油田注采作业易引发地下储层压力变化,易导致油田地表局部形变并诱发剪切套损,开展油田地表形变监测可以识别油田主要形变区和可能套损区,对油田作业合理规划和油田高质量可持续发展具有重要意义。合成孔径干涉测量(InSAR)技术可实现大面积、高精度的地表形变监测,但传统时序InSAR技术在地表覆盖复杂的油田区面临测量点不足且空间分布不均的问题。以大庆油田为例,针对上述问题以及季节性冻土影响,将周期模型融入DS-InSAR技术开展地表形变监测,并分析地表形变与油田注采量之间的相关性。结果表明:(1)油田区因注采等差异导致其地表形变分布不均匀且呈显著的非线性特征,最大沉降速率约为47 mm/a,最大抬升速率约为45 mm/a;(2)油田区地表形变与油田注采作业高度相关,注液作业使得地下储层压力增大,地表抬升,采油作业使得储层压力减小,造成地表沉降。该研究可为油田注采生产策略优化提供科学数据依据,进一步拓展了InSAR技术在油田区的应用。
油田注采作业易引发地下储层压力变化,易导致油田地表局部形变并诱发剪切套损,开展油田地表形变监测可以识别油田主要形变区和可能套损区,对油田作业合理规划和油田高质量可持续发展具有重要意义。合成孔径干涉测量(InSAR)技术可实现大面积、高精度的地表形变监测,但传统时序InSAR技术在地表覆盖复杂的油田区面临测量点不足且空间分布不均的问题。以大庆油田为例,针对上述问题以及季节性冻土影响,将周期模型融入DS-InSAR技术开展地表形变监测,并分析地表形变与油田注采量之间的相关性。结果表明:(1)油田区因注采等差异导致其地表形变分布不均匀且呈显著的非线性特征,最大沉降速率约为47 mm/a,最大抬升速率约为45 mm/a;(2)油田区地表形变与油田注采作业高度相关,注液作业使得地下储层压力增大,地表抬升,采油作业使得储层压力减小,造成地表沉降。该研究可为油田注采生产策略优化提供科学数据依据,进一步拓展了InSAR技术在油田区的应用。
油田注采作业易引发地下储层压力变化,易导致油田地表局部形变并诱发剪切套损,开展油田地表形变监测可以识别油田主要形变区和可能套损区,对油田作业合理规划和油田高质量可持续发展具有重要意义。合成孔径干涉测量(InSAR)技术可实现大面积、高精度的地表形变监测,但传统时序InSAR技术在地表覆盖复杂的油田区面临测量点不足且空间分布不均的问题。以大庆油田为例,针对上述问题以及季节性冻土影响,将周期模型融入DS-InSAR技术开展地表形变监测,并分析地表形变与油田注采量之间的相关性。结果表明:(1)油田区因注采等差异导致其地表形变分布不均匀且呈显著的非线性特征,最大沉降速率约为47 mm/a,最大抬升速率约为45 mm/a;(2)油田区地表形变与油田注采作业高度相关,注液作业使得地下储层压力增大,地表抬升,采油作业使得储层压力减小,造成地表沉降。该研究可为油田注采生产策略优化提供科学数据依据,进一步拓展了InSAR技术在油田区的应用。
The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.