1887

Abstract

The artificial neural networks have proven to be a powerful tool to solving a wide variety of optimization<br>problems. In this work we develop a recurrent network with no self-feedback loops and no hidden neurons for<br>seismic signal processing where this neural network gives us reflectivity location and reflectivity magnitude<br>estimation. The most important advantage of this neural network is the use a type of activation function which<br>permits three possible states of neuron to estimate the position of the seismic reflectors in such way to<br>reproduce its true polarities. The operational evaluation of this neural network architecture is accomplished in<br>synthetic data obtained through the ray theory.

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/content/papers/10.3997/2214-4609-pdb.215.sbgf065
1999-08-15
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.215.sbgf065
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