Deblending multisource seismic data with periodically varying cosine code and sparse inversion

2020 ◽  
Author(s):  
Mengyao Jiao ◽  
Tianyue Hu ◽  
Weikang Kuang ◽  
Yang Liu ◽  
Shaohuan Zu
Keyword(s):  
Seismic Data ◽  
Sparse Inversion

3D surface-related multiple elimination using parabolic sparse inversion

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. V145-V152 ◽  
Author(s):  
Ketil Hokstad ◽  
Roger Sollie
Keyword(s):  
Seismic Data ◽  
Alternative Formulation ◽  
Practical Implementation ◽  
3D Seismic ◽  
Systems Of Equations ◽  
Production Scale ◽  
Seismic Acquisition ◽  
Sparse Inversion ◽  
Multiple Elimination ◽  
3D Seismic Data

The basic theory of surface-related multiple elimination (SRME) can be formulated easily for 3D seismic data. However, because standard 3D seismic acquisition geometries violate the requirements of the method, the practical implementation for 3D seismic data is far from trivial. A major problem is to perform the crossline-summation step of 3D SRME, which becomes aliased because of the large separation between receiver cables and between source lines. A solution to this problem, based on hyperbolic sparse inversion, has been presented previously. This method is an alternative to extensive interpolation and extrapolation of data. The hyperbolic sparse inversion is formulated in the time domain and leads to few, but large, systems of equations. In this paper, we propose an alternative formulation using parabolic sparse inversion based on an efficient weighted minimum-norm solution that can be computed in the angular frequency domain. The main advantage of the new method is numerical efficiency because solving many small systems of equations often is faster than solving a few big ones. The method is demonstrated on 3D synthetic and real data with reflected and diffracted multiples. Numerical results show that the proposed method gives improved results compared to 2D SRME. For typical seismic acquisition geometries, the numerical cost running on 50 processors is [Formula: see text] per output trace. This makes production-scale processing of 3D seismic data feasible on current Linux clusters.

Reflectivity decomposition: Theory and application

2021 ◽  
pp. 1-57
Author(s):  
Chen Liang ◽  
John Castagna ◽  
Marcelo Benabentos
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reflection Coefficients ◽  
3D Seismic ◽  
Decomposition Theory ◽  
Midland Basin ◽  
Reflection Seismic ◽  
Sparse Inversion ◽  
Reflectivity Inversion

Sparse reflectivity inversion of processed reflection seismic data is intended to produce reflection coefficients that represent boundaries between geological layers. However, the objective function for sparse inversion is usually dominated by large reflection coefficients which may result in unstable inversion for weak events, especially those interfering with strong reflections. We propose that any seismogram can be decomposed according to the characteristics of the inverted reflection coefficients which can be sorted and subset by magnitude, sign, and sequence, and new seismic traces can be created from only reflection coefficients that pass sorting criteria. We call this process reflectivity decomposition. For example, original inverted reflection coefficients can be decomposed by magnitude, large ones removed, the remaining reflection coefficients reconvolved with the wavelet, and this residual reinverted, thereby stabilizing inversions for the remaining weak events. As compared with inverting an original seismic trace, subtle impedance variations occurring in the vicinity of nearby strong reflections can be better revealed and characterized when only the events caused by small reflection coefficients are passed and reinverted. When we apply reflectivity decomposition to a 3D seismic dataset in the Midland Basin, seismic inversion for weak events is stabilized such that previously obscured porous intervals in the original inversion, can be detected and mapped, with good correlation to actual well logs.

Simultaneous reconstruction of seismic reflections and diffractions using a global hyperbolic Radon dictionary

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. V315-V323 ◽  
Author(s):  
Amr Ibrahim ◽  
Paolo Terenghi ◽  
Mauricio D. Sacchi
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Small Data ◽  
Inversion Algorithm ◽  
Efficient Tool ◽  
Reconstruction Methods ◽  
Simultaneous Reconstruction ◽  
Sparse Inversion ◽  
Data Windows ◽  
Seismic Reflections

We have developed a new transform with basis functions that closely resemble seismic reflections and diffractions. The new transform is an extension of the classic hyperbolic Radon transform and accounts for the apex shifts of the seismic reflection hyperbolas and the asymptote shifts of the seismic diffraction hyperbolas. The adjoint and forward operators of the proposed transform are computed using Stolt operators in the frequency domain to increase the computational efficiency of the transform. This new transform is used, in conjunction with a sparse inversion algorithm, to reconstruct common-shot gathers. Our tests indicate that this new transform is an efficient tool for interpolating coarsely sampled seismic data in cases in which one cannot use small data windows to validate the linear event assumption that is often made by Fourier-based reconstruction methods.

