ALBERT

Description: Continuous gravimetric observations have been made with three successive generations of superconducting gravimeter over 20 yr at Syowa Station ( $39.6\deg$ E, $69.0\deg$ S), East Antarctica. The third-generation instrument, OSG#058, was installed in January 2010 and was calibrated by an absolute gravimeter during January and February, 2010. The estimated scale factor was –73.823 ± 0.053 μGal V –1 (1 μGal = 10 –8 m s –2 ). The first 5 yr of OSG#058 data from 2010 January 7 to 2015 January 10 were decomposed into tidal waves (M3 to Ssa) and other non-tidal components by applying the Bayesian tidal analysis program BAYTAP. Long-term non-tidal gravity residuals, which were obtained by subtracting annual and 18.6 year tidal waves and the predicted gravity response to the Earth's variable rotation, showed significant correlation with the accumulated snow depth measured at Syowa Station. The greatest correlation occurred when the gravity variations lagged the accumulated snow depth by 21 d. To estimate the gravitational effect of the accumulated snow mass, we inferred a conversion factor of 3.13 ± 0.08 μGal m –1 from this relation. The accumulated snow depth at Syowa Station was found to represent an extensive terrestrial water storage (the snow accumulation) around Syowa Station, which was estimated from the Gravity Recovery and Climate Experiment satellite gravity data. The snow accumulation around Syowa Station was detectable by the superconducting gravimeter.

Description: Gravity variations associated with Earth's oblateness ( J 2 ) have been observed by satellite laser ranging (SLR) since 1976. The J 2 time-series has been used to measure and help understand many geophysical processes within the Earth system ranging from the mantle to the atmosphere. While post glacial rebound and the Earth climate system are believed to be the primary driving forces of long-term and seasonal J 2 variations, the physical cause of decadal and longer timescale J 2 variations has remained uncertain, although recent evidence indicates that polar ice mass changes are important. In this study, we estimate a variety of climate contributions to J 2 over the period 1979–2010, and find that ice mass variations in Greenland and Antarctica are the dominant cause of observed decadal and longer J 2 variations. Residual variations at periods near 10–11 years may reflect limitations of numerical climate models in estimating mass change variability at long periods, but are also suggestive of potential contribution related to variable solar activity.

Description: A new approach based on energy conservation principle for satellite gravimetry mission has been developed and yields more accurate estimation of in situ geopotential difference observables using K-band ranging (KBR) measurements from the Gravity Recovery and Climate Experiment (GRACE) twin-satellite mission. This new approach preserves more gravity information sensed by KBR range-rate measurements and reduces orbit error as compared to previous energy balance methods. Results from analysis of 11 yr of GRACE data indicated that the resulting geopotential difference estimates agree well with predicted values from official Level 2 solutions: with much higher correlation at 0.9, as compared to 0.5–0.8 reported by previous published energy balance studies. We demonstrate that our approach produced a comparable time-variable gravity solution with the Level 2 solutions. The regional GRACE temporal gravity solutions over Greenland reveals that a substantially higher temporal resolution is achievable at 10-d sampling as compared to the official monthly solutions, but without the compromise of spatial resolution, nor the need to use regularization or post-processing.

Description: Two types of signals are clearly visible in continuous GPS (cGPS) time-series in Iceland, in particular in the vertical component. The first one is a yearly seasonal cycle, usually sinusoid-like with a minimum in the spring and a maximum in the fall. The second one is a trend of uplift, with higher values the closer the cGPS stations are to the centre of Iceland and ice caps. Here, we study the seasonal cycle signal by deriving its average at 71 GPS sites in Iceland. We estimate the annual and semi-annual components of the cycle in their horizontal and vertical components using a least-squares adjustment. The peak-to-peak amplitude of the cycle of the vertical component at the studied sites ranges from 4 mm near the coastline up to 27 mm at the centre of the Vatnajökull, the largest ice cap in Iceland. The minimum of the seasonal cycle occurs earlier in low lying areas than in the central part of Iceland, consistent with snow load having a large influence on seasonal deformation. Modelling shows that the seasonal cycle is well explained by accounting for elastically induced surface displacements due to snow, atmosphere, reservoir lake and ocean variations. Model displacement fields are derived considering surface loads on a multilayered isotropic spherical Earth. Through forward and inverse modelling, we were able to reproduce a priori information on the average seasonal cycle of known loads (atmosphere, snow in non-glaciated areas and lake reservoir) and get an estimation of other loads (glacier mass balance and ocean). The seasonal glacier mass balance cycle in glaciated areas and snow load in non-glaciated areas are the main contributions to the seasonal deformation. For these loads, induced seasonal vertical displacements range from a few millimetres far from the loads in Iceland, to more than 20 mm at their centres. Lake reservoir load also has to be taken into account on local scale as it can generate up to 20 mm of vertical deformation. Atmosphere load and ocean load are observable and generate vertical displacements in the order of a few millimetres. Inversion results also shows that the Iceland crust is less rigid than the world average. Interannual deviation from the GPS seasonal cycle can occur and are caused by unusual weather conditions over extended period of time.

