Spatiotemporal slip distributions associated with the 2018–2019 Bungo Channel long-term slow slip event inverted from GNSS data

Long-term slow slip events (L-SSEs) have repeatedly occurred beneath the Bungo Channel in southwestern Japan with durations of several months to a couple of years, with a recurrence interval of approximately 6 years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE by inverting processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2002–2004 L-SSE. However, the slip expanded in the northeast and southwest directions in the latter half of the second subevent. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 28 cm, 54 cm/year, $$4.4 \times 10^{19}$$ 4.4 × 10 19 Nm, and 7.0, respectively, all of which were the largest among the 1996–1998, 2002–2004, 2009–2011, and 2018–2019 L-SSEs.

Towards operational multi-GNSS tropospheric products at GFZ Potsdam

The assimilation of global navigation satellite system (GNSS) data has been proven to have a positive impact on weather forecasts. However, the impact is limited due to the fact that solely the zenith total delays (ZTDs) or integrated water vapor (IWV) derived from the GPS satellite constellation are utilized. Assimilation of more advanced products, such as slant total delays (STDs), from several satellite systems may lead to improved forecasts. This study shows a preparation step for the assimilation, i.e., the analysis of the multi-GNSS tropospheric advanced parameters: ZTDs, tropospheric gradients and STDs. Three solutions are taken into consideration: GPS-only, GPS–GLONASS (GR) and GPS–GLONASS–Galileo (GRE). The GNSS estimates are calculated using the operational EPOS.P8 software developed at GFZ. The ZTDs retrieved with this software are currently being operationally assimilated by weather services, while the STDs and tropospheric gradients are being tested for this purpose. The obtained parameters are compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The results show that all three GNSS solutions show similar level of agreement with the ERA5 model. For ZTDs, the agreement with ERA5 results in biases of approx. 2 mm and standard deviations (SDs) of 8.5 mm. The statistics are slightly better for the GRE solution compared to the other solutions. For tropospheric gradients, the biases are negligible, and SDs are equal to approx. 0.4 mm. The statistics are almost identical for all three GNSS solutions. For STDs, the agreement from all three solutions is very similar; however it is slightly better for GPS only. The average bias with respect to ERA5 equals approx. 4 mm, with SDs of approx. 26 mm. The biases are only slightly reduced for the Galileo-only estimates from the GRE solution. This study shows that all systems provide data of comparable quality. However, the advantage of combining several GNSS systems in the operational data assimilation is the geometry improvement by adding more observations, especially for low elevation and azimuth angles.

Comment on: “Interseismic Strain Accumulation near Lisbon (Portugal) from Space Geodesy” by Fonseca et al. (2021)

The paper by Fonseca et al. (2021), hereafter referred as FON21, published in Geophysical Research Letters2 make several conclusions that are not convincingly supported by the evidence of the data that is made available. In this comment we will address the following statements: 1) FON21 “provides new evidence of sinistral simple shear driven by a NNE-SSW first-order tectonic lineament; 2) “PSInSAR vertical velocities corroborate qualitatively the GNSS strain-rate field, showing uplift/subsidence where the GNSS data indicate contraction/extension”; 3) FON21 proposes “the presence of a small block to the W of Lisbon moving independently toward the SW with a relative velocity of 0.96 ± 0.20 mm/yr”; 4) FON21 shows “that the contribution of intraplate faults to the seismic hazard in the LMA is more important than currently assumed”. We conclude that more evidence needs to be collected to confirm or infirm FON21 statements and conclusions. For the moment the proposal of an autonomous crustal block moving with significant velocity in relation to the neighboring domain should be considered speculative and unproved.

First considerations on post processing kinematic GNSS data during a geophysical oceanographic cruise

<p class="Abstract">Differential GNSS positioning on vessels is of considerable interest in various fields of application as navigation aids, precision positioning for geophysical surveys or sampling purposes especially when high resolution bathymetric surveys are conducted. However ship positioning must be considered a kinematic survey with all the associated problems. The possibility of using high-precision differential GNSS receivers in navigation is of increasing interest, also due to the very recent availability of low-cost differential receivers that may soon replace classic navigation ones based on the less accurate point positioning technique. The availability of greater plano-altimetric accuracy, however, requires an increasingly better understanding of planimetric and altimetric reference systems. In particular, the results allow preliminary considerations on the congruence between terrestrial reference systems (which the GNSS survey can easily refer to) and marine reference systems (connected to National Tidegauge Network). In spite of the fluctuations due to the physiological continuous variation of the ship's attitude, GNSS plot faithfully followed the trend of the tidal variations and highlighted the shifts between GNSS plot and the tide gauges due to the different materialization of the relative reference systems.</p><p class="Abstract"><span lang="EN-US"><br /></span></p>

