Coherent Noise Suppression using Interferometric Cross-Correlation

2017 ◽  
Author(s):  
Milad I. Akhlaghi ◽  
Aristide Dogariu
Keyword(s):  
Cross Correlation ◽  
Noise Suppression ◽  
Coherent Noise

Suppression of Single Vector Hydrophone Coherent-Noise Based on the Cross-SCF

2012 ◽  
Vol 588-589 ◽  
pp. 948-952
Author(s):  
Wei Zhang ◽  
Jin Fang Cheng ◽  
Jie Xu
Keyword(s):  
Cross Correlation ◽  
Sound Pressure ◽  
Noise Suppression ◽  
Vibration Velocity ◽  
Coherent Noise ◽  
Correlation Spectrum ◽  
Vector Hydrophone ◽  
Correlation Processing ◽  
The Cross ◽  
Better Than

At present the cross-correlation processing can only suppress the isotropic noise by vector hydrophone sound pressure and vibration velocity combined. The coherent composition of the actual ambient noise makes the detection ability of cross-correlation spectrum reduced. Use XWVD theory, proposed a cross symmetry-correlation function (Cross-SCF). Analysis of simulation data under different SNR and Different nature noise combination proving that the noise suppression Performance of suggested Cross-SCF has nothing to do with noise properties, and compared with the cross-correlation processing have indeed better than coherent noise suppression ability.

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. V1-V10
Author(s):  
Julián L. Gómez ◽  
Danilo R. Velis ◽  
Juan I. Sabbione
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Department Of Energy ◽  
Time Slice ◽  
3D Seismic ◽  
Coherent Noise ◽  
Data Set ◽  
Amplitude Data ◽  
3D Volume ◽  
2D And 3D

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve the time direction in the computation of the signal envelopes needed for the iterative sifting process. Instead, we apply the sifting algorithm spatially in the inline-crossline plane. At each time slice, we calculate the upper and lower signal envelopes by means of a filter whose length is adapted dynamically at each sifting iteration according to the spatial distribution of the extrema. The denoising of a 3D volume is achieved by removing the most oscillating modes of each time slice from the noisy data. We determine the performance of the algorithm by using three public-domain poststack field data sets: one 2D line of the well-known Alaska 2D data set, available from the US Geological Survey; a subset of the Penobscot 3D volume acquired offshore by the Nova Scotia Department of Energy, Canada; and a subset of the Stratton 3D land data from South Texas, available from the Bureau of Economic Geology at the University of Texas at Austin. The results indicate that random and coherent noise, such as footprint signatures, can be mitigated satisfactorily, enhancing the reflectors with negligible signal leakage in most cases. Our method, called empirical-mode filtering (EMF), yields improved results compared to other 2D and 3D techniques, such as [Formula: see text] EMD filter, [Formula: see text] deconvolution, and [Formula: see text]-[Formula: see text]-[Formula: see text] adaptive prediction filtering. EMF exploits the flexibility of EMD on seismic data and is presented as an efficient and easy-to-apply alternative for denoising seismic data with mild to moderate structural complexity.

Detection and Estimation of Radar Reflectivity from Weak Echo of Precipitation in Dual-Polarized Weather Radars

2014 ◽  
Vol 31 (8) ◽  
pp. 1677-1693 ◽  
Author(s):  
Reino Keränen ◽  
V. Chandrasekar
Keyword(s):  
Cross Correlation ◽  
Noise Suppression ◽  
Geometric Mean ◽  
Intrinsic Noise ◽  
Diagonal Element ◽  
Radar Reflectivity ◽  
Consistent Estimate ◽  
Dual Polarization ◽  
Weather Radars ◽  
Finite Sums

Abstract In operational weather radar, precipitation echoes are often weak when compared to the underlying noise. Coherence properties of dual polarization can be used for enhancing the detection and for the improved estimation of weak echoes of precipitation. The enhanced detectability results from utilizing coherent averages of precipitation signals, while the uncorrelated noise vanishes asymptotically, explicit in the off-diagonal element Rhv of the echo covariance matrix. In finite sums, the noise terms as well as the uncertainties associated with them are suppressed. A signal can be detected in weaker echo by an analytically derived censoring policy. The coherent sums are readily available as the cross-correlation function of the antenna voltages H and V, which estimates Rhv in the mode of simultaneous transmission and reception. The magnitude of Rhv is a consistent estimate of the copolar echo power, leading to the copolar radar reflectivity of precipitation, which refers to the geometric mean of the reflectivities in H and V polarizations. Because of the intrinsic noise suppression, estimates of the copolar reflectivity are, in relative terms, more precise and more accurate than the corresponding estimates of reflectivity in specific channels, for weak signals of precipitation. These aspects are discussed quantitatively with validation of the key features in real conditions. The advances suggest for dedicated dual-polarization surveillance scans of weak echo of precipitation.

