Global Positioning System Displacement Research Articles

Early postseismic slip of the 21 November 2022 Mw 5.6 Cianjur, Indonesia, earthquake based on GPS measurements

ABSTRACT Global Positioning System (GPS) data after the 21 November 2022 Mw 5.6 Cianjur earthquake are crucial for understanding the physical processes during the postseismic phase. New continuous GPS stations were installed and recorded data for periods ranging from five to twenty-five days after the mainshock. The daily time series data at GPS stations shows an evolving logarithmic decay over time, as expected due to frictional afterslip. We inverted the postseismic displacement data to solve the postseismic slip distribution, utilising Akaike’s Bayesian information criterion. A cumulative aseismic slip moment of 1.54 × 1016 N·m, equivalent to Mw 4.7, is modelled during the period from five to twenty-five days after the mainshock. This aseismic slip moment is equivalent to approximately 7% of the seismic moment during the mainshock. Our analysis on the newly identified active fault using GPS displacements highlights the imperative need to identify active faults prior to future earthquake occurrences, especially when conducting hazard mitigation in populated regions in Indonesia.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Mapping Glacier Basal Sliding Applying Machine Learning

AbstractDuring the RESOLVE project (“High‐resolution imaging in subsurface geophysics: development of a multi‐instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass‐filtered copies of the continuous seismic waveforms, we compute energy‐based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on‐ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

Studying spatio-temporal patterns of vertical displacements caused by groundwater mass changes observed with GPS

The sensitivity of Global Positioning System (GPS) technique to detect displacements caused by Total Water Storage (TWS) changes has already been analyzed, while its sensitivity to individual TWS components, such as groundwater, is still in question. We use the probabilistic Principal Component Analysis (pPCA) to examine the spatio-temporal variations of vertical displacements from 98 GPS stations located in 9 regions of the world recognized as those where changes in groundwater masses are the most significant. To study the Earth's crust displacements induced by changes in groundwater masses, we remove from GPS displacements those induced by changes in masses of other water storage components using the WaterGAP Global Hydrology Model (WGHM). For validation purposes, displacements predicted by the Gravity Recovery and Climate Experiment (GRACE) missions from which the WGHM compartments other than groundwater are subtracted are also used. We estimate an average global trend of vertical displacements induced by groundwater mass changes equal to 0.4 ± 0.17 mm/yr for GRACE-WGHM differences. We observe that this trend is overestimated by groundwater-induced displacements estimated for GPS-WGHM differences for several regions around the world and mostly underestimated by WGHM. Spatio-temporal patterns are very coherent between GPS-WGHM differences and GRACE-WGHM differences with significant signatures observed for periods of intense natural and/or human-induced groundwater mass changes. We show that the determined GRACE-WGHM differences and GPS-WGHM differences capture most of the wet and dry periods reflected by the Standardized Precipitation Evapotranspiration Index (SPEI). We also find that groundwater-induced displacements estimated from the three datasets we use, show a prominent 6-year cycle. Finally, we demonstrate that GPS displacements can add very essential information to study and monitor local displacements due to groundwater mass changes and may successfully contribute to the climate-related research.

Strong Ground Motion Recorded by High-Rate GPS during the 2021 Ms 6.4 Yangbi, China, Earthquake

Abstract The 2021 Ms 6.4 Yangbi earthquake, Yunnan, China, was recorded by the Dali Global Positioning System (GPS) network comprising 37 permanent stations within 100 km of the epicenter. All of these GPS stations recorded 1 Hz data, and 12 of them also recorded 5 Hz data. Using sophisticated data processing strategies, especially the method to overcome ionospheric and multipath errors, near-field waveforms of epoch-by-epoch displacement of all of the GPS stations within an epicentral distance of ∼50 km were derived from high-rate GPS observations with a root mean square error of 3.8, 4.2, and 8.2 mm for the east–west, north–south, and up components, respectively. The peak ground displacement of up to 14 cm in the horizontal direction and 4.2 cm in the vertical direction and the largest coseismic displacement of 3.1 cm in the horizontal direction and 3.2 cm in the vertical direction were observed for the stations within 6–9 km of the epicenter. The waveforms, with dominant periods between 7 and 10 s, present a systematic change in shape as a function of distance from the source, which demonstrates that the high-rate GPS observations can provide reliable relative-displacement response spectra at the periods needed in the design of large structures for resisting strong earthquakes. Comparisons of the GPS displacement time series at 1, 2.5, and 5 Hz suggest that 5 Hz data are able to capture strong ground information that are of interest to both earthquake seismologists and engineers. We conclude that the displacement waveforms and motion trajectories from high-rate GPS observations provide unique additional information of the near-field strong ground motion that is valuable to seismologists in unraveling the dynamic process of fault rupture and to engineers for designing large structures with very long period response.

