GPS Stations Research Articles

Analysis of the common model error on velocity field under Colored noise model by GPS and InSAR: A case study in the Nepal and everest region

The accuracy of the velocity field will be affected by the noise model and common mode errors through GPS time series analysis. In order to analyze the influence of these two factors on the accuracy of the velocity field, two kinds of data are used, including the three-year observation from 20 permanent GPS stations with high spatial correlation in the Everest, which is about 650 km from north to south and 1068 km from east to west, and three-year 80 ascending images and 141 descending images from sentinel-1A, which are processed by GAMIT/GLOBK software and Small Baseline Subset-Interferometric Synthetic Aperture Radar method (SBAS-InSAR), respectively. The vertical deformation rate is solved by time series analysis through a self-made adaptive algorithm. In the analysis, the linear change rate, period, half period coefficient, and residual sequence of all stations are solved by using James L. Davis periodic model. The noise type of residual sequence is analyzed by the power spectrum model. The spatio-temporal correlated noise, Common Mode Error (CME), is extracted by the Principal Component Analysis (PCA) and Karhunen-Loeve (KLE) methods. The results show that noises can be best described by “flicker noise + white noise” model. After the removal of CME, the R2 estimates of all stations are above 0.8, with RMS value of velocity field decreasing from 1.428 mm/yr to 1.062 mm/yr and 1.063 mm/yr to 0.815 mm/yr, in N and E directions, respectively, indicating that the influence of CME can't be ignored in the extraction of the high-precision velocity field in the Nepal and Everest region.

Microseismicity along Xiaojiang Fault Zone (Southeastern Tibetan Plateau) and the characterization of interseismic fault behavior

The Xiaojiang Fault (XJF) Zone locates in the southeastern of Tibetan Plateau and defines the boundary between the South China and Sichuan-Yunnan blocks. Historical large earthquakes were hosted on the XJF, though its seismic hazard in the near future is under debate. In this study, we utilize portable broad-band seismic network to unravel the microseismic activities along XJF, and to further investigate the fault structures and their properties. Adopting PALM, a newly developed earthquake detection algorithm, we obtained ~13,000 relocated events. The micro-seismicity reveals widespread off-fault structures showing conjugate geometry, while the major faults present low seismicity. The fault branches conjugate to the main-fault present intensive microseismicity, which hosts repeating events and presents high b-value. Regional GPS stations reflect slips are mostly concentrated along the XJF, while the slip rate on off-fault branches correlates with seismic activities on these structures. Combining with other recent seismological and magnetotellurics evidences, we suggest a low strength on these off-fault structures, which may partially release tectonic stress loading and serve as a barrier for future big earthquakes. On the XJF, the microseismic events are clustered on the fault junctions with low b-value. A special set of clusters between 25°N to 25.5°N show an along-strike variation of depth from 10 to 25-km, imaging the boundary between creeping and locked fault portions. We revisit the seismic hazard problem of XJF, and conclude that XJF is at the late stage of inter-seismic period.

Solar activity influence on the temporal and spatial variations of the Arctic and Antarctic ionosphere

Based on Total Electron Content (TEC) data in polar areas derived from Global Ionospheric Maps (GIMs) and observations of single GPS stations from 2000 to 2020, combined with solar activity parameters F10.7, the influence of solar activity on the polar ionosphere is studied. Firstly, the spatiotemporal variation characteristics of the Arctic and Antarctic ionosphere TEC are analyzed in different solar activity intensities. Secondly, the correlations between polar TEC and F10.7 are studied in different solar activity stages. Thirdly, the variations of TEC during the polar day and polar night in year of very low solar activity (2008) are analyzed. The results show the monthly average value of the polar ionospheric TEC reaches its maximum in May and June under the influence of solar activity. However, during the peak year of solar activity, the monthly average peak value of TEC may arrive early, reaching its maximum value in March or April. The dependence of polar TEC on the solar activity varies at different periods of solar activity (rising or declining period). The correlation between polar TEC and F10.7 is higher in the declining period of solar activity than in the rising period of solar activity. During the rising period of solar activity, the TEC at different times under the same solar activity intensity is also different, and the low value of TEC has a lower correlation with solar activity. The ionosphere at the Arctic and Antarctic is quiet during polar night in 2008, and ionization patch appears in the Arctic. During the polar days, the ionospheric peaks of the Arctic and Antarctic occur in the areas with a high solar irradiation altitude angle. However, the ionospheric peak region in the Antarctic area from 10:00 UT to 20:00 UT does not follow this rule.

