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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Image-based velocity estimation of rock using Convolutional Neural Networks
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Image-based velocity estimation of rock using Convolutional Neural Networks

机译：卷积神经网络基于图像的速度估计

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摘要

Digital images of rock samples have been using extensively in Digital Rock Physics (DRP) to evaluate physical parameters of rock such as permeability, P- and S-wave velocities and formation factor. The parameters are numerically computed by simulation of the corresponding physical processes through segmented image of rock, which provide a direct and accurate evaluation of rock properties. However, recent advances in machine learning and Convolutional Neural Networks (CNN) allow using images as input. Such networks, however, require a considerable number of images as input. In this paper, CNNs are used to estimate the P- and S-wave velocities from images of rock medium. To deal with lack of input data, a hybrid pattern- and pixel-based simulation (HYPPS) is used as an efficient data augmentation method to increase the training data set. For each input image, 10 stochastic realizations are produced. Compare to the case wherein the stochastic models are not used, the new results from the enhanced network indicate a sharp improvement in the estimations such that R-2 is increased to 0.94. Furthermore, the newly developed CNN network, unlike the one with the small data set (R-2 = 0.75), manifests no over/underestimation. The estimated properties, in comparison with the computational results, indicate that CNNs perform outstandingly in predicting the physical parameters of rock without conducting any time-demanding forward modeling if enough input data are provided. (C) 2018 Elsevier Ltd. All rights reserved.
机译：岩石样品的数字图像已广泛使用数字岩石物理（DRP），以评估岩石的物理参数，如渗透率，P-和S波速度和形成系数。通过仿真通过岩石分段图像模拟相应的物理过程来数量计算，这提供了对岩石性质的直接和准确的评估。然而，机器学习和卷积神经网络（CNN）的最新进步允许使用图像作为输入。然而，这种网络需要相当数量的图像作为输入。在本文中，CNNS用于估计来自岩石介质图像的P速度和S波速度。要处理缺少输入数据，混合模式和基于像素的仿真（Hypps）用作有效的数据增强方法，以增加训练数据集。对于每个输入图像，产生10个随机实现。与未使用随机模型的情况进行比较，来自增强网络的新结果表明估计的急剧改善，使得R-2增加到0.94。此外，新开发的CNN网络与具有小数据集（R-2 = 0.75）的CNN网络不同，表明否/低估。与计算结果相比，估计属性表明CNNS在预测岩石的物理参数时表现出突出的情况下，如果提供足够的输入数据，则在不进行任何时间要求的前瞻性建模的情况下进行突出的。 （c）2018年elestvier有限公司保留所有权利。

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