Machine learning-based estimation and clustering of statistics within stratigraphic models as exemplified in Denmark

IF 2 4区 地球科学 Q1 GEOLOGY
Frederik Alexander Falk, Rasmus Bødker Madsen
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引用次数: 0

Abstract

Estimating a covariance model for kriging purposes is traditionally done using semivariogram analyses, where an empirical semivariogram is calculated, and a chosen semivariogram model, usually defined by a sill and a range, is fitted. We demonstrate that a convolutional neural network can estimate such a semivariogram model with comparable accuracy and precision by training it to recognise the relationship between realisations of Gaussian random fields and the sill and range values that define it, for a Gaussian type semivariance model. We do this by training the network with synthetic data consisting of many such realisations with the sill and range as the target variables. Because training takes time, the method is best suited for cases where many models need to be estimated since the actual estimation itself is about 70 times faster with the neural network than with the traditional approach. We demonstrate the viability of the method in three ways: (1) we test the model’s performance on the validation data, (2) we do a test where we compare the model to the traditional approach and (3) we show an example of an actual application of the method using the Danish national hydrostratigraphic model.
丹麦地层模型中基于机器学习的估算和统计聚类示例
为克里金目的估算协方差模型的传统方法是使用半变量图分析,即计算经验半变量图,并拟合选定的半变量图模型(通常由边际和范围定义)。我们通过训练卷积神经网络来识别高斯类型的半方差模型中高斯随机场的现实化与定义半方差模型的边际值和范围值之间的关系,从而证明卷积神经网络能够以相当的准确度和精确度估算出这样的半方差模型。为此,我们使用合成数据对网络进行了训练,合成数据由许多此类变现组成,并将边际和范围作为目标变量。由于训练需要时间,这种方法最适合需要估计许多模型的情况,因为使用神经网络进行实际估计本身就比传统方法快 70 倍。我们从三个方面证明了该方法的可行性:(1)在验证数据上测试模型的性能;(2)将该模型与传统方法进行比较;(3)使用丹麦国家水文地层模型展示该方法的实际应用实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geus Bulletin
Geus Bulletin GEOLOGY-
CiteScore
2.80
自引率
17.60%
发文量
8
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