Creep-dilatancy development at a transform plate boundary

How tectonic plates slip slowly and episodically along their boundaries, is a major, open question in earthquake science. Here, we use offshore in-situ sediment pore-pressure acquired in the proximity of the active offshore Main Marmara Fault and onshore geodetic time-series data set from a single GPS station to demonstrate the pore-pressure/deformation coupling during a 10-month slow-slip event. We show that pore pressure fluctuations are the expression of hydro-mechanical process affecting the deep seismogenic zone and indicate that small disturbances in geodetic data may have important meaning in terms of transient deformations. These results have major implications in understanding the spatial impact of slow-slip processes and their role in earthquake cycles. We demonstrate that piezometers measuring along a transform fault can help define the time scale regulating the coupling between slow-slip events and earthquake nucleation process.

Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods

This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations.

Greenland Mass Trends From Airborne and Satellite Altimetry During 2011-2020.

We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea‐level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea‐level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer‐term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice‐loss product results in a significantly different GIA interpretation from a previous ice‐loss product.

Long-term changes in crustal movements and deformations before and during the 2016 Kumamoto earthquake sequence

The Kyushu Island, as well as whole Japanese archipelago, is equipped with dense GPS network (GEONET). It allows us to track the movements and deformations of the earth’s surface over long-term time intervals. In this study, based on daily determinations of the coordinates of GPS stations, analysis has been made on long-term trends in the accumulation of movements and deformations in large areas of the Kyushu Island before the series of April 14–16, 2016 Kumamoto earthquakes to identify deformation precursors and locked, immobile fault zones. The study of the seismic deformation process was performed using the data from 70 continuous permanent GPS stations for the period 2009–2016.The movement and deformation features found characterize the kinematics of the axial zone of the southwestern part of the island arc of the Japanese archipelago. The combination of coseismic compression and uplift in the center of the formed triad of deformation extrema and the consistency between subsidence and extensions at its edges demonstrate the mechanism of growth of the central region of the island arc under compression and the role of volcanism. Of particular interest in the context of the development of movements and deformations during the generation of the Kumamoto earthquakes is the behavior of the minimum displacement moduli of the GNSS sites. Analysis of their kinematics shows the formation of a zone of minimum displacements, in which subsequent strong seismic events were localized. It is shown that rather dense and extensive GNSS networks allow observing and studying the seismic-deformation process at the stages of seismic generation, discharge and relaxation, thereby providing an empirical basis for the development of models for predicting large seismic events.

Adaptive Fusion of Multi-Source Tropospheric Delay Estimates for InSAR Deformation Measurements

Atmospheric propagation delay correction is the key to improving the accuracy of deformation measurement of satellite interferometric synthetic aperture radar (InSAR). The empirical phase-elevation models and external data-based models present uneven performances of atmospheric delay correction for InSAR deformation monitoring. In this study, based on our previous fusion of delays predicted by multiple weather models (FDWM), we propose a new approach of adaptive fusion of multi-source tropospheric delay (AFMTD) estimates derived from multiple models over wide areas, i.e., ERA5, GACOS, WRF, MERRA2, NARR, MODIS, Linear model, and Powerlaw model. The spatially varying scaling algorithm is employed to refine the tropospheric delays predicted by the weather models. Meanwhile, we adopt a multiple-window strategy to cope with the spatially lateral variation of tropospheric delays. The AFMTD not only improves the spatial heterogeneity of tropospheric delay, but also adaptively combines multiple models to achieve a more reliable delay estimation. This AFMTD method is incorporated into the StaMPS-SBAS procedure. We compared the AFMTD with other single models using ENVISAT ASAR and Sentinel-1 datasets over Los Angeles of Southern California. The result of ASAR first demonstrates the effectiveness and reliability of the AFMTD method by referring to the assumed ground truth of simultaneous MERIS observations. The results of Sentinel-1 data show that over 95% of unwrapped interferograms have the minimum root-mean-square values after AFMTD correction for both descending and ascending tracks. The validation against GPS observation presents that the RMSEs of InSAR displacement time series after AFMTD correction decreases at more than 90% of 125 GPS stations. The average reductions of RMSE are 35.79% and 36.28% for descending and ascending data, respectively, and the maximum improvement is more than 70%. Overall, the proposed AFMTD method outperforms any single model for InSAR tropospheric delay correction and provides an open framework to fuse multi-source tropospheric delay estimates.

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.

