scholarly journals GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michela Ravanelli ◽  
Giovanni Occhipinti ◽  
Giorgio Savastano ◽  
Attila Komjathy ◽  
Esayas B. Shume ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Data Stream ◽  
Electron Content ◽  
Warning Systems ◽  
Time Data ◽  
Total Electron ◽  
Ground Shaking ◽  
Variometric Approach ◽  
Gnss Data

AbstractGlobal Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events. The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC. Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms. We apply the TVA to the Mw 8.3 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario. Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 9.5 min after the rupture respectively. The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source. The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation.

scholarly journals Advantages of Geostationary Satellites for Ionospheric Anomaly Studies: Ionospheric Plasma Depletion Following a Rocket Launch

2019 ◽  
Vol 11 (14) ◽  
pp. 1734 ◽  
Author(s):  
Giorgio Savastano ◽  
Attila Komjathy ◽  
Esayas Shume ◽  
Panagiotis Vergados ◽  
Michela Ravanelli ◽  
...  
Keyword(s):  
Real Time ◽  
Ionospheric Plasma ◽  
Early Warning Systems ◽  
Electron Content ◽  
Total Electron ◽  
Rocket Launch ◽  
Gps Satellites ◽  
Geo Satellites ◽  
Air Force Base ◽  
Variometric Approach

In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 Falcon 9 rocket launch from Vandenberg Air Force Base in California. VARION is a real-time Global Navigation Satellites Systems (GNSS)-based algorithm that can be used to detect various ionospheric disturbances associated with natural hazards, such as tsunamis and earthquakes. A noise reduction algorithm was applied to the VARION-GEO solutions to remove the satellite-dependent noise term. Our analysis showed that the interactions of the exhaust plume with the ionospheric plasma depleted the total electron content (TEC) to a level comparable with nighttime TEC values. During this event, the geometry of the satellite-receiver link is such that GEO satellites measured the depleted plasma hole before any GPS satellites. We estimated that the ionosphere relaxed back to a pre-perturbed state after about 3 h, and the hole propagated with a mean speed of about 600 m/s over a region of 700 km in radius. We conclude that the VARION-GEO approach can provide important ionospheric TEC real-time measurements, which are not affected by the motion of the ionospheric pierce points (IPPs). Furthermore, the VARION-GEO measurements experience a steady noise level throughout the entire observation period, making this technique particularly useful to augment and enhance the capabilities of well-established GNSS-based ionosphere remote sensing techniques and future ionospheric-based early warning systems.

scholarly journals Real-Time Geophysical Applications with Android GNSS Raw Measurements

2019 ◽  
Vol 11 (18) ◽  
pp. 2113 ◽  
Author(s):  
Marco Fortunato ◽  
Michela Ravanelli ◽  
Augusto Mazzoni
Keyword(s):  
Real Time ◽  
Noise Level ◽  
Vertical Component ◽  
Satellite System ◽  
Electron Content ◽  
Dual Frequency ◽  
Total Electron ◽  
Variometric Approach ◽  
Global Navigation Satellite ◽  
Slant Total Electron Content

The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.

IGS RTS for real-time global ionospheric total electron content modeling: Method and Applications

2020 ◽  
Author(s):  
Ningbo Wang ◽  
Zishen Li ◽  
Liang Wang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Global Scale ◽  
Global Network ◽  
Single Frequency ◽  
Space Representation ◽  
Electron Content ◽  
Time Data ◽  
Total Electron ◽  
Real Time Mode

