Many geological hazards are associated with ground deformations. Prompt and accurate detection and interpretation of ground deformation is therefore vital to geohazard mitigation. Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an effective geodetic technique for monitoring ground deformation. However, accurate computation and interpretation of deformation using InSAR are often hindered by various errors and a lack of expert knowledge. We present a new advanced deep learning model based on a multi-task vision transformer (MT-ViT) to automatically detect, locate, and interpret deformation using single interferograms. To address the issue of limited training data in InSAR applications, the proposed model utilizes pre-trained weights from optical images and transfers them to a simulated InSAR dataset. Then real interferograms are used to fine-tune the weights in the network. An overall loss function is designed, which considers the classification and localization losses in the model. The effectiveness of the proposed model is demonstrated using both simulated and real InSAR datasets that contain either coseismic or volcanic deformation. The experimental results from the model are also compared with the state-of-the-art convolutional neural network (CNN) based techniques. The results show significant improvement in both the accuracy of the results and the computational efficiency over the CNN-based approaches. The MT-ViT model achieved 99.4 % classification accuracy, 54.1 % mean intersection over union (IOU), and 0.9 km localization accuracy. A comprehensive evaluation of the hyperparameters in training the MT-ViT model was carried out, which will inform future research in this direction. The research results highlight the promising capabilities of MT-ViT in near real-time deformation monitoring and automated deformation interpretation.

中文翻译：

---

使用 InSAR 数据和多任务 ViT 模型进行自动变形检测和解释

许多地质灾害都与地面变形有关。因此，及时、准确地检测和解释地面变形对于减轻地质灾害至关重要。多时相干涉合成孔径雷达（MT-InSAR）是监测地面变形的有效大地测量技术。然而，利用 InSAR 对形变的精确计算和解释常常受到各种错误和缺乏专业知识的阻碍。我们提出了一种基于多任务视觉变换器 (MT-ViT) 的新型高级深度学习模型，可使用单个干涉图自动检测、定位和解释变形。为了解决 InSAR 应用中训练数据有限的问题，所提出的模型利用光学图像中的预训练权重并将其传输到模拟 InSAR 数据集。然后使用真实的干涉图来微调网络中的权重。设计了整体损失函数，考虑了模型中的分类和定位损失。使用包含同震或火山变形的模拟和真实 InSAR 数据集证明了所提出模型的有效性。该模型的实验结果还与最先进的基于卷积神经网络（CNN）的技术进行了比较。结果表明，与基于 CNN 的方法相比，结果的准确性和计算效率都有显着提高。 MT-ViT 模型实现了 99.4% 的分类精度、54.1% 的平均交并集 (IOU) 和 0.9 公里的定位精度。对训练 MT-ViT 模型的超参数进行了综合评估，这将为该方向的未来研究提供信息。研究结果凸显了 MT-ViT 在近实时变形监测和自动变形解释方面的潜力。