连续重力观测异常模式的多分辨率识别算法  

Multiresolution Recognition Algorithm for Abnormal Patterns of Continuous Gravity Observation

作　　者：刘子维[1] 张晓彤[1] 张锐 江颖[1] 韦进[1] 张坤[1] LIU Ziwei;ZHANG Xiaotong;ZHANG Rui;JIANG Ying;WEI Jin;ZHANG Kun(Key Laboratory of Earthquake Geodesy, Institute of Seismology, CEA, Wuhan 430071, China;National Earthquake Infrastructure Service, Beijing 100036, China)

机构地区：[1]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),湖北武汉430071 [2]地壳运动监测工程研究中心,北京100036

出　　处：《武汉大学学报（信息科学版）》2018年第6期840-846,942,共8页Geomatics and Information Science of Wuhan University

基　　金：国家重大科学仪器设备开发专项(2012YQ10022506);国家自然科学基金(41374088,41304018,41404065,41404064);地震科技星火计划(XH15028)~~

摘　　要：在小波多孔算法的基础上,提出了一种综合信号频率信息和幅值信息的连续重力观测数据多分辨率异常模式识别算法,利用小波分解得到高频区域的能量作为频率指标,与幅值相结合,对信号及其多孔小波分解结果进行多分辨率异常模式识别。利用模拟数据和实际超导重力观测数据对算法的有效性进行了验证,结果表明,该算法能够准确地在带有噪声的信号中识别模拟数据的异常模式,可应用于连续重力观测台网数据分析处理,对于提升台网观测数据质量以及地震预测等实际应用都具有重要意义。用此方法分析拉萨和武汉的3台超导重力仪2015-04-25尼泊尔地震前一天的秒采样数据后,得到一段27min的在频率指标上有超过90%相似性的异常模式,这一结果的更深层次物理解释仍需要进一步研究。Based onàtrous algorithm,we propose a multiresolution algorithm,using the energy of high frequency domain of wavelet decomposition as the frequency index,combining with amplitude,to search for the abnormal pattern in the signal and its wavelet decomposition results.Simulated data and actual superconducting gravimeter（SG）results verify the effectiveness of this algorithm.Results show that the algorithm can accurately identify anomalies in the simulated data with noise.Using this algorithm when processing of continuous gravity observation data can improving the quality of China gravity network observation data with significance in earthquake prediction and other applications.We found 27-minute anomalies after analyzing three SG datasets for Lhasa and Wuhan after the 2015Nepal earthquake,with a similarity of over 90%.The reason of this phenomenon still needs further research in future.

关 键 词：连续重力观测 时间序列 多分辨率算法 异常模式识别 小波变化 时频分析 多孔算法 

分 类 号：P223.7[天文地球—大地测量学与测量工程]