Assessment of Contemporary Antarctic GIA Models Using High-Precision GPS Time Series

Past redistributions of the Earth’s mass resulting from the Earth’s viscoelastic response to the cycle of deglaciation and glaciation reflect the process known as glacial isostatic adjustment (GIA). GPS data are effective at constraining GIA velocities, provided that these data are accurate, have adequate spatial coverage, and account for competing geophysical processes, including the elastic loading of ice/snow ablation/accumulation. GPS solutions are significantly affected by common mode errors (CMEs) and the choice of optimal noise model, and they are contaminated by other geophysical signals due primarily to the Earth’s elastic response. Here, independent component analysis is used to remove the CMEs, and the Akaike information criterion is used to determine the optimal noise model for 79 GPS stations in Antarctica, primarily distributed across West Antarctica and the Antarctic Peninsula. Next, a high-resolution surface mass variation model is used to correct for elastic deformation. Finally, we use the improved GPS solution to assess the accuracy of seven contemporary GIA forward models in Antarctica. The results show that the maximal GPS crustal displacement velocity deviations reach 4.0 mm yr−1, and the mean variation is 0.4 mm yr−1 after removing CMEs and implementing the noise analysis. All GIA model-predicted velocities are found to systematically underestimate the GPS-observed velocities in the Amundsen Sea Embayment. Additionally, the GPS vertical velocities on the North Antarctic Peninsula are larger than those on the South Antarctic Peninsula, and most of the forward models underestimate the GIA impact on the Antarctic Peninsula.

Performance of NeQuick-2 and IRI-Plas 2017 Models during Solar Maximum Years in 2013–2014 over Equatorial and Low Latitude Regions

This paper carries out a comparative investigation of the Total Electron Content (TEC) values calculated by using the NeQuick-2 and IRI-Plas 2017 models. The investigation was carried out for the solar maximum year of 2013–2014 with data from eight GPS stations within the equatorial and low latitude regions. The results show that both models agree quite well with the observed TEC values obtained from GPS measurements in all the stations, although with some overestimations and underestimations observed during the daytime and nighttime hours. The NeQuick-2 model, in general, performed better in months, seasons, and in most of the stations when the IRI-Plas overestimates the GPS-TEC. However, it is interesting to know that with an increase in solar activity in some seasons, the quality of forecasting IRI-Plas can improve, whereas for the NeQuick-2 model, it decreases, but this is not true for all the seasons and all the stations. Factors causing the discrepancies in the IRI-Plas data model might be caused by the plasmaspheric part included in the IRI, and it is found to be maximum at the MBAR (34%) station, whereas that of the NeQuick-2 data model is found to be maximum at the ADIS (47.7%) station. There is a latitudinal dependence for both models in which the prediction error decreases with the increasing latitudes.

Quality assessment of Continuous Operating Reference Stations (CORS) - GPS stations in Mexico

The present analysis provided the quality assessment of 65 Continuous Operating Reference Stations (CORS) situated in and around Mexico using the data of about ten years (January 2010 to January 2020). To accomplish the assessment, we considered 4 quality indexes for performing the quality check, incluing the multipath effect on L1 and L2 bands, signal-to-noise ratio in both bands, cycle slips, and integrity. Meanwhile, a new reference parameter named regional values for Mexico (RVM) was calculated for the stations situated in Mexico. Additionally, an exhaustive analysis of the quality indicators in the worst and best cases was performed. The signal degradation was proved by a long-term time series and crustal deformation analysis in GAMIT/GLOBK platform. The results showed a strong correlation between integrity, cycle slips and daily observations time, and the multipath effect is strong in the case of outdated systems. The study indicates that the estimated quality indexes and values could be the basis for establishing new CORS in Mexico, and the errors corrections which cannot be mitigated in the post-processing stage would greatly help utilize the data for different scientific applications. The results were supported by deformation analysis in part of Baja California Peninsula in Mexico indicating northern and eastern velocity vector of −3.08 ± 0.02 mm/yr, −7.85 ± 0.02 mm/yr and −0.07 ± 0.03 mm/yr, −9.33 ± 0.02 mm/yr respectively at MPR1 and INEG stations.

