Thomas A. Smith

For other people named Thomas A. Smith, see Thomas A. Smith (disambiguation).

Thomas A. Smith received a B.S. and M.S. degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984[1] to develop PC software to support training workshops which subsequently and there led to the development of the KINGDOM software suite for seismic interpretation.

The SEG recognized awarded Dr. Smith’s work with the SEG Enterprise Award in 2000,[2] and in 2010, GSH awarded him its Honorary Membership. Iowa State University awarded him its highest award, Distinguished Alumni Award in 2015, after department recognition Distinguished Alumnus Lecturer Award in 1996 and college recognition Citation of Merit for National and International Recognition in 2002.

In 2008, Dr. Smith founded Geophysical Insights where he leads a team of geoscientists and computer software engineers in developing adaptive learning technologies for fundamental geophysical problems. In 2013, Geophysical Insights launched Paradise,[3] a software product embodying machine learning processes for multi-attribute seismic analysis.

Dr. Smith has been a member of the SEG since 1967 and is also a member of the SEG, GSH, HGS, EAGE, SIPES, AAPG, GSH, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013. On January 25, 2016, he was recognized by the Houston Geological Society (HGS) at Legends Night[4] for his contributions to advancing the body of geoscience knowledge and his outstanding career achievements.[5][6][7][8][9]

Publications

"Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps." Interpretation, Vol 3, No. 4 (November 2015); p. SAE59-SAE83. doi: 10.1190/INT-2015-0037.1.

"Delighting in Geophysics." GeoExpro, Vol. 11, No. 4 (September 2014); p.104.

Dutta, A. "From Insights to Foresights." New Technology Magazine (October 2011); p. 15-16.

Smith, T., Sarcey, D. "Seismic attribute analysis can benefit from unsupervised neural network." Offshore (September 2011); p. 24-25.

Kliewer, G. "Neural network notices anomalies in seismic data." Offshore (September 2011); p. 26.

Smith, T., "Unsupervised neural networks—disruptive technology for seismic interpretation." Oil and Gas Journal (October 4, 2010).

Smith, T., Neidell, N. "Improved seismic resolution of stratigraphically complex reservoirs through modeling and color displays." SEG Technical Program Expanded Abstracts (1990); p. 357. doi: 10.1190/1.1890196.

Smith, T., Sendlein, L. "An Algorithm for the Best Fit Solution of a System of Linear Regression Equations for Seismic Refraction Data." GEOPHYSICS, Vol. 38, No. 6 (December 1973); p. 1062-1069. doi: 10.1190/1.1440396.

References

  1. ↑ Seismic Micro-Technology “Board of Directors” 2009. Retrieved on 22 February 2011.
  2. ↑ Proubasta, D., GeoScience World. The Leading Edge; May 2000; v. 19; no. 5; p. 545-558
  3. ↑ "Advances Opening New Possibilities". www.aogr.com. Retrieved 2016-01-25.
  4. ↑ HGS Legends Night 2016
  5. ↑ Oil & Gas Financial Journal SMT founder launches Geophysical Insights to provide seismic interpretation technology Houston. July 20, 2010. Retrieved on 22-03-2011.
  6. ↑ Green, Hal. Geophysical Insights, LLC, launches to provide advanced technology for the interpretation of seismic data 21 July 2010. Retrieved on 22 February 2011
  7. ↑ Hart Energy. Geophysical Insights Launches Advanced Seismic Interpretation Clinics Based on Unsupervised Neural Networks Technology 5 July 2011. Retrieved on 22 February 2011
  8. ↑ Houston Geological Society.
  9. ↑ Digital Energy Journal. Using Neural Networks for Seismic Interpretation p.6-8. Feb/March 2011. Retrieved on 22 February 2011
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