scholarly journals ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

2016 ◽  
Vol XLI-B8 ◽  
pp. 449-452
Author(s):  
S. S. Zhao ◽  
N. Wang ◽  
J. Hui ◽  
X. Ye ◽  
Q. Qin
Keyword(s):  
Coalbed Methane ◽  
Random Noise ◽  
Estimation Method ◽  
Low Frequency ◽  
Least Square ◽  
Electromagnetic Signal ◽  
Gaussian Component ◽  
Observation Data ◽  
Electromagnetic Data ◽  
Class B

Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

scholarly journals ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

2016 ◽  
Vol XLI-B8 ◽  
pp. 449-452
Author(s):  
S. S. Zhao ◽  
N. Wang ◽  
J. Hui ◽  
X. Ye ◽  
Q. Qin
Keyword(s):  
Coalbed Methane ◽  
Random Noise ◽  
Estimation Method ◽  
Low Frequency ◽  
Least Square ◽  
Electromagnetic Signal ◽  
Gaussian Component ◽  
Observation Data ◽  
Electromagnetic Data ◽  
Class B

Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

scholarly journals Calibration of MEMS Triaxial Accelerometers Based on the Maximum Likelihood Estimation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yifan Sun ◽  
Xiang Xu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Random Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Least Square ◽  
Triaxial Accelerometer ◽  
Zero Bias ◽  
Estimation Function ◽  
Maximum Likelihood Estimation Method

As a widely used inertial device, a MEMS triaxial accelerometer has zero-bias error, nonorthogonal error, and scale-factor error due to technical defects. Raw readings without calibration might seriously affect the accuracy of inertial navigation system. Therefore, it is necessary to conduct calibration processing before using a MEMS triaxial accelerometer. This paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. The error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. The calibration parameters are obtained by the Newton iteration method, which is more efficient and accurate. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. Moreover, the proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. The proposed method has the potential to be applied to other triaxial inertial sensors.

scholarly journals Overcoming the Existence of Extreme Outlier Data by Using Robust MM Method Based on The Objective Function of Tukey bisquare

2018 ◽  
Vol 1 (1) ◽  
pp. 022-032
Author(s):  
Science Nature
Keyword(s):  
High Efficiency ◽  
Robust Regression ◽  
Weighting Function ◽  
Estimation Method ◽  
Breakdown Point ◽  
Error Component ◽  
Least Square ◽  
Model Parameters ◽  
Observation Data ◽  
Extreme Outlier

A widely used estimation method in estimating regression model parameters is the ordinary least square (OLS) that minimizes the sum of the error squares. In addition to the ease of computing, OLS is a good unbiased estimator as long as the error component assumption ()  in the given model is met. However, in the application, it is often encountered violations of assumptions. One of the violation types is the violation of distributed error assumption which is caused by the existence of the outlier on observation data. Thus, a solid method is required to overcome the existence of outlier, that is Robust Regression. One of the Robust Regression methods commonly used is robust MM method. Robust MM method is a combination of breakdown point and high efficiency. Results obtained based on simulated data generated using SAS software 9.2, shows that the use of objective weighting function tukey bisquare is able to overcome the existence of extreme outlier. Furthermore, it is determined that the value of tuning constant c with Robust MM method is 4.685 and it is obtained95% of efficiency. Thus, the obtained breakdown point is 50%.    

scholarly journals Overcoming the Existence of Extreme Outlier Data by Using Robust MM Method Based on The Objective Function of Tukey bisquare

2018 ◽  
Vol 1 (1) ◽  
pp. 022-032
Author(s):  
Science Nature
Keyword(s):  
High Efficiency ◽  
Robust Regression ◽  
Weighting Function ◽  
Estimation Method ◽  
Breakdown Point ◽  
Error Component ◽  
Least Square ◽  
Model Parameters ◽  
Observation Data ◽  
Extreme Outlier

A widely used estimation method in estimating regression model parameters is the ordinary least square (OLS) that minimizes the sum of the error squares. In addition to the ease of computing, OLS is a good unbiased estimator as long as the error component assumption ()  in the given model is met. However, in the application, it is often encountered violations of assumptions. One of the violation types is the violation of distributed error assumption which is caused by the existence of the outlier on observation data. Thus, a solid method is required to overcome the existence of outlier, that is Robust Regression. One of the Robust Regression methods commonly used is robust MM method. Robust MM method is a combination of breakdown point and high efficiency. Results obtained based on simulated data generated using SAS software 9.2, shows that the use of objective weighting function tukey bisquare is able to overcome the existence of extreme outlier. Furthermore, it is determined that the value of tuning constant c with Robust MM method is 4.685 and it is obtained95% of efficiency. Thus, the obtained breakdown point is 50%.    

