Sensor placement for microseismic event location
| dc.contributor.advisor | Daku, Brian L. | en_US |
| dc.contributor.committeeMember | Saadat Mehr, Aryan | en_US |
| dc.contributor.committeeMember | Merriam, James B. | en_US |
| dc.contributor.committeeMember | Klymyshyn, David M. | en_US |
| dc.contributor.committeeMember | Chen, Li | en_US |
| dc.creator | Errington, Angus Frank Charles | en_US |
| dc.date.accessioned | 2006-11-06T12:12:15Z | en_US |
| dc.date.accessioned | 2013-01-04T05:07:54Z | |
| dc.date.available | 2006-11-07T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T05:07:54Z | |
| dc.date.created | 2006-10 | en_US |
| dc.date.issued | 2006-10-26 | en_US |
| dc.date.submitted | October 2006 | en_US |
| dc.description.abstract | Mining operations can produce highly localized, low intensity earthquakes that are referred to as microseismic events. Monitoring of microseismic events is useful in predicting and comprehending hazards, and in evaluating the overall performance of a mine design. A robust localization algorithm is used to estimate the source position of the microseismic event by selecting the hypothesized source location that maximizes an energy function generated from the sum of the time--aligned sensor signals. The accuracy of localization for the algorithm characterized by the variance depends in part upon the configuration of sensors. Two algorithms, MAXSRC and MINMAX, are presented that use the variance of localization error, in a particular direction, as a performance measure for a given sensor configuration.The variance of localization error depends, in part, upon the energy spectral density of the microseismic event. The energy spectral density characterization of sensor signals received in two potash mines are presented and compared using two spectral estimation techniques: multitaper estimation and combined time and lag weighting. It is shown that the difference between the the two estimation techniques is negligible. However, the differences between the two mine characterizations, though not large, is significant. An example uses the characterized energy spectral densities to determine the variance of error for a single step localization algorithm.The MAXSRC and MINMAX algorithms are explained. The MAXSRC sensor placement algorithm places a sensor as close as possible to the source position with the maximum variance. The MINMAX sensor placement algorithm minimizes the variance of the source position with the maximum variance after the sensor has been placed. The MAXSRC algorithm is simple and can be solved using an exhaustive search while the MINMAX algorithm uses a genetic algorithm to find a solution. These algorithms are then used in three examples, two of which are simple and synthetic. The other example is from Lanigan Potash Mine. The results show that both sensor placement algorithms produce similar results, with the MINMAX algorithm consistently doing better. The MAXSRC algorithm places a single sensor approximately 100 times faster than the MINMAX algorithm. The example shows that the MAXSRC algorithm has the potential to be an efficient and intuitively simple sensor placement algorithm for mine microseismic event monitoring. The MINMAX algorithm provides, at an increase in computational time, a more robust placement criterion which can be solved adequately using a genetic algorithm. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-11062006-121215 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | energy spectral density | en_US |
| dc.subject | location estimation | en_US |
| dc.subject | microseismic events | en_US |
| dc.title | Sensor placement for microseismic event location | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Electrical Engineering | en_US |
| thesis.degree.discipline | Electrical Engineering | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |