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Advancing spatial-temporal rock fracture prediction with virtual camera-based data augmentation
Tunnelling and Underground Space Technology ( IF 6.7 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.tust.2025.106400
Jiawei Xie, Baolin Chen, Jinsong Huang, Yuting Zhang, Cheng Zeng

Predicting rock fractures in unexcavated areas is a critical yet challenging aspect of geotechnical projects. This task involves forecasting the fracture mapping sequences for unexcavated rock faces using the sequences from excavated ones, which is well-suited for spatial–temporal deep learning techniques. Fracture mapping sequences for deep learning model training can be achieved based on field photography. However, the main obstacle lies in the insufficient availability of high-quality photos. Existing data augmentation techniques rely on slices taken from Discrete Fracture Network (DFN) models. However, slices differ significantly from actual photos taken from the field. To overcome this limitation, this study introduces a new framework that uses Virtual Camera Technology (VCT) to generate “virtual photos” from DFN models. The external (e.g., camera location, direction) and internal parameters (e.g., focal length, resolution, sensor size) of cameras can be considered in this method. The “virtual photos” generated from the VCT and conventional slicing method have been extensively compared. The framework is designed to adapt to any distribution of field fractures and camera settings, serving as a universal tool for practical applications. The whole framework has been packaged as an open-source tool for rock “photos” generation. An open-source benchmark database has also been established based on this tool. To validate the framework’s feasibility, the Predictive Recurrent Neural Network (PredRNN) method is applied to the generated database. A high degree of similarity is observed between the predicted mapping sequences and the ground truth. The model successfully captured the dynamic changes in fracture patterns across different sections, thereby confirming the framework’s practical utility. The source code and dataset can be freely downloaded from GitHub repository (https://github.com/GEO-ATLAS/Rock-Camera).

中文翻译:


通过基于虚拟相机的数据增强推进时空岩石断裂预测



预测未开挖区域的岩石裂缝是岩土工程项目的一个关键但具有挑战性的方面。这项任务涉及使用已挖掘岩面的序列来预测未开挖岩面的裂缝映射序列,这非常适合时空深度学习技术。深度学习模型训练的裂缝映射序列可以基于现场摄影来实现。然而,主要障碍在于高质量照片的可用性不足。现有的数据增强技术依赖于从离散断裂网络 (DFN) 模型中获取的切片。但是,切片与从现场拍摄的实际照片有很大不同。为了克服这一限制,本研究引入了一个新框架,该框架使用虚拟相机技术 (VCT) 从 DFN 模型生成 “虚拟照片”。该方法可以考虑相机的外部(例如,相机位置、方向)和内部参数(例如,焦距、分辨率、传感器尺寸)。VCT 生成的“虚拟照片”和传统切片方法已被广泛比较。该框架旨在适应场裂缝和相机设置的任何分布,作为实际应用的通用工具。整个框架已被打包为用于生成岩石 “照片” 的开源工具。基于此工具还建立了一个开源基准测试数据库。为了验证框架的可行性,将预测递归神经网络 (PredRNN) 方法应用于生成的数据库。在预测的映射序列和地面实况之间观察到高度相似。 该模型成功地捕捉了不同截面断裂模式的动态变化,从而证实了该框架的实际效用。源代码和数据集可以从 GitHub 存储库 (https://github.com/GEO-ATLAS/Rock-Camera) 免费下载。
更新日期:2025-01-17
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