UltraViolet SANitizing System for Sterilization of Ambulances Fleets and for Real-Time Monitoring of Their Sterilization Level

Background: The contamination of ambulances with pathogenic agents represents a potential threat for the public health, not only for common pathogens but also for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aim of this project was to exploits the germicidal effect of the UVC radiation at 254 nm to sanitize the patient’s compartment of ambulances with an advanced UltraViolet SANitizing System (UV-SAN) and assess its relevance for avoiding the spread of COVID-19 and other drug resistant pathogens. Methods: The system is equipped with UVC lamps that are activated when the ambulance compartment is empty and sanitize the environment in less than 15 min. An Ozone sensor continuously monitors the gas concentration, ensuring it does not exceed threshold value harmful for patients and operators’ health. The system is relying on GNSS data and a satellite communication link, which allow to monitor and record traceability (when, where and what) of all the sanitation operations performed. This information is real-time monitored from a dedicated web-application. Results: UVC irradiation efficiently reduced SARS-CoV-2 virus titer (>99.99%), on inanimate surfaces such as plastic, stainless steel or rubber, with doses ranging from 5.5 to 24.8 mJ/cm2 and the UV-SAN system is effective against multi drug resistant (MDR) bacteria up to >99.99%, after 10 to 30 min of irradiation. Conclusions: UV-SAN can provide rapid, efficient and sustainable sanitization procedures of ambulances.

Improvement of Multi-GNSS Precision and Success Rate Using Realistic Stochastic Model of Observations

With the advancement of multi-constellation and multi-frequency global navigation satellite systems (GNSSs), more observations are available for high precision positioning applications. Although there is a lot of progress in the GNSS world, achieving realistic precision of the solution (neither too optimistic nor too pessimistic) is still an open problem. Weighting among different GNSS systems requires a realistic stochastic model for all observations to achieve the best linear unbiased estimation (BLUE) of unknown parameters in multi-GNSS data processing mode. In addition, the correct integer ambiguity resolution (IAR) becomes crucial in shortening the Time-To-Fix (TTF) in RTK, especially in challenging environmental conditions. In general, it is required to estimate various variances for observation types, consider the correlation between different observables, and compensate for the satellite elevation dependence of the observable precision. Quality control of GNSS signals, such as GPS, GLONASS, Galileo, and BeiDou can be performed by processing a zero or short baseline double difference pseudorange and carrier phase observations using the least-squares variance component estimation (LS-VCE). The efficacy of this method is investigated using real multi-GNSS data sets collected by the Trimble NETR9, SEPT POLARX5, and LEICA GR30 receivers. The results show that the standard deviation of observations depends on the system and the observable type in which a particular receiver could have the best performance. We also note that the estimated variances and correlations among different observations are also dependent on the receiver type. It is because the approaches utilized for the recovery techniques differ from one type of receiver to another kind. The reliability of IAR will improve if a realistic stochastic model is applied in single or multi-GNSS data processing. According to the results, for the data sets considered, a realistic stochastic model can increase the computed empirical success rate to 100% in multi-GNSS as well as a single system. As mentioned previously, the realistic precision of the solution can be achieved with a realistic stochastic model. However, using the estimated stochastic model, in fact, leads to better precision and accuracy for the estimated baseline components, up to 39% in multi-GNSS.

No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.

Postglacial strain rate – stress paradox, example of the Western Alps active faults

The understanding of the origins of seismicity in intraplate regions is crucial to better characterize seismic hazards. In formerly glaciated regions such as Fennoscandia North America or the Western Alps, stress perturbations from Glacial Isostatic Adjustment (GIA) have been proposed as a major cause of large earthquakes. In this study, we focus on the Western Alps case using numerical modeling of lithosphere response to the Last Glacial Maximum icecap. We show that the flexural response to GIA induces present-day stress perturbations of ca. 1–2 MPa, associated with horizontal extension rates up to ca. 2.5 × 10−9 yr−1. The latter is in good agreement with extension rates of ca. 2 × 10−9 yr−1 derived from high-resolution geodetic (GNSS) data and with the overall seismicity deformation pattern. In the majority of simulations, stress perturbations induced by GIA promote fault reactivation in the internal massifs and in the foreland regions (i.e., positive Coulomb Failure Stress perturbation), but with predicted rakes systematically incompatible with those from earthquake focal mechanisms. Thus, although GIA explains a major part of the GNSS strain rates, it tends to inhibit the observed seismicity in the Western Alps. A direct corollary of this result is that, in cases of significant GIA effect, GNSS strain rate measurements cannot be directly integrated in seismic hazard computations, but instead require detailed modeling of the GIA transient impact.