The attenuation factor or quality factor(Q-factor or Q) has been used to measure the energy attenuation of seismic waves propagating in underground media. Many methods are used to estimate the Q-factor. We propose a m...The attenuation factor or quality factor(Q-factor or Q) has been used to measure the energy attenuation of seismic waves propagating in underground media. Many methods are used to estimate the Q-factor. We propose a method to calculate the Q-factor based on the prestack Q-factor inversion and the generalized S-transform. The proposed method specifies a standard primary wavelet and calculates the cumulative Q-factors; then, it finds the interlaminar Q-factors using the relation between Q and offset(QVO) and the Dix formula. The proposed method is alternative to methods that calculate interlaminar Q-factors after horizon picking. Because the frequency spectrum of each horizon can be extracted continuously on a 2D time–frequency spectrum, the method is called the continuous spectral ratio slope(CSRS) method. Compared with the other Q-inversion methods, the method offers nearly effortless computations and stability, and has mathematical and physical significance. We use numerical modeling to verify the feasibility of the method and apply it to real data from an oilfield in Ahdeb, Iraq. The results suggest that the resolution and spatial stability of the Q-profile are optimal and contain abundant interlaminar information that is extremely helpful in making lithology and fluid predictions.展开更多
We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was ...We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was more reliable than traditional methods. The data required for the method were near the resonant frequency,not at the half-power points of the reflection coefficient curve or Smith chart. We applied the new method to measure a resonant cavity with an unloaded Q factor of^100,000, obtaining good agreement between the measured and theoretical results.展开更多
All-optical regenerators can be used to suppress amplified spontaneous emission(ASE) noise introduced by cascaded erbium doped fiber amplifiers(EDFAs) in optical fiber communication systems and lead to the improvement...All-optical regenerators can be used to suppress amplified spontaneous emission(ASE) noise introduced by cascaded erbium doped fiber amplifiers(EDFAs) in optical fiber communication systems and lead to the improvement of optical receiver sensitivity. By introducing the Q-factor transfer function(QTF), we evaluate the Q-factor performance of degenerate four-wave mixing(DFWM) regenerators with clock pump and reveal the differences between the optimal input powers determined from the static and dynamic power tranfer function(PTF) and the QTF curves. Our simulation shows that the clock-pump regnerator is capable of improving the Q-facor and receiver sensitivity for 40 Gbit/s ASE-degraded return-to-zero on-off keying(RZ-OOK) signal by 2.58 dB and 4.2 d B, respectively.展开更多
The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common...The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.展开更多
Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- a...Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.展开更多
We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute t...We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.展开更多
基金supported by The National Key Research and Development Program Plane(No.2017YFC0601505)National Natural Science Foundation(No.41672325)Science&Technology Department of Sichuan Province Technology Project(No.2017GZ0393)
文摘The attenuation factor or quality factor(Q-factor or Q) has been used to measure the energy attenuation of seismic waves propagating in underground media. Many methods are used to estimate the Q-factor. We propose a method to calculate the Q-factor based on the prestack Q-factor inversion and the generalized S-transform. The proposed method specifies a standard primary wavelet and calculates the cumulative Q-factors; then, it finds the interlaminar Q-factors using the relation between Q and offset(QVO) and the Dix formula. The proposed method is alternative to methods that calculate interlaminar Q-factors after horizon picking. Because the frequency spectrum of each horizon can be extracted continuously on a 2D time–frequency spectrum, the method is called the continuous spectral ratio slope(CSRS) method. Compared with the other Q-inversion methods, the method offers nearly effortless computations and stability, and has mathematical and physical significance. We use numerical modeling to verify the feasibility of the method and apply it to real data from an oilfield in Ahdeb, Iraq. The results suggest that the resolution and spatial stability of the Q-profile are optimal and contain abundant interlaminar information that is extremely helpful in making lithology and fluid predictions.
基金supported by the National Key Research and Development Program of China(No.2016YFA0401902)
文摘We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was more reliable than traditional methods. The data required for the method were near the resonant frequency,not at the half-power points of the reflection coefficient curve or Smith chart. We applied the new method to measure a resonant cavity with an unloaded Q factor of^100,000, obtaining good agreement between the measured and theoretical results.
基金supported by the National Natural Science Foundation of China(No.61671108)the Fundamental Research Funds for the Central Universities(No.ZYGX2014J005)the Program for Changjiang Scholars and Innovative Research Team in University
文摘All-optical regenerators can be used to suppress amplified spontaneous emission(ASE) noise introduced by cascaded erbium doped fiber amplifiers(EDFAs) in optical fiber communication systems and lead to the improvement of optical receiver sensitivity. By introducing the Q-factor transfer function(QTF), we evaluate the Q-factor performance of degenerate four-wave mixing(DFWM) regenerators with clock pump and reveal the differences between the optimal input powers determined from the static and dynamic power tranfer function(PTF) and the QTF curves. Our simulation shows that the clock-pump regnerator is capable of improving the Q-facor and receiver sensitivity for 40 Gbit/s ASE-degraded return-to-zero on-off keying(RZ-OOK) signal by 2.58 dB and 4.2 d B, respectively.
文摘The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.
文摘Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
文摘We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.
