Biography
Brian Russell holds a B.Sc. from the University of Saskatchewan (1975), a M.Sc. from Durham University (1978), U.K., and a Ph.D. from the University of Calgary (2004), all in geophysics. He joined Chevron in Calgary as an exploration geophysicist in 1975 and subsequently worked for Teknica and Veritas before co-founding Hampson-Russell Software with Dan Hampson in 1987. Hampson-Russell is now a subsidiary of CGG, where Brian is Vice President, GeoSoftware and a CGG Fellow.
Brian is involved in the research of new AVO, rock physics, inversion and seismic attribute techniques as well as giving presentations on seismic theory and software throughout the world. He is a Past-President of both the SEG and CSEG and has received Honorary Membership from both societies, as well as the Cecil Green Enterprise Award from SEG (jointly with Dan Hampson) and the 1999 CSEG Medal.
Brian is an Adjunct Professor in the Department of Geoscience at the University of Calgary and registered as a Professional Geophysicist (P. ]Geoph.) in Alberta. He is also currently on the Board of the M.Sc. in Integrated Petroleum Geosciences (IPG) at the University of Alberta.
Description
Although the theory of neural networks, or artificial intelligence (AI) as it is sometimes called, dates back to the backpropagation algorithm of the 1980’s, it is only recently that hardware requirements have finally caught up to the algorithms, turning the promise of AI into reality. Thus, there has been a reawakening of interest in neural networks and AI, and it has inspired yet another popular term: machine learning. But at the heart of all of these methods are several straightforward ideas that were developed by the electrical engineering and statistical scientific communities. The purpose of the course is to look at the history and basic algorithms involved in neural networks and machine learning and to apply these algorithms to petroleum exploration. Each section will include both numerical exercises and real data examples taken from the geosciences. The objective of the course is to get behind all the current “hype” around the subject and understand the fundamentals of neural networks and machine learning. The level of the course is aimed at students with an undergraduate degree in one of the geoscience disciplines. Although the course will include mathematics, the mathematics will be of a basic nature and will be fully explained. Some familiarity with linear algebra would be helpful.
Target audience
Geoscientists with a reasonable background in mathematics
