采用长短时记忆网络训练的卫星重力数据粗差探测方法与应用  

OUTLIER DETECTION METHOD AND APPLICATION OF SATELLITE GRAVITY DATA TRAINED BY LONG SHORT-TERM MEMORY NETWORK

作　　者：杨玉 吴云龙 姚运生[1] 单维锋 YANG Yu;WU Yun-long;YAO Yun-sheng;SHAN Wei-feng(Institute of Disaster Prevention,Sanhe 065201,China;Key Laboratory of Earthquake Geodesy,Institute of Seismology of China Earthquake Administration,Wuhan 430071,China)

机构地区：[1]防灾科技学院,三河065201 [2]中国地震局地震研究所,中国地震局地震大地测量重点实验室,武汉430071

出　　处：《地震地质》2021年第5期1326-1338,共13页Seismology and Geology

基　　金：国家自然科学基金项目(41974096,41931074);国家重点研发计划(2018YFC1503503)共同资助。

摘　　要：粗差探测是卫星重力数据预处理环节的关键步骤。针对海量观测数据如卫星重力梯度数据,原有的粗差探测方法存在时间消耗长、准确率较低等不足。文中基于长短时记忆(LSTM)网络方法,提出了可用于重力梯度数据粗差探测的机器学习方法,实现了对长时间序列观测数据的粗差识别问题,避免了粗差对观测数据的影响。计算结果显示,LSTM训练模型的预测精度达99.4%,在预测过程中,扩大训练数据量或增加LSTM神经元的个数都可提高预测效果,且损失函数、学习率、迭代次数等是影响预测效果的主要模型参数。训练模型识别粗差实验结果表明:LSTM模型能够很好地应用于卫星重力梯度测量观测数据的粗差探测。Outlier detection is a key step in satellite gravity data preprocessing.As the theory and practice of GOCE satellite gravity gradient measurement get more and more sophisticated,the spatial resolution of satellite gravity data can reach the order of 1mgal and the accuracy of 1~2cm.However,due to the interference of various uncertain factors and the characteristics of massive observation,the satellite gravity gradient data often have some outliers.Simulation studies have shown that outliers will adversely affect the interpretation of various physical phenomena.In addition,the existing outlier detection methods have the disadvantages of high time consumption and low accuracy,which reduces the reliability of data analysis and affects the accuracy of the results.Therefore,outliers need to be eliminated.In recent years,with the in-depth development of artificial intelligence technology in earth science research and applications,many new methods and achievements in geoscience have been obtained at home and abroad.Inspired by the fact that long short-term memory networks can capture long-term or short-term information in data sequences,in this paper,a long short-term memory(LSTM)network for outlier detection of gravity gradient data is proposed.This network is a special type of cyclic neural network that can avoid long-term dependence.It adopts the special gate structure of LSTM network,trains the sample characteristics through the calculation of forgetting gate,input gate and output gate,and the LSTM network selectively updates or discards the neuron vector so as to preserve the long-term state of neurons and make LSTM network perform better on long-time series.In order to prove the reliability of extracting outliers by long short-term memory neural network method,the simulated satellite gravity data can be used for the analysis.Firstly,through the 300-order EMG96 model,the normal ellipsoid GRS80 simulates the gravity gradient data with a sampling rate of 5s and a length of 1 day,and by selecting the function whose exp

关 键 词：卫星重力梯度 粗差探测 长短时记忆网络 模型参数 

分 类 号：P315.726[天文地球—地震学]