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.

Seismic processing and imaging of the new 2D marine reflection seismic data in the Polish sector of the Baltic Sea

2020 ◽  
Author(s):  
Quang Nguyen ◽  
Michal Malinowski ◽  
Piotr Krzywiec ◽  
Christian Huebscher
Keyword(s):  
High Resolution ◽  
Baltic Sea ◽  
Shallow Water ◽  
Seismic Data ◽  
Seismic Profile ◽  
Constant Thickness ◽  
The Baltic Sea ◽  
Seismic Processing ◽  
Fault Systems ◽  
The Baltic

<p>Geological structure and tectonics of the Phanerozoic sedimentary cover within the transition zone between the Precambrian and Paleozoic platform in the Polish sector of the Baltic Sea was imaged using new 2D high-resolution multi-channel seismic reflection data. The new seismic data were acquired in 2016 during the course of RV Maria S. Merian expedition MSM52 within the framework of the BALTEC project. Eight profiles (with the total length of ca. 850km) covered the tectonics blocks located within the Polish Exclusive Economic Zone, stretching from the East European Craton (EEC) to the Paleozoic platform across the Teisseyre-Torquist Zone (TTZ).</p><p>Our in-house seismic processing workflow focused on removing multiples contaminating this shallow-water data, both water bottom and interbed related. Various demultiple techniques such as SRME, TAU-P domain deconvolution, high resolution parabolic Radon demultiple and SWDM (Shallow water demultiple) have been tested. Combination of all those techniques at different stages of the processing with some modifications based on a particular seismic profile proved to be the most effective. Consequently, multiples obscuring seismic sections were efficiently reduced. Data were processed up to Kirchhoff pre-stack time migration.</p><p>The longest seismic profile (line BGR16-212, ca. 240 km long) crosses almost perpendicularly majority of Precambrian and Paleozoic fault systems bordering the tectonic blocks of the EEC basement, so fault systems could be easily interpreted. EEC Precambrian basement is characterized by a regional flexure towards the TTZ. Cambrian-Ordovician exhibits similar geometry and is characterized by a relatively constant thickness related to deposition on the Tornquist Ocean passive margin. Thick Silurian succession is characterized by a regional divergent pattern caused by deposition within the Caledonian foredeep basin. Structural pattern within the W part of the study area is much more complex as this area underwent Late Paleozoic extension/transtension, Variscan inversion, Permo-Mesozoic subsidence and Late Cretaceous inversion.</p><p>This study was funded by the Polish National Science Centre grant no UMO-2017/27/B/ST10/02316.</p>

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.

Enhancing resolution of nonstationary seismic data by molecular-Gabor transform

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. V31-V41 ◽  
Author(s):  
Lingling Wang ◽  
Jinghuai Gao ◽  
Wei Zhao ◽  
Xiudi Jiang
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Field Data ◽  
Optimization Problem ◽  
Gabor Transform ◽  
Frequency Bandwidth ◽  
Time Frequency ◽  
Text Model ◽  
Amplitude Compensation

We propose an adaptive spectrum-broadening method (ASBM) to improve the resolution of nonstationary seismic data. This method assumes that a seismic trace can be split into segments, each of which can be considered as approximately stationary. We construct a set of specific windows, called molecular-Gabor (MG) windows, by solving an optimization problem, such that the seismic trace in each of the MG windows is stationary. A time-frequency (t-f) transform, called MG transform, can be obtained from a MG frame constructed using the MG windows. For a seismic trace, we first transform it into the t-f domain, then spectrum-broadening and/or amplitude compensation are performed in each of the MG windows. Subsequently, a high-resolution version of the nonstationary seismic trace will be obtained after the inverse MG transform. Applications of this method to synthetic and field data show that the ASBM works well for a general earth [Formula: see text]-model that varies with traveltime. It can restore the attenuated energy and expand the frequency bandwidth of a nonstationary seismic trace effectively. One significant advantage of our method is that it automatically estimates all the parameters that are optimal for each trace.

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.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

Topographic corrections to slowness measurements in parsimonious migration of land data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. S77-S80 ◽  
Author(s):  
Ibrahim Z. Basi ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Field Data

Parsimonious migration requires that incident and emergent angles be measured, via apparent slownesses, from the seismic data being migrated. When slownesses are measured from land data, it is the apparent slowness along the topography, not the horizontal slowness that is being measured. Thus, errors are introduced into the incident and emergent angle estimates, which are defined via horizontal slownesses. These errors can be corrected using the local topographic dips. A 2D field data example shows that, after correction, a migrated image has significantly improved coherency.

Reducing risk and improving production in unconventional plays

2013 ◽  
Vol 1 (2) ◽  
pp. SB3-SB14 ◽  
Author(s):  
Tony Rebec ◽  
Marino Pareja ◽  
Zhiyong Zhao
Keyword(s):  
Seismic Data ◽  
Seismic Processing ◽  
Business Values ◽  
Improve Production ◽  
Reduce Risk ◽  
The Right

Using seismic data to reduce risk and improve production in unconventional plays requires careful preplanning based on the nature of the play, plus acquiring the right seismic, processing the seismic correctly, extracting the optimum information, and then transforming the information into business values. We discuss these criteria and focus on extracting the optimum information.

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