Split shear-wave analysis using an artificial neural network

Dai, Heng; MacBeth, Colin. 1994 Split shear-wave analysis using an artificial neural network. First Break, 12 (12). 605-613.

Full text not available from this repository. (Request a copy)

Official URL: http://fb.eage.org/content.php?id=28365

Abstract/Summary

Artificial 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.

Item Type:	Publication - Article
Programmes:	BGS Programmes > Seismology and Geomagnetism
NORA Subject Terms:	Earth Sciences
Date made live:	16 Oct 2012 14:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/20004

Item Type:

Publication - Article

Programmes:

BGS Programmes > Seismology and Geomagnetism

NORA Subject Terms:

Earth Sciences

Date made live:

16 Oct 2012 14:59 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/20004