National Seismological Network in India for Real-Time Earthquake Monitoring

2021 ◽  
Author(s):  
Brijesh K. Bansal ◽  
Ajeet P. Pandey ◽  
Ajay P. Singh ◽  
Gaddale Suresh ◽  
Ravi K. Singh ◽  
...  
Keyword(s):  
Real Time ◽  
Source Parameters ◽  
Focal Depth ◽  
Streaming Data ◽  
Small Aperture ◽  
Earthquake Parameters ◽  
Seismic Stations ◽  
National Network ◽  
Earthquake Source Parameters ◽  
Seismological Network

Abstract The National Seismological Network (NSN) of India has a history of more than 120 yr. During the last two decades, the NSN has gone through a significant modernization process, involving installation of seismic stations equipped with a broadband seismograph (BBS) and a strong-motion accelerograph (SMA). Each station has a very-small-aperture terminal connectivity for streaming data in real time to the central receiving station (CRS) in New Delhi. Seismic data recorded by the network are analyzed continuously on 24×7 basis to monitor the earthquakes in India and its adjoining regions. In this article, we present details of BBS and SMA network configurations; data streaming from the field seismic stations to the CRS for analysis; and the automatic and manual publication of the earthquake parameters including location coordinates, focal depth, time of occurrence, and magnitude, etc. Details of historically significant analog seismic charts and the seismic catalog, which includes more than 34,000 events with magnitude Mw 1.7–9.3 since 1505, are provided. The national network of India has been strengthened over the years and is now capable of estimating the main earthquake source parameters within ∼5–10min with an average of about 8.0 min. The spatial analysis of minimum magnitude of completeness further indicates a significant enhancement in minimum threshold magnitude detection capability of the network in recent decades.

scholarly journals The use of parallel computing for the high-resolution determination of earthquake source parameters

2019 ◽  
pp. 68-75
Author(s):  
A. S. Fomochkina ◽  
V. G. Bukchin
Keyword(s):  
Parallel Computing ◽  
Focal Mechanism ◽  
Source Parameters ◽  
Nodal Plane ◽  
Earthquake Parameters ◽  
Earthquake Source Parameters ◽  
Detailed Search ◽  
High Degree ◽  
Special Case

Alongside the determination of the focal mechanism and source depth of an earthquake by direct examination of their probable values on a grid in the parameter space, also the resolution of these determinations can be estimated. However, this approach requires considerable time in the case of a detailed search. A special case of a shallow earthquake whose one nodal plane is subhorizontal is an example of the sources that require the use of a detailed grid. For studying these events based on the records of the long-period surface waves, the grids with high degree of detail in the angles of the focal mechanism are required. We discuss the application of the methods of parallel computing for speeding up the calculations of earthquake parameters and present the results of studying the strongest aftershock of the Tohoku, Japan, earthquake by this approach.

Insights into deep learning for earthquake magnitude and location estimation

2021 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Early Warning ◽  
Source Parameters ◽  
Spectral Composition ◽  
Learning Model ◽  
Location Estimation ◽  
Training Set ◽  
Earthquake Source Parameters ◽  
Deep Learning Model

<p><span>The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and </span><span>can</span><span> learn features and their relation from data.</span></p><p><span>A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.</span></p><p><span>Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks </span><span>(station spacing down to 10 km)</span><span> of strong motion sensors, and North Chile with a sparser network </span><span>(station spacing around 40 km) </span><span>of broadband stations. </span></p><p><span>We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. </span>Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. <span>Third, the model fails for events with few similar training examples. For magnitude, this means that the largest event</span><span>s</span><span> are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. </span><span>These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.</span></p>

scholarly journals Large submarine earthquakes that occurred worldwide in a 1-year period (June 2013 to June 2014) – a contribution to the understanding of tsunamigenic potential

2015 ◽  
Vol 15 (10) ◽  
pp. 2183-2200 ◽  
Author(s):  
R. Omira ◽  
D. Vales ◽  
C. Marreiros ◽  
F. Carrilho
Keyword(s):  
Numerical Modeling ◽  
Subduction Zones ◽  
Source Parameters ◽  
Tsunami Generation ◽  
Earthquake Parameters ◽  
Shallow Earthquakes ◽  
Earthquake Source Parameters ◽  
Tsunami Numerical Modeling ◽  
Submarine Earthquakes ◽  
Earthquake Model

Abstract. This paper is a contribution to a better understanding of the tsunamigenic potential of large submarine earthquakes. Here, we analyze the tsunamigenic potential of large earthquakes which have occurred worldwide with magnitudes around Mw = 7.0 and greater during a period of 1 year, from June 2013 to June 2014. The analysis involves earthquake model evaluation, tsunami numerical modeling, and sensors' records analysis in order to confirm the generation of a tsunami (or lack thereof) following the occurrence of an earthquake. We also investigate and discuss the sensitivity of tsunami generation to the earthquake parameters recognized to control tsunami occurrence, including the earthquake location, magnitude, focal mechanism and fault rupture depth. Through this analysis, we attempt to understand why some earthquakes trigger tsunamis and others do not, and how the earthquake source parameters are related to the potential of tsunami generation. We further discuss the performance of tsunami warning systems in detecting tsunamis and disseminating the alerts. A total of 23 events, with magnitudes ranging from Mw = 6.7 to Mw = 8.1, have been analyzed. This study shows that about 39 % of the analyzed earthquakes caused tsunamis that were recorded by different sensors with wave amplitudes varying from a few centimeters to about 2 m. Tsunami numerical modeling shows good agreement between simulated waveforms and recorded waveforms, for some events. On the other hand, simulations of tsunami generation predict that some of the events, considered as non-tsunamigenic, caused small tsunamis. We find that most generated tsunamis were caused by shallow earthquakes (depth < 30 km) and thrust faults that took place on/near the subduction zones. The results of this study can help the development of modified and improved versions of tsunami decision matrixes for various oceanic domains.

