Finding patterns in subsurface using Bayesian machine learning approach
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TL;DR: In this work, anisotropy and heterogeneity are considered as possible patterns that inherently exist in the observations, and these are inferred and described in a Bayesian manner.
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About: This article is published in Underground Space. The article was published on 01 Mar 2020. and is currently open access. The article focuses on the topics: Seismic inversion & Cluster analysis.
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Citations
3D geological structure inversion from Noddy-generated magnetic data using deep learning methods
Jiateng Guo,Yunqiang Li,Mark Jessell,Jeremie Giraud,Chaoling Li,Lixin Wu,Fengdan Li,Shanjun Liu +7 more
TL;DR: In this paper, a 3D geological structure inversion method using convolutional neural networks (CNNs) is proposed to predict the parameters of a geological structure for constructing 3D model.
57
Genetic programming model for estimating soil suction in shallow soil layers in the vicinity of a tree
TL;DR: In this paper, a computational model consisting of a drying-cycle model and a wet-wetting model was developed by means of a genetic programming method to depict variations in soil suction using select influential parameters.
56
Bayesian machine learning-based method for prediction of slope failure time
TL;DR: In this article , a Bayesian machine learning (BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction, through which the probabilistic distribution of the SFT can be obtained.
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Training image selection for development of subsurface geological cross-section by conditional simulations
TL;DR: In this paper, a data-driven method based on edge orientation detection is proposed for selection of the optimal training image, which can effectively combine prior geological knowledge as training images with site-specific measurements for spatial interpolation.
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Estimation of spatiotemporal response of rooted soil using a machine learning approach
TL;DR: A machine learning method is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters and the results indicate that the model can give a reasonable estimation for the spatiotemporal variations of land suction around a tree with acceptable errors.
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