中南大学学报（自然科学版）2011，Vol.42Issue(12)：3797-3803,7.
重力梯度张量的拟BP神经网络反演
Quasi-BP neural network inversion of gravity gradient tensor
摘要
Abstract
Based on the fact that gravity gradient tensor is a parameter which can reflect the spatial variation of gravity field, and that it has a higher resolution compared to the traditional gravity anomaly, a method for interpretation of gravity gradient tensor was proposed. The method is a kind of quasi-BP neural network algorithm which is based on RPROP algorithm. A three-layer network and the hidden layer neurons denote physics value were used. The physics value was automatically modified according to RPROP algorithm, and the physical distribution of field source was gotten. The results show that the method has a fast convergence speed and little dependence on initial model used in the inversion of gravity gradient tensor date, and can reflect the shape and density characters of anomalous body.关键词
重力梯度张量/拟BP神经网络/RPROP算法/反演Key words
gravity gradient tensor/ quasi-BP neural network/ RPROP algorithm/ inversion分类
天文与地球科学引用本文复制引用
郭文斌,朱自强,鲁光银..重力梯度张量的拟BP神经网络反演[J].中南大学学报（自然科学版）,2011,42(12):3797-3803,7.基金项目
国家自然科学基金资助项目(41174061) （41174061）
国家高技术研究发展计划("863计划")项目(2007AA06Z102) （"863计划"）
中南大学自由探索计划项目(2011QNZT011) （2011QNZT011）
