An ensemble learning paradigm for subsurface stratigraphy from sparse measurements and augmented training images

• المؤلفون الرئيسيون: Shi, Chao, Wang, Yu, Yang, Haoqing
• مؤلفون آخرون: School of Civil and Environmental Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2024
• الموضوعات: Engineering; Data augmentation; Generative modelling
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/180730
• المؤسسة: Nanyang Technological University

The performance of computer vision-based techniques for stratigraphic modeling relies heavily on qualified training images to capture the complex stratigraphic connectivity. In geotechnical engineering, only limited training images are available for a specific site. Stochastic simulation modelling b...