Source Process Featuring Asymmetric Rupture Velocities of the 2021 Mw 7.4 Maduo, China, Earthquake from Teleseismic and Geodetic Data

AbstractOn 21 May 2021, an Mw 7.4 left-lateral strike-slip earthquake occurred within the Bayan Har block in the Tibetan plateau. To learn about the source rupture process, we collected the teleseismic waveforms and utilized the backprojection method to investigate the rupture kinematics of the earthquake. The results indicate that the earthquake was a bilateral rupture event with asymmetric rupture velocities. The rupture velocity in the east of the epicenter was uniform and in the range of 2.72–3.67 km/s, whereas, in the west, it was in the range of 1.39–1.78 km/s in the first 20 km and then increased to 2.82–3.17 km/s. The slip distribution constrained by the Interferometric Synthetic Aperture Radar and Global Positioning System displacements clearly reveals kinematic coseismic slip in greater detail, which makes up for the limitations of the backprojection method. Two main asperities in the east verify the results of the backprojection method. The rupture depth in the west was slightly shallower than that in the east, which may be the reason for the asymmetry of rupture velocities. The initial rupture point was updated based on the asymmetric velocities and geodetic slip distribution. The multiple-point-source moment tensors based on the rupture velocities and new initial rupture point not only match the fault geometries determined by relocated aftershocks but also fit well with the released energy distribution, which proves the asymmetry of rupture velocities.

A Loading Correction Model for GPS Measurements Derived from Multiple-Data Combined Monthly Gravity

Time-dependent loading deformations of the Earth’s surface, due to nontidal changes in the atmosphere, ocean, land water/ice, etc., contribute significantly to the seasonal and secular Global Positioning System (GPS) site displacements, especially for the up component. While loading deformations derived from general circulation model (GCM) outputs are usually used to correct loading signals in the GPS site displacements, this study aims to provide a loading correction model based on the multiple-data combined monthly gravity products LDCmgm90. We have adopted GPS measurements from 249 IGS reference frame stations and 3 different GCM-based loading models to test the reliability of the LDCmgm90 model. Compared to the GCM-based models, the LDCmgm90 loading correction is more effective in attenuating seasonal (especially annual) loading signals and can bring more significant improvements to most stations for both the data-trend-removed and the data-trend-retained cases. Thus, we have validated the LDCmgm90 model from the loading aspect and proved it to be a reliable loading-correction model for GPS displacements. The relatively better secular loading signals provided by the LDCmgm90 loading model may provide us a chance to study the long-term, nonloading signals in GPS data.

Rapid Earthquake Source Description Using Variometric-Derived GPS Displacements toward Application to the 2019 Mw 7.1 Ridgecrest Earthquake

AbstractUsing near-field high-rate Global Positioning System (GPS) displacements to invert for earthquake fault slips in real time has the potential to improve the accuracy of earthquake early warning or tsunami early warning. For such applications, real-time retrieval of high-accuracy GPS displacements is essential. Here, we report on rapid modeling of the 2019 Mw 7.1 Ridgecrest earthquake with real-time GPS displacements derived from a variometric approach with readily available broadcast ephemeris. This method calculates station variations in real time by differencing continuous phase observations and does not rely on precise orbit and clock information. The phase ambiguity is also removed, and thus the method does not suffer from a relatively long convergence time. To improve the accuracy of variometric displacements, we use a local spatial filter to decrease the influence of residual errors that cannot be removed completely by the time difference. We invert for the centroid moment tensor, static fault slips, and fault rupture process from the derived displacements. Our results show that all inverted models are available within about 65 s after the origin time of the earthquake and are comparable with models inverted by real-time precise point positioning displacements. This study highlights the great value of variometric displacements for the rapid earthquake source description with only broadcast ephemeris.