Description: Measurements of ground deformation can be used to identify and interpret geophysical processes occurring at volcanoes. Most studies rely on a single geodetic technique, or fit a geophysical model to the results of multiple geodetic techniques. Here we present a methodology that combines GPS, Total Station measurements and InSAR into a single reference frame to produce an integrated 3-D geodetic velocity surface without any prior geophysical assumptions. The methodology consists of five steps: design of the network, acquisition and processing of the data, spatial integration of the measurements, time series computation and finally the integration of spatial and temporal measurements. The most significant improvements of this method are (1) the reduction of the required field time, (2) the unambiguous detection of outliers, (3) an increased measurement accuracy and (4) the construction of a 3-D geodetic velocity field. We apply this methodology to ongoing motion on Arenal's western flank. Integration of multiple measurement techniques at Arenal volcano revealed a deformation field that is more complex than that described by individual geodetic techniques, yet remains consistent with previous studies. This approach can be applied to volcano monitoring worldwide and has the potential to be extended to incorporate other geodetic techniques and to study transient deformation.

Description: Apparent acceleration in Gravity Recovery and Climate Experiment (GRACE) Antarctic ice mass time-series may reflect both ice discharge and surface mass balance contributions. However, a recent study suggests there is also contamination from errors in atmospheric pressure de-aliasing fields [European Center for Medium-Range Weather Forecast (ECMWF) operational products] used during GRACE data processing. To further examine this question, we compare GRACE atmospheric pressure de-aliasing (GAA) fields with in situ surface pressure data from coastal and inland stations. Differences between the two are likely due to GAA errors, and provide a measure of error in GRACE solutions. Time-series of differences at individual weather stations are fit to four presumed error components: annual sinusoids, a linear trend, an acceleration term and jumps at times of known ECMWF model changes. Using data from inland stations, we estimate that atmospheric pressure error causes an acceleration error of about +7.0 Gt yr –2 , which is large relative to prior GRACE estimates of Antarctic ice mass acceleration in the range of –12 to –14 Gt yr –2 . We also estimate apparent acceleration rates from other barometric pressure (reanalysis) fields, including ERA-Interim, MERRA and NCEP/DOE. When integrated over East Antarctica, the four mass acceleration estimates (from GAA and the three reanalysis fields) vary considerably (by ~2–16 Gt yr –2 ). This shows the need for further effort to improve atmospheric mass estimates in this region of sparse in situ observations, in order to use GRACE observations to measure ice mass acceleration and related sea level change.

Description: Several attempts have been made to obtain a radiographic image inside volcanoes using cosmic-ray muons (muography). Muography is expected to resolve highly heterogeneous density profiles near the surface of volcanoes. However, several prior works have failed to make clear observations due to contamination by background noise. The background contamination leads to an overestimation of the muon flux and consequently a significant underestimation of the density in the target mountains. To investigate the origin of the background noise, we performed a Monte Carlo simulation. The main components of the background noise in muography are found to be low-energy protons, electrons and muons in case of detectors without particle identification and with energy thresholds below 1 GeV. This result was confirmed by comparisons with actual observations of nuclear emulsions. This result will be useful for detector design in future works, and in addition some previous works of muography should be reviewed from the view point of background contamination.

