scholarly journals Earthquake Magnitude Estimation using Precise Point Positioning

2021 ◽  
Vol 906 (1) ◽  
pp. 012107
Author(s):  
Jakub Nosek ◽  
Pavel Václavovic
Keyword(s):  
Ground Motion ◽  
Magnitude Estimation ◽  
Precise Point Positioning ◽  
Early Warning Systems ◽  
Earthquake Magnitude ◽  
Parameters Estimation ◽  
Moment Magnitude ◽  
Accurate Estimation ◽  
Magnitude Scaling ◽  
Point Positioning

Abstract An accurate estimation of an earthquake magnitude plays an important role in targeting emergency services towards affected areas. Along with the traditional methods using seismometers, site displacements caused by an earthquake can be monitored by the Global Navigation Satellite Systems (GNSS). GNSS can be used either in real-time for early warning systems or in offline mode for precise monitoring of ground motion. The Precise Point Positioning (PPP) offers an optimal method for such purposes, because data from only one receiver are considered and thus not affected by other potentially not stable stations. Precise external products and empirical models have to be applied, and the initial convergence can be reduced or eliminated by the backward smoothing strategy or integer ambiguity resolution. The product for the magnitude estimation is a peak ground displacement (PGD). PGDs observed at many GNSS stations can be utilized for a robust estimate of an earthquake magnitude. We tested the accuracy of estimated magnitude scaling when using displacement waveforms collected from six selected earthquakes between the years 2016 and 2020 with magnitudes in a range of 7.5–8.2 Moment magnitude MW. We processed GNSS 1Hz and 5Hz data from 182 stations by the PPP method implemented in the G-Nut/Geb software. The precise satellites orbits and clocks corrections were provided by the Center for Orbit Determination in Europe (CODE). PGDs derived on individual GNSS sites formed the basis for ground motion parameters estimation. We processed the GNSS observations by the combination of the Kalman filter (FLT) and the backward smoother (SMT), which significantly enhanced the kinematic solution. The estimated magnitudes of all the included earthquakes were compared to the reference values released by the U. S. Geological Survey (USGS). The moment magnitude based on SMT was improved by 20% compared to the FLT-only solution. An average difference from the comparison was 0.07 MW and 0.09 MW for SMT and FLT solutions, respectively. The corresponding standard deviations were 0.18 MW and 0.22 MW for SMT and FLT solutions, which shows a good consistency of our and the reference estimates.

scholarly journals Determination of earthquake magnitude using GPS displacement waveforms from real-time precise point positioning

2013 ◽  
Vol 196 (1) ◽  
pp. 461-472 ◽  
Author(s):  
Rongxin Fang ◽  
Chuang Shi ◽  
Weiwei Song ◽  
Guangxing Wang ◽  
Jingnan Liu
Keyword(s):  
Real Time ◽  
Precise Point Positioning ◽  
Earthquake Magnitude ◽  
Point Positioning

scholarly journals Single Frequency Ionosphere-free Precise Point Positioning: A Cross-correlation Problem?

2011 ◽  
Vol 1 (4) ◽  
pp. 314-323 ◽  
Author(s):  
S. Choy ◽  
D. Silcock
Keyword(s):  
Cross Correlation ◽  
Precise Point Positioning ◽  
Parameters Estimation ◽  
Phase Delay ◽  
Single Frequency ◽  
Noise Characteristics ◽  
Correlation Problem ◽  
Phase Combination ◽  
Point Positioning ◽  
Free Code

Single Frequency Ionosphere-free Precise Point Positioning: A Cross-correlation Problem?This research investigates the feasibility of applying the code and the ionosphere-free code and phase delay observables for single frequency Precise Point Positioning (PPP) processing. Two observation models were studied: the single frequency ionosphere-free code and phase delay, termed the quasi-phase observable, and the code and quasi-phase combination. When implementing the code and quasi-phase combination, the cross-correlation between the observables must be considered. However, the development of an appropriate weight matrix, which can adequately describe the noise characteristics of the single frequency code and quasi-phase observations, is not a trivial task. The noise in the code measurements is highly dependent on the effects of the ionosphere; while the quasi-phase measurements are basically free from the effects of the ionospheric error. Therefore, it is of interest to investigate whether the correlation between the two measurements can be neglected when the code measurements were re-introduced to constrain the initial parameters estimation and thereby improving the phase ambiguities initialization process. It is revealed that the assumed uncorrelated code and quasi-phase combination provided comparable if not better positioning precision than the quasi-phase measurement alone. The level of improvement in the estimated positions is between 1 - 18 cm RMS.

