| 基于循环神经网络的重力异常数据推估研究 |
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| 引用本文： | 佘雅文,付广裕.基于循环神经网络的重力异常数据推估研究[J].大地测量与地球动力学,2021,41(3):234-237. |
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| 作者姓名： | 佘雅文 付广裕 |
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| 作者单位： | 南京大学地球科学与工程学院,南京市仙林大道163号,210023;中国地震局地震预测研究所地震预测重点实验室,北京市复兴路63号,100036;中国地震局地震预测研究所地震预测重点实验室,北京市复兴路63号,100036 |
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| 基金项目： | 中国地震局地震预测研究所基本科研业务费专项;国家自然科学基金;国家重点研发计划 |
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| 摘 要： | 利用鄂尔多斯西南缘的重力观测数据对长短期记忆循环神经网络(long short-term memory, LSTM)进行训练，结果表明，该神经网络可基于有限的数据获取较好的推估结果。基于自由空气重力异常数据，对比分析长短期记忆循环神经网络和传统克里金方法的推估结果发现，神经网络的推估能力优于传统克里金方法，但运算效率低于后者。利用自由空气重力异常对整个区域进行推估，结果表明，LSTM方法明显优于克里金方法，加入高程数据作为约束条件可有效提升LSTM方法推估自由空气重力异常场的精度。
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| 关 键 词： | 长短期记忆循环神经网络 自由空气重力异常 克里金方法 |
Estimation of Gravity Anomaly Data Based on Recurrent Neural Network |
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| SHE Yawen,FU Guangyu.Estimation of Gravity Anomaly Data Based on Recurrent Neural Network[J].Journal of Geodesy and Geodynamics,2021,41(3):234-237. |
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| Authors: | SHE Yawen FU Guangyu |
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| Institution: | (School of Earth Sciences and Engineering,Nanjing University,Nanjing 210023,China;Key Laboratory of Earthquake Forecasting,Institute of Earthquake Forecasting,Beijing 100036,China) |
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| Abstract: | We adopt the gravity observation data of the southwestern margin of Ordos to train the long short-term memory recurrent neural network(LSTM).The results show that LSTM can obtain good estimation results based on limited data.Through comparing and analyzing the estimated results of LSTM and ordinary Kriging method based on free-air gravity anomaly data,we find that the estimation ability of neural network is better than ordinary Kriging method,but Kriging method performs better in terms of computing efficiency.Using the free-air gravity anomaly data to estimate the entire area,we show that LSTM method is significantly better than Kriging method.Adding elevation data as a constraint can effectively improve the accuracy of free-air gravity anomaly field estimated by LSTM method. |
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| Keywords: | long short-term memory recurrent neural network free-air gravity anomaly Kriging method |
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