Surface‐related multiple elimination: Application on real data

1991 ◽  
Author(s):  
D. J. Verschuur ◽  
A. J. Berkhout ◽  
C. P. A. Wapenaar
Keyword(s):  
Real Data ◽  
Multiple Elimination

Bayesian wavefield separation by transform-domain sparsity promotion

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. A33-A38 ◽  
Author(s):  
Deli Wang ◽  
Rayan Saab ◽  
Özgür Yilmaz ◽  
Felix J. Herrmann
Keyword(s):  
Bayesian Approach ◽  
Real Data ◽  
Transform Domain ◽  
Coherent Noise ◽  
Noise Sources ◽  
Wavefield Separation ◽  
Ground Roll ◽  
Successful Removal ◽  
Multiple Elimination ◽  
Made In

Successful removal of coherent-noise sources greatly determines seismic imaging quality. Major advances have been made in this direction, e.g., surface-related multiple elimination (SRME) and interferometric ground-roll removal. Still, moderate phase, timing, amplitude errors, and clutter in predicted signal components can be detrimental. Adopting a Bayesian approach, along with assuming approximate curvelet-domain independence of the to-be-separated signal components, we construct an iterative algorithm that takes predictions produced by, for example, SRME as input and separates these components in a robust manner. In addition, the proposed algorithm controls the energy mismatch between separated and predicted components. Such a control, lacking in earlier curvelet-domain formulations, improves results for primary-multiple separation on synthetic and real data.

Adaptive multiple subtraction based on multiband pattern coding

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. V69-V78 ◽  
Author(s):  
Jinlin Liu ◽  
Wenkai Lu
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Subtraction Problem ◽  
Frequency Bands ◽  
Main Challenge ◽  
Multiple Elimination ◽  
Pattern Coding

Adaptive multiple subtraction is the key step of surface-related multiple elimination methods. The main challenge of this technique resides in removing multiples without distorting primaries. We have developed a new pattern-based method for adaptive multiple subtraction with the consideration that primaries can be better protected if the multiples are differentiated from the primaries. Different from previously proposed methods, our method casts the adaptive multiple subtraction problem as a pattern coding and decoding process. We set out to learn distinguishable patterns from the predicted multiples before estimating the multiples contained in seismic data. Hence, in our method, we first carried out pattern coding of the predicted multiples to learn the special patterns of the multiples within different frequency bands. This coding process aims at exploiting the key patterns contained in the predicted multiples. The learned patterns are then used to decode (extract) the multiples contained in the seismic data, in which process those patterns that are similar to the learned patterns were identified and extracted. Because the learned patterns are obtained from the predicted multiples only, our method avoids the interferences of primaries in nature and shows an impressive capability for removing multiples without distorting the primaries. Our applications on synthetic and real data sets gave some promising results.

Multiple subtraction using statistically estimated inverse wavelets

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB247-WB254 ◽  
Author(s):  
Yike Liu ◽  
Degang Jin ◽  
Xu Chang ◽  
Peng Li ◽  
Hongchuan Sun ◽  
...  
Keyword(s):  
Real Data ◽  
Least Square ◽  
Second Step ◽  
Multiple Events ◽  
Amplitude Distortion ◽  
Computational Costs ◽  
Multiple Elimination

Surface-related multiple elimination (SRME) typically consists of two steps: The first step is prediction and the second step is subtraction. In subtraction, it is important to effectively attenuate multiple events and preserve primary events. When multiples cross with or overlap on primaries, least-square subtraction usually cannot subtract multiples effectively and may also damage the primaries. When multiples overlap with primaries, least-square subtraction cannot always subtract multiples accurately and often damages the primaries. To remedy this problem, we propose to statistically estimate the inverse source wavelet, correct for errors in the estimate of the inverse wavelet, and then use the corrected inverse wavelets for multiple subtraction. Synthetic tests and real data examples show that the proposed method can effectively attenuate multiples, while they also preserve the continuity of reflection events and successfully avoid amplitude distortion. The proposed method is characterized by low computational costs and ease of implementation.

Multiple attenuation in the image space

Geophysics ◽  
2005 ◽  
Vol 70 (1) ◽  
pp. V10-V20 ◽  
Author(s):  
Paul Sava ◽  
Antoine Guitton
Keyword(s):  
Real Data ◽  
Velocity Model ◽  
Image Space ◽  
Radon Transforms ◽  
Data Space ◽  
Multiple Attenuation ◽  
3D Data ◽  
Propagation Effects ◽  
Wavefield Extrapolation ◽  
Multiple Elimination

Multiples can be suppressed in the angle-domain image space after migration. For a given velocity model, primaries and multiples have different angle-domain moveout and, therefore, can be separated using techniques similar to the ones employed in the data space prior to migration. We use Radon transforms in the image space to discriminate between primaries and multiples and employ accurate functions describing angle-domain moveouts. Since every individual angle-domain common-image gather incorporates complex 3D propagation effects, our method has the advantage of working with 3D data and complicated geology. Therefore, our method offers an alternative to the more expensive surface-related multiple-elimination (SRME) approach operating in the data space. Radon transforms are cheap but not necessarily ideal for separating primaries and multiples, particularly at small angles where the moveout discrepancy between the two kinds of events are not large. Better techniques involving signal/noise separation using prediction-error filters can be employed as well, although such approaches fall outside the scope of this paper. We demonstrate, using synthetic and real data examples, the power of our method in discriminating between primaries and multiples after migration by wavefield extrapolation, followed by transformation to the angle domain.

