Integrated bedrock model combining airborne geophysics and sparse drillings based on an artificial neural network

Lysdahl, Asgeir Olaf Kydland; Christensen, Craig W.; Pfaffhuber, Andreas Aspmo; Vöge, Malte; Andresen, Lars; Skurdal, Guro Huun; Panzner, Martin

doi:10.1016/j.enggeo.2021.106484

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…In the literature, we can find quantitative interpretations of inverted geophysical data integrating ground truth data and which are based on machine learning (Lysdahl et al, 2022;Moghadas and Badorreck, 2019;Whiteley et al, 2021) and exclusively on statistics or probability theory (Dewar and Knight, 2020;Hermans and Irving, 2017;Isunza Manrique et al, 2023).…”

Section: Classification Of the Field Datamentioning

confidence: 99%

Integrated Methodology to Link Geochemical and Geophysical-Lab Data in a Geophysical Investigation of a Slag Heap for Resource Quantification

Isunza Manrique,

Hermans,

Caterina

et al. 2023

Preprint

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Section: Classification Of the Field Datamentioning

confidence: 99%

Integrated Methodology to Link Geochemical and Geophysical-Lab Data in a Geophysical Investigation of a Slag Heap for Resource Quantification

Isunza Manrique,

Hermans,

Caterina

et al. 2023

Preprint

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“…Supervised machine learning algorithms (e.g. random forests and artificial neural networks) can be applied to construct 3D lithological models by training from labelled geophysical and geological datasets (Jia et al, 2021;Lysdahl et al, 2022). Despite obtaining encouraging results with supervised machine learning algorithms, most studies have not addressed the following critical challenges regarding supervised machine learning algorithms for 3D geological modelling:…”

Section: Introductionmentioning

confidence: 99%

“…2. The labelled geological datasets are mainly composed of borehole data from early exploration phases (Jia et al, 2021;Lysdahl et al, 2022). The number of lithological sample categories in drilling datasets is commonly imbalanced.…”

Section: Introductionmentioning

confidence: 99%

GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data

Guo,

Xu,

Wang

et al. 2024

Geosci. Model Dev.

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Abstract. Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data.

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“…Friedel et al (2016) and Friedel (2016) used machine learning to estimate aquifer distributions and hydrostratigraphic units using AEM, borehole and hydrogeological data. Lysdahl et al (2022) combined AEM with borehole data to map a bedrock model using an artificial neural network. Machine learning can also be combined with multiple-point statistics to model facies architectures.…”

Section: Introductionmentioning

confidence: 99%

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

Kawo,

Korus,

Kishawi

et al. 2024

Water Resources Research

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Characterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine‐scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi‐Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K‐means clustering was used to translate the Stacking Classifier model into a 4‐class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer‐aquifer and stream‐aquifer connections.

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Integrated bedrock model combining airborne geophysics and sparse drillings based on an artificial neural network

Cited by 7 publications

References 24 publications

Integrated Methodology to Link Geochemical and Geophysical-Lab Data in a Geophysical Investigation of a Slag Heap for Resource Quantification

Integrated Methodology to Link Geochemical and Geophysical-Lab Data in a Geophysical Investigation of a Slag Heap for Resource Quantification

GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

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