We aim to better understand the overriding plate deformation during the megathrust earthquake cycle. We estimate the spatial patterns of interseismic GNSS velocities in South America, Southeast Asia, and northern Japan and the associated uncertainties due to data gaps and velocity uncertainties. The interseismic velocities with respect to the overriding plate generally decrease with distance from the trench with a steep gradient up to a “hurdle”, beyond which the gradient is distinctly lower and velocities are small. The hurdle is located 500–1000 km away from the trench, for the trench-perpendicular velocity component, and either at the same distance or closer for the trench-parallel component. Significant coseismic displacements were observed beyond these hurdles during the 2010 Maule, 2004 Sumatra-Andaman, and 2011 Tohoku earthquakes. We hypothesize that both the interseismic hurdle and the coseismic response result from a mechanical contrast in the overriding plate. We test our hypothesis using physically consistent, generic, three-dimensional finite element models of the earthquake cycle. Our models show a response similar to the interseismic and coseismic observations for a compliant near-trench overriding plate and an at least 5 times stiffer overriding plate beyond the contrast. The model results suggest that hurdles are more prominently expressed in observations near strongly locked megathrusts. Previous studies inferred major tectonic or geological boundaries and seismological contrasts located close to the observed hurdles in the studied overriding plates. The compliance contrast probably results from thermal, compositional and thickness contrasts and might cause the observed focusing of smaller-scale deformation like backthrusting.
Surface deformation accompanying dike intrusions is dominated by uplift and horizontal motion directly related to the intrusions. In some cases, it includes subsidence due to associated magma reservoir deflation. When reservoir deflation is large enough, it can form, or reactivate pre-existing, caldera ring-faults. Ring-fault reactivation, however, is rarely observed during moderate-sized eruptions. On February 21st, 2015 at Ambrym volcano in Vanuatu, a basaltic dike intrusion produced more than 1 meter of co-eruptive uplift, as measured by InSAR, SAR correlation, and Multiple Aperture Interferometry (MAI). Here we show that an average of ∼40 cm of slip occurred on a normal caldera ring-fault during this moderate-sized (VEI < 3) event, which intruded a volume of ∼24 million cubic meters and erupted ∼9.3 million cubic meters of lava (DRE). Using the 3D Mixed Boundary Element Method, we explore the stress change imposed by the opening dike and the depressurizing reservoir on a passive, frictionless fault. Normal fault slip is promoted when stress is transferred from a depressurizing reservoir beneath one of Ambrym’s main craters. After estimating magma compressibility, we provide an upper-bound on the critical fraction (f = 7%) of magma extracted from the reservoir to trigger fault slip. We infer that broad basaltic calderas may form in part by hundreds of subsidence episodes no greater than a few meters, as a result of magma extraction from the reservoir during moderate- sized dike intrusions.
Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ~2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.
Previous studies have demonstrated that tides are subject to considerable changes on secular time scales. However, these studies rely on sea level observations from tide gauges that are predominantly located in coastal and shelf regions and therefore, the large-scale patterns remain uncertain. Now, for the first time, satellite radar altimetry (TOPEX/Poseidon & Jason series) has been used to study worldwide linear trends in tidal harmonic constants of four major tides (M2, S2, O1, and K1). This study demonstrates both the potential and challenges of using satellite data for the quantification of such long-term changes. Two alternative methods were implemented. In the first method, tidal harmonic constants were estimated for consecutive four-year periods, from which the linear change was then estimated. In the second method, the estimation of linear trends in the tidal constants of the four tides was integrated in the harmonic analysis. First, both methods were assessed by application to tide gauge data that were sub-sampled to the sampling scheme of the satellites. Thereafter the methods were applied to the real satellite data. Results show both statistically significant decreases and increases in amplitude up to 1 mm/year and significant phase changes up to ~0.1 deg/year. The level of agreement between altimeter-derived trends and estimates from tide gauge data differs per region and per tide.
