Projects per year
Abstract
Delineation of a subsurface geological cross-section can be accomplished by conditional simulations, which can effectively combine prior geological knowledge as training images with site-specific measurements for spatial interpolation. Valuable prior geological knowledge may be concisely represented by a single training image, and the evaluation and optimal selection of a representative training image is crucial for a successful application of conditional simulation methods. In this study, a data-driven method based on edge orientation detection is proposed for selection of the optimal training image. The stratigraphic soil boundaries of all candidate training images (CTIs) and site-specific measurements are scanned by an edge detector. The derived edge orientations are quantified and compared between CTIs and site-specific measurements. Among all CTIs, the CTI that has the minimal difference of edge orientation distribution with respect to the site-specific measurements is selected as the optimal one. The proposed method is validated using both an illustrative example and a real case. It is demonstrated that edge orientation successfully differentiates soil stratigraphic patterns between different training images, and the derived edge orientation distribution can be used as a quantitative indicator for selection of the optimal training image.
Original language | English |
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Article number | 106415 |
Journal | Engineering Geology |
Volume | 295 |
Online published | 21 Oct 2021 |
DOIs | |
Publication status | Published - 20 Dec 2021 |
Research Keywords
- Convolutional neural network
- Edge orientation detection
- Image-based method, Sparse measurements
- XGBoost
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Dive into the research topics of 'Training image selection for development of subsurface geological cross-section by conditional simulations'. Together they form a unique fingerprint.Projects
- 2 Finished
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GRF: Multiscale Machine Learning of Subsurface Stratigraphy from Limited Site-specific Measurements and Prior Geological Knowledge using Iterative Convolutional Neural Networks (CNN)
WANG, Y. (Principal Investigator / Project Coordinator)
1/01/22 → 2/10/24
Project: Research
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GRF: Development of Machine Learning Methods for Planning of Geotechnical Site Investigation and Analytics of Site Investigation Data
WANG, Y. (Principal Investigator / Project Coordinator)
1/01/20 → 9/08/23
Project: Research