Description: Normal mode treatments of the Earth's body tide response were developed in the 1980s to account for the effects of Earth rotation, ellipticity, anelasticity and resonant excitation within the diurnal band. Recent space-geodetic measurements of the Earth's crustal displacement in response to luni-solar tidal forcings have revealed geographical variations that are indicative of aspherical deep mantle structure, thus providing a novel data set for constraining deep mantle elastic and density structure. In light of this, we make use of advances in seismic free oscillation literature to develop a new, generalized normal mode theory for the tidal response within the semi-diurnal and long-period tidal band. Our theory involves a perturbation method that permits an efficient calculation of the impact of aspherical structure on the tidal response. In addition, we introduce a normal mode treatment of anelasticity that is distinct from both earlier work in body tides and the approach adopted in free oscillation seismology. We present several simple numerical applications of the new theory. First, we compute the tidal response of a spherically symmetric, non-rotating, elastic and isotropic Earth model and demonstrate that our predictions match those based on standard Love number theory. Second, we compute perturbations to this response associated with mantle anelasticity and demonstrate that the usual set of seismic modes adopted for this purpose must be augmented by a family of relaxation modes to accurately capture the full effect of anelasticity on the body tide response. Finally, we explore aspherical effects including rotation and we benchmark results from several illustrative case studies of aspherical Earth structure against independent finite-volume numerical calculations of the semi-diurnal body tide response. These tests confirm the accuracy of the normal mode methodology to at least the level of numerical error in the finite-volume predictions. They also demonstrate that full coupling of normal modes, rather than group coupling, is necessary for accurate predictions of the body tide response.

Description: On 2008 October 5, a magnitude 6.6 earthquake struck the eastern termination of the intermontane Alai valley between the southern Tien Shan and the northern Pamir of Kyrgyzstan. The shallow thrust earthquake occurred in the footwall of the Main Pamir thrust, where the Pamir orogen is colliding with the southern Tien Shan mountains. We measure the coseismic surface displacements using SAR (Synthetic Aperture RADAR) data; the results show clear gradients in the vertical and horizontal directions along a complex pattern of surface ruptures and active faults. To integrate and to interpret these observations in the context of the regional tectonics, we complement the SAR data analysis with seismological data and geological field observations. While the main moment release of the Nura earthquake appears to be on the Pamir Frontal thrust, the main surface displacements and surface rupture occurred in the footwall along the NE–SW striking Irkeshtam fault. With InSAR data from ascending and descending tracks along with pixel offset measurements, we model the Nura earthquake source as a segmented rupture. One fault segment corresponds to high-angle brittle faulting at the Pamir Frontal thrust and two more fault segments show moderate-angle and low-friction thrusting at the Irkeshtam fault. Our integrated analysis of the coseismic deformation argues for rupture segmentation and strain partitioning associated to the earthquake. It possibly activated an orogenic wedge in the easternmost segment of the Pamir-Alai collision zone. Further, the style of the segmentation may be associated with the presence of Palaeogene evaporites.

Description: A thorough understanding of time-dependent noise in Global Navigation Satellite System (GNSS) position time-series is necessary for computing uncertainties in any signals found in the data. However, estimation of time-correlated noise is a challenging task and is complicated by the difficulty in separating noise from signal, the features of greatest interest in the time-series. In this paper, we investigate how linear trends affect the estimation of noise in daily GNSS position time-series. We use synthetic time-series to study the relationship between linear trends and estimates of time-correlated noise for the six most commonly cited noise models. We find that the effects of added linear trends, or conversely de-trending, vary depending on the noise model. The commonly adopted model of random walk (RW), flicker noise (FN) and white noise (WN) is the most severely affected by de-trending, with estimates of low-amplitude RW most severely biased. FN plus WN is least affected by adding or removing trends. Non-integer power-law noise estimates are also less affected by de-trending, but are very sensitive to the addition of trend when the spectral index is less than one. We derive an analytical relationship between linear trends and the estimated RW variance for the special case of pure RW noise. Overall, we find that to ascertain the correct noise model for GNSS position time-series and to estimate the correct noise parameters, it is important to have independent constraints on the actual trends in the data.