A machine learning model for structural trend fields

Goncalves I.G.; Guadagnin F.; Kumaira S.; Da Silva S.L.

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A machine learning model for structural trend fields

机译：结构趋势领域的机器学习模型

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摘要

This work presents a Gaussian process model (a Bayesian derivation of kriging) for the interpolation of structural field data (dip and strike measurements). The structural data are treated as the directional derivatives of a latent potential field. The latent field's isosurfaces characterize the general structural trend in a region, and the predictive variance can be used as a measure of uncertainty. The model's parameters are optimized via maximum likelihood, avoiding the need for a variogram analysis. The model is tested using the orientation vectors of metamorphic foliation in meta-volcanic rocks of the Passo Feio Metamorphic Complex, in southern Brazil. An open-source implementation is available.

机译：这项工作提出了一种高斯过程模型（Kriging的贝叶斯推导），用于插值结构场数据（DIP和Strike测量）。结构数据被视为潜在潜在场的定向衍生物。潜在的领域的等气文件表征了一个地区的一般结构趋势，并且可以将预测方差用作不确定性的量度。模型的参数通过最大可能性进行优化，避免需要变形仪分析。在巴西南部的Passo Feio Metalymormalphic Complex的Meta-Volcanic复合物中的变质叶的定向载体测试了该模型。可以使用开源实现。

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来源
《Computers & geosciences》 |2021年第4期|104715.1-104715.9|共9页
作者
Goncalves I.G.; Guadagnin F.; Kumaira S.; Da Silva S.L.;
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作者单位

Campus Caçapava do Sul Universidade Federal do Pampa Brazil;

Campus Caçapava do Sul Universidade Federal do Pampa Brazil;

Campus Caçapava do Sul Universidade Federal do Pampa Brazil;

Campus Caçapava do Sul Universidade Federal do Pampa Brazil;

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正文语种 eng
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关键词
Gaussian Process; Implicit modeling; Kriging; Machine learning; Structural trend; Vector field;

机译：高斯进程;隐含建模;克里格;机器学习;结构趋势;矢量领域;

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A machine learning model for structural trend fields

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