The key to understanding Clastic reservoirs in Paradise starts with good synthetic ties to the wavelet data. How to Use Paradise to Interpret Clastic Reservoirs AAPG honored him with a Distinguished Service award for promoting geophysics among the geological community. He wrote the AAPG Explorer column on geophysics for 6 years. SEG has awarded him a Special Commendation, Life Membership, and Honorary Membership. His Board service was as SEG Editor (2 years), followed by 1-year terms as First VP, President Elect, President, and Past President. He was the first person to serve 6 years on the Board of Directors of the Society of Exploration Geophysicists (SEG). He has published books on VSP, cross-well profiling, seismic stratigraphy, and multicomponent seismic technology. He then established a multicomponent seismic research laboratory at the Bureau of Economic Geology and served The University of Texas at Austin as a Senior Research Scientist for 28 years. He moved to WesternAtlas and worked 3 years as Vice President of Geophysical Development and Marketing. Upon completing his university studies, he worked at Phillips Petroleum Company for 23 years and was Exploration Manager for Asia and Latin America when he left Phillips. His thesis work focused on high-velocity micro-meteoroid impact on space vehicles, which required trips to Goddard Space Flight Center to do finite-difference modeling on dedicated computers. Hardage received a PhD in physics from Oklahoma State University. This research strategy of analyzing VSP data with unsupervised machine learning will now expand to horizontal-geophone VSP data.īob A. This initial study indicates that unsupervised machine learning, when applied to VSP data, is a powerful tool for understanding the physics of seismic reflectivity at a prospect. The internal attribute fabric of these key down-going and up-going neurons are then compared to expose their similarities and differences. These SOM results show that a small number of specific neurons build the down-going direct-P illuminating wavefield, and another small group of neurons build up-going P primary reflections and early-arriving down-going P multiples. A self-organizing map (SOM) approach is then taken to illustrate how unsupervised machine learning describes the fabric of down-going and up-going events embedded in vertical-geophone VSP data. The investigation then applies principal component (PCA) analysis to VSP data and shows that direct-S and direct-P wavefields that are created simultaneously at a vertical-vibrator baseplate have the same dominant principal components. This backtracking confirms that the points-of-origin of direct-P and direct-S are identical. This study shows how to use instantaneous attributes to backtrack down-going direct-P and direct-S illuminating wavelets to the vibrator baseplate inside the near-field zone. The zero-offset VSP data used in this investigation were acquired in a hard-rock, fast-velocity, environment that caused the shallowest 2 or 3 geophones to be inside the near-field radiation zone of a vertical-vibrator baseplate. This concept is introduced in this paper by applying unsupervised learning to VSP data to better understand the physics of P and S reflection seismology. Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine LearningĮxamination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric of down-going, illuminating, P and S wavefields with the fabric of up-going reflections and interbed multiples created by these wavefields.
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