Rapid developments in SQUID-based technology make it possible for geophysical exploration to direct measure, inverse and interpret magnetic gradient tensor data. This contribution introduces a novel three-dimensional hybrid regularization method for inversion of magnetic gradient tensor data, which is based on the minimum support functional and total variation functional. Compared to the existing stabilizers, for example, the minimum support stabilizer, the minimum gradient support stabilizer or the total variation stabilizer, our proposed hybrid stabilizer, in association with boundary penalization, improves the revision result greatly, including higher spatial and depth resolution, more clear boundaries, more highlighted images and more evident structure depiction. Moreover, suitable selection of model parameter λ will further improve the image quality of the recovered model. We verify our proposed hybrid method with various synthetic magnetic models. Experiment results prove that this method gives more accurate results, exhibiting advantages of less computational costs even when less prior information of magnetic sources are provided. Comparison of results with different types of magnetic data with and without remanence indicates that our inversion algorithm can obtain more detailed information on the source structure based on rational estimation of total magnetization direction. Finally, we present a case study for inverting SQUID-based magnetic tensor data acquired at Da Hinggan Mountains area, inner Mongolia, China. The result also certifies that the method is reliable and efficient for real cases. 相似文献
Jiuzhaigou, located in the transitional area between the Qinghai–Tibet Plateau and the Sichuan Basin, is highly prone to geological hazards (e.g., rock fall, landslide, and debris flow). High-performance-based hazard prediction models, therefore, are urgently required to prevent related hazards and manage potential emergencies. Current researches mainly focus on susceptibility of single hazard but ignore that different types of geological hazards might occur simultaneously under a complex environment. Here, we firstly built a multi-geohazard inventory from 2000 to 2015 based on a geographical information system and used satellite data in Google earth and then chose twelve conditioning factors and three machine learning methods—random forest, support vector machine, and extreme gradient boosting (XGBoost)—to generate rock fall, landslide, and debris flow susceptibility maps. The results show that debris flow models presented the best prediction capabilities [area under the receiver operating characteristic curve (AUC 0.95)], followed by rock fall (AUC 0.94) and landslide (AUC 0.85). Additionally, XGBoost outperformed the other two methods with the highest AUC of 0.93. All three methods with AUC values larger than 0.84 suggest that these models have fairly good performance to assess geological hazards susceptibility. Finally, evolution index was constructed based on a joint probability of these three hazard models to predict the evolution tendency of 35 unstable slopes in Jiuzhaigou. The results show that these unstable slopes are likely to evolve into debris flows with a probability of 46%, followed by landslides (43%) and rock falls (29%). Higher susceptibility areas for geohazards were mainly located in the southeast and middle of Jiuzhaigou, implying geohazards prevention and mitigation measures should be taken there in near future.
Journal of Geographical Sciences - Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields. Timely and accurate measures are... 相似文献