, does not covary with changes in amplitude. These observations place phase-relation diversity on the map as a prominent and general property of neural oscillations that, moreover, can be studied with noninvasive methods in healthy human volunteers. This opens important new avenues for investigating how neural oscillations contribute to the neural implementation of cognition and behavior.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

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