Autocorrelogram migration: Theory

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1685-1694 ◽  
Author(s):  
Gerard T. Schuster ◽  
Fred Followill ◽  
Lewis J. Katz ◽  
Jianhua Yu ◽  
Zhaojun Liu
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Seismic Profile ◽  
Coherent Noise ◽  
Direct Wave ◽  
Pilot Signal ◽  
Source Signal ◽  
2D And 3D ◽  
Direct Primary ◽  
Imaging Conditions

We present the equations for migrating inverse‐vertical‐seismic‐profile‐while‐drilling and common‐midpoint autocorrelograms. These equations partly generalize the 1D autocorrelation imaging methods of Katz and Claerbout to 2D and 3D media, and also provide a formal mathematical procedure for imaging the reflectivity distribution from autocorrelograms. The imaging conditions are designed to migrate specific events in the autocorrelograms, either the direct‐primary correlations or the direct‐ghost correlations. Here, direct stands for direct wave, primary stands for primary reflections, and ghost denotes free‐surface ghost reflections. The main advantage in migrating autocorrelograms is that the source wavelet does not need to be known, which is the case for seismic data generated by a rotating drill bit or for vibroseis data with a corrupted pilot signal. Another advantage is that the source and receiver static problems are mitigated by autocorrelation migration. Two limitations are that autocorrelation of traces amplifies coherent noise such as surface waves, and produces undesirable coherent noise denoted as “virtual multiples.” Similar to “physical multiples,” such noise can, in principle, be partially suppressed by filtering and stacking of migration images obtained from many different shot gathers. Results with both synthetic and field data validate this conjecture, and show that autocorrelogram migration can be a viable alternative to standard migration when the source signal is not adequately known or there are severe static problems.

Interferometric prediction and subtraction of surface waves with a nonlinear local filter

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. SI1-SI8 ◽  
Author(s):  
Yanwei Xue ◽  
Shuqian Dong ◽  
Gerard T. Schuster
Keyword(s):  
Surface Waves ◽  
Seismic Data ◽  
Field Data ◽  
Matched Filter ◽  
Parameter Selection ◽  
Coherent Noise ◽  
Interferometric Method ◽  
3D Data ◽  
2D And 3D ◽  
Selection Of

Surface waves are a form of coherent noise that can obscure valuable reflection information in exploration records. It is sometimes difficult to eliminate these surface waves by traditional filtering approaches, such as an [Formula: see text] filter, without damaging the useful signals. As a partial remedy, we propose an interferometric method to predict and subtract surface waves in seismic data. The removal of surface waves by the proposed interferometric method consists of three steps: (1) remove most of the surface waves by a nonlinear local filter; (2) predict the residual surface waves by the interferometric method; (3) separate the residual surface waves from the result of step 2 by a nonlinear local filter, and remove the residual surface waves by a matched filter from the result of step 1. Field data tests for 2D and 3D data show that the method effectively suppresses surface waves and preserves the reflection information. Results suggest that the effectiveness of this method is sensitive to the parameter selection of the nonlinear local filter.

A procedure for optimally removing localized coherent noise

Geophysics ◽  
1995 ◽  
Vol 60 (1) ◽  
pp. 191-203 ◽  
Author(s):  
A. Frank Linville ◽  
Robert A. Meek
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Noise Attenuation ◽  
Signal Distortion ◽  
Coherent Noise ◽  
Vertical Seismic Profile ◽  
Seismic Records ◽  
Signal Distortions ◽  
Notch Filtering ◽  
Phase Differences

Primary reflections in seismic records are often obscured by coherent noise making processing and interpretation difficult. Trapped water modes, surface waves, scattered waves, air waves, and tube waves to name a few, must be removed early in the processing sequence to optimize subsequent processing and imaging. We have developed a noise canceling algorithm that effectively removes many of the commonly encountered noise trains in seismic data. All currently available techniques for coherent noise attenuation suffer from limitations that introduce unacceptable signal distortions and artifacts. Also, most of those techniques impose the dual stringent requirements of equal and fine spatial sampling in the field acquisition of seismic data. Our technique takes advantage of characteristics usually found in coherent noise such as being localized in time, highly aliased, nondispersive (or only mildly so), and exhibit a variety of moveout patterns across the seismic records. When coherent noise is localized in time, a window much like a surgical mute is drawn around the noise. The algorithm derives an estimate of the noise in the window, automatically correcting for amplitude and phase differences, and adaptively subtracts this noise from the window of data. This signal estimate is then placed back in the record. In a model and a land data example, the algorithm removes noise more effectively with less signal distortion than does f-k filtering or velocity notch filtering. Downgoing energy in a vertical seismic profile (VSP) with irregular receiver spacing is also removed.

