Source location error analysis and optimization methods
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TL;DR: A comparison study of two optimization methods shows that the distribution with this character constrains the input errors and minimizes their impact, which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors.
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Abstract: The efficiency of an optimization method for acoustic emission/microseismic (AE/MS) source location is determined by the compatibility of its error definition with the errors contained in the input data. This compatibility can be examined in terms of the distribution of station residuals. For an ideal distribution, the input error is held at the station where it takes place as the station residual and the error is not permitted to spread to other stations. A comparison study of two optimization methods, namely the least squares method and the absolute value method, shows that the distribution with this character constrains the input errors and minimizes their impact, which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors. When the errors in the input data are systematic and/or extreme in that the basic data structure is altered by these errors, none of the optimization methods are able to function. The only means to resolve this problem is the early detection and correction of these errors through a data screening process. An efficient data screening process is of primary importance for AE/MS source location. In addition to its critical role in dealing with those systematic and extreme errors, data screening creates a favorable environment for applying optimization methods.
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Citations
Sectional velocity model for microseismic source location in tunnels
TL;DR: In this paper, a suitable sectional velocity model for MS source location in tunnels is proposed, where the velocities from the MS source to the sensors in any one group are almost the same but those to different groups of sensors may be different.
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Theoretical and Experimental Studies of Localization Methodology for AE and Microseismic Sources Without Pre-Measured Wave Velocity in Mines
TL;DR: Results of the pencil lead break tests, the thermal fracture experiment in granite, and the blasting experiments have demonstrated that the proposed method can not only decrease the location errors induced by measurement deviation of velocity, but also locate the MS/AE source in real time, which is a beneficial complement to the method TM in mines.
76
Evaluation of the acoustic emission 3D localisation accuracy for the mechanical damage monitoring in concrete
Antoine Boniface,Jacqueline Saliba,Zoubir Mehdi Sbartaï,Narintsoa Ranaivomanana,Jean-Paul Balayssac +4 more
TL;DR: The accuracy of AE sources localisation in concrete is studied and several methods related to the choice of the onset detection of AE signals and to the location algorithm are evaluated and compared.
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Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network
Longjun Dong,Li Xibing +1 more
TL;DR: In this paper, an analytical solution of the acoustic emission/microseismic (AE/MS) source location coordinates was optimized and simplified, and a location method with P-wave velocity by analytical solutions was obtained with these equations.
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Locating single-point sources from arrival times containing large picking errors (LPEs): the virtual field optimization method (VFOM).
TL;DR: The virtual field optimization method (VFOM) for locating single-point sources using passive techniques such as passive sonar detection and acoustic emission can obtain more precise and stable solutions than traditional methods when the input data contain LPEs.
References
Probability and statistics in engineering and management science
TL;DR: In this article, the authors introduce Probability One-Dimension Random Variables Functions of One Random Variable and Expectation Joint Probability Distributions Some Important Discrete Distributions some Important Continuous Distributions The Normal Distribution Random Samples and Sampling Distributions Parameter Estimation Tests of Hypotheses Design and Analysis of Single Factor Experiments: The Analysis of Variance Design of Experiments with Several Factors Simple Linear Regression and Correlation Multiple Regression Nonparametric Statistics Statistical Quality Control and Reliability Engineering Stochastic Processes and Queueing Statistical Decision Theory References
550
•Book
Probability and Statistics in Engineering and Management Science
A. Narayanan
- 23 Sep 1981
TL;DR: In this article, the authors introduce Probability One-Dimension Random Variables Functions of One Random Variable and Expectation Joint Probability Distributions Some Important Discrete Distributions some Important Continuous Distributions The Normal Distribution Random Samples and Sampling Distributions Parameter Estimation Tests of Hypotheses Design and Analysis of Single Factor Experiments The Analysis of Variance Design of Experiments with Several Factors Simple Linear Regression and Correlation Multiple Regression Nonparametric Statistics Statistical Quality Control and Reliability Engineering Stochastic Processes and Queuing Statistical Decision Theory
443
Efficient mine microseismic monitoring
TL;DR: In this paper, the important issues for efficient mine microseismic monitoring programs are discussed from three aspects: monitoring planning, data processing, and micro seismic event location.
242
Microearthquake location: A nonlinear approach that makes use of a simplex stepping procedure
TL;DR: In this paper, an earthquake location scheme that uses the simplex algorithm in the solution is presented, which can be used for any velocity structure for which source to geophone times can be calculated.
136
Robust earthquake location using M-estimates
TL;DR: In this paper, the authors compared the robustness of least squares and bisquare estimators when applied to the earthquake location problem and showed that error at one station can inflate the residuals at other stations so that a true outlier may be hidden.
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