Development of Qube: A Low-Cost Internet of Things Device for On-Site and Regional Earthquake Warning

2021 ◽  
Author(s):  
Vivien He
Keyword(s):  
Internet Of Things ◽  
Ground Motion ◽  
Low Cost ◽  
Early Warning Systems ◽  
Earthquake Magnitude ◽  
Text Messages ◽  
Seismic Network ◽  
Motion Velocity ◽  
Local Earthquake ◽  
Local Earthquake Magnitude

Abstract Earthquakes are a major global risk. The current earthquake early warning systems based on public seismic stations face challenges such as high cost, low density, high latency, no alert zone, and difficulty in predicting ground motions at the location of the user. This article pursues an alternative consumer-based approach. An Internet of Things consumer device, called a “Qube,” was built for a cost below $100 and is about the size of a Rubik’s cube. The Qube successfully detected earthquakes and issued earthquake warnings through sounding the onboard alarm for on-site warning and sending text messages to local subscribers for regional warning. The Qube is highly sensitive. During nine months of testing from September 2020 to May 2021, it detected all earthquakes over M 3.0 magnitude around Los Angeles, as well as nearby earthquakes down to M 2.3. The Qube uses a geophone for ground-motion velocity sensing and captures earthquake waveforms consistent with a nearby broadband seismometer in the Southern California Seismic Network. By analyzing data of the earthquakes detected by the Qube, an empirical logarithmic formula that is used to estimate local earthquake magnitude based on detected ground-motion amplitude in digital counts was developed. Although the Qube’s response in digital counts to ground-motion velocity in μm/s has not been determined, the empirical formula between Qube’s output and local earthquake magnitude suggests the Qube’s consistency in ground-motion measurement. The Qube has Wi-Fi connectivity and is controllable via a smartphone or computer. The combination of low cost, high sensitivity, and integrated alarm function of the Qube is intended to enable a consumer-based approach with the potential for mass adoption and use in dense networks, creating new opportunities for seismic network, earthquake warning, and educational applications.

Timeliness of earthquake magnitude estimation from the prompt elasto-gravity signal  using Deep Learning

2021 ◽  
Author(s):  
Andrea Licciardi ◽  
Quentin Bletery ◽  
Bertrand Rouet-Leduc ◽  
Jean-Paul Ampuero ◽  
Kévin Juhel
Keyword(s):  
Deep Learning ◽  
Magnitude Estimation ◽  
Early Warning ◽  
Early Warning Systems ◽  
Earthquake Magnitude ◽  
Reliable Estimate ◽  
Origin Time ◽  
Tsunami Early Warning ◽  
Gravity Signal ◽  
Large Earthquakes

<p>Mass redistribution during large earthquakes produces a prompt elasto-gravity signal (PEGS) that travels at the speed of light and can be observed on seismograms before the arrival of P-waves. PEGS carries information about earthquake magnitude and the temporal evolution of seismic moment, therefore it could be used to both improve the accuracy of current early source estimation systems and speed-up early warning. However, PEGS has been detected for only a handful of very large earthquakes so far, and its potential use for operational early warning remains to be established. In this work, we study the timeliness of magnitude estimation for subduction earthquakes in Japan using PEGS waveforms by means of Deep Learning and Bayesian uncertainty analysis. Given the paucity of PEGS observations, we train the model on a database of synthetic seismograms augmented with empirical noise in order to simulate more realistic waveforms. We use about 80 stations from the Japanese F-Net network and from networks with data available through IRIS.</p><p>Under this experimental setup, we find that our model is able to track the moment release for earthquakes with a final Mw above 8.0, with a system latency that depends on the signal-to-noise ratio of PEGS. The application of our model to the Mw=9.1 Tohoku-Oki earthquake shows a latency of about 50 s after which the model is able to track well the evolving Mw of the earthquake. After about 2 minutes from the earthquake origin time, a reliable estimate of its final Mw is obtained. Similar performances in terms of timeliness of final Mw estimation are observed for the relatively smaller Hokkaido earthquake (Mw=8.1) although with higher uncertainty.</p><p>Our results highlight the potential of PEGS to enhance the performance of existing tsunami early warning systems where estimating the magnitude of very large earthquakes within few minutes is vital.</p><p> </p>

Moment Magnitude Estimation of Large Earthquakes Based on Long-Period Ground Motion Prediction Equations and Preassumed Fault Models

2016 ◽  
Vol 10 (02) ◽  
pp. 1640004
Author(s):  
Rami Ibrahim ◽  
Hongjun Si ◽  
Kazuki Koketsu ◽  
Hiroe Miyake
Keyword(s):  
Ground Motion ◽  
Magnitude Estimation ◽  
Moment Magnitude ◽  
Motion Prediction ◽  
Fault Models ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Long Period ◽  
Large Earthquakes

We proposed a simple method of moment magnitude estimation based on long-period (5–30[Formula: see text]s) ground motion prediction equations (GMPEs). We constructed empirical fault models for different trial magnitudes. The source-to-site distance from the preassumed fault models to seismic stations was estimated. The best magnitude estimate was determined with reference to the minimum residual between the magnitude estimated from the GMPEs and the trial magnitude used to define the fault model. Using this method, the moment magnitudes of six large interplate earthquakes that occurred around the Japan Islands were estimated. The estimated magnitudes obtained in this study are in good agreement with those calculated from the global centroid moment tensor project. The method has potential to be used for rapid moment magnitude estimation of large earthquakes.

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