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- Volume 12, Issue 12, 1994
First Break - Volume 12, Issue 12, 1994
Volume 12, Issue 12, 1994
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Seismic velocities - a critique
More LessTechniques for estimating the velocity in the ground are almost as old as the seismic method itself - be it reflection or refraction. The geophysicist working on singlefold data made considerable use of such techniques as the T²_X² and T-AT methods, for velocity estimation, throughout the pre-digital processing era (see, for example Steele, 1941). Sometimes, a special shotpointgeophone arrangement using a field outlay that resembled a present-day CMP configuration was shot, with the objective of obtaining velocity estimates to successive reflectors (e.g. Gardner 1947). The process of velocity estimation was carried out manually, usually by means of graphical plots. These techniques culminated in the work of Dix (1955) which, being designed in common depth point geometry fashion, became directly applicable to multi-fold data when these became commonplace shortly afterwards.
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Plotting log data from deviated and horizontal wells: a spreadsheet-based method
Authors N.T. Grant and H. SloanThe drilling of horizontal and highly deviated wells has become a regular technique in the development of both oil and gas fields, enabling greater well bore access to reservoirs and thereby yielding greater well productivity. It seems likely that highly deviated and horizontal wells will also become common place for field appraisal, as they can yield valuable information on both the lateral properties and dimensions of reservoirs (Robertson et al. 1992). This information is not necessarily obtained from correlations of vertical wells, especially in heterogeneous formations.
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Split shear-wave analysis using an artificial neural network
Authors H. Dai and C. MacBethArtificial neural networks (ANNs) are simple models that attempt to simulate the operation of neurons in the brain. Although ANNs are relatively new in seismology, their origins can be traced back to the 1940s when psychologists began developing models of human learning. One of the most exciting developments in ANNs was the advent of the Perceptron, the idea that a network of elemental processors arrayed in a marmer reminiscent of biological neural networks might be able to learn how to recognize and classify patterns in an autonomous manner. However, in 1969, Marvin Minsky, one of the founding fathers of artificial intelligence, proved mathematically that perceptrons were incapable of solving many simpIe problems.
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Volumes & issues
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Volume 42 (2024)
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Volume 41 (2023)
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Volume 40 (2022)
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Volume 39 (2021)
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Volume 38 (2020)
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Volume 37 (2019)
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Volume 36 (2018)
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Volume 35 (2017)
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Volume 34 (2016)
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Volume 33 (2015)
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Volume 32 (2014)
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Volume 31 (2013)
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Volume 30 (2012)
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Volume 29 (2011)
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Volume 28 (2010)
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Volume 27 (2009)
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Volume 26 (2008)
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Volume 25 (2007)
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Volume 24 (2006)
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Volume 23 (2005)
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Volume 22 (2004)
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Volume 21 (2003)
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Volume 20 (2002)
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Volume 19 (2001)
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Volume 18 (2000)
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Volume 17 (1999)
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Volume 16 (1998)
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Volume 15 (1997)
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Volume 14 (1996)
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Volume 13 (1995)
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Volume 12 (1994)
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Volume 11 (1993)
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Volume 10 (1992)
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Volume 9 (1991)
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Volume 8 (1990)
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Volume 7 (1989)
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Volume 6 (1988)
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Volume 5 (1987)
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Volume 4 (1986)
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Volume 3 (1985)
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Volume 2 (1984)
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Volume 1 (1983)