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

Application of Fitting Estimation Method in GPS Height Fitting

2013 ◽  
Vol 694-697 ◽  
pp. 2545-2549 ◽  
Author(s):  
Qian Wen Cheng ◽  
Lu Ben Zhang ◽  
Hong Hua Chen
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Least Square ◽  
Least Squares Collocation ◽  
Normal Height ◽  
Geodetic Height ◽  
The Given ◽  
Surveying And Mapping

The key point researched by many scholars in the field of surveying and mapping is how to use the given geodetic height H measured by GPS to obtain the normal height. Although many commonly-used fitting methods have solved many problems, they all value the pending parameters as the nonrandom variables. Figuring out the best valuations, according to the traditional least square principle, only considers its trend or randomness, which is theoretically incomprehensive and have limitations in practice. Therefore, a method is needed not only considers its trend but also takes randomness into account. This method is called the least squares collocation.

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Shadow banking from China's perspective: an empirical analysis of bank-issued wealth management products

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Mehmood Raza Shah ◽  
Qiang Fu ◽  
Ghulam Abbas ◽  
Muhammad Usman Arshad
Keyword(s):  
Commercial Banks ◽  
Fixed Effects ◽  
Banking Sector ◽  
Balance Sheet ◽  
Estimation Method ◽  
Least Square ◽  
Shadow Banking ◽  
Content Type ◽  
Wealth Management ◽  
Chinese Banks

PurposeWealth Management Products (WMPs) are the largest and most crucial component of China's Shadow banking, which are off the balance sheet and considered as a substitute for deposits. Commercial banks in China are involved in the issuance of WMPs mainly to; evade the regulatory restrictions, move non-performing loans away from the balance sheet, chase the profits and take advantage of yield spread (the difference between WMPs yield and deposit rate).Design/methodology/approachIn this study, the authors investigate what bank related characteristics and needs; influenced and prompted the issuance of WMPs. By using a quarterly panel data from 2010 to 2019, this study performed the fixed effects approach favored by the Hausman specification test, and a feasible generalized least square (FGLS) estimation method is employed to deal with any issues of heteroscedasticity and auto-correlation.FindingsThis study found that there is a positive and significant association between the non-performing loan ratio and the issuance of WMPs. Moreover, profitability and spread were found to play an essential role in the issuance of WMPs. The findings of this study suggest that WMPs are issued for multi-purpose, and off the balance sheet status of these products makes them very lucrative for regulated Chinese commercial banks.Research limitations/implicationsNon-guaranteed WMPs are considered as an item of shadow banking in China, as banks do not consolidate this type of WMPs into their balance sheet; due to that reason, there is no individual bank data available for the amount of WMPs. The authors use the number of WMPs issued by banks as a proxy for the bank's exposure to the WMPs business.Practical implicationsFrom a regulatory perspective, this study helps regulators to understand the risk associated with the issuance of WMPs; by providing empirical evidence that Chinese banks issue WMPs to hide the actual risk of non-performing loans, and this practice could mislead the regulators to evaluate the bank credit risk and loan quality. This study also identifies that Chinese banks issue WMPs for multi-purpose; this can help potential investors to understand the dynamics of WMPs issuance.Originality/valueThis research is innovative in its orientation because it is designed to investigate the less explored wealth management products (WMPs) issued by Chinese banks. This study's content includes not only innovation but also contributes to the existing literature on the shadow banking sector in terms of regulatory arbitrage. Moreover, the inclusion of FGLS estimation models, ten years of quarterly data, and the top 30 Chinese banks (covers 70% of the total Chinese commercial banking system's assets) make this research more comprehensive and significant.

Non-stationary local slope estimation via forward-backward space derivative calculation

Geophysics ◽  
2021 ◽  
pp. 1-91
Author(s):  
Hang Wang ◽  
Liuqing Yang ◽  
Xingye Liu ◽  
Yangkang Chen ◽  
Wei Chen
Keyword(s):  
Finite Difference ◽  
Random Noise ◽  
Estimation Method ◽  
Order Derivative ◽  
Space Domain ◽  
Local Slope ◽  
First Order ◽  
Slope Estimation ◽  
Difference Calculation ◽  
Seismic Images

The local slope estimated from seismic images has a variety of meaningful applications. Slope estimation based on the plane-wave destruction (PWD) method is one of the widely accepted techniques in the seismic community. However, the PWD method suffers from its sensitivity to noise in the seismic data. We propose an improved slope estimation method based on the PWD theory that is more robust in the presence of strong random noise. The PWD operator derived in the Z-transform domain contains a phase-shift operator in space corresponding to the calculation of the first-order derivative of the wavefield in the space domain. The first-order derivative is discretized based on a forward finite difference in the traditional PWD method, which lacks the constraint from the backward direction. We propose an improved method by discretizing the first-order space derivative based on an averaged forward-backward finite-difference calculation. The forward-backward space derivative calculation makes the space-domain first-order derivative more accurate and better anti-noise since it takes more space grids for the derivative calculation. In addition, we introduce non-stationary smoothing to regularize the slope estimation and to make it even more robust to noise. We demonstrate the performance of the new slope estimation method by several synthetic and field data examples in different applications, including 2D/3D structural filtering, structure-oriented deblending, and horizon tracking.

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