<p>To enable GNSS applications with low or no time latency, real-time services (RTS) of the International GNSS Services (IGS) has been launched since 2013. The IGS RTS provides real-time data streams with latencies of less than few seconds, containing multi-frequency and multi-constellation GNSS measurements from a global network of high-quality GNSS receivers, which provides the opportunity to reconstruct global ionospheric models in real-time mode. For the computation of real-time global ionospheric maps (RT-GIM), a 2-day predicted global ionospheric model is introduced along with real-time slant ionospheric delays extracted from real-time IGS global stations. GPS and GLONASS L1+L2, BeiDou B1+B2 and Galileo E1+E5a signals with a sampling rate of 1 Hz are used to extract slant TEC (STEC) estimates. Spherical harmonic expansion up to degree and order 15 is employed for global vertical TEC (VTEC) modeling by combining the observed and predicted ionospheric data in real-time mode. Real-time ionospheric State Space Representation (SSR) corrections are then distributed in RTCM 1264 message (123.56.176.228:2101/CAS05) aside from the generation of RT-GIM in IONEX v1.0 format (available at ftp://ftp.gipp.org.cn/product/ionex/). The quality of CAS RT-GIMs is assessed during an 18-month period starting from August 2017, by comparison with GPS differential slant TECs at the selected IGS stations over continental areas, Jason-3 VTECs over the oceans and IGS combined final GIMs on a global scale, respectively. Results show that CAS’s RT-GIM products exhibit a relative error of 13.9%, which is only approximately 1-2% worse than the final ones during the test period. Additionally, the application of RT-GIM on the single-frequency precise point positioning (PPP) of smartphones is also presented.</p>

An innovative methodology for locating ionosphere layer height: case study on 2011 Tohoku-Oki earthquake and tsunami

2021 ◽  
Author(s):  
Michela Ravanelli ◽  
Giovanni Occhipinti
Keyword(s):  
Real Time ◽  
Physical Models ◽  
Electron Content ◽  
Ionospheric Disturbances ◽  
Mean Velocity ◽  
Total Electron ◽  
Ionospheric Models ◽  
Innovative Methodology ◽  
Variometric Approach ◽  
Pierce Point

<p>One of the main issues in GNSS ionosphere seismology is to localize the exact height of the single thin layer (H<sub>ion</sub>) with which the ionosphere is approximated. H<sub>ion</sub> is generally assumed to be the altitude of the maximum ionospheric ionization (hmF2), i.e., in the ionospheric F-layer. In this sense, H<sub>ion</sub> is often  be presumed from physical principles or ionospheric models. The determination of  H<sub>ion </sub>is, therefore, fundamental since it affects the coordinates of the ionospheric pierce point (IPP) and subsequentely of the sub-ionospheric pierce point (SIP).</p><p>In this work, we present a new developed methodology to determine the exact localization of H<sub>ion.</sub> We tested this approach on the TIDs (Travelling ionospheric disturbances) connected with the 2011 Tohoku-Oki earthquake and tsunami [1]. In detail, we computed the slant Total Electron Content (sTEC) variations at different H<sub>ion </sub>(in the range from 100 to 600 km) with the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm [2,3], then we interpolated the different pattern in sTEC values related to different waves detected in the ionosphere (AGW<sub>epi</sub>, IGW<sub>tsuna</sub> and AW<sub>Rayleigh</sub>) finding the mean velocity value of these waves. Subsequentely, the minimized difference between the estimated propagation velocity and the values from physical models fix us the correct H<sub>ion.</sub></p><p>Our results show a H<sub>ion </sub>of 370 km, while ionopshere model IRI 2006 located the maximum of ionospheric ionization at an height of 270 km. This difference is important to understand how a different H<sub>ion</sub> can impact on the location of the sTEC perturbation, affecting the shape and the extent of the source from TEC observations.</p><p> </p><p> </p><p> </p><p> </p><p><strong>References</strong></p><p>[1] https://earthquake.usgs.gov/earthquakes/eventpage/official20110311054624120_30/executive</p><p>[2] Giorgio Savastano, Attila Komjathy, Olga Verkhoglyadova, Augusto Mazzoni, Mattia Crespi, Yong Wei, and Anthony J Mannucci, “Real-time detection of tsunami ionospheric disturbances with a stand-alone gnss receiver: A preliminary feasibility demonstration, ”Scientific reports, vol. 7, pp. 46607, 2017.</p><p>[3] Giorgio Savastano, Attila Komjathy, Esayas Shume, Panagiotis Vergados, Michela Ravanelli, Olga Verkhoglyadova, Xing Meng, and Mattia Crespi, “Advantages of geostationary satellites for ionospheric anomaly studies: Ionospheric plasma depletion following a rocket launch,”Remote Sensing, vol. 11, no. 14, pp. 1734, 2019</p>

