The Homogenized Instrumental Seismic Catalog (HORUS) of Italy from 1960 to Present

2020 ◽  
Vol 91 (6) ◽  
pp. 3208-3222 ◽  
Author(s):  
Barbara Lolli ◽  
Daniele Randazzo ◽  
Gianfranco Vannucci ◽  
Paolo Gasperini
Keyword(s):  
Real Time ◽  
Moment Tensor ◽  
Regression Method ◽  
Online Resources ◽  
Time Interval ◽  
Empirical Relationships ◽  
Chi Square ◽  
Centroid Moment Tensor ◽  
Different Sources ◽  
Seismic Catalog

Abstract We implemented an automatic procedure to update in near-real time (daily to hourly) a homogeneous catalog of Italian instrumental seismicity to be used for forecasting experiments and other statistical analyses. The magnitudes of all events are homogeneously revalued to be consistent with Mw standard estimates made by the Global Centroid Moment Tensor project. For the time interval from 1960 to 15 April 2005, catalogs and online resources available for the Italian area were merged and all magnitudes were homogenized to Mw according to empirical relationships computed using the chi-square regression method, which properly consider the uncertainties of both variables. From 16 April 2005 to the present, an automatic procedure periodically downloads the data of the online bulletin of the Istituto Nazionale di Geofisica e Vulcanologia and of online moment tensor catalogs from respective websites, merges the different sources, and applies traditional magnitude conversions to Mw. The final catalog is provided on a website for public dissemination.

Application of high-rate GPS for earthquake rapid response and modelling: a case in the 2019 Mw 7.1 Ridgecrest earthquake

2020 ◽  
Vol 222 (3) ◽  
pp. 1923-1935
Author(s):  
Jin Fang ◽  
Caijun Xu ◽  
Jianfei Zang ◽  
Yangmao Wen ◽  
Chuang Song ◽  
...  
Keyword(s):  
Real Time ◽  
Moment Tensor ◽  
Propagation Speed ◽  
Source Model ◽  
High Rate ◽  
Centroid Moment Tensor ◽  
Magnitude Scaling ◽  
Global Positioning ◽  
Slip Event ◽  
Kaikoura Earthquake

SUMMARY The 2019 Mw 7.1 Ridgecrest earthquake opens an opportunity to investigate how soon we can produce a reliable fault geometry and subsequently a robust source model based on high-rate Global Positioning System (GPS) data. In this study, we conduct peak ground displacement (PGD) magnitude scaling, real-time centroid moment tensor (CMT) calculation and rapid kinematic slip inversion. We conclude that a four-station PGD warning with a magnitude of Mw 7.03 can be issued at 24 s after initiation of the rupture. Fast CMT inversion can initially recover the correct nodal planes at 30 s. The kinematic slip model reveals that the Mw 7.1 earthquake is a predominant dextral strike-slip event with both normal and thrust components resolved. The earthquake shows a bilateral rupture with a low propagation speed of ∼2.1 km s−1 and a slip maxima of ∼4 m. The total moment is 5.18 × 1019 N m (Mw 7.11). We further suggest that a reasonable source model will be available in a simulated real-time mode within 30 s after the earthquake occurring, without using full high-rate GPS waveforms. This research highlights the significance of high-rate GPS for rapid earthquake response and modelling of kinematic rupture, which is also generalized by the hypothetical real-time GPS analysis for the 2016 Mw 7.8 Kaikoura earthquake and the 2010 Mw 7.2 El Mayor-Cucapah earthquake.

Rapid Earthquake Source Description Using Variometric-Derived GPS Displacements toward Application to the 2019 Mw 7.1 Ridgecrest Earthquake

2021 ◽  
Author(s):  
Jianfei Zang ◽  
Caijun Xu ◽  
Yangmao Wen ◽  
Xiaohang Wang ◽  
Kefeng He
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Moment Tensor ◽  
Continuous Phase ◽  
Earthquake Source ◽  
High Rate ◽  
Convergence Time ◽  
Centroid Moment Tensor ◽  
Origin Time ◽  
Broadcast Ephemeris

Abstract Using near-field high-rate Global Positioning System (GPS) displacements to invert for earthquake fault slips in real time has the potential to improve the accuracy of earthquake early warning or tsunami early warning. For such applications, real-time retrieval of high-accuracy GPS displacements is essential. Here, we report on rapid modeling of the 2019 Mw 7.1 Ridgecrest earthquake with real-time GPS displacements derived from a variometric approach with readily available broadcast ephemeris. This method calculates station variations in real time by differencing continuous phase observations and does not rely on precise orbit and clock information. The phase ambiguity is also removed, and thus the method does not suffer from a relatively long convergence time. To improve the accuracy of variometric displacements, we use a local spatial filter to decrease the influence of residual errors that cannot be removed completely by the time difference. We invert for the centroid moment tensor, static fault slips, and fault rupture process from the derived displacements. Our results show that all inverted models are available within about 65 s after the origin time of the earthquake and are comparable with models inverted by real-time precise point positioning displacements. This study highlights the great value of variometric displacements for the rapid earthquake source description with only broadcast ephemeris.

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

Profile, Gaming Usage and Purposes of Gaming of Internet Café Users in Manila

2013 ◽  
Vol 5 (4) ◽  
pp. 43-58
Author(s):  
Ana Clariza Natanauan ◽  
Jenmart Bonifacio ◽  
Mikael Manuel ◽  
Rex Bringula ◽  
John Benedic Enriquez
Keyword(s):  
Real Time ◽  
Exploratory Study ◽  
Roman Catholic ◽  
Descriptive Statistics ◽  
Future Research ◽  
Chi Square ◽  
Middle Income ◽  
Income Class ◽  
College Degrees ◽  
Strategy Games

This descriptive-exploratory study attempted to give the readers a portrait of cyber café gamers in Manila. It determined the profile of gamers, their gaming usage, and their purposes of cyber café gaming. Descriptive statistics revealed that most of the respondents were Manila settlers, students, pursuing or had obtained college degrees, male, young, Roman Catholic, single, belonged to middle-income class, and played games in cyber cafés in the afternoon once to twice a week. One-way chi-square showed that frequency of gaming was not equally distributed in a week and gamers showed tendency to play games in a cyber in a particular time of the day. Real-time strategy games were the most frequently played games in cyber cafés. To recreate, to relieve boredom, and to have fun were the top three reasons in playing games in cyber cafés. Conclusions and directions for future research were also presented.

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