南海北部油气化探研究——以甲烷浓度为例     

王庆光  徐瑞松  王洁  陈彧 《海洋科学》2010,34(6):9-15

利用油气化探方法分析南海北部采样点海水中的甲烷气体的浓度,发现在甲烷浓度分布图中有7个异常高的位置,其中的一些区域已经探明有油气藏的存在,结果表明通过海水甲烷浓度来探测油气藏是可行的,对今后的海洋油气探测具有重要的指导意义。  相似文献   

Geo-environment-aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas     

Liguo Zhang  Haowei Zeng  Yulin Ding  Han Hu  Li Chen  Junxiao Zhang  Yan Zhou 《Transactions in GIS》2023,27(5):1418-1440

Landslide susceptibility evaluation (LSE) is a critical issue for disaster prevention. Limited by labor cost and observation technology, landslide samples are extremely limited in dense vegetation-covered and remote areas, making the common supervised learning model underfit with limited samples. Therefore, the reliability of analysis results in mountainous areas is low. Transfer learning can achieve reliable assessment without the need for representative samples. However, transfer learning suffers from environmental heterogeneity in regional LSE and may transfer incorrect classification knowledge of landslide features from dissimilar environments. Aiming at these challenges, we proposed a geo-environment-aware LSE method based on unsupervised adversarial transfer learning. The key is to consider the difference in landslide features in different geo-environments. The study areas were first divided into multiple sub-environments, and the similarity between the sub-environments was calculated. Then an environment-aware adversarial transfer model was built for fine-grained aligning of the landslide feature with similar sub-environments and for reducing negative transfer between dissimilar environments. The fitted classification model was employed to predict the target regions and to generate the final LSE. The experimental results indicated that the proposed method achieves reliable LSE for sample-free regions. The accuracy of the proposed method is 7–12% better than commonly used methods such as support vector machines, random forests, and artificial neural networks. The performance of the proposed method is even close to the results of supervised learning with the presence of representative samples, and it also performs more globally and objectively in susceptibility mapping. These results reveal that the proposed method effectively transfers the knowledge of landslide susceptibility from other regions to the sample-free region.  相似文献