The continuous redistribution of water mass involved in the hydrologic cycle leads to deformation of The continuous redistribution of water involved in the hydrologic cycle leads to deformation of the solid Earth. On a global scale, this deformation is well explained by the loading imposed by hydrological mass variations and can be quantified to first order with space-based gravimetric and geodetic measurements. At the regional scale, however, aquifer systems also undergo poroelastic deformation in response to groundwater fluctuations. Disentangling these related but distinct 3D deformation fields from geodetic time series is essential to accurately invert for changes in continental water mass, to understand the mechanical response of aquifers to internal pressure changes as well as to correct time series for these known effects. Here, we demonstrate a methodology to accomplish this task by considering the example of the well-instrumented Ozark Plateaus Aquifer System (OPAS) in central United States. We begin by characterizing the most important sources of groundwater level variations in the spatially heterogeneous piezometer dataset using an Independent Component Analysis. Then, to estimate the associated poroelastic displacements, we project geodetic time series corrected for hydrological loading effects onto the dominant groundwater temporal functions. We interpret the extracted displacements in light of analytical solutions and a 2D model relating groundwater level variations to surface displacements. In particular, the relatively low estimates of elastic moduli inferred from the poroelastic displacements and groundwater fluctuations may be indicative of aquifer layers with a high fracture density. Our findings suggest that OPAS undergoes significant poroelastic deformation, including highly heterogeneous horizontal poroelastic displacements.
It has been standard practice for about two decades to compute GPS-based station velocity uncertainties using the apparent noise statistics of the non-linear position residuals rather than assume white noise (WN) behavior. The latter choice would yield unrealistic velocity uncertainties. The most common noise types used are power-law, usually close to flicker noise (FN), over most frequencies mixed with WN at the shortest periods. The complicating impact of offsets in the position time series, mostly caused by equipment changes or tectonic events, has not been fully appreciated. These are far less benign than recently suggested. In addition to contributing a pseudo-random walk noise (RW) component to the velocity errors, estimating offset parameters changes the apparent noise color towards whiter. Spectral power is effectively drained by offsets at periods longer than roughly the mean span between them. This consequently promotes a Gauss-Markov process as the apparently preferred noise model and, importantly, obscures the presence of RW and long-period Earth deformation in the series. Both effects can lead to potentially under-estimated velocity uncertainties. The full value of decadal-long GPS time series for geodynamical applications is thereby greatly eroded by recurring offsets, especially when they occur quasi-regularly. In addition, contrary to common assumption, the noise color is generally not fixed with time, but clearly becomes whiter in more recent data. The origin of the colored noise and its whitening over time remain elusive.
There is an approximately one-year observation gap of terrestrial water storage anomalies (TWSAs) between the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO). This poses a challenge for water resources management, as discontinuity in the TWSA observations may introduce significant biases and uncertainties in hydrological model predictions and consequently mislead decision making. To tackle this challenge, a Bayesian convolutional neural network (BCNN) is proposed in this study to bridge this gap using climatic data as inputs. Enhanced by integrating recent advances in deep learning, BCNN can efficiently extract important features for TWSA predictions from multi-source input data. The predicted TWSAs are compared to the hydrological model outputs and three recent TWSA prediction products. Results suggest the superior performance of BCNN in bridging the gap. The extreme dry and wet events during the gap period are also successfully identified by BCNN.
Interferometric synthetic aperture radar (InSAR) has been successfully used to map ground displacements associate with landslides. One challenge with InSAR is that the basic measurement of interferometric phase takes values between 0 and 2π instead of values representing total displacement relative to some stable reference frame. Phase unwrapping is necessary to reconstruct measurements of total displacement for use in quantitative analysis. Unwrapping approaches often assume that the absolute phase difference between two neighboring pixels should be a small fraction of a cycle (π or less). In the presence of noise or high strain rates associated with fast-moving landslides, aliasing of the phase (under-sampling of the wrapped signal) can result in unwrapping errors and under- or overestimates of total displacement. Here we use a pattern-based strategy for phase unwrapping of InSAR observations of fast-moving landslides, where we determine the unwrapped deformation field that is most similar to a scaled reference displacement map. We also describe a range of metrics that we use to evaluate the most appropriate scaling for each interferogram and demonstrate the range of conditions where they perform well using synthetic data. For evaluation of the results, we generated UAVSAR wrapped interferograms over the Slumgullion landslide in Colorado where phase aliasing for interferograms with temporal baselines larger than seven days is common. We show the interferograms unwrapped with our approach and compare them against results from range offsets (pixel tracking), demonstrating that our approach can be used for time spans well beyond those where traditional phase unwrapping performs well.
