摘要
当前重力背景场构建所需的高精度、高分辨率重力数据,已可通过多种方式获取。神经网络与重力场结合的研究方兴未艾,考虑到神经网络使用的特点,其适用于重力数据处理的插值拟合过程。本文针对重力离散数据格网化插值过程精度降低的问题,提出一种基于正交多项式神经网络对重力数据进行插值的新方法。勒让德多项式神经网络(LPNN)模型具有复杂非线性映射能力,能够对重力数据推估建模,但由于其单层结构,LPNN的计算复杂度低。通过与现有方法进行比较,本文方法获得结果的准确性和可靠性,最后在中国南海实验区域上进行高精度重力数据插值成图,进一步验证了方法的可行性。
The high precision and high resolution gravity data required for the construction of the current gravity background field can be obtained in a variety of ways.The combination of neural network and gravity field is in the ascendant and considering the characteristics of neural network usage,it is suitable for the interpolation and fitting process of gravity data processing.This paper proposes a new method for interpolating gravity data based on orthogonal polynomial neural networks to address the issue of reduced accuracy in the grid interpolation process of gravity discrete data.Legendre polynomial neural network(LPNN)model has complex nonlinear mapping ability and can predict and model gravity data.Moreover,due to its single-layer structure,LPNN has low computational complexity.Compared with the existing methods,the accuracy and reliability of the results obtained by the proposed method are proved.Finally,high precision gravity data interpolation is performed in the South China Sea experiment area to further verify the feasibility of the method.
作者
谢心和
赵东明
刘长青
XIE Xinhe;ZHAO Dongming;LIU Changqing(School of Geospatial Information,University of Information Engineering,Zhengzhou 450001,China)
出处
《测绘与空间地理信息》
2023年第12期19-23,共5页
Geomatics & Spatial Information Technology
基金
国家自然科学基金(42174008)资助。
作者简介
谢心和(1998-),男,广东惠州人,测绘工程专业硕士研究生,主要研究方向为物理大地测量。;通信作者:赵东明(1976-),男,河南郑州人,副教授,博士,2004年毕业于信息工程大学大地测量学与测量工程专业,主要从事物理大地测量方面的教学研究工作。
