(1. 湖南科技大学 页岩气资源利用湖南重点实验室，湘潭 411201；
2. 中南大学 地球科学与信息物理学院，长沙 410083；
3. 湖南科技大学 资源环境与安全工程学院，湘潭 411201)
摘 要: 针对在重力梯度张量正演中计算耗时过长和核矩阵内存消耗过大等制约反演实施的瓶颈问题，在L1范数的基础上，引入种植反演，用累加求和分析替换迭代求解，避免计算或存储反演核矩阵，以减少内存占用和加快反演迭代；针对种植反演容易导致相邻异常源相互侵入的问题，引入一个基于位场水平衰减特性加权函数来限制密度吸引子的作用范围，以期使密度吸引子忽略较远的异常源，抑制相邻异常源相互干扰。反演结果及分析表明重力及重力梯度张量种植反演所需计算机内存小和水平衰减特性加权函数能有效的抑制相邻异常源的侵入。
关键字: 种植反演；水平加权特性函数；重力梯度张量；L1范数
（1. Hunan Provincial Key Laboratory of Shale Gas Resource Utilization, Hunan University of Science and Technology, Xiangtan 411201, China;
2. School of Geosciences and Info-physics, Central South University, Changsha 410083, China;
3. School of Resource Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China）
Abstract:Large-scale inversion of gravity gradient tensor data is a time-consuming problem with high demands on computational and physical memory usage. To avoid extraordinary matrix-vector multiplications in each inverse iteration and to speed up the forward of geophysical models, planting inversion is introduced and conjugate gradient iteration replaced by accumulation summary based on L1 norm. The planting inversion easily leads to adjacent anomalies mutually invasive, a horizontal weighted function is proposed to suppress mutual interference between the adjacent anomaly sources. These results of the inversions and analysis results show that planting inversion with horizontal weighted function obtain a meaningful geophysical model. And these methods require little memory and high efficiency.
Key words: planting inversion; horizontal weighted function; gravity gradient tensor; L1 norm
