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.

An application of the Marchenko internal multiple elimination scheme formulated as a least-squares problem

Geophysics ◽  
2021 ◽  
pp. 1-70
Author(s):  
Rodrigo S. Santos ◽  
Daniel E. Revelo ◽  
Reynam C. Pestana ◽  
Victor Koehne ◽  
Diego F. Barrera ◽  
...  
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Neumann Series ◽  
Convergence Criterion ◽  
Coherent Noise ◽  
Data Set ◽  
Least Squares Problem ◽  
Time Migration ◽  
Multiple Elimination ◽  
Seismic Images

Seismic images produced by migration of seismic data related to complex geologies, suchas pre-salt environments, are often contaminated by artifacts due to the presence of multipleinternal reflections. These reflections are created when the seismic wave is reflected morethan once in a source-receiver path and can be interpreted as the main coherent noise inseismic data. Several schemes have been developed to predict and subtract internal multiplereflections from measured data, such as the Marchenko multiple elimination (MME) scheme,which eliminates the referred events without requiring a subsurface model or an adaptivesubtraction approach. The MME scheme is data-driven, can remove or attenuate mostof these internal multiples, and was originally based on the Neumann series solution ofMarchenko’s projected equations. However, the Neumann series approximate solution isconditioned to a convergence criterion. In this work, we propose to formulate the MMEas a least-squares problem (LSMME) in such a way that it can provide an alternative thatavoids a convergence condition as required in the Neumann series approach. To demonstratethe LSMME scheme performance, we apply it to 2D numerical examples and compare theresults with those obtained by the conventional MME scheme. Additionally, we evaluatethe successful application of our method through the generation of in-depth seismic images,by applying the reverse-time migration (RTM) algorithm on the original data set and tothose obtained through MME and LSMME schemes. From the RTM results, we show thatthe application of both schemes on seismic data allows the construction of seismic imageswithout artifacts related to internal multiple events.

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 Adaptive overburden elimination with the multidimensional Marchenko equation

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. T265-T284 ◽  
Author(s):  
Joost van der Neut ◽  
Kees Wapenaar
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Velocity Model ◽  
Thin Layers ◽  
Multiple Reflections ◽  
Data Set ◽  
Marchenko Equation ◽  
Recorded Data ◽  
Internal Multiples ◽  
Multiple Elimination

Iterative substitution of the multidimensional Marchenko equation has been introduced recently to integrate internal multiple reflections in the seismic imaging process. In so-called Marchenko imaging, a macro velocity model of the subsurface is required to meet this objective. The model is used to back-propagate the data during the first iteration and to truncate integrals in time during all successive iterations. In case of an erroneous model, the image will be blurred (akin to conventional imaging) and artifacts may arise from inaccurate integral truncations. However, the scheme is still successful in removing artifacts from internal multiple reflections. Inspired by these observations, we rewrote the Marchenko equation, such that it can be applied early in a processing flow, without the need of a macro velocity model. Instead, we have required an estimate of the two-way traveltime surface of a selected horizon in the subsurface. We have introduced an approximation, such that adaptive subtraction can be applied. As a solution, we obtained a new data set, in which all interactions (primaries and multiples) with the part of the medium above the picked horizon had been eliminated. Unlike various other internal multiple elimination algorithms, the method can be applied at any specified target horizon, without having to resolve for internal multiples from shallower horizons. We successfully applied the method on synthetic data, where limitations were reported due to thin layers, diffraction-like discontinuities, and a finite acquisition aperture. A field data test was also performed, in which the kinematics of the predicted updates were demonstrated to match with internal multiples in the recorded data, but it appeared difficult to subtract them.

LONG-PERIOD SURFACE-RELATED MULTIPLE SUPPRESSION IN 2D MARINE SEISMIC DATA USING PREDICTIVE DECONVOLUTION AND COMBINATION OF SURFACE-RELATED MULTIPLE ELIMINATION AND PARABOLIC RADON FILTERING

2021 ◽  
Author(s):  
Pimpawee Sittipan ◽  
Pisanu Wongpornchai
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Petroleum Reservoirs ◽  
The United States ◽  
Multiple Reflections ◽  
Long Period ◽  
Predictive Deconvolution ◽  
Short Period ◽  
Marine Seismic ◽  
Multiple Elimination

Some of the important petroleum reservoirs accumulate beneath the seas and oceans. Marine seismic reflection method is the most efficient method and is widely used in the petroleum industry to map and interpret the potential of petroleum reservoirs. Multiple reflections are a particular problem in marine seismic reflection investigation, as they often obscure the target reflectors in seismic profiles. Multiple reflections can be categorized by considering the shallowest interface on which the bounces take place into two types: internal multiples and surface-related multiples. Besides, the multiples can be categorized on the interfaces where the bounces take place, a difference between long-period and short-period multiples can be considered. The long-period surface-related multiples on 2D marine seismic data of the East Coast of the United States-Southern Atlantic Margin were focused on this research. The seismic profile demonstrates the effectiveness of the results from predictive deconvolution and the combination of surface-related multiple eliminations (SRME) and parabolic Radon filtering. First, predictive deconvolution applied on conventional processing is the method of multiple suppression. The other, SRME is a model-based and data-driven surface-related multiple elimination method which does not need any assumptions. And the last, parabolic Radon filtering is a moveout-based method for residual multiple reflections based on velocity discrimination between primary and multiple reflections, thus velocity model and normal-moveout correction are required for this method. The predictive deconvolution is ineffective for long-period surface-related multiple removals. However, the combination of SRME and parabolic Radon filtering can attenuate almost long-period surface-related multiple reflections and provide a high-quality seismic images of marine seismic data.