GPS Constraints on Drought‐Induced Groundwater Loss Around Great Salt Lake, Utah, With Implications for Seismicity Modulation

AbstractGreat Salt Lake (GSL), Utah, lost 1.89 ± 0.04 m of water during the 2012–2016 drought. During this timeframe, data from the Gravity Recovery and Climate Experiment mission underestimate this mass loss, while nearby Global Positioning System (GPS) stations exhibit significant shifts in position. We find that crustal deformation, from unloading the Earth's crust consistent with the observed GSL water loss alone, does not explain the GPS displacements, suggesting contributions from additional water storage loss surrounding GSL. This study applies a damped least squares inversion to the three‐dimensional GPS displacements to test a range of distributions of groundwater loads to fit the observations. When considering the horizontal and vertical displacements simultaneously, we find a realistic distribution of water loss while also resolving the observed water loss of the lake. Our preferred model identifies mass loss up to 64 km from the lake via two radial rings. The contribution of exterior groundwater loss is substantial (10.9 ± 2.8 km3 vs. 5.5 ± 1.0 km3 on the lake), and greatly improves the fit to the observations. Nearby groundwater wells exhibit significant water loss during the drought, which substantiates the presence of significant water loss outside of the lake, but also highlights greater spatial variation than our model can resolve. We observe seismicity modulation within the inferred load region, while the region outside the (un)loading reveals no significant modulation. Drier periods exhibit higher quantities of events than wetter periods and changes in trend of the earthquake rate are correlated with regional mass trends.

The Complex Love Numbers of Long‐Period Zonal Tides Retrieved From Global GPS Displacements: Applications for Determining Mantle Anelasticity

AbstractLong‐period zonal tidal Love numbers are more sensitive to lower‐mantle anelasticity (which is difficult to determine even from laboratory); however, the Love numbers of the zonal tides have not been adequately determined. In this study, we use the optimal sequence estimation method with the Global Positioning System (GPS) displacements to estimate the complex Love numbers of the long‐period zonal tides. For the first time, we retrieve the complex Love numbers h20 and l20 for the Msm, Msf, SN, Mstm, Mtm, Msqm, and Mqm tides from the Up‐ and North‐components of the global GPS displacements; we also re‐estimate the same complex Love numbers for the Mm (monthly) and Mf (fortnightly) tides with a higher precision. Based on the robust estimated h20 and l20 values for the nine zonal tides, we inverse their corresponding mantle anelastic parameters fr and fi, respectively. We finally obtain that the material‐dependent parameter α for the lower mantle is 0.189 ± 0.07, and find that the 18.6‐year nodal tide and the Chandler wobble, as well as the long‐period zonal tides, can be represented by a single absorption band. Our estimates for fr, fi, and α can be further used to construct attenuation and velocity models of the Earth's interior.

Improved Hydrological Loading Models in South America: Analysis of GPS Displacements Using M-SSA

Environmental loading, in particular from continental water storage changes, induces geodetic station displacements up to several centimeters for the vertical components. We investigate surface deformation due to loading processes in South America using a set of 247 permanent GPS (Global Positioning System) stations for the 2003–2016 period and compare them to loading estimates from global circulation models. Unfortunately, some of the hydrological components, and in particular surface waters, may be missing in hydrological models. This is especially an issue in South America where almost half of the seasonal water storage variations are due to surface water changes, e.g., rivers and floodplains. We derive river storage variations by rerouting runoffs of global hydrology models, allowing a better agreement with the mass variations observed from GRACE (Gravity Recovery and Climate Experiment) mission. We extract coherent seasonal GPS displacements using Multichannel Singular Spectrum Analysis (M-SSA) and show that modeling the river storage induced loading effects significantly improve the agreement between observed vertical and horizontal displacements and loading models. Such an agreement has been markedly achieved in the Amazon basin. Whilst the initial models only explained half of the amplitude of GPS, the new ones compensate for these gaps and remain consistent with GRACE.

Identifying the sensitivity of GPS to non-tidal loadings at various time resolutions: examining vertical displacements from continental Eurasia

We examine the sensitivity of the Global Positioning System (GPS) to non-tidal loading for a set of continental Eurasia permanent stations. We utilized daily vertical displacements available from the Nevada Geodetic Laboratory (NGL) at stations located at least 100 km away from the coast. Loading-induced predictions of displacements of earth’s crust are provided by the Earth-System-Modeling Group of the GFZ (ESMGFZ). We demonstrate that the hydrological loading, supported by barystatic sea-level changes to close the global mass budget (HYDL + SLEL), contributes to GPS displacements only in the seasonal band. Non-tidal atmospheric loading, supported by non-tidal oceanic loading (NTAL + NTOL), correlates positively with GPS displacements for almost all time resolutions, including non-seasonal changes from 2 days to 5 months, which are often considered as noise, intra-seasonal and seasonal changes with periods between 4 months and 1.4 years, and, also, inter-annual signals between 1.1 and 3.0 years. Correcting the GPS vertical displacements by NTAL leads to a reduction in the time series variances, evoking a whitening of the GPS stochastic character and a decrease in the standard deviation of noise. Both lead, on average, to an improvement in the uncertainty of the GPS vertical velocity by a factor of 2. To reduce its impact on the GPS displacement time series, we recommend that NTAL is applied at the observation level during the processing of GPS observations. HYDL might be corrected at the observation level or remain in the data and be applied at the stage of time series analysis.