An amplitude-preserving deblending approach for simultaneous sources

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V185-V196 ◽  
Author(s):  
Chengbo Li ◽  
Charles C. Mosher ◽  
Yongchang Ji
Keyword(s):  
Seismic Data ◽  
Line Search ◽  
Inversion Problem ◽  
Coherent Noise ◽  
Amplitude Variation With Offset ◽  
Alternating Direction ◽  
Line Search Techniques ◽  
Sparse Inversion ◽  
Improved Performance ◽  
Blended Data

A goal of simultaneous shooting is to acquire high-quality seismic data more efficiently, while reducing operational costs and improving acquisition efficiency. Effective sampling and deblending techniques are essential to achieve this goal. Inspired by compressive sensing (CS), we have formulated deblending as an analysis-based sparse inversion problem. We solve the inversion problem with an algorithm derived from the classic alternating direction method (ADM), associated with variable splitting and nonmonotone line-search techniques. In our testing, the analysis-based formulation together with nonmonotone ADM algorithm provides improved performance compared with synthesis-based approaches. A major issue for all deblending approaches is how to deal with real-world variations in seismic data caused by static shifts and amplitude imbalances. We evaluate the concept of including static and amplitude corrections obtained from surface-consistent solutions into the deblending formulation. We implement solutions that use a multistage inversion scheme to overcome the practical issues embedded in the field-blended data, such as strong coherent noise, statics, and shot-amplitude variations. The combination of these techniques gives high-fidelity deblending results for marine and land data. We use two field-data examples acquired with simultaneous sources to demonstrate the effectiveness of the proposed approach. Imaging and amplitude variation with offset quantitative analysis are carried out to indicate the amplitude-preserving character of deblended data with this methodology.

Simultaneous vibroseis data separation through sparse inversion

2019 ◽  
Vol 38 (8) ◽  
pp. 625-629 ◽  
Author(s):  
Jiawen Song ◽  
Peiming Li ◽  
Zhongping Qian ◽  
Mugang Zhang ◽  
Pengyuan Sun ◽  
...  
Keyword(s):  
Seismic Data ◽  
High Fidelity ◽  
Data Sets ◽  
Sparse Constraint ◽  
Coherent Signal ◽  
Additional Processing ◽  
Seismic Acquisition ◽  
Sparse Inversion ◽  
Data Separation ◽  
Simultaneous Source

Compared with conventional seismic acquisition methods, simultaneous-source acquisition utilizes independent shooting that allows for source interference, which reduces the time and cost of acquisition. However, additional processing is required to separate the interfering sources. Here, we present an inversion-based deblending method, which distinguishes signal from blending noise based on coherency differences in 3D receiver gathers. We first transform the seismic data into the frequency-wavenumber-wavenumber domain and impose a sparse constraint to estimate the coherent signal. We then subtract the estimated signal from the original input to predict the interference noise. Driven by data residuals, the signal is updated iteratively with shrinking thresholds until the signal and noise fully separate. We test our presented method on two 3D field data sets to demonstrate how the method proficiently separates interfering vibroseis sources with high fidelity.

Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion

Geophysics ◽  
2015 ◽  
Vol 80 (6) ◽  
pp. WD129-WD141 ◽  
Author(s):  
Raphael Sternfels ◽  
Ghislain Viguier ◽  
Regis Gondoin ◽  
David Le Meur
Keyword(s):  
Seismic Data ◽  
Low Rank ◽  
Noise Attenuation ◽  
Sparse Inversion

Estimation of primaries by sparse inversion from passive seismic data

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. SA61-SA69 ◽  
Author(s):  
G. J. van Groenestijn ◽  
D. J. Verschuur
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Synthetic Data ◽  
Point Sources ◽  
Impulse Responses ◽  
Inversion Process ◽  
Amplitude Estimation ◽  
Passive Seismic ◽  
Sparse Inversion ◽  
Data Surface

For passive seismic data, surface multiples are used to obtain an estimate of the subsurface responses, usually by a crosscorrelation process. This crosscorrelation process relies on the assumption that the surface has been uniformly illuminated by subsurface sources in terms of incident angles and strengths. If this is not the case, the crosscorrelation process cannot give a true amplitude estimation of the subsurface response. Furthermore, cross terms in the crosscorrelation result are not related to actual subsurface inhomogeneities. We have developed a method that can obtain true amplitude subsurface responses without a uniform surface-illumination assumption. Our methodology goes beyond the crosscorrelation process and estimates primaries only from the surface-related multiples in the available signal. We use the recently introduced estimation of primaries by sparse inversion (EPSI) methodology, in which the primary impulse responses are considered to be the unknowns in a large-scale inversion process. With some modifications, the EPSI method can be used for passive seismic data. The output of this process is primary impulse responses with point sources and receivers at the surface, which can be used directly in traditional imaging schemes. The methodology was tested on 2D synthetic data.

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