Migration with reduced artifacts from internal multiple reflections

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. A25-A29
Author(s):  
Lele Zhang
Keyword(s):  
Seismic Reflection ◽  
Computational Cost ◽  
Data Sets ◽  
Square Root ◽  
Multiple Reflections ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recent Developments ◽  
Multiple Elimination

Migration of seismic reflection data leads to artifacts due to the presence of internal multiple reflections. Recent developments have shown that these artifacts can be avoided using Marchenko redatuming or Marchenko multiple elimination. These are powerful concepts, but their implementation comes at a considerable computational cost. We have derived a scheme to image the subsurface of the medium with significantly reduced computational cost and artifacts. This scheme is based on the projected Marchenko equations. The measured reflection response is required as input, and a data set with primary reflections and nonphysical primary reflections is created. Original and retrieved data sets are migrated, and the migration images are multiplied with each other, after which the square root is taken to give the artifact-reduced image. We showed the underlying theory and introduced the effectiveness of this scheme with a 2D numerical example.

scholarly journals Data-driven internal multiple elimination and its consequences for imaging: A comparison of strategies

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S365-S372 ◽  
Author(s):  
Lele Zhang ◽  
Jan Thorbecke ◽  
Kees Wapenaar ◽  
Evert Slob
Keyword(s):  
Inverse Scattering ◽  
Computational Cost ◽  
Multiple Reflection ◽  
Data Driven ◽  
Transmission Losses ◽  
Multiple Reflections ◽  
Data Set ◽  
Numerical Examples ◽  
Inverse Scattering Series ◽  
Multiple Elimination

We have compared three data-driven internal multiple reflection elimination schemes derived from the Marchenko equations and inverse scattering series (ISS). The two schemes derived from Marchenko equations are similar but use different truncation operators. The first scheme creates a new data set without internal multiple reflections. The second scheme does the same and compensates for transmission losses in the primary reflections. The scheme derived from ISS is equal to the result after the first iteration of the first Marchenko-based scheme. It can attenuate internal multiple reflections with residuals. We evaluate the success of these schemes with 2D numerical examples. It is shown that Marchenko-based data-driven schemes are relatively more robust for internal multiple reflection elimination at a higher computational cost.

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.

Predictive removal of scattered noise

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Gérard C. Herman ◽  
Colin Perkins
Keyword(s):  
Mathematical Model ◽  
Wave Propagation ◽  
Surface Wave ◽  
Seismic Data ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Unique Data ◽  
Surface Wave Propagation ◽  
Petroleum Development

Land seismic data can be severely contaminated with coherent noise. We discuss a deterministic technique to predict and remove scattered coherent noise from land seismic data based on a mathematical model of near-surface wave propagation. We test the method on a unique data set recorded by Petroleum Development of Oman in the Qarn Alam area (with shots and receivers on the same grid), and we conclude that it effectively reduces scattered noise without smearing reflection energy.

Seismic characterization of a Mississippi Lime resource play in Osage County, Oklahoma, USA

2013 ◽  
Vol 1 (2) ◽  
pp. SB97-SB108 ◽  
Author(s):  
Benjamin L. Dowdell ◽  
J. Tim Kwiatkowski ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Analysis ◽  
High Porosity ◽  
Fracture Density ◽  
Coherent Noise ◽  
High Fracture ◽  
Data Set ◽  
Horizontal Drilling ◽  
Prestack Inversion ◽  
Osage County

With the advent of horizontal drilling and hydraulic fracturing in the Midcontinent, USA, fields once thought to be exhausted are now experiencing renewed exploitation. However, traditional Midcontinent seismic analysis techniques no longer provide satisfactory reservoir characterization for these unconventional plays; new seismic analysis methods are needed to properly characterize these radically innovative play concepts. Time processing and filtering is applied to a raw 3D seismic data set from Osage County, Oklahoma, paying careful attention to velocity analysis, residual statics, and coherent noise filtering. The use of a robust prestack structure-oriented filter and spectral whitening greatly enhances the results. After prestack time migrating the data using a Kirchhoff algorithm, new velocities are picked. A final normal moveout correction is applied using the new velocities, followed by a final prestack structure-oriented filter and spectral whitening. Simultaneous prestack inversion uses the reprocessed and time-migrated seismic data as input, along with a well from within the bounds of the survey. With offsets out to 3048 m and a target depth of approximately 880 m, we can invert for density in addition to P- and S-impedance. Prestack inversion attributes are sensitive to lithology and porosity while surface seismic attributes such as coherence and curvature are sensitive to lateral changes in waveform and structure. We use these attributes in conjunction with interpreted horizontal image logs to identify zones of high porosity and high fracture density.

Structurally constrained impedance inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T577-T589 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky
Keyword(s):  
Objective Function ◽  
Seismic Data ◽  
Field Data ◽  
A Priori ◽  
Structural Constraints ◽  
Inversion Algorithm ◽  
Data Set ◽  
Impedance Inversion ◽  
Single Trace ◽  
Complex Geology

In complex geology, the presence of highly dipping structures can complicate impedance inversion. We have developed a structurally constrained inversion in which a computationally well-behaved objective function is minimized subject to structural constraints. This approach allows the objective function to incorporate structural orientation in the form of dips into our inversion algorithm. Our method involves a multitrace impedance inversion and a rotation of an orthogonal system of derivative operators. Local dips used to constrain the derivative operators were estimated from migrated seismic data. In addition to imposing structural constraints on the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We investigated this algorithm on a complex synthetic 2D model as well as a seismic field data set. We compared the result obtained with this approach with the results from single trace-based inversion and laterally constrained inversion. The inversion carried out using dip information produces a model that has higher resolution that is more geologically realistic compared with other methods.

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