Zhen Li

In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes.

In order to study the seismic-ionospheric coupling relationship, this paper selects window values based on wavelet power spectrum method, and obtains the 27-day periodicity of wavelet power spectrum in the full time domain that passes 95% significance test. Then, sliding quartile method is used to analyze the earthquakes above Mw6.5 in Japan from 2011 to 2022, which exclude hybrid effects. Sunspot number (SSN), 10.7cm radio flux (F10.7), solar wind velocity (Vsw), storm ring current index (DST), and global Geomagnetic activity index (KP) are excluded as interference terms to eliminate the influence of solar and geomagnetic activity. According to the lithosphere-atmosphere-ionospheric coupling mechanism (LAIC) and global TECmap, the abnormal changes on the 29th day before the earthquake (February 15, 2022), which do not conform to the previous research rules, are analyzed to prove that TEC anomalies caused by earthquakes. The statistical analysis of ionospheric TEC anomalies shows that the magnitude of earthquake is positively correlated with the amplitude of TEC anomalies, and the occurrence time of ionospheric anomalies tends to lag as the magnitude increases.

Interferometric Synthetic Aperture Radar (InSAR) phase unwrapping error is a major limiting factor on the InSAR-derived tectonic deformation velocity. This is particularly the case when atmospheric turbulence, large deformation gradient and strong phase noise exist. To address limitations of existing phase unwrapping error correction methods which are not supported for multi-looked InSAR data, here we present a new algorithm that integrates decorrelation phase correction, triplet phase closure (TPC) test and integer linear programming (ILP) to overcome this limit. The rationale behind is that we mitigate the phase inconsistence using decorrelation correction and then detect the phase unwrapping error magnitude using TPC. Next we borrow the ILP from Compressed Sensing that converts the phase unwrapping error correction to a sparse signal recovery problem. We demonstrated the validity of our method by using synthetic data and 5-years Sentinel-1 real data covering the Central San Andreas Fault creep section, where exists obvious tectonic deformation, strong atmospheric disturbance and decorrelated scatterers, and the inverted long-term creep model constrained by InSAR velocity after correction shows a lower uncertainty than that constrained by the uncorrected one.

This paper comprehensively analyzes the effect of surface mass loading on GPS height time series in terms of weighted root mean square (WRMS), annual timescale, velocity and noise properties. Considering the complexity of the hydrological process, we adopt the same atmospheric loading and nontidal ocean loading estimated from the TUGO-m barotropic model (ATMMO) and different hydrological loadings estimated from the Global Land Data Assimilation System (GLDAS) and the Global Land Surface Discharge Model (LSDM). The results indicate that the combined ATMMO & GLDAS outperforms the combined ATMMO & LSDM in reducing the WRMS of GPS height times series due to the annual phase asynchrony between GPS height and ATMMO & LSDM. Furthermore, cross-wavelet transform-based (XWT-based) semblance analysis is adopted to quantitatively express the annual phase relationship between GPS height time series and different loading model combinations. The results show that (1) mass loading is the main driving force for the annual fluctuation in GPS height, and (2) the annual term of ATMMO & GLDAS better matches the GPS height time series than ATMMO & LSDM. Consequently, it is concluded that the LSDM may not work well in Eurasia with its current accuracy. Finally, we investigate the impacts of the mass loading correction on site velocity and noise properties. For most sites, the velocity change can reach 0.1 mm/year after the loading correction, indicating that the influence of mass loading should not be ignored in the construction of millimeter-level earth reference frames. However, for some sites, the velocity uncertainty is increased due to undesirable changes in noise properties, suggesting that using the mass loading model to correct GPS height time series is potentially feasible but not sufficient.

We investigated the effect of mass loading (atmospheric, oceanic and hydrological loading (AOH)) on Global Positioning System (GPS) height time series from 30 GPS stations in the Eurasian plate. Wavelet coherence (WTC) was employed to inspect the correlation and the time-variable relative phase between the two signals in the time–frequency domain. The results of the WTC-based semblance analysis indicated that the annual fluctuations in the two signals for most sites are physically related. The phase asynchrony at the annual time scale between GPS heights and AOH displacements indicated that the annual oscillation in GPS heights is due to a combination of mass loading signals and systematic errors (AOH modelling errors, geophysical effects and/or GPS system errors). Moreover, we discuss the impacts of AOH corrections on GPS noise estimation. The results showed that not all sites have an improved velocity uncertainty due to the increased amplitude of noise and/or the decreased spectral index after AOH corrections. Therefore, the posterior mass loading model correction is potentially feasible but not sufficient.

Sunspot number is an important parameter for presenting the intensity of solar activity. Based on the sunspot number series, which has been replaced by a new improved version since 2015, we confirm that the sunspot number has significant variations at 11-year and 112-year periods. The sunspot number has also increased from 1700 to 2016 with 0.08 annual increments on the basis of wavelet analysis and least-square fitting. We further confirm that global temperatures are remarkable in 22-year and 64-year cycles. The result of wavelet transform coherence (WTC) analysis suggests that solar activity has a positive lag effect on global temperatures in the period band of 22 years with a 3-year lag. However, the linearly increasing global temperature has hampered WTC analysis since 1960. Aiming to solve this problem, we apply wavelet decomposition and cross correlation to determine whether the aforementioned lag effect in the period band of 22 years has a 2-year lag rather than a 3-year lag. We find that the 22-year magnetic field solar cycle plays a greater role in global climate change than the 11-year sunspot cycle. In addition, we notice that the solar activity is not a representation of the driving force of the upward trend of global temperature after the industrial age. The Granger causality test results demonstrate that the phenomenon of the global warming is caused by excessive CO₂ emissions.