| 基于神经网络方法获得最优化月球内部结构模型 |
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| 引用本文： | 廖彬彬,徐建桥,陈晓东,孙和平,周江存.基于神经网络方法获得最优化月球内部结构模型[J].地球物理学报,2022,65(3):939-951. |
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| 作者姓名： | 廖彬彬 徐建桥 陈晓东 孙和平 周江存 |
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| 作者单位： | 中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室,武汉430077;中国科学院大学,北京100049,中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室,武汉430077 |
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| 基金项目： | 中国科学院战略性先导科技专项(B类)(XDB41000000);重点部署项目(KFZD-SW-428);国家自然科学基金(41974023,42104006,41874094,41874026,42174101,41674083)资助。 |
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| 摘 要： | 由于观测手段有限,目前对月球内部结构的认识还存在很大的不确定性,至今仍没有一个被广泛认可的内部结构模型,且现有对月球内部结构模型的研究几乎很少关注观测值对观测精度的影响.本研究采用混合密度神经网络方法得到了月球内部结构模型的后验概率密度分布,获得了平均月球内部结构模型(Mean模型)、最大后验概率对应的月球内部结构模型(MAP模型)以及满足1-σ准则的月球内部结构模型(1-σ模型),其中MAP模型即为本文给出的最优化月球内部结构模型.此外,研究结果表明月球低速区S波波速低于月幔S波波速,因此本文结果支持月幔底部存在一个低速区的观点.不同观测值观测精度对模型影响的研究结果表明,勒夫数k2存在一个约为0.0220的下边界,且其观测精度对月球内部结构模型的影响显著大于平均密度和平均转动惯量.
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| 关 键 词： | 月球内部结构 混合密度神经网络(MDN) 月球低速区 贝叶斯反演 谱元法 |
Optimal lunar internal structure model obtained by a neural network method |
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| LIAO BinBin,XU JianQiao,CHEN XiaoDong,SUN HePing,ZHOU JiangCun.Optimal lunar internal structure model obtained by a neural network method[J].Chinese Journal of Geophysics,2022,65(3):939-951. |
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| Authors: | LIAO BinBin XU JianQiao CHEN XiaoDong SUN HePing ZHOU JiangCun |
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| Institution: | (State Key Laboratory of Geodesy and Earth′s Dynamics,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences,Wuhan 430077,China;University of Chinese Academy of Sciences,Beijing 100049,China) |
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| Abstract: | Because of limited observation techniques,current understanding of the lunar internal structure is so uncertain that no lunar internal structure model has been widely recognized and adopted until nowadays.In addition,little attention had been paid to the effects of different observations accuracies on the structure model.Using the mixture density neural network(MDN)method,the posterior probability density distributions of the lunar models are obtained in this study.Based on the density distribution,the average lunar internal structure model(Mean model),the maximum posterior probability model(MAP model)and the lunar model range satisfying the 1-σ(1-σModel)are computed,and the MAP model is the optimal lunar internal structure model given in this paper.In addition,our results show that the S-wave velocity in the low-velocity zone of the moon is lower than that of the lunar mantle,which supports the opinion of the existence of a low-velocity zone at the bottom of the lunar mantle.Numerical results of the influences of different observation accuracies on lunar model show that the Love number k2 has a lower boundary with a value of about 0.0220.In addition,the accuracy of k2 has a significant influence on the lunar model comparing with influences of the average density and the average moment of inertia. |
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| Keywords: | Lunar internal structure Mixture Density Neural Network(MDN) Lunar low-velocity zone Bayesian inverse problem Spectral element method |
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