摘要
重力梯度张量的定义是对重力位求二阶导数,相比于传统的布格重力异常,它在反映地下密度异常分布上有着更高的灵敏度,且能够进一步准确直接地反映目标体的边界。但对单个张量分量作反演时,可能会丢失一部份有用的信息,从而造成反演结果的误差。而全张量反演是将重力的五个梯度张量联合起来进行反演,这样做可以综合更丰富的场源信息。相比于传统做法中的布格重力异常反演和单重力张量分量反演,得到的反演结果不仅有了更高的分辨率,在识别目标体特征上也有更好的效果。粒子群算法是一种基于群体智能的优化迭代算法,这里利用粒子群算法对重力张量单分量、布格重力异常和全张量分别进行反演,并对结果进行简要分析。
Gravity gradient tensor is the second derivative of gravity position,compared with the conventional Bouguer gravity anomaly,which reflects the underground density anomalies for higher sensitivity and more accurately reflect the field directly to the source body boundaries.However,a single tensor inversion data is easy to lose useful information resulting in an incorrect interpretation of information.The full tensor inversion of gravity gradient tensor with five independent components of all joint inversion interpretation is proposed,a more comprehensive multi-component field source information.Compared to a single gravity tensor inversion and the traditional sense of the Bouguer gravity anomaly inversion,the inversion results by the proposed methods show the higher resolution,and better capability to identify the field source characteristics.PSO is an iterative optimization based on swarm intelligence algorithm,and this paper will use the particle swarm algorithm for single-component tensor gravity,Bouguer gravity anomaly and full tensor inversion respectively,and give a brief analysis of the results.
出处
《物探化探计算技术》
CAS
CSCD
2013年第2期128-133,117,共6页
Computing Techniques For Geophysical and Geochemical Exploration
基金
国家自然科学基金项目资助(41174061)
中南大学自由探索计划资助(2011QNZT011)
关键词
重力张量
粒子群优化算法
反演
gravity tensor
particle swarm optimization(PSO)
inversion
