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.

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.

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.

scholarly journals Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S197-S205 ◽  
Author(s):  
Zhaolun Liu ◽  
Abdullah AlTheyab ◽  
Sherif M. Hanafy ◽  
Gerard Schuster
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
3D Data ◽  
Strong Surface ◽  
Migration Method ◽  
Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

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.

An elastodynamic inverse scattering method for removing scattered surface waves from field data

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1897-1905 ◽  
Author(s):  
Bastian Blonk ◽  
Gerard C. Herman ◽  
Guy G. Drijkoningen
Keyword(s):  
Surface Waves ◽  
Inverse Scattering ◽  
Seismic Data ◽  
Field Data ◽  
Inverse Scattering Method ◽  
Scattering Method ◽  
Data Sets ◽  
Propagation Characteristics ◽  
Fitting Procedure ◽  
Separate Source

In an earlier paper, we introduced a 3-D inverse scattering method for removing scattered surface waves from seismic data that was based on a tomographic imaging of the scattered surface waves by a data‐fitting procedure that used as much of the seismic data as possible. After this imaging step, the scattered surface waves can be computed and removed for each separate source‐receiver pair. We now apply the method to two field‐data sets. The method requires a knowledge of the source waveform and shallow propagation characteristics, and these input requirements are estimated from the direct surface wave. We conclude that the method effectively attenuates crossline scattered surface waves without affecting deeper reflections.

Spectral structure-oriented filtering of seismic data with self-adaptive paths

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V271-V280
Author(s):  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Noise Removal ◽  
Quality Data ◽  
Spectral Structure ◽  
Data Sets ◽  
Coherent Noise ◽  
Edge Preserving ◽  
3D Data ◽  
Self Adaptive

We have developed an algorithm to perform structure-oriented filtering (SOF) in 3D seismic data by learning the data structure in the frequency domain. The method, called spectral SOF (SSOF), allows us to enhance the signal structures in the [Formula: see text]-[Formula: see text]-[Formula: see text] domain by running a 1D edge-preserving filter along curvilinear self-adaptive trajectories that connect points of similar characteristics. These self-adaptive paths are given by the eigenvectors of the smoothed structure tensor, which are easily computed using closed-form expressions. SSOF relies on a few parameters that are easily tuned and on simple 1D convolutions for tensor calculation and smoothing. It is able to process a 3D data volume with a 2D strategy using basic 1D edge-preserving filters. In contrast to other SOF techniques, such as anisotropic diffusion, anisotropic smoothing, and plane-wave prediction, SSOF does not require any iterative process to reach the denoised result. We determine the performance of SSOF using three public domain field data sets, which are subsets of the well-known Waipuku, Penobscot, and Teapot surveys. We use the Waipuku subset to indicate the signal preservation of the method in good-quality data when mostly background random noise is present. Then, we use the Penobscot subset to illustrate random noise and footprint signature attenuation, as well as to show how faults and fractures are improved. Finally, we analyze the Teapot stacked and depth-migrated subsets to show random and coherent noise removal, leading to an improvement of the volume structural details and overall lateral continuity. The results indicate that random noise, footprints, and other artifacts can be successfully suppressed, enhancing the delineation of geologic structures and seismic horizons and preserving the original signal bandwidth.

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.

Data-driven edge detectors for seismic data interpretation

Geophysics ◽  
2021 ◽  
pp. 1-41
Author(s):  
Julián L. Gómez ◽  
Lucía E. N. Gelis ◽  
Danilo R. Velis
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Data Interpretation ◽  
Data Driven ◽  
Data Sets ◽  
3D Data ◽  
Edge Detectors ◽  
Novel Method ◽  
2D And 3D ◽  
Structure Enhancement

We present a novel method to assist in seismic interpretation. The algorithm learns data-driven edge-detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local and structural information retrieved from patches taken randomly from the input time slices. The filters conform to an orthogonal family that behaves as structure-aware Sobel-like edge detectors, and the user can set their size and number. The results from marine Canada and New Zealand 3D seismic data demonstrate that the proposed algorithm allows the semblance attribute to improve the delineation of the subsurface channels. This fact is further supported by testing the method with realistic synthetic 2D and 3D data sets containing channeling and meandering systems. We contrast the results with standard plain Sobel filtering, multidirectional Sobel filters of variable size, and the dip-oriented plane-wave destruction Sobel attribute. The proposed method gives results that are comparable or superior to those of Sobel-based approaches. In addition, the obtained filters can adapt to the geological structures present in each time slice, which reduces the number of unwanted artifacts in the final product.

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