The VERE framework was designed and developed in Python to generate hazard confidence maps for seismic-induced landslides, leveraging advanced data analysis and machine learning capabilities. A Virtual Environment (VE) and a Real Environment (RE) containing, respectively, datasets and map sets, are the core of the framework. The Virtual Environment (VE) comprises datasets including morphometric, geotechnical, and hydrological metadata, which are generated assuming a normal distribution, based on representative recurrent values of these parameters in the study area. The Real Environment (RE) includes grid datasets with a common resolution, obtained through analytical preprocessing of various spatial data distributions, including InSAR (Interferometric Synthetic Aperture Radar) data. This data is processed to detect ongoing slope instability and the activity state of surveyed landslides. The framework employs numerical machine learning, trained on meta-solutions derived from an advanced simplified physical model. The model accounts for viscoplastic behavior as well as the reduction of shear strengths toward the residual state during seismic-induced sliding. Hazard confidence maps are produced through an ML-based prediction, considering co-seismic displacements and post-seismic mobility under different initial porewater pressures and seismicity scenarios. The test-site region is the Sele River valley located in an inter-Apennine sector of southern Italy, a seismic-prone area known for its recent seismic activity.

中文翻译：

---

VERE Py 框架：InSAR 驱动的地震滑坡灾害测绘中物理信息机器学习的双环境


VERE 框架是在 Python 中设计和开发的，用于利用高级数据分析和机器学习功能为地震诱发的山体滑坡生成灾害置信度图。虚拟环境 （VE） 和真实环境 （RE） 分别包含数据集和地图集，是该框架的核心。虚拟环境 （VE） 包括包括形态测量、岩土工程和水文元数据的数据集，这些数据集是根据研究区域中这些参数的代表性循环值生成的，假设呈正态分布。真实环境 （RE） 包括具有通用分辨率的网格数据集，这些数据集是通过对各种空间数据分布的分析预处理获得的，包括 InSAR （干涉合成孔径雷达） 数据。处理这些数据以检测持续的边坡不稳定性和已调查滑坡的活动状态。该框架采用数值机器学习，在源自高级简化物理模型的元解决方案上进行训练。该模型考虑了粘塑性行为以及地震诱发滑动过程中剪切强度向残余状态的降低。灾害置信度图是通过基于 ML 的预测生成的，考虑了不同初始孔隙水压力和地震活动情景下的同震位移和地震后移动性。测试地点区域是位于意大利南部亚平宁山脉间地区的 Sele 河谷，这是一个地震多发地区，以其最近的地震活动而闻名。