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Research on the prediction and disaster risk assessment of surface deformation in open-pit mines serves to improve the accuracy of disaster warning and make safety control decisions. In recent years, the development of big data, cloud computing and artificial intelligence methods has provided technical support for the intelligent transformation of traditional mines. This paper summarizes the research progress of surface deformation hazards in open-pit mines from three aspects: intelligent perception, intelligent prediction and disaster risk evaluation of surface deformation. Specifically, by reviewing the intelligent monitoring technologies of mine surface deformation, this study indicates that the choice of intelligent monitoring methods should factor in data accuracy, installation cost and post-processing speed, reviews the intelligent modeling methods of surface deformation prediction regarding the methodological combination of traditional deformation prediction and intelligent optimization, machine learning and deep learning, and summarizes the mechanism behind the typical risk assessment method of mine deformation hazards. Based on the current research progress, we discuss the existing research gap and prospects of intelligent prediction and disaster risk assessment of surface deformation in open-pit mines so as to offer reference to the intelligent upgrading of mine disaster prevention and control.

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of 'sky', 'air', and 'ground' , as well as integrated collaborative monitoring method of 'sky-air-ground'. The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the 'sky-air-ground' is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.

Diffusion and seepage collectively govern the mass transfer behavior of gases in the CO2 enhancing coalbed methane recovery (CO2-ECBM) process, significantly influencing both coalbed methane extraction efficiency and CO2 sequestration capacity. Conventional theoretical models typically assume a uniform gas distribution within the coal matrix during injection. However, extensive field studies have revealed inconsistent conclusions. This paper delves into the gas equilibrium time lag property during the CO2-ECBM process, introducing the concept of sub-matrix mass transfer behavior to describe the non-uniform distribution of pressure, and construct the modified binary gas flow control equations and the permeability evolution model that takes this behavior into account. This model is used to investigate how sub-matrix mass transfer influences gas seepage characteristics. Findings show that the equilibrium time lag property intensify with coalbed extension but diminish over time. Despite an increased sub-matrix proportion enhancing CO2 adsorption capacity, the difference fades away due to the decay of the gas equilibrium time lag property. Furthermore, fixed-point monitoring reveals that a higher sub-matrix proportion aggravates permeability evolution, constraining fluid flow capacity. Based on these observations, a hypothesis of multi-level diffusion behavior within the coalbed is proposed, alongside an exploration of optimized CO2 injection strategies, providing new theoretical insights for CO2 sequestration in deep coalbeds. [ABSTRACT FROM AUTHOR]