[CCoE Notice] MS Thesis Presentation

[Abercrombie, Irene F]

MS Thesis Defense
Automatic First Break Detection by Spectral Decomposition using Minimum Uncertainty Wavelets
Sunil Kapur
Date: Monay, November 19th, 2012

Location: ME Small Conference Room
Time: 12:30 pm

Committee Members:
Dr. Donald Kouri
Dr. Haleh Ardebili
Dr. Jagannatha Rao


Seismic Signal Processing can be effectively utilized to determine the micro-seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating the micro-seismic events. The measured data collected gathers far more information that can be extracted by human operators and their interpretations consume a lot of time. The transformation in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detection that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian mu wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 mu seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically.



-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20121113/cfe572df/attachment.html