Automatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets

Seismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ 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 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Houston

Year dc:date.issued

2012

Author dc:creator

• Kapur, Sunil 1988-

Advisor dc:contributor.advisor

• Kouri, Donald J.

Committee members dc:contributor.committeemember

• Rao, Jagannatha R.
• Ardebili, Haleh

• Super Gaussian Mu-Wavelets
• First Break Detection
• Spectral decomposition
• Heisenberg Uncertainty Principle
• Singular Value Decomposition Method
• Cn transform

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10657/2184

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/2184