Variational Gaussian processes for implicit geological modeling

Gonçalves, Ítalo Gomes; Guadagnin, Felipe; Cordova, Diogo Peixoto

doi:10.1016/j.cageo.2023.105323

Cited by 4 publications

(1 citation statement)

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“…This is accomplished by creating intermediate 3D geological models that correspond to a subset of the data and then consolidating them into a unified, comprehensive 3D geological model while complying with data constraints and geological principles. Presently, the primary aim of implicit modeling is the generation and simulation of geological boundaries, accomplished through automatic construction employing techniques like radial basis functions, variational Gaussian processes, and other methodologies [27][28][29]. To address concerns regarding overlap and gaps that arise in the modeling process, Li and colleagues [30] introduced a reconstruction algorithm based on the labeling of voxel points.…”

Section: Implicit Geological Modelingmentioning

confidence: 99%

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

Liu,

Li,

2024

Applied Sciences

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Three-dimensional (3D) models provide the most intuitive representation of geological conditions. Traditional modeling methods heavily depend on technicians’ expertise and lack ease of updating. In this study, we introduce a deep learning-based method for 3D geological implicit modeling, leveraging a substantial dataset of geological drilling data. By applying resampling and normalization techniques, we standardize drilling data and significantly expand the dataset, making it suitable for training deep neural networks. Utilizing the characteristics of the sample data, we design and establish the network structure, loss function, and parameter configurations, resulting in the training of a deep neural network with high accuracy and robust generalization capability. Ultimately, we utilize the dataset generated from the network’s predictions to render and construct the 3D geological model. The research in this paper demonstrates the significant promise of deep neural networks in addressing geological challenges. The deep learning-based implicit 3D modeling method surpasses traditional approaches in terms of generalization, convenience, and adaptability.

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