The GeoSciFramework project (GSF), funded by the NSF Office of Advanced Cyberinfrastructure and NSF EarthCube programs, aims to improve intermediate-to-short term forecasts of catastrophic natural hazard events, allowing researchers to instantly detect when an event has occurred and reveal more suppressed, long-term motions of Earth’s surface at unprecedented spatial and temporal scales. These goals will be accomplished by training machine learning algorithms to recognize patterns across various data signals during geophysical events and deliver scalable, real-time data processing proficiencies for time series generation. The algorithm will employ an advanced convolutional neural network method wherein spatio-temporal analyses are informed both by physics-based models and continuous datasets, including Interferometric Synthetic Aperture Radar (InSAR), seismic, GNSS, tide gauge, and gas-emission data. The project architecture accommodates increasingly large datasets by implementing similar software packages already proven to support internet searches and intelligence gathering. This talk will focus primarily on the Differential InSAR (DInSAR) time-series analysis component, which quantifies line-of-sight (LOS) ground deformation at mm-cm spatial resolution. Here, we compare time series products generated under three different processing techniques. The first, an automated version of InSAR processing using the small baseline subset (SBAS) method performed in parallel on systems such as Generic Mapping Tool SAR (GMT5SAR) and the Generic InSAR Analysis Toolbox (GIAnT). The second method will resemble the first but will implement different processing systems for performance comparison using the InSAR Scientific Computing Environment (ISCE) and the Miami InSAR Time Series Software in Python (MintPy). The final strategy, developed by Drs. Zheng and Zebker from Stanford University, concentrates on the topographic phase component of the SAR signal so that simple cross multiplication returns an observation sequence of interferograms in geographic coordinates [Zebker, 2017]. Our results provide high-resolution views of ground motions and measure LOS deformation over both short and long periods of time.
The Xianshuihe (XSH) fault in eastern Tibet is one of the most active faults in China, with the next large earthquake most likely to occur along its SE part, where the fault splits into three parallel branches: Yalahe, Selaha and Zheduotang. Precisely quantifying their slip rates at various timescales is essential to evaluate regional earthquake hazard. Here, we expand our previous work on the Selaha fault, to the nearby Zheduotang and Moxi faults, and add observations on the Yalahe fault and on the newly discovered Mugecuo South fault zone. Using tectonic-geomorphology approaches with 10Be dating, we had determined average late Quaternary slip rates of 9.75±0.15 and 4.4±0.5 mm/yr along the NW and SE Selaha fault, respectively. Using the same methods here, we determine a slip rate of 3.7-5.4 mm/yr on the Zheduotang fault and of 10.4-14.8 mm/yr on the Moxi fault. This is consistent with the southeastward slip rate increase we had proposed along the XSH fault system from 6-8 mm/yr (Ganzi fault) to ~10 mm/yr (Selaha fault), and >10.4 mm/yr (Moxi fault). We eventually propose a new model for the SE Xianshuihe fault, where the large-scale Mugecuo pull-apart basin lies within an even larger scale compressive uplift zone in the XSH fault’s restraining bend, where the highest peak in eastern Tibet is located (Gongga Shan, 7556 m). Our slip rates determination allows to estimate a relatively high regional earthquake hazard of Mw~7 at present in the SE Xianshuihe fault.
Isolating the source of non-tidal oceanographic noise in seafloor pressure data is critical for improving the use of these data for seafloor geodetic applications. Residuals between nearby bottom pressure records have typically been used to remove the non-tidal components, as these are largely common-mode. To evaluate the similarities between pairs of observed bottom pressure records at a range of water depths, we calculate the standard deviations of the time series of residuals between data from all site pairs, recorded during a recent experiment offshore New Zealand. Similar to a recent study offshore Cascadia, we find that the magnitude of the standard deviation depends more on relative water depth than the distance between sites. This confirms that non-tidal components are more similar along isobaths even if the distance between sites is large. We show that the depth range varies with the depth of the deeper site of the pairs under restrictions.
