Physical Wavelet Frame Denoising

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 225-231 ◽  
Author(s):  
Rongfeng Zhang ◽  
Tadeusz J. Ulrych
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Real Data ◽  
Wavelet Denoising ◽  
Seismic Signals ◽  
Wavelet Frame ◽  
New Approach ◽  
Design And Implementation ◽  
Ground Roll ◽  
Acoustic Processing

This paper deals with the design and implementation of a new wavelet frame for noise suppression based on the character of seismic data. In general, wavelet denoising methods widely used in image and acoustic processing use well‐known conventional wavelets which, although versatile, are often not optimal for seismic data. The new approach, physical wavelet frame denoising uses a wavelet frame that takes into account the characteristics of seismic data both in time and space. Synthetic and real data tests show that the approach is effective even for seismic signals contaminated by strong noise which may be random or coherent, such as ground roll or air waves.

Relative time seislet transform

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V223-V232 ◽  
Author(s):  
Zhicheng Geng ◽  
Xinming Wu ◽  
Sergey Fomel ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Computational Cost ◽  
Lifting Scheme ◽  
Real Data ◽  
Data Sets ◽  
Relative Time ◽  
New Approach ◽  
New Formulation ◽  
Wavelet Lifting ◽  
Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

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.

Pitfalls in seismic processing: An application of seismic modeling to investigate acquisition footprint

2016 ◽  
Vol 4 (2) ◽  
pp. SG1-SG9 ◽  
Author(s):  
Marcus P. Cahoj ◽  
Sumit Verma ◽  
Bryce Hutchinson ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Seismic Attributes ◽  
Velocity Analysis ◽  
Seismic Modeling ◽  
Seismic Processing ◽  
The Real ◽  
Considerable Research ◽  
Data Volume ◽  
Ground Roll

The term acquisition footprint is commonly used to define patterns in seismic time and horizon slices that are closely correlated to the acquisition geometry. Seismic attributes often exacerbate footprint artifacts and may pose pitfalls to the less experienced interpreter. Although removal of the acquisition footprint is the focus of considerable research, the sources of such artifact acquisition footprint are less commonly discussed or illustrated. Based on real data examples, we have hypothesized possible causes of footprint occurrence and created them through synthetic prestack modeling. Then, we processed these models using the same workflows used for the real data. Computation of geometric attributes from the migrated synthetics found the same footprint artifacts as the real data. These models showed that acquisition footprint could be caused by residual ground roll, inaccurate velocities, and far-offset migration stretch. With this understanding, we have examined the real seismic data volume and found that the key cause of acquisition footprint was inaccurate velocity analysis.

Three-parameter Radon transform based on shifted hyperbolas

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V39-V48 ◽  
Author(s):  
Ali Gholami ◽  
Toktam Zand
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
High Performance ◽  
Fourier Transforms ◽  
Real Data ◽  
Data Sets ◽  
Radon Transforms ◽  
Slice Theorem ◽  
Ground Roll ◽  
Zero Offset

The focusing power of the conventional hyperbolic Radon transform decreases for long-offset seismic data due to the nonhyperbolic behavior of moveout curves at far offsets. Furthermore, conventional Radon transforms are ineffective for processing data sets containing events of different shapes. The shifted hyperbola is a flexible three-parameter (zero-offset traveltime, slowness, and focusing-depth) function, which is capable of generating linear and hyperbolic shapes and improves the accuracy of the seismic traveltime approximation at far offsets. Radon transform based on shifted hyperbolas thus improves the focus of seismic events in the transform domain. We have developed a new method for effective decomposition of seismic data by using such three-parameter Radon transform. A very fast algorithm is constructed for high-resolution calculations of the new Radon transform using the recently proposed generalized Fourier slice theorem (GFST). The GFST establishes an analytic expression between the [Formula: see text] coefficients of the data and the [Formula: see text] coefficients of its Radon transform, with which a very fast switching between the model and data spaces is possible by means of interpolation procedures and fast Fourier transforms. High performance of the new algorithm is demonstrated on synthetic and real data sets for trace interpolation and linear (ground roll) noise attenuation.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

