Dr. Tom Smith

President & CEO

Dr. Tom Smith received a BS and MS 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 to develop PC software to support training workshops which subsequently led to development of the KINGDOM Software Suite for integrated geoscience interpretation with world-wide success.

The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award.  The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award.

In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists, geologists and computer scientists in developing advanced technologies for fundamental geophysical problems.  The company launched the Paradise® multi-attribute analysis software in 2013, which uses Machine Learning and pattern recognition to extract greater information from seismic data.

Dr. Smith has been a member of the SEG since 1967 and is a professional member of SEG, GSH, HGS, EAGE, SIPES, AAPG, 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) as a geophysicist who has made significant contributions to the field of geology.  He currently serves on the SEG President-Elect’s Strategy and Planning Committee and the ISU Foundation Campaign Committee for Forever True, For Iowa State.

Published Work by Dr. Tom Smith:

Geobodies in Paradise: a Machine Learning Application

Geobodies in Paradise: a Machine Learning Application

Dr. Tom Smith presents "Geobodies in Paradise: a Machine Learning Application" at the 2018 SEG Convention in Anaheim, California. Dr ...
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3-d seismic image

Machine Learning Revolutionizing Seismic Interpretation

The science of petroleum geophysics is changing, driven by the nature of the technical and business demands facing geoscientists as ...
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2D Seismic Line Processed by SOM Analysis

Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition ...
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seismic interpretation software - SOM

Delighting in Geophysics

Dr. Thomas A. Smith founded Seismic Micro-Technology (SMT) in 1984 and led the development of the widely adopted Kingdom Suite ...
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Time slice

Seismic Attribute Analysis Can Benefit From Unsupervised Neural Network

Process identifies anomalies from original data without bias using Unsupervised Neural Networks in Greenfield Exploration ...
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Introduction to Self-Organzing Maps in Multi-Attribute Seismic Data

Introduction to Self-Organzing Maps in Multi-Attribute Seismic Data

Unsupervised neural network searches multi-dimensional data for natural clusters. Neurons are attracted to areas of higher information density. The SOM ...
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Unsupervised Neural Networks - Disruptive Technology for Seismic Interpretation

Unsupervised Neural Networks – Disruptive Technology for Seismic Interpretation

Reducing the risk and time in finding oil and gas using machine learning techniques via unsupervised neural networks ...
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