InSAR time-series (InSAR-TS) analysis enables us to obtain the displacement time-series by using a number of SAR images repeatedly acquired on the area. Among the factors affecting the accuracy of the InSAR-TS analysis, this study focuses on three factors that may severely affect the signal detection limit: 1) the selection of the reference point (determining the offset in each interferogram), 2) ramp-type artifact that originate from inaccuracy in the orbit data or ionospheric disturbance, and 3) altitude-correlated tropospheric noise. Fukushima et al. (2019, Earth, Planets and Space) proposed an InSAR-TS analysis method to simultaneously solve for the displacement time-series and the error terms mentioned above as well as the error in the digital elevation model. In the proposed method, the unwrapped phase in interferograms is assumed to be composed of a linear combination of the LOS displacement, offset, planar ramp, altitude-correlated phase, and error in the used digital elevation model. A set of unwrapped small-baseline interferograms is then inverted to simultaneously obtain the displacement time-series and the parameters describing the error terms under the minimum norm condition on the displacement time-series. In this study, I applied the above-mentioned method after some updates such as introduction of the temporal constraint adopted by the NSBAS algorithm (Doin et al., 2011) and data masking, on the ALOS-2 data acquired around the Arima-Takatsuki fault zone in western Honshu, Japan. Data of four different Paths (20 and 21 from descending orbit, 127 and 128 from ascending orbit) obtained between August 2014 and March 2021 were analyzed. Some of the original interferograms contained severe noise such as a phase ramp equivalent to approximately 25 cm of LOS displacements. The average velocity field obtained by applying the method captured a relative range decrease of a few mm/year on the southern side of the fault, consistent with the results obtained from Sentinel-1 data analysis. Given the fact that the Sentinel-1 dataset had much favorable conditions (much larger number of data and much smaller ionospheric noise), the consistency in the average velocity field suggests the effectiveness of the proposed approach.
Ice shelves control the stability of ice sheets and regulate ice sheet contribution to sea level rise by buttressing ice �ow. Most of Greenland’s ice shelves have already been lost, and many ice shelves around Antarctica are thinning and retreating. Ice shelves are increasingly vulnerable to thinning and destabilization due to surface and basal melting, and these processes may be exacerbated by the presence of basal channels, which are deep grooves that entrain meltwater at the base of ice shelves. Basal channels have been observed alongside spatial and temporal changes in grounding line geometry, strain rates and stress transfer, and the incidence and advection of other surface and basal features. The relationships between these processes, and their implications for ice shelf stability, remain largely unknown due to the lack of observations of su�ciently high spatial and temporal resolution. Our methodology employs high temporal and spatial resolution digital elevation models (DEMs) from REMA and ArcticDEM, laser altimetry from ICESat-2, radar sounding and laser altimetry from Operation IceBridge, and velocity data derived from interferometry, enabling us to constrain the morphology and evolution of channels and other ice shelf features at the fringes of both ice sheets. We intend to investigate how the relationships between channels, grounding line processes, and rifts and crevasses impact the persistence of ice shelf area necessary to maintain a “safety band”, or su�cient buttressing force, against grounded ice. Where time-evolving grounding line position data are sparse, we use the DEMs to track the boundary of hydrostatic equilibrium, which we use as a proxy for changes in grounding line position in order to investigate changes in ice shelf geometry. We have completed analysis of three ice shelves and plan to observe at least twelve more in order to develop an inventory of at- risk ice shelves. Based on our preliminary results, we hypothesize that rapidly evolving basal channels are associated with high rates of change in the grounding zone. This work is integral to assessing past and future ice shelf stability, and it will help the glacier dynamics community more accurately account for small-scale ice shelf processes in computational models which predict ice sheet contribution to sea level rise.
The window remove-restore technique, suggested by Abd-Elmotaal and Kühtreiber (2003) to get rid of the double consideration of the topographic-isostatic masses within the data window, is implemented for the African gravity field recovery in the framework of the activities of the IAG Sub-Commission on the gravity and geoid in Africa. Within the course of the window technique, one needs to compute the effect of the topographic-isostatic masses (terrain correction) for the full data window. Since the African data window is fairly large (-42º ≤ φ ≤ 44º; -22º ≤ λ ≤ 62º), the computation of the effect of the topographic-isostatic masses of the full data window consumes very long CPU time using the common TC-program (Forsberg, 1984). This investigation proposes an optimal terrain correction software for the window remove-restore technique. It uses three radii around the computational point. The first radius is used for the innermost zone utilizing the finest DTM for a relatively short radius (around 2 km). The second radius is used for the inner zone up to a short radius (10-15 km). Here a reasonably fine DTM is sufficient. The third radius is used for the rest of the full data window utilizing a coarse DTM. A thorough comparison between the developed software and the TC-program is performed to assess the quality of the developed technique and to compare the needed CPU time to perform the terrain correction for Africa.