Ionosphere Monitoring with Multi-Frequency and Multi-GNSS Android Smartphone: A Feasibility Study Towards GNSS Big Data Applications for Geosciences

2020 ◽  
Author(s):  
Marco Fortunato ◽  
Michela Ravanelli ◽  
Augusto Mazzoni
Keyword(s):  
Real Time ◽  
Electron Content ◽  
Monitoring Networks ◽  
Atmospheric Conditions ◽  
Mass Market ◽  
Total Electron ◽  
Ionosphere Monitoring ◽  
Variometric Approach ◽  
Gnss Measurements ◽  
Static Conditions

<p>The release of Android GNSS Raw Measurements API, (2016) and the growing technological development introduced by the use of multi-GNSS and multi-frequency GNSS chipsets – changed the hierarchies within the GNSS mass-market world. In this sense, Android smartphones became the new leading products. Positioning performances and quality of raw GNSS measurements have been studied extensively. Despite the greater susceptibility to multipath and cycle slip due to the low cost antenna used, a positioning up to sub-meter accuracy can be achieved. Among the improvements in positioning and navigation, the availability of GNSS measurements from Android smartphones paved new ways in geophysical applications: e.g. periodic fast movements reconstruction and ionospheric perturbances detection.  In fact, considering the number of Android smartphones compatible with the Google API, additional costless information can be used to densify the actual networks of GNSS permanent stations used to monitor atmospheric conditions. However, an extensively analysis on the reconstruction of ionospheric conditions with Android raw measurements is necessary to prove the accuracy achievable in future ionosphere monitoring networks based on both permanent GNSS station and Android smartphone.</p><p>The aim of this work is to assess the performance of multi-frequency and multi-GNSS smartphone – in particular, Xiaomi Mi 8 and Huawei Mate 20 X – in the reconstruction of real-time sTEC (slant Total Electron Content) variations meaningful of ionospheric perturbations. A 24-hour dataset of 1Hz GNSS measurements in static conditions was collected from the two smartphones in addition to data collected from M0SE, one of the EUREF/IGS permanent stations. The VARION (Variometric Approach for Real-time Ionosphere Observations) algorithm, based on the variometric approach and developed within the Geodesy and Geomatics Division of Sapienza University of Rome, was used to retrieve sTEC variations for all the observation periods.</p><p>The results, although preliminary, show that it is possible to study also from the smarthphone the trend of sTEC variations with elevation: lower elevation angles cause noisier sTEC variations. RMSE of the order of 0.02 TECU/s are found for elevation angles higher than 20 degrees as it happens for permanent stations. At the same time, the sTEC variations were compared to the overall measurements noise, due to both environmental and receiver noise, in order to statistically define the correlation between RMSE and derived sTEC variation.</p><p>Although the results obtained in this work are encouraging, still further analyses need to be carried out especially at latitudes where ionosphere conditions and perturbations play a major role. However, the possibility to perform such analyses on datasets collected worldwide is prevented from their availability. The last part of this work is therefore focused on the identification of a methodology to share with the GNSS community to collect, store and share GNSS measurements from Android smartphones to enable the researchers to enlarge the spatial and temporal boundaries of their research in the field of ionosphere modelling with mass-market devices. </p>

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

2018 ◽  
pp. 200-221
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam
Keyword(s):  
Big Data ◽  
Real Time ◽  
Performance Improvement ◽  
Data Stream ◽  
Real Data ◽  
Stream Computing ◽  
Time Data ◽  
Real Time Data ◽  
Computing Platforms ◽  
Time And Energy

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

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