A pinboard by
Charles Mborah

PhD Candidate, Missouri University of Science & Technology


The acoustic emission/microseismic technique (AE/MS) has emerged as one of the most important techniques in recent decades, and has found wide applications in the mining and petroleum industries as well as aerospace engineering. In the mining industry for instance, the technique has been used in applications involving slope stability analysis (surface mining). In underground mining, it has been used for ground control, rockburst, and coal bump monitoring. Uncertainties associated with rock mass stability are generally high in regions affected by mining operations. Hence, the AE/MS technique is usually employed to provide a means of establishing zones of instability. In general, when solid bodies/materials are under stress, they emit low-level seismic signals. The AE/MS technique uses the signals generated as a result of a material being under stress to study fracture/failure processes. The most difficult part of the AE/MS technique is signal processing. Among various signal processing issues, the most important one is seismic event extraction with precise timing. In addition to the fact that this is the first step and also the foundation for signal processing, there is no efficient method for processing AE/MS data.On the other hand, the basic processing of AE/MS data is normally accomplished using proprietary commercial software packages. In most cases, however, such commercial software packages might not be readily available to academic institutions for research purposes. Therefore, the AE/MS research group in the Department of Mining and Nuclear Engineering at Missouri University of Science and Technology (Missouri S&T) aims to develop a processing software to fill this gap and to aid in research and teaching in the field of AE/MS data processing. The main objective of this research is therefore to develop a highly efficient method to resolve the problems of background noise and outburst activities characteristic of AE/MS data. The method is a hybrid technique that encompasses four recent and sophisticated techniques, including the characteristic function, high order statistics, wavelet analysis, and a phase association theory. The specific objectives shall include the following: i. the design and development of an algorithm for filtering AE/MS data using the stationary discrete wavelet transform; ii. the formulation of a characteristic function derived from high order statistics; iii. the design of a phase association theory to improve accuracy


A nonlinear microseismic source location method based on Simplex method and its residual analysis

Abstract: Source location is one of the most valuable features of the microseismic technique due to its ability to delineate the unstable areas. In this paper, the precise formulas of the station residual and event residual are derived for the L1 norm statistical standard and the L2 norm statistical standard based on the residual analysis. Then, the error space for microseismic source location is proposed and analyzed. Based on the above research, a nonlinear microseismic source location method using the Simplex method is developed. This new method can search the microseismic source directly in the error space through four deformations of the simplex figures, and it is able to make use of both P-wave and S-wave velocities. Finally, the performance of the Simplex microseismic source location method is tested and verified by laboratory experiments. Test results show that the Simplex microseismic source location method can improve the accuracy and stability of the source location greatly when P-wave and S-wave velocities are involved simultaneously and correctly. The results also demonstrate that the L1 norm statistical standard always provides more accurate and reliable solutions than the L2 norm statistical standard when there are some major but isolated errors in the input data. However, none of the optimization methods are able to function when the errors in the input data are systematic and extreme, which indicates that an early detection and correction of these errors is of primary importance for microseismic source location.

Pub.: 21 Sep '13, Pinned: 27 Jun '17

Two types of multiple solutions for microseismic source location based on arrival-time-difference approach

Abstract: The arrival-time-difference approach is the dominant source location approach used in the microseismic source location area. Multiple solutions problem is one of the major concerns in microseismic source location, which is closely related to the microseismic network. This paper categorizes the multiple solutions into two types based on the origin times when using the arrival-time-difference approach. Type I multiple solutions are those which have the same origin time; type II multiple solutions are those with different origin times. The sufficient and necessary conditions to produce type I multiple solutions are that all sensors are located in a straight line for two-dimensional cases and on a plane for three-dimensional cases. The sufficient and necessary conditions to produce type II multiple solutions are that all sensors are located on a hyperbola for two-dimensional cases and on a hyperboloid for three-dimensional cases. Furthermore, the proofs indicate that type I multiple solutions are preventable, while a microseismic network consisting of the minimum number of sensors can never be free of type II multiple solutions. It means, besides the minimum number of sensors, at least one more sensor which is not on this hyperbola or hyperboloid is needed to uniquely determine a source. The results from field tests and applications indicate that when the sensors of a network lie on a hyperbola, the type II multiple solutions may not be the necessary outcome under the influence of errors in real data. However, the accuracy of the microseismic source location is affected significantly by this kind of networks. The results also show that not only the multiple solutions problem can be avoided effectively, but more importantly, the accuracy of the source location will be greatly improved by the optimization of network based on the characteristics of the microseismic network and field conditions.

Pub.: 06 Mar '14, Pinned: 27 Jun '17