The atmospheric drag and the Radiation Pressure are the dominant forces acting on LEO satellites. Many different approaches have been followed for the modelling of these non-gravitational forces, based on the physics and the satellite characteristics, but in many cases large inconsistencies are present between the models and the accelerometer measurements. Atmospheric drag is considered as the most difficult force to model, and the Radiation Pressure models show large deviations from the measurements depending on the b′ angle and the position of the satellite near the entrance and the exit from the Earth’s shadow. Numerous models have been presented for GRACE satellites but none for GRACE-FO. The innovation of this study is the development of an atmospheric drag and a Radiation Pressure data-driven model based only on the accelerometer measurements of GRACE-C satellite, using least squares principles. The atmospheric drag is modelled using accelerometer measurements from the shadow segment of the orbit. An additional weighted constraint is that near the middle of the sun segment of the orbit, the drag in the x-direction should be equal to the actual measurements due to Radiation Pressure being nearly zero. Subsequently, we subtract the modelled drag from the real measurements in order to estimate the Radiation Pressure which, consequently, is modelled using a least squares frequency-domain analysis. The residual series proceeded from the subtraction of these two models from the actual measurements of GRACE-C accelerometer, are analyzed by taking into consideration the local time, the spatial information and the variations of b΄ angle, as well as their connection with electromagnetic changes in the upper atmosphere. The proposed models have been tested for different time periods in the last three years of GRACE C and the rms of the residual series along the x and the z axes of the accelerometer is ~2.5 nm/s2, while the y-axis exhibits an rms of ~1 nm/s^2.
GRACE-FO (GFO) and Swarm are two LEO missions that, among others, provide non-gravitational acceleration measurements required for geopotential model development and modelling of non-gravitational forces acting upon them. Unfortunately, the performance of the accelerometers on board for both missions is not the expected. Measurements from both missions present dominant bias jumps that occur on all accelerometer axes and they have been linked to the satellites’ entrance to and exit from the Earth’s shadow. These jumps are estimated and corrected at Level 1A of GFO C and at Level 2 of Swarm C in an optimal way using Least Squares methodology. The corresponding variances of the jumps are also calculated. Furthermore, the measurements contain spurious signals and dominant spikes mostly connected with thruster activation, mainly in the equatorial region or high temperature sensitivity. These disturbances have a significant impact on the data analysis. We propose an alternative weighting filter methodology to generate Level 1B data from Level 1A for GFO C that includes the attenuated spikes and their corresponding variances and does not involve the removal of the spikes nor does it include any interpolation to fill data gaps. This methodology is used for Swarm C accelerometer Level 2 dataset as well. Using spectral domain methods, we show that the newly generated GFO Level 1B and Swarm Level 2 data are not contaminated by the presence of spikes and data jumps. In the polar regions, mostly at the South pole, spikes in the measurements are connected to magnetic disturbances when the satellites enter these regions. Our proposed methodology contains an optimal and unbiased dataset of non-gravitational acceleration measurements that can be used for the estimation of geopotential models and also for the investigation of the accelerometer’s response to electromagnetic disturbances and the modelling of other non-gravitational accelerations to derive thermospheric neutral densities.
The Chandler Wobble (CW) period is considered a single time-invariable constant, ~1.2 years, but the possible time-variability has not been examined using modern space-geodetic data. We first examined whether the Chandler period could vary with time on the assumption of minimum excitation power. Unexpectedly, the estimated Chandler period has been shortened by more than 60 days since about 2005. Moreover, by simple least-squares modeling, we found that CW started to be weaker in 2005 and almost disappeared in 2015. We interpret these results in both excitation and wobble domains as caused by the absence of CW for the first time in the observation history. Assuming no excitations of CW since 2005, the rather abrupt damping suggests the Q-value is below 25. Meanwhile, the analyses of the available atmospheric and oceanic angular momentum (AAM/OAM) functions indicate the significant amplitude of CW even after 2005, implying that the AAM/OAM functions are incomplete.
The vertical accuracy of eight different freely accessible DEMs has been evaluated across different physiographic divisions and the river basins of Nepal. Results revealed that MERIT is superior to other DEMs (RMSE 9m) in the low-lying Terai plains of Nepal where the elevation range is lower. In High mountains and High Himalayas having higher elevation range, SRTM90m outperformed all its counterparts. Meanwhile, in Siwalik and middle mountains, both SRTM90m and HYDROSHEDS exhibited almost similar RMSE indicating their compatible uses in these regions. Meanwhile, the accuracy assessment across different river basins of Nepal discerned that the accuracy of SRTM90m was above others in larger river basins like Koshi (RMSE 224m), Narayani (RMSE 215m), and Karnali (RMSE 265m) where the range of elevation is greater. In the smaller to medium-sized basins like Kankai, Kamala, Bagmati, West Rapti, and Babai, HYDROSHEDS was preferable along with SRTM90m. Based on different error statistics, the DEMs were ranked in order of their accuracy.