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.

A COMPARISON OF PHYSICAL MODEL WITH FIELD DATA OVER OLIVER FIELD, VULCAN GRABEN

1995 ◽  
Vol 35 (1) ◽  
pp. 26 ◽  
Author(s):  
B.J. Evans ◽  
B.F. Oke ◽  
M. Urosevic ◽  
K. Chakraborty
Keyword(s):  
Physical Model ◽  
Seismic Data ◽  
Field Data ◽  
Joint Venture ◽  
Survey Design ◽  
Three Dimensional ◽  
Physical Modelling ◽  
Field Work ◽  
Physical Models ◽  
2D And 3D

Physical models representing the three dimensional geology of oil fields can be built from materials such as plastics and resins. Using ultrasound transmitters and receivers, 2D and 3D seismic surveys can be simulated to aid in the survey design of field work, provide insight into data processing, and can test interpretation concepts. Such modelling simulates most aspects of both land and marine seismic.In 1993 BHP Petroleum, on behalf of the AC/P6 Joint Venture, contracted Curtin University's Geophysics Group to build a 1:40,000 scale, 11-layer, 2.5D model of the Oliver Field so that 2D and 3D field data acquisition and processing could be simulated. A 2.5D model is invariant in the strike direction, but can answer most of the questions of a true 3D model at a fraction of the effort and cost. This was the first such model built in Australia, and one of the most complex physical models ever built.Of interest was the quality of imaging under the fault shadow near reservoir level, and whether the application of dip or strike 3D acquisition and processing approaches could improve the seismic data quality. Consequently, both dip (2D) and strike (2.5D) seismic data were acquired over the model using similar parameters to those used in conventional offshore acquisition. The data were processed to migration stage and compared with the field seismic data. Numerical model and field VSP data were also processed and compared with the field and physical model seismic data.The good agreement between processed physical model seismic and field seismic shows that physical modelling of geology has application in both two and three dimensional interpretation, acquisition planning, and processing testing and optimisation.This physical model experiment proved conclusively that shallow faults with a relatively large velocity contrast across them cause 'back' faults on the seismic data which do not exist in reality. Furthermore, this experiment proved for the first time using a physical model that strike 3D marine recording is preferable to dip 3D marine recording.

Prestack Bayesian statistical inversion constrained by reflection features

Geophysics ◽  
2020 ◽  
pp. 1-98
Author(s):  
Bo Yu ◽  
Hui Zhou ◽  
Lingqian Wang ◽  
Wenling Liu
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Posterior Probability ◽  
Probability Distributions ◽  
Seismic Profile ◽  
Inversion Method ◽  
Reflection Coefficients ◽  
Geological Structures ◽  
Practical Applications ◽  
Geological Information

Bayesian statistical inversion can integrate diverse datasets to infer the posterior probability distributions of subsurface elastic properties. However, certain existing methods may suffer from two issues in practical applications, namely spatial discontinuities and the uncertainty caused by the low-quality seismic traces. These limitations are evident in prestack statistical inversion since some traces in prestack angle gathers may be missing or low-quality. We propose a prestack Bayesian statistical inversion method constrained by reflection features to alleviate these issues. Based on a Bayesian linearized inversion framework, the proposed inversion approach is implemented by integrating the prestack seismic data with reflection features. The reflection features are captured from the poststack seismic profile, and they represent the relationships of the reflection coefficients between different traces. By utilizing the proposed approach, we are able to achieve superior inversion results and to evaluate inversion uncertainty simultaneously even with the low-quality prestack seismic data. The results of the synthetic and field data tests confirm the theoretical and practical effects of the reflection features on improving inversion continuity and accuracy and reducing inversion uncertainty. Moreover, this work gives a novel way to integrate the information of geological structures in statistical inversion methods. Other geological information, which can be linearized accurately or approximately, can be utilized in this manner.