Refocusing migrated seismic images in absorptive media

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. S103-S110 ◽  
Author(s):  
Changjun Zhang ◽  
Tadeusz J. Ulrych
Keyword(s):  
Seismic Data ◽  
Fast Algorithm ◽  
Real Data ◽  
Seismic Exploration ◽  
Seismic Signals ◽  
Time Step ◽  
Seismic Migration ◽  
Blurring Effect ◽  
Nonstationary Deconvolution ◽  
Seismic Images

In seismic exploration, received seismic signals usually experience absorption during their propagation. However, seismic migration algorithms seldom take into account seismic absorption in their implementations. We have investigated the blurring effect in migrated images that occurs when using a regular migration algorithm to migrate those seismic data with absorption. The blurring functions can be calculated using a numerical method; and for layered media, a fast algorithm exists for updating the blurring function from one time step to another. The deblurring process is formulated as a problem of multidimensional nonstationary deconvolution. We use a least-squares inverse scheme to remove the absorption blurring effect and in turn refocus migrated images. The refocusing algorithm is stable, and convergence is achieved with a few iterations at each wavenumber. Experiments on synthetic and real data show that our refocusing technique is valid when compensating for seismic absorption after migration.

Automatic nonhyperbolic velocity analysis by polynomial chaos expansion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. U79-U88 ◽  
Author(s):  
Mostafa Abbasi ◽  
Ali Gholami
Keyword(s):  
Seismic Data ◽  
Polynomial Chaos ◽  
Seismic Velocity ◽  
Real Data ◽  
Velocity Analysis ◽  
Data Set ◽  
New Approach ◽  
Transverse Isotropic ◽  
Two Parameters ◽  
Vti Media

Seismic velocity analysis is one of the most crucial and, at the same time, the most laborious tasks during seismic data processing. This becomes even more difficult and time-consuming when nonhyperbolicity has to be considered in the velocity analysis. Nonhyperbolic velocity analysis provides very useful information during the processing and interpretation of seismic data. The most common approach for considering anisotropy during velocity analysis is to describe the moveout based on a nonhyperbolic equation. The nonhyperbolic moveout equation in vertically transverse isotropic (VTI) media is defined by two parameters: normal moveout (NMO) velocity [Formula: see text] and anellipticity [Formula: see text] (or horizontal velocity [Formula: see text]). We have developed a new approach based on polynomial chaos (PC) expansion for automating nonhyperbolic velocity analysis of common-midpoint (CMP) data in VTI media. For this purpose, we use the PC expansion to approximate the nonhyperbolic semblance function with a very fast-to-simulate function in terms of [Formula: see text] and [Formula: see text]. Then, using particle swarm optimization, we stochastically look for the optimum NMO and horizontal velocities that provide the maximum semblance. In contrary to common approaches for nonhyperbolic velocity analysis in which the two parameters are estimated iteratively in an alternating fashion, we find [Formula: see text] and [Formula: see text] simultaneously. This approach is tested on various data including a simple convolutional model, an anisotropic benchmark model, and a real data set. In all cases, the new method provided acceptable results. Reflections in the CMP corrected using the optimum velocities are properly flattened, and almost no residual moveout is observed.

scholarly journals Application of an improved particle filter for random seismic noise suppression

2021 ◽  
Vol 18 (6) ◽  
pp. 943-953
Author(s):  
Jingquan Zhang ◽  
Dian Wang ◽  
Peng Li ◽  
Shiyu Liu ◽  
Han Yu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Seismic Data ◽  
Particle Filtering ◽  
Seismic Noise ◽  
Firefly Algorithm ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Seismic Signals ◽  
And Performance

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

Sign in / Sign up

Export Citation Format

Share Document