Displacements Before and After Great Earthquakes: Geodetic and Seismic Viewpoints

Analysis of geodetic data obtained by the global positioning system (GPS) leads to better understanding of earthquake origins and helps to interpret their sequences. For this, we cross-compare the pre- and postseismic displacements associated with great earthquakes and derived from GPS observations and seismic catalogs. In a pilot study of the 2004 Sumatra–Andaman Mw9.2 earthquake in the Indian Ocean, the 2011 Tohoku Mw9.1 earthquake in Japan, the 2010 offshore Maule Mw8.8 and the 2015 Illapel Mw8.3 earthquakes in Chile, the 2018 Kodiak Mw7.9 earthquake in the Gulf of Alaska, and the 2016 Kaikoura Mw7.8 earthquake in New Zealand, we consider GPS data from stations of the Global Navigation Satellite System (GNSS) along with integral characteristics of the regional seismic regime, including the accumulated displacement derived from the catalogs of earthquake hypocenter parameters. We did not find any prominent transient pattern in the daily geodetic measurements registered before the six great earthquakes at the nearest GNSS stations. We found that (a) the six cases exhibit different GPS versus seismic displacement correlation patterns before and after the great earthquakes, (b) the observed high variability of the correlation between geodetic ρ(t) and seismic ∑(t) integrals appears indicative of a partial contribution of earthquakes to naturally haphazard sporadic movement of lithospheric blocks of different size, and (c) GPS data confirm the existence of intermittent long periods of regionally stable levels of seismic regime reflected in the control parameter of the Unified Scaling Law for Earthquakes that may change as a result of mid- or even short-term bursts of activity associated with catastrophic events.

Common-mode error and multipath mitigation for subdaily crustal deformation monitoring with high-rate GPS observations

High-rate precise point positioning with a global positioning system (GPS) is recognized as a powerful tool for large earthquake monitoring. However, its ability to monitor subdaily subtle deformation is still limited by unmodeled errors, which primarily contain the common-mode error (CME) and multipath. In this study, a combination of modified principal component analysis and the sidereal filtering method is employed to separate and extract the CME and multipath from high-rate GPS displacements. The effectiveness of the proposed method is validated by processing 1 Hz GPS data at 131 static stations over 30 days, during which the 4 and 6 July 2019 Ridgecrest earthquakes occurred. The results show no obvious early afterslip within the first 5 h following the 6 July Mw 7.1 earthquake. The CME mainly consists of the first principal component and can be attributed to satellite orbit and clock errors. The proportion of the CME is higher than that of the multipath in the high-rate displacements, namely 36.7% versus 14.4%, 23.8% versus 18.1%, and 36.1% versus 12.5% for the east, north, and up components, respectively. By applying this method, the high-rate displacements can achieve precisions of 0.47, 0.50, and 1.60 cm in the three components. The results of the rapid fault-slip inversions indicate that the PPP fixed solution augmented with spatiotemporal filtering can improve fault-slip inversion and moment magnitude estimation and may lead to new findings for geophysics.

InSAR 3-D Coseismic Displacement Field of the 2015 Mw 7.8 Nepal Earthquake: Insights into Complex Fault Kinematics during the Event

