摘要
为了揭示地球固体潮中谐波成分间的相关乘性调制关系与非相关叠加关系,并根据这些关系来分析重力固体潮信号中隐含的潮汐谐波。根据地球、月球与太阳旋转轨道的位置关系,建立了一个引潮力的正交分解模型。进一步,基于改进单形进化智能优化算法的独立分量分析的重力固体潮正交分解模型上,利用谱相关方法,对重力固体潮的独立成分进行谱相关分析,从而完整实现了潮汐谐波加性正交分解之后的乘性解调。最后,结合实际观测数据,并引入理论信号作为参考背景,利用以上模型与算法进行对比处理和分析。研究表明,所提出的模型与方法可以有效地实现潮汐谐波的正交分解,凸显能量较弱的长周期谐波分量,并从谱相关图谱中反映潮汐谐波调制关系的变化。
In order to reveal the correlation between the harmonic components in the earth’s solid tidal wave and the non-correlation superposition relationship,and based on these relations,the tidal harmonic implicit in the gravitational solid tidal signal was analyzed. According to the position relationship among the Earth,the moon and the sun’s rotating orbit, an orthogonal decomposition model of tidal force was established.Furthermore,on the orthogonal decomposition model of gravity solid tidal wave based on the independent component analysis of the improved SSSE intelligent optimization algorithm,the spectral correlation method was used to analyze the independent components of gravity solid tidal wave. Thus, the multiplicative demodulation after the orthogonal decomposition of the conformal wave was completely realized. Finally,the above model and algorithm were used to compare and analyze the actual observation data and the theoretical signal as reference background. The results show that the proposed model and method can effectively achieve the orthogonal decomposition of tidal harmonics, highlight the weak energy of the long period harmonics component,and reflect the change of tidal harmonic modulation relationship from the spectral correlation map.
作者
矣昕宝
魏巍
全海燕
Yi Xinbao;Wei Wei;Quan Haiyan(Xishuangbanna Vocational Technical College Teachers College Public Section,Yunnan Xishuangbanna666100,China;Faculty of Information and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《地球科学进展》
CAS
CSCD
北大核心
2019年第2期148-155,共8页
Advances in Earth Science
基金
国家自然科学基金项目"提取重力固体潮信号中地球物理信息和地震前兆信息的关键信号处理算法研究"(编号:41364002)资助~~
关键词
重力固体潮
分解模型
独立成分分析
谱相关
潮汐谐波
Gravity solid tides
Decomposition model
Independent component analysis
Spectral correlation
Tidal harmonics