loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification

Pirot, Guillaume; Joshi, Ranee; Giraud, Jérémie; Lindsay, Mark; Jessell, Mark

doi:10.5194/gmd-15-4689-2022

Cited by 9 publications

(2 citation statements)

References 58 publications

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“…Although the existing implicit methods can generate various models by perturbing the inputs to characterize uncertainties, they might not explore a broad range of possible geological patterns and structural relationships in nature through a single model suit for stochastic simulation (Jessell et al, 2022). Working on the automation of modeling workflow, our CNN is beneficial for a flexible interpretation of aleatory and epistemic uncertainties (Pirot et al, 2022) by generating diverse modeling realizations instead of one best realization due to its high computational efficiency.…”

Section: Structural Uncertainty Characterizationmentioning

confidence: 99%

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

et al. 2022

Geosci. Model Dev.

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Abstract. Implicit structural modeling using sparse and unevenly distributed data is essential for various scientific and societal purposes, ranging from natural source exploration to geological hazard forecasts. Most advanced implicit approaches formulate structural modeling as least squares minimization or spatial interpolation, using various mathematical methods to solve for a scalar field that optimally fits all the inputs under an assumption of smooth regularization. However, these approaches may not reasonably represent complex geometries and relationships of structures and may fail to fit a global structural trend when the known data are too sparse or unevenly distributed. Additionally, solving a large system of mathematical equations with iterative optimization solvers could be computationally expensive in 3-D. To deal with these issues, we propose an efficient deep learning method using a convolution neural network to create a full structural model from the sparse interpretations of stratigraphic interfaces and faults. The network is beneficial for the flexible incorporation of geological empirical knowledge when trained by numerous synthetic models with realistic structures that are automatically generated from a data simulation workflow. It also presents an impressive characteristic of integrating various types of geological constraints by optimally minimizing a hybrid loss function in training, thus opening new opportunities for further improving the structural modeling performance. Moreover, the deep neural network, after training, is highly efficient for the generation of structural models in many geological applications. The capacity of our approach for modeling complexly deformed structures is demonstrated by using both synthetic and field datasets in which the produced models can be geologically reasonable and structurally consistent with the inputs.

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Section: Structural Uncertainty Characterizationmentioning

confidence: 99%

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

et al. 2022

Geosci. Model Dev.

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show abstract

Section: Structural Uncertainty Characterizationmentioning

confidence: 99%

DeepISMNet: Three-Dimensional Implicit Structural Modeling with Convolutional Neural Network

et al. 2022

Preprint

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Abstract. Implicit structural modeling using sparse and unevenly distributed data is essential for various scientific and societal purposes ranging from natural source exploration to geological hazard forecasts. Most advanced implicit approaches formulate structural modeling as least-squares minimization or spatial interpolation problem and solve partial differential equations (PDEs) for a scalar field that optimally fits all the input data under smooth regularization assumption. However, the PDEs in these methods might be insufficient to model highly complex structures in practice and may fail to reasonably fit a global structure trend when the known data are too sparse. In addition, solving the PDEs with iterative optimization solvers could be computationally expensive in 3-D. In this study, we propose an efficient deep learning method using a convolution neural network to predict a scalar field from sparse structural data associated with multiple distinct stratigraphic layers and faults. Our deep learning architecture is beneficial for the flexible incorporation of empirical geological knowledge by training with numerous and realistic structural models that are automatically generated from a data simulation workflow. It also presents an impressive characteristic of integrating various types of structural constraints by optimally minimizing a hybrid loss function to compare predicted and reference structural models, opening new opportunities for further improving geological modeling. Moreover, the deep neural network, after training, is highly efficient to predict implicit structural models in practical applications. The capacity of our approach for modeling highly deformed geological structures is verified by using both synthetic and real-world datasets, where the produced models are geologically reasonable and structurally consistent with the inputs.

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Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction

Liu

et al. 2023

Bull Eng Geol Environ

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loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification

Cited by 9 publications

References 58 publications

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

DeepISMNet: Three-Dimensional Implicit Structural Modeling with Convolutional Neural Network

Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction

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