The 2015 Mw 7.8 Gorkha, Nepal, earthquake occurred in the central Himalayan collisional orogenic belt, which demonstrated complex fault kinematics and significant surface deformation. The coseismic deformation has been well documented by previous studies using Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data. However, due to some limitations of spatially sparse GPS stations and InSAR only-one-dimensional observation in the line-of-sight (LOS) direction, the complete distribution and detailed spatial variation of the three-dimensional surface deformation field are still not fully understood. In this study, we reconstructed the three-dimensional coseismic deformation fields using multi-view InSAR observations and investigated the refined surface deformation characteristics during this event. We firstly obtained four ascending and descending InSAR coseismic deformation maps from both Sentinel-1A/B and ALOS-2 data. Secondly, we obtained the synthetic north-south deformation field from our best-fitting slip distribution inversions. Finally, we calculated three-dimensional deformation fields, which were consistent with coseismic GPS displacements but with higher resolution. We found that the surface deformation is dominated by horizontal southward motion and vertical uplift and subsidence, with minor east-west deformation. In the north-south direction, the whole deformation area reaches at least 150 × 150 km with a maximum displacement of ~1.5 m. In the vertical direction, two areas, including uplift in the south and subsidence in the north, are mapped with a peak displacement of 1.5 and −1.0 m, respectively. East-west deformation presented a four-quadrant distribution with a maximum displacement of ~0.6 m. Complex thrusting movement occurred on the seismogenic fault; overall, there was southward push motion and wave-shaped fold motion.

A possible precursor prior to the Lushan earthquake from GPS observations in the southern Longmenshan

Global Positioning System (GPS) stations installed in and around the epicenter of the Lushan earthquake (Mw 6.7), which occurred almost 5 years after the 2008 Wenchuan earthquake, recorded preseismic deformation corresponding to the Lushan earthquake within the southern Longmenshan thrust belt. A half-space dislocation model is used to simulate the theoretical values of the postseismic displacements caused by the 2008 Wenchuan earthquake, and after transforming the reference frame and filtering the GPS displacement time series, the theoretical and observed GPS values are compared to identify the geodetic anomaly preceding the Lushan earthquake. The abnormal extent of this geodetic anomaly decreases with increasing epicentral distance for each GPS site. This geodetic signal reflects preslip along a locked section of the 2013 seismogenic fault, which caused the accumulation of elastic strain energy until the faulting strength was overcome, thereby generating the Lushan earthquake. Hence, this anomaly might be used as an observable and identifiable precursor to forecast an impending earthquake within a period of less than two and half years before its occurrence.

Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections

The 2016 Mw 7.8 Kaikōura earthquake represents an extremely complex event involving over ten major crustal faults, altering conventional understanding of multi-fault ruptures. Although evidence for coseismic slip on the Hikurangi subduction interface is controversial, we present afterslip on the subduction zone beneath Marlborough using 13 months of Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) observations. The spatially and temporally correlated atmospheric errors in SAR interferograms are problematic, and hence a new InSAR time series approach, combining the Generic Atmospheric Correction Online Service (GACOS) with a spatial-temporal Atmospheric Phase Screen (APS) filter to facilitate the InSAR time series analysis, is developed. For interferograms with over 250 km spatial extent, we achieve a 0.77 cm displacement RMS difference against GPS, improving 61% from the conventional InSAR time series method (TS). Comparisons between the overlapping region of two independent tracks show an RMS difference of 1.1 cm for the TS-GACOS-APS combined method, improving 54% from the TS method and 27% from using TS with an APS filter only. The APS filter reduces the short wavelength residuals substantially, but fails to remove the long wavelength error even after the ramp removal, revealing that the GACOS correction has played a key role in mitigating long wavelength atmospheric effects. The resultant InSAR displacements, together with the GPS displacements, are used to recover the time-dependent afterslip distribution on the Hikurangi subduction interface, which provides insights for reviewing the co-seismic slip sources, the present status of the subduction plate boundary and future seismic hazards.

An Improved GPS-Inferred Seasonal Terrestrial Water Storage Using Terrain-Corrected Vertical Crustal Displacements Constrained by GRACE

Based on a geophysical model for elastic loading, the application potential of Global Positioning System (GPS) vertical crustal displacements for inverting terrestrial water storage has been demonstrated using the Tikhonov regularization and the Helmert variance component estimation since 2014. However, the GPS-inferred terrestrial water storage has larger resulting amplitudes than those inferred from satellite gravimetry (i.e., Gravity Recovery and Climate Experiment (GRACE)) and those simulated from hydrological models (e.g., Global Land Data Assimilation System (GLDAS)). We speculate that the enlarged amplitudes should be partly due to irregularly distributed GPS stations and the neglect of the terrain effect. Within southwest China, covering part of southeastern Tibet as a study region, a novel GPS-inferred terrestrial water storage approach is proposed via terrain-corrected GPS and supplementary vertical crustal displacements inferred from GRACE, serving as "virtual GPS stations" for constraining the inversion. Compared to the Tikhonov regularization and Helmert variance component estimation, we employ Akaike’s Bayesian Information Criterion as an inverse method to prove the effectiveness of our solution. Our results indicate that the combined application of the terrain-corrected GPS vertical crustal displacements and supplementary GRACE spatial data constraints improves the inversion accuracy of the GPS-inferred terrestrial water storage from the Helmert variance component estimation, Tikhonov regularization, and Akaike’s Bayesian Information Criterion, by 55%, 33%, and 41%, respectively, when compared to that of the GLDAS-modeled terrestrial water storage. The solution inverted with Akaike’s Bayesian Information Criterion exhibits more stability regardless of the constraint conditions, when compared to those of other inferred solutions. The best Akaike’s Bayesian Information Criterion inverted solution agrees well with the GLDAS-modeled one, with a root-mean-square error (RMSE) of 3.75 cm, equivalent to a 15.6% relative error, when compared to 39.4% obtained in previous studies. The remaining discrepancy might be due to the difference between GPS and GRACE in sensing different surface water storage components, the remaining effect of the water storage changes in rivers and reservoirs, and the internal error in the geophysical model for elastic loading.

