Suppress random seismic noise by edge-preserving polynomial fitting method

2012 ◽  
Author(s):  
Alireza Yaghoubizadeh ◽  
Hamidreza Siahkoohi
Keyword(s):  
Seismic Noise ◽  
Polynomial Fitting ◽  
Edge Preserving ◽  
Fitting Method

Edge-preserving polynomial fitting method to suppress random seismic noise

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. V69-V73 ◽  
Author(s):  
Yan-hong Lu ◽  
Wen-kai Lu
Keyword(s):  
Seismic Noise ◽  
Real Data ◽  
Data Sets ◽  
Polynomial Fitting ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Fitting Method ◽  
Final Estimate ◽  
Good Signal ◽  
Fitting Error

This paper focuses on suppressing random seismic noise while preserving signals and edges. We propose an edge-preserving polynomial fitting (EPPF) method leading to good signal and edge preservation. The EPPF method assumes that a 1D signal can be modeled by a polynomial. A series of shifted windows are used to estimate any sample in a 1D signal. After that, the window with the minimum fitting error is selected and its output is assigned as the final estimate for this sample. For a point in 2D seismic data, several 1D signals are extracted along different directions first and then are processed by the EPPF method. After that, we select the direction with a minimum fitting error and assign its output as the final estimate for this point. Applications with synthetic and real data sets show that the EPPF method suppresses the random seismic noise effectively while preserving the signals and edges. Comparisons of results obtained by the EPPF method, the edge-preserving smoothing (EPS) method, and the polynomial fitting (PF) method show that the EPPF method outperforms EPS and PF methods in these tests.

scholarly journals High-Order Polynomial Fitting Assistance for Fast Double-Peak Finding in Brillouin-Distributed Sensing

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 187
Author(s):  
Marcelo A. Soto ◽  
Alin Jderu ◽  
Dorel Dorobantu ◽  
Marius Enachescu ◽  
Dominik Ziegler
Keyword(s):  
Optical Fibers ◽  
High Order ◽  
Polynomial Fitting ◽  
Gaussian Fitting ◽  
Peak Finding ◽  
Fitting Method ◽  
Fold Reduction ◽  
Order Polynomial ◽  
Fitting In ◽  
Distributed Brillouin Sensor

A high-order polynomial fitting method is proposed to accelerate the computation of double-Gaussian fitting in the retrieval of the Brillouin frequency shifts (BFS) in optical fibers showing two local Brillouin peaks. The method is experimentally validated in a distributed Brillouin sensor under different signal-to noise ratios and realistic spectral scenarios. Results verify that a sixth-order polynomial fitting can provide a reliable initial estimation of the dual local BFS values, which can be subsequently used as initial parameters of a nonlinear double-Gaussian fitting. The method demonstrates a 4.9-fold reduction in the number of iterations required by double-Gaussian fitting and a 3.4-fold improvement in processing time.

Polynomial Moving Fitting Method for Edge Identification

2011 ◽  
Vol 308-310 ◽  
pp. 2560-2564 ◽  
Author(s):  
Xiang Rong Yuan
Keyword(s):  
Edge Detection ◽  
Curve Fitting ◽  
Polynomial Function ◽  
High Order ◽  
Polynomial Fitting ◽  
Fitting Method ◽  
Order Polynomial ◽  
Better Than ◽  
Low Order

A moving fitting method for edge detection is proposed in this work. Polynomial function is used for the curve fitting of the column of pixels near the edge. Proposed method is compared with polynomial fitting method without sub-segment. The comparison shows that even with low order polynomial, the effects of moving fitting are significantly better than that with high order polynomial fitting without sub-segment.

Seismic noise attenuation using nonstationary polynomial fitting

2010 ◽  
Author(s):  
Guochang Liu ◽  
Xiaohong Chen ◽  
Jingye Li ◽  
Jing Du ◽  
Jiawen Song
Keyword(s):  
Seismic Noise ◽  
Noise Attenuation ◽  
Polynomial Fitting

An Improved Iterative Polynomial Fitting Algorithm for Baseline Correction in X-Ray Spectrum

2021 ◽  
Vol 105 ◽  
pp. 90-98
Author(s):  
Xiao Yu Jiang ◽  
Qing Ya Wang ◽  
Mu Qiang Xu ◽  
Jun Hao
Keyword(s):  
Fluorescence Spectrum ◽  
Soil Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Polynomial Fitting ◽  
X Ray ◽  
Iterative Processes ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Fitting In

An iterative polynomial fitting method is proposed for the estimate of the baseline of the X-ray fluorescence spectrum signal. The new method generates automatic thresholds by comparing the X-ray fluorescence spectrum signal with the calculated signal from polynomial fitting in the iterative processes. The signal peaks are cut out consecutively in the iterative processes so the polynomial fitting will finally give a good estimation of the baseline. Simulated data and real data from the soil analysis spectrum are used to demonstrate the feasibility of the proposed method.

scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

Robust polynomial fitting method for regional gravity estimation

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 80-89 ◽  
Author(s):  
J. F. Beltrão ◽  
J. B. C. Silva ◽  
J. C. Costa
Keyword(s):  
Gravity Data ◽  
Crustal Thickening ◽  
Polynomial Fitting ◽  
Shallow Subsurface ◽  
Residual Field ◽  
Fitting Method ◽  
Definition Of ◽  
Regional Gravity ◽  
Lower Crustal ◽  
Polynomial Surface

Standard polynomial fitting methods are inconsistent in their formulation. The regional field is approximated by a polynomial fitted to the observed field. As a result, in addition to the nonuniqueness in the definition of the regional field, the fitted polynomial is strongly influenced by the residual field (observed field minus regional field). We present a regional‐residual separation method for gravity data which uses a robust procedure to determine the coefficients of a polynomial fitted to the observations. Under the hypothesis that the regional can be modeled correctly by the polynomial surface, the proposed method minimizes the influence of the residual field in the fitted surface. The proposed method was applied to real gravity data from Ceará state, Brazil, and produced information on zones of possible crustal thickening and the occurrence of lower‐crustal granulitic rocks thrust into the shallow subsurface.

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