Regional application of C1 finite element interpolation method in modeling of ionosphere total electron content over Europe

• A novel approach for regional ionosphere grid model with high spatial-temporal resolution is suggested. • The results of the new model is analyzed at different disturb geomagnetic and solar conditions. • A comparative analysis between the results of new model, GIM, NeQuick and GPS-TEC is made. In this paper, a novel approach based on C 1 finite element (FE) interpolation is used to construct a regional ionospheric grid model (RIGM) with high spatio-temporal resolution over Europe. The spatial resolution of the new model is 0.5 0 for a longitude and latitude and a temporal resolution is 15 min. Observations of 38 GPS stations at different days and seasons with different geomagnetic and solar activity conditions are used to evaluate the new method. All stations are used to discretize the whole domain of the problem into separate triangular patches using a Delaunay triangulation algorithm. In this case, GPS stations would be the vertices of the triangular patches. Also, the position of stations in sun-fixed coordinate system with coordinates of latitude and longitude are chosen in order to consider the temporal variations of vertical total electron content (VTEC). To model the behavior of VTEC in each triangular patch, a particular shape function is defined which is fitted to VTEC values at three vertices of each triangle while it satisfies C 1 continuity of VTEC at the common edges of the adjacent triangles. The results of the new model are compared with the VTEC of GPS at 4 control stations as well as global ionosphere map (GIM) and NeQuick models. Statistical parameters, including correlation coefficient, root mean square error (RMSE), standard deviation and dVTEC = |VTEC GPS - VTEC model | have been used to evaluate the error of the models. In low geomagnetic activity (KP < 4), the average RMSE at 4 control stations for FE, GIM and NeQuick models are 0.89, 1.05 and 6.42 TECU, respectively, while in the high geomagnetic activity (KP > 4), they are 1.24, 1.68 and 7.62 TECU, respectively. For further analysis, the accuracy of three models in precise point positioning (PPP) has been also evaluated. In PPP method, there is an improvement of about 1–16 mm compared to GIM and NeQuick models at coordinate components of control stations in high geomagnetic activity. The results of the analysis performed in this paper show that the FE model has a very high capability and accuracy in regional VTEC modeling in high and low geomagnetic conditions. The regional accuracy of this model is higher than GIM and NeQuick models and therefore can be considered as a new model for the ionosphere.

Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

Time-Series InSAR Dynamic Analysis With Robust Sequential Adjustment

Nowadays, SAR instruments such as ESA’s Sentinel-1 constellation, ICEYE’s constellation of small and agile radar satellites, and the upcoming ALOS-4 and NASA/ISRO SAR missions provide new opportunities for near-real-time monitoring of geohazards with enhanced spatiotemporal resolution. Sequential dynamic adjustment model is regarded as an effective way to rapidly update time-series InSAR measurements. However, the accuracy of geophysical parameters of interest estimated from the conventional sequential least squares is greatly sensitive to the anomalous observations and/or anomalous prior parameter information. This letter aims to introduce the robust sequential adjustment method based on the M-estimation principle into near-real-time InSAR deformation monitoring to mitigate the effect of anomalous errors. Using both synthetic and real Sentinel-1 SAR datasets over Echigo plain in Japan, we fully evaluate the performance of the robust sequential estimation approach with respect to unwrapping errors in the SAR data stack. Measurements at 9 GPS stations located in the study area are used to validate the results. We find that the averaged RMSE of robust sequential adjustment is reduced by 15% in comparison with that of the conventional sequential least-squares method.

A Linear Regression of Differential PWV Calibration Model to Improve the Accuracy of MODIS NIR All-Weather PWV Products Based on Ground-Based GPS PWV Data

Precipitable water vapor (PWV) products derived from the near-infrared (NIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, onboard Aqua and Terra satellites, were calibrated using a linear differential PWV (LinearDP) calibration model based on GPS-retrieved PWV observations. All MODIS NIR PWV pixels were classified into two groups according to the cloud mask of each pixel. For each group, MODIS NIR PWV products and ground-based PWV data from 453 GPS sites in Australia from January 2017 to December 2018 were utilized to determine the differential PWV by subtracting GPS PWV from MODIS PWV. Then, empirical regression relationship between the differential PWV data and the MODIS PWV products was developed using a linear regression approach. The LinearDP model coefficients were independently obtained from each month for each group. The period for model validation spans from January to December in 2019. Comparison of calibrated MODIS NIR PWV versus GPS-derived PWV over Australia showed that the root-mean-square error (RMSE) of Aqua has reduced 42.61% for clear group, 41.43% for cloudy group, and 41.45% for both clear and cloudy groups; and has respectively reduced 53.76%, 37.03%, and 39.33% for Terra. By comparing against ERA5 PWV data, the RMSE reduced 37.21%–43.14% for Aqua and 38.73%–53.87% for Terra. The improvement of MODIS NIR PWV products is further validated in China, with an RMSE reduction of 24.53%–31.78% for Aqua and 28.26%–38.69% for Terra against reference PWV from 214 GPS stations. The mean bias was reduced to −0.415–0.752 mm in Australia and to −0.382–2.013 mm in China.