Retrieving geophysical signals from GPS in the La Plata River region

Over the last few years, efforts to model short-term deformations of the Earth’s crust have multiplied. Sudden water level rise can cause sporadic, but significant, motions in the solid Earth’s surface. In this work, we address the problem of retrieving reliable estimates of the vertical displacement of a Global Positioning System (GPS) station located very close to the eastern shore of the La Plata River, during a strong storm surge event. Capturing sub-daily GPS displacements demands an elaborate processing strategy because several highly correlated parameters must be estimated simultaneously. We present a successful strategy that reduces the number of unknowns that have to be estimated simultaneously, by using an empirical model that describes the elastic response of the Earth’s crust to the hydrological load variations. We incorporate this model into the observation equations so that, instead of estimating the station position, we estimate every epoch a single parameter of the empirical model, i.e., the empirical elastic parameter EEP, that is assumed to be a constant of the Earth’s crust in the region of the GPS station. We verify that the estimated parameter agrees well with the value calculated from the CRUST 1.0-A model of the Earth’s crust. The GPS receiver was tied to an external cesium clock, which allowed us to process the data according to two different strategies: (a) estimating the receiver clock error (Δt) as an epoch-wise free parameter, which is equivalent to ignoring the presence of the external clock, and (b) conditioning the variability of that estimate with a small a priori variance compatible with the external clock’s variability. We find that, without having an external atomic clock, the estimation of all the parameters, i.e., the zenith tropospheric delay, Δt, and the EEP, worsens when the GPS station is affected by sub-daily vertical displacements.

Fault behavior and lower crustal rheology inferred from the first seven years of postseismic GPS data after the 2008 Wenchuan earthquake

Long-term and wide-area geodetic observations may allow identifying distinct postseismic deformation processes following large earthquakes, and thus can reveal fault behavior and permit quantifying complexities in lithospheric rheology. In this paper, the first 7 years of GPS (Global Positioning System) displacement data following the 2008 Mw7.9 Wenchuan earthquake are used to study the relevant mechanisms of postseismic deformation. Two simple models that consider either afterslip or viscoelastic relaxation as the unitary mechanism of the postseismic deformation are tested at first. After analyzing the limitations and complementarity of these two separated models, a combined model incorporating the two main mechanisms is presented. In contrast to previous studies, which mostly assume that afterslip and viscoelastic relaxation are independent, our combined model includes the secondary viscoelastic relaxation effect induced by transient afterslip. Modeling results suggest that the middle- to far-field postseismic deformation is mainly induced by viscoelastic relaxation of the coseismic stress change in the lower crust, whereas the near-field displacement is dominantly caused by stress-driven aseismic afterslip. The seismic moment released by the transient afterslip corresponds to an Mw7.4 earthquake, or 25% of that released by the Mw7.9 main shock. With a characteristic decay time of 1.2 yr, most afterslip (∼80%) is released in the first 2 years. Negligible aseismic afterslip is observed in the seismic gap between the Wenchuan and Lushan earthquakes, indicating the locked state of the fault within this segment. The effective lower crustal viscosity of the eastern Tibetan Plateau is estimated to be 2.0×1018Pas, at least two orders of magnitude smaller than that of the adjacent Sichuan Basin. This finding is consistent with previous observations of low seismic velocity and high electrical conductivity in this region, all of which support the assumption that the crustal thickening in the eastern Tibetan Plateau is dominantly caused by ductile lower crustal flow, with important implications for understanding both long- and short-term crustal deformation processes.