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Features of the Neural Network for Determining the Position and Geometric Characteristics of Cavernous Inclusions

机译:确定海绵状夹杂物位置和几何特征的神经网络特征

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This article describes the process of developing a neural network (NN) capable of restoring the structure of the geological and physical model of the medium (GPMM) based on a known picture of the propagation of a wave field. The architecture of the NN itself and its components are indicated, information on the process of its training is provided. Also, the time of operation of the NN on different devices and the results obtained are shown. The work was supported by the Novosibirsk State Technical University (Project C-19, 2018).
机译:本文介绍了开发神经网络(NN)的过程,该过程可以基于已知的波场传播图片来恢复介质的地质和物理模型(GPMM)的结构。指示了NN本身的体系结构及其组件,并提供了有关其训练过程的信息。此外,还显示了NN在不同设备上的运行时间以及获得的结果。这项工作得到了新西伯利亚国立技术大学的支持(C-19项目,2018年)。

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