scholarly journals Convolutional sparse coding for noise attenuation in seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. V23-V30
Author(s):  
Zhaolun Liu ◽  
Kai Lu
Keyword(s):  
Sparse Coding ◽  
Seismic Data ◽  
Field Data ◽  
Synthetic Data ◽  
Basis Functions ◽  
High Signal ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Denoising Method ◽  
Data Set

We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.

scholarly journals A field data example of Marchenko multiple elimination

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. S65-S70 ◽  
Author(s):  
Lele Zhang ◽  
Evert Slob
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Field Data ◽  
Multiple Reflections ◽  
Coherent Noise ◽  
Data Set ◽  
Model Independent ◽  
Multiple Elimination

Internal multiple reflections have been widely considered as coherent noise in measured seismic data, and many approaches have been developed for their attenuation. The Marchenko multiple elimination (MME) scheme eliminates internal multiple reflections without model information or adaptive subtraction. This scheme was originally derived from coupled Marchenko equations, but it was modified to make it model independent. It filters primary reflections with their two-way traveltimes and physical amplitudes from measured seismic data. The MME scheme is applied to a deepwater field data set from the Norwegian North Sea to evaluate its success in removing internal multiple reflections. The result indicates that most internal multiple reflections are successfully removed and primary reflections masked by overlapping internal multiple reflections are recovered.

Transform-domain noise synthesis and normal moveout-stack deconvolution approach to ground roll attenuation

Geophysics ◽  
2020 ◽  
Vol 86 (1) ◽  
pp. V15-V22
Author(s):  
Felix Oghenekohwo ◽  
Mauricio D. Sacchi
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Field Data ◽  
Transform Domain ◽  
Coherent Noise ◽  
Least Squares Problem ◽  
Fourier Filtering ◽  
Ground Roll ◽  
Seismic Reflections ◽  
Strong Ground

Ground roll is coherent noise in land seismic data that contaminates seismic reflections. Therefore, it is essential to find efficient ways that remove this noise and still preserve reflections. To this end, we have developed a signal and noise separation framework that uses a hyperbolic moveout assumption on reflections, coupled with the synthesis of coherent ground roll. This framework yields a least-squares problem, which we solve using a sparsity-promoting program that gives coefficients capable of modeling the signal and noise. Subtraction of the predicted noise from the observed data produces data with amplitude-preserved reflections. We develop this technique on synthetic and field data contaminated by weak and strong ground roll noise. Compared to conventional Fourier filtering techniques, our method accurately removes the ground roll while preserving the amplitude of the signal.

Non-stationary local signal-and-noise orthogonalization

Geophysics ◽  
2021 ◽  
pp. 1-96
Author(s):  
Yangkang Chen ◽  
Sergey Fomel
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Optimization Problem ◽  
Seismic Profile ◽  
New Method ◽  
Successful Performance ◽  
Stationary Model ◽  
Seismic Processing ◽  
Orthogonalization Method ◽  
Low Pass

The local signal-and-noise orthogonalization method has been widely used in the seismic processing and imaging community. In the local signal-and-noise orthogonalization method, a fixed triangle smoother is used for regularizing the local orthogonalization weight, which is based on the assumption that the energy is homogeneously distributed across the whole seismic profile. The fixed triangle smoother limits the performance of the local orthogonalization method in processing complicated seismic datasets. Here, we propose a new local orthogonalization method that uses a variable triangle smoother. The non-stationary smoothing radius is obtained by solving an optimization problem, where the low-pass filtered seismic data are matched by the smoothed data in terms of the local frequency attribute. The new local orthogonalization method with non-stationary model smoothness constraint is called the non-stationary local orthogonalization method. We use several synthetic and field data examples to demonstrate the successful performance of the new method.

A Single Bit Matters: Coherent Noise of Seismic Data Loggers

2015 ◽  
Vol 86 (3) ◽  
pp. 901-907 ◽  
Author(s):  
R. Takagi ◽  
K. Nishida ◽  
Y. Aoki ◽  
T. Maeda ◽  
K. Masuda ◽  
...  
Keyword(s):  
Seismic Data ◽  
Coherent Noise ◽  
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