On the global ionospheric diurnal double maxima based on GPS vertical total electron content

Ionospheric diurnal double maxima (DDM) is a twin-peak pattern in the ionospheric electron density/total electron content (TEC) during the daytime. Understanding the characteristics of DDM is essential to study the physical mechanisms of the ionosphere. In this paper, the vertical TEC data (VTEC) in 2019–2020 derived from 537 globally distributed GPS stations were used to investigate the DDM phenomenon. The results reveal that the occurrence rate of DDMs is roughly quasi-symmetrical about the magnetic equator. In the northern hemisphere, it first increases, then decreases, and finally increases with the increase of magnetic latitude. The DDM phenomenon also exhibits significant seasonal variation. It mainly appears in summer/winter in the northern/southern hemisphere, and the valley and the second peak usually appear earlier in winter and later in summer. According to the difference in the magnitude of the two peaks of DDM, the DDM phenomenon is mainly manifested as the front peak significant type or the posterior peak significant type. The probability of the former shows an M-shaped variation with increasing longitude in the middle and high latitudes of the northern hemisphere and an inverted V-shaped variation in the high latitudes of the southern hemisphere within 180°W~60°W. The probability of the posterior peak significant type shows a trend opposite to the front peak significant type in each area. The occurrence time of DDM structures is usually about one hour later in low-latitude regions than in other regions, and the duration is usually shorter than in other regions. The relative magnitude of the DDM’s twin peaks in low-latitude regions is usually smaller than in other regions.

Coseismic and postseismic deformation from the 2007 Bengkulu earthquake based on GPS Data

On September 12, 2007, a M8.5 megathrust earthquake occurred along the Sunda trench near Bengkulu, West Sumatra. GPS data in Sumatra have indicated the coseismic and postseismic deformations resulting from this earthquake. Our estimate of coseismic displacements suggests that the earthquake displaced up to ~1.8m at GPS stations located north of the epicenter. Moreover, our principal strain estimation in the region suggests that the maximum coseismic extensional strain is ~40 ppm. Our analysis of GPS data in the region suggests that the postseismic decay of the 2007 Bengkulu earthquake was 46 days, estimated using a logarithmic function.

Volcanic Origin of a Long‐Lived Swarm in the Central Bransfield Basin, Antarctica

AbstractUnderstanding the extensional processes in tectonic context at the transition from continental to oceanic spreading is fundamental to obtain new insights about formations of new oceans. To that scope, we study a large and long‐lived earthquake swarm occurring in 2020–2021 in a back‐arc rift (the Bransfield Basin) south of the South Shetland Islands, Antarctica. We make use of one local seismological station to detect more than 36,000 small earthquakes, occurring from August 2020 to June 2021. Together with the occurrence of earthquakes, we observe a significant, geodetic deformation at a nearby GPS station. By joint interpretation of b‐value, spatiotemporal evolution of seismicity and geodetic deformation, we infer a volcanic origin for this swarm that takes place close to the ridge axis. Our study suggests that beyond the 7 mm/yr deformation reported at the Bransfield Basin ridge, transient deformation episodes localized at the ridge axial volcanic structure also modulate the extension.

Geometry-dependent rupture process of the 2015 Gorkha, Nepal, earthquake determined using a dip-varying inversion approach with teleseismic, high-rate GPS, static GPS and InSAR data

SUMMARYFault geometry is widely recognized as one of the most important factors that affect the rupture process and damages of earthquakes. However, there have been few earthquake cases in which the close relation between the fault geometry and rupture process is resolved from inversions of seismic and geodetic observations. In this study, we develop an approach to simultaneously estimate the rupture process and dip-angle variation on the fault. The effectiveness of our new approach was validated through inverse numerical tests. We apply the new approach to the 2015 Mw7.8 Gorkha earthquake and obtain a dip-varying rupture model by jointly inverting the teleseismic, near-fault high-rate GPS, static co-seismic GPS and InSAR data. Our results show a ramp–flat décollement-ramp fault geometry of the earthquake. The shallow ramp may have prevented the rupture from breaking through to the surface. The variation of dip angle changing with depth leads to significantly different rupture velocities and rupture lengths at shallow and deep fault portions. Particularly, the northeastern downdip ramp behaves as a geometric barrier and rapidly slows down the rupture propagation in 35–45 s after the rupture initiation. In contrast, the rupture duration and fault length in the updip portion are relatively long since there is no significant lateral dip change. Furthermore, the approach can improve our understanding of the relationship between rupture behaviour and fault geometry for other thrust low-dip-angle (dip &amp;lt; 45°) earthquakes.