scholarly journals GROUND ROLL NOISE ATTENUATION IN 3D LAND SEISMIC DATA IN PARTS OF NIGER DELTA, NIGERIA

2020 ◽  
Vol 5 (1) ◽  
pp. 04-06
Author(s):  
Bridget L. Lawrence ◽  
Etim D. Uko ◽  
Chibuogwu L. Eze ◽  
Chicozie Israel-Cookey ◽  
Iyeneomie Tamunobereton-ari ◽  
...  
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Noise Suppression ◽  
Seismic Source ◽  
Signal Amplitude ◽  
Average Frequency ◽  
Frequency Offset ◽  
Receiver Design ◽  
Coherent Noise ◽  
Ground Roll

Three-dimensional (3D) land seismic datasets were acquired from Central Depobelt in the Niger Delta region, Nigeria, with with the aim of attenuating ground roll noise from the dataset. The Omega (Schlumberger) software 2018 version was used along with frequency offset coherent noise suppression (FXCNS) and Anomalous Amplitude Attenuation (AAA) algorithms for ground roll attenuation. From the results obtained, Frequency Offset Coherent Noise Suppression (FXCNS) attenuates ground roll while AAA algorithm attenuates the residual high amplitude noise from the seismic data. Average frequency of the ground roll in the seismic data is 10.50Hz which falls within the actual range of ground roll frequency which is within the range of 3.00 – 18.00Hz. The average velocity of the ground roll in the seismic data is 477.36ms-1 while the velocity of ground roll ranges between 347.44 and 677.37ms-1. The wavelength of ground roll in the seismic data is 50.28m. The amplitude of the ground roll of -6.24dB is maximum at 4.2Hz. Frequency of signal ranges between 10.21 and 25.12Hz with an average of 17.67Hz. Signal amplitude of -8.32dB is maximum at 6.30Hz, while its wavelength is 57.12m. The results of this work can be used in the seismic source-receiver design for application in the area of study. Moreover, with ground roll noise attenuated, a better image of the subsurface geology is obtained hence reducing the risk of obtaining a wild cat drilling.

scholarly journals Coherent Noise Suppression Using Adaptive Homomorphic Filtering for Wideband Electromagnetic Imaging System

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4469
Author(s):  
Yanju Zhu ◽  
Shuguo Xie
Keyword(s):  
Electromagnetic Interference ◽  
Noise Suppression ◽  
Dynamic Range ◽  
Imaging System ◽  
Minimum Description Length ◽  
Coherent Noise ◽  
Noise Pattern ◽  
Electromagnetic Imaging ◽  
Homomorphic Filtering

The wideband electromagnetic imaging system using a parabolic reflector is a device for detecting and locating electromagnetic interference sources (EMIS). When multiple coherent interference sources are detected, the confusion will occur due to the coherent noise that is caused by interference phenomenons. Previous works have removed the coherent noise by using iterative techniques, but they face a limitation in removing noise in that the coherent noise pattern changes with frequency in a wideband. In this paper, an adaptive homomorphic filtering is proposed to overcome the limitations of conventional methods from 1 GHz–6 GHz. The coherent noise existing in the several electromagnetic images is studied, and it is confirmed that the variation of the coherent noise pattern is related to the position, the number, and the frequency of EMIS. Then, by analyzing the probability density of coherent noise intensity, an adaptive Gaussian filter is carefully designed to remove coherent noise. The filter parameters are selected by the minimum description length criterion (MDL) to apply to compute directly the local amount of Gaussian smoothing at each pixel of each image. The results of the experiments and simulations demonstrate that the proposed method can significantly improve the quality of electromagnetic images in terms of maximum sidelobe level (MSL) by 15 dB and dynamic range (DR) of the system over 20 dB, compared with conventional narrowband denoising methods.

scholarly journals Coherent noise suppression by learning and analyzing the morphology of the data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V397-V411 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Morphological Diversity ◽  
Seismic Signal ◽  
Coherent Noise ◽  
Data Set ◽  
Redundant Dictionary ◽  
Manual Search ◽  
Mechanical Noise ◽  
Learned Dictionary

We have developed a method for suppressing coherent noise from seismic data by using the morphological differences between the noise and the signal. This method consists of three steps: First, we applied a dictionary learning method on the data to extract a redundant dictionary in which the morphological diversity of the data is stored. Such a dictionary is a set of unit vectors called atoms that represent elementary patterns that are redundant in the data. Because the dictionary is learned on data contaminated by coherent noise, it is a mix of atoms representing signal patterns and atoms representing noise patterns. In the second step, we separate the noise atoms from the signal atoms using a statistical classification. Hence, the learned dictionary is divided into two subdictionaries: one describing the morphology of the noise and the other one describing the morphology of the signal. Finally, we separate the seismic signal and the coherent noise via morphological component analysis (MCA); it uses sparsity with respect to the two subdictionaries to identify the signal and the noise contributions in the mixture. Hence, the proposed method does not use prior information about the signal and the noise morphologies, but it entirely adapts to the signal and the noise of the data. It does not require a manual search for adequate transforms that may sparsify the signal and the noise, in contrast to existing MCA-based methods. We develop an application of the proposed method for removing the mechanical noise from a marine seismic data set. For mechanical noise that is coherent in space and time, the results show that our method provides better denoising in comparison with the standard FX-Decon, FX-Cadzow, and the curvelet-based denoising methods.

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