scholarly journals Adaptive curvelet-domain primary-multiple separation

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. A17-A21 ◽  
Author(s):  
Felix J. Herrmann ◽  
Deli Wang ◽  
Dirk J. (Eric) Verschuur
Keyword(s):  
Least Squares ◽  
High Frequency ◽  
Real Data ◽  
Adaptive Method ◽  
Phase Errors ◽  
Successful Separation ◽  
Data Adaptive ◽  
Offset Time ◽  
Multiple Elimination

In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function of offset, time, and dip), they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band), and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

Exergy and sensibility analysis of each individual effect in a kraft multiple effect evaporator

TAPPI Journal ◽  
2019 ◽  
Vol 18 (10) ◽  
pp. 607-618
Author(s):  
JÉSSICA MOREIRA ◽  
BRUNO LACERDA DE OLIVEIRA CAMPOS ◽  
ESLY FERREIRA DA COSTA JUNIOR ◽  
ANDRÉA OLIVEIRA SOUZA DA COSTA
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Kraft Pulping ◽  
Steam Temperature ◽  
Input Flow ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Multiple Effect ◽  
Sensibility Analysis ◽  
Exergetic Analysis

The multiple effect evaporator (MEE) is an energy intensive step in the kraft pulping process. The exergetic analysis can be useful for locating irreversibilities in the process and pointing out which equipment is less efficient, and it could also be the object of optimization studies. In the present work, each evaporator of a real kraft system has been individually described using mass balance and thermodynamics principles (the first and the second laws). Real data from a kraft MEE were collected from a Brazilian plant and were used for the estimation of heat transfer coefficients in a nonlinear optimization problem, as well as for the validation of the model. An exergetic analysis was made for each effect individually, which resulted in effects 1A and 1B being the least efficient, and therefore having the greatest potential for improvement. A sensibility analysis was also performed, showing that steam temperature and liquor input flow rate are sensible parameters.

THE DEVELOPMENT OF THE MATHEMATICAL MODEL OF PREDICTING THE INDICATORS OF ACCREDITATION OF TECHNICAL UNIVERSITIES IN THE RUSSIAN FEDERATION

2017 ◽  
pp. 27-38
Author(s):  
Olga Mikhaylovna Tikhonova ◽  
Alexander Fedorovich Rezchikov ◽  
Vladimir Andreevich Ivashchenko ◽  
Vadim Alekseevich Kushnikov
Keyword(s):  
Mathematical Model ◽  
System Dynamics ◽  
Regression Model ◽  
Oriented Graph ◽  
Real Data ◽  
Educational Process ◽  
Proposed Model ◽  
Linear Differential ◽  
The University ◽  
The Mathematical Model

The paper presents the system of predicting the indicators of accreditation of technical universities based on J. Forrester mechanism of system dynamics. According to analysis of cause-and-effect relationships between selected variables of the system (indicators of accreditation of the university) there was built the oriented graph. The complex of mathematical models developed to control the quality of training engineers in Russian higher educational institutions is based on this graph. The article presents an algorithm for constructing a model using one of the simulated variables as an example. The model is a system of non-linear differential equations, the modelling characteristics of the educational process being determined according to the solution of this system. The proposed algorithm for calculating these indicators is based on the system dynamics model and the regression model. The mathematical model is constructed on the basis of the model of system dynamics, which is further tested for compliance with real data using the regression model. The regression model is built on the available statistical data accumulated during the period of the university's work. The proposed approach is aimed at solving complex problems of managing the educational process in universities. The structure of the proposed model repeats the structure of cause-effect relationships in the system, and also provides the person responsible for managing quality control with the ability to quickly and adequately assess the performance of the system.

Investigation of Timing Anomalies in the Russian Stock Market in the Post-Crisis Period

2019 ◽  
Vol 12 (3) ◽  
pp. 37-47
Author(s):  
I. Ya. Lukasevich
Keyword(s):  
Stock Market ◽  
Information Technologies ◽  
Real Data ◽  
Weak Form ◽  
Fair Value ◽  
Presidential Decree ◽  
Day Of The Week ◽  
Russian Stock Market ◽  
Economic Growth Rates ◽  
Global And Local

The implementation of the May presidential decree aimed at Russia’s joining the top five global economies and achieving economic growth rates above the world’s average while maintaining macroeconomic stability requires a highly developed and efficient stock market ensuring the accumulation of capital and its deployment in the most promising and productive sectors of the economy.The subject of the research is timing anomalies in the Russian stock market in 2012–2018. The relevance of the research is due to the information inefficiency of the Russian stock market and its imperfections leading to significant price deviations from the «fair» value of assets and depriving investors of the opportunity to form various strategies for deriving additional revenues not related to fundamental economic factors and objective processes occurring in the global and local economies and the economy of an individual business entity. Based on the trend analysis of the Broad Market USD Index (RUBMI), the paper demonstrates a methodology for simulating the analysis of price anomalies on large arrays of real data using statistical data processing methods and modern information technologies. The paper concludes that though the Russian stock market lacks even the weak form of efficiency, such well-known timing anomalies as the “day-of-the-week” effect and the “month” effect have not been observed in the recent years. Therefore, investors could not use these anomalies to derive regular revenues above the market average.

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