基于GNSS时空数据的瞬态无震蠕滑信息检测

利用分布活动断层带的GNSS位移时空序列,基于卡尔曼滤波和主成分分析,研究了一套集GNSS时空噪声处理与断层瞬态无震蠕滑信息时空分布检测于一体的方法.基于一阶高斯马尔科夫理论表述了瞬时蠕滑信息与时空噪声的运动状态方程,通过卡尔曼滤波提高了时间域信噪比;根据断层形变高空间相关性的特点,通过主成分时空分析,提高了空间域信噪比.模拟试验结果表明,当断层滑移引起的地表位移大小至少与噪声水平相当时,均可检测得到断层瞬态无震蠕滑时空分布及其滑移特征.以Cascadia慢滑移事件为例,清晰地检测到了两次无震蠕滑信息,分析其蠕滑特征与相关研究结果吻合.通过对滇西2011—2013年陆态网GNSS连续站数据的处理,分析发现了期间有微弱的震后余滑信息,主要表现在澜沧江断裂、红河断裂和小江断裂的南段.其时空分布特征与2011年3月24日的缅甸MW7.2级地震相对应.因此,得出了2011—2013年期间云南地区的断层活动可能与缅甸地震带有着密切的关系的结论.

Detection of transient aseismic slip signals from GNSS spatial-temporal data

The aseismic slip or slow earthquakes is a very important component of the seismogenic process of faults. However, it is difficult to detect these small transient signals because of the low signal-to-noise ratio (SNR) in GNSS observation data. Based on the rich GNSS spatial-temporal data in active faults, a method of integrating processing of spatial-temporal noise and detection of transient aseismic slip signals is proposed.The transient signal and spatiotemporal correlated noise are expressed by the first-order Gaussian Markov process (FOGM). The SNR in the time domain is enhanced by Kalman filtering (KF). According to the high spatial coherence of fault deformation, the SNR in the space domain is enhanced by the principal component analysis (PCA). Combining spatiotemporal filtering of PCA with KF, the SNR of GNSS observation data is further improved.The result of simulations shows that the method can properly eliminate the effect of the linear trend, the year and half year cycle, further improve the space-time SNR of GNSS spatial-temporal data, and realize the detection of the transient aseismic slip signals even though the SNR is 1∶1. Taking the slow slip event in Cascadia as an example, two aseismic slip events that happened in January 2007 and April 2008 were detected clearly. The interval of the two events was about 15 months and each event shows reverse displacements. The duration was about 18 days, and the displacement was about 8 mm,mainly distributed in the south edge of Cascadia subduction zone nearly in the range of 200 km. The slip characteristics were analyzed to be consistent with the relevant literature. Using the GNSS data in western Yunnan province from 2011 to 2013 provided by Crustal Movement Observation Network of China, the weak postseismic slip signals were detected. By principal component time response analysis, from March 2011 to June 2012, the displacements of NS and EW components exhibit obvious abnormal deviations, about 6 mm and 8 mm respectively. By space response analysis, the creep deformation is mainly distributed in the south section of the Lancangjiang fault, Red River fault and Xiaojiang fault. The spatial-temporal distribution corresponds to Burma M_W7.2 earthquake on March 24, 2011. It is concluded that the fault activity in western Yunnan province is closely related to the Burma earthquake.Although it is difficult to improve the SNR of the single station GNSS observation data, using the spatial-temporal series of the whole GNSS network, according to the high spatial coherence of the fault deformation, based on KF combining with PCA, allows us to reduce the space-time irrelevant noise effectively and to reveal the temporal-spatial distribution characteristics of the transient aseismic slip. It would provide an important constraint and priori information for further fine inversion of fault parameters.