scholarly journals Registration opportunities of the temporary seismological network of IDG RAS on EEC

2020 ◽  
Vol 2 (2) ◽  
pp. 84-90
Author(s):  
Andrey Goev ◽  
Sergey Volosov ◽  
Irina Sanina ◽  
Nataliya Konstantinovskaya ◽  
Margarita Nesterkina
Keyword(s):  
Triple Junction ◽  
Deep Structure ◽  
Observation System ◽  
Seismic Section ◽  
Small Aperture ◽  
Seismic Stations ◽  
East European ◽  
Temporary Network ◽  
Collision Zone ◽  
Seismological Network

In 2017, as a part of the study of the deep structure of the central part of the East European craton (EEC), three temporary seismic observation points were installed. They were equipped with broadband three-component sensors. The position of the stations is due to the need to build a seismic section in the sub-latitudinal direction in order to study the collision zone of the triple junction of mega blocks in the central part of the EEC. Together with the small-aperture seismic array "Mikhnevo" (MHVAR), temporary seismic stations form an area observation system with distances between stations of the order of 100 km. In 2018, the stations of the temporary network of the IDG RAS had registered 765 events of various nature: 222 industrial explosions and 543 earthquakes. During the year, the "Mikhnevo" array records about 5000 events, of which about 1000 are earthquakes at teleseismic and regional distances, and about 900 are identified as industrial explosions. Mutual processing of observed data on the temporary network and on the "Mikhnevo" in some cases (17%) made it possible to specify the results of the location of industrial explosions obtained previously at the "Mikhnevo" over 10 km.

scholarly journals NEAR REAL-TIME DETERMINATION OF EARTHQUAKE SOURCE PARAMETERS FOR TSUNAMI EARLY WARNING FROM GEODETIC OBSERVATIONS

2016 ◽  
Vol XLI-B8 ◽  
pp. 117-120
Author(s):  
Sunanda Manneela ◽  
T. Srinivasa Kumar ◽  
Shailesh R. Nayak
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Rapid Determination ◽  
Moment Tensor ◽  
Source Parameters ◽  
Critical Role ◽  
Tsunami Source ◽  
Tsunami Early Warning ◽  
Earthquake Source Parameters

Exemplifying the tsunami source immediately after an earthquake is the most critical component of tsunami early warning, as not every earthquake generates a tsunami. After a major under sea earthquake, it is very important to determine whether or not it has actually triggered the deadly wave. The near real-time observations from near field networks such as strong motion and Global Positioning System (GPS) allows rapid determination of fault geometry. Here we present a complete processing chain of Indian Tsunami Early Warning System (ITEWS), starting from acquisition of geodetic raw data, processing, inversion and simulating the situation as it would be at warning center during any major earthquake. We determine the earthquake moment magnitude and generate the centroid moment tensor solution using a novel approach which are the key elements for tsunami early warning. Though the well established seismic monitoring network, numerical modeling and dissemination system are currently capable to provide tsunami warnings to most of the countries in and around the Indian Ocean, the study highlights the critical role of geodetic observations in determination of tsunami source for high-quality forecasting.

scholarly journals Predicting approximate seismic responses in multistory buildings from real-time earthquake source information, for earthquake early warning applications

2021 ◽  
Author(s):  
Gemma Cremen ◽  
Omar Velazquez ◽  
Benazir Orihuela ◽  
Carmine Galasso
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Early Warning ◽  
Multistory Buildings ◽  
Building Model ◽  
Beam Combination ◽  
Earthquake Source Parameters ◽  
Wide Range

AbstractRegional earthquake early warning (EEW) alerts and related risk-mitigation actions are often triggered when the expected value of a ground-motion intensity measure (IM), computed from real-time magnitude and source location estimates, exceeds a predefined critical IM threshold. However, the shaking experienced in mid- to high-rise buildings may be significantly different from that on the ground, which could lead to sub-optimal decision-making (i.e., increased occurrences of false and missed EEW alarms) with the aforementioned strategy. This study facilitates an important advancement in EEW decision-support, by developing empirical models that directly relate earthquake source parameters to resulting approximate responses in multistory buildings. The proposed models can leverage real-time earthquake information provided by a regional EEW system, to provide rapid predictions of structure-specific engineering demand parameters that can be used to more accurately determine whether or not an alert is triggered. We use a simplified continuum building model consisting of a flexural/shear beam combination and vary its parameters to capture a wide range of deformation modes in different building types. We analyse the approximate responses for the building model variations, using Italian accelerometric data and corresponding source parameter information from 54 earthquakes. The resulting empirical prediction equations are incorporated in a real-time Bayesian framework that can be used for building-specific EEW applications, such as (1) early warning of floor-shaking sensed by occupants; and (2) elevator control. Finally, we demonstrate the improvement in EEW alert accuracy that can be achieved using the proposed models.

scholarly journals Appraising the Early-est earthquake monitoring system for tsunami alerting at the Italian Candidate Tsunami Service Provider

2015 ◽  
Vol 15 (9) ◽  
pp. 2019-2036 ◽  
Author(s):  
F. Bernardi ◽  
A. Lomax ◽  
A. Michelini ◽  
V. Lauciani ◽  
A. Piatanesi ◽  
...  
Keyword(s):  
Service Provider ◽  
Epicentral Distance ◽  
Source Parameters ◽  
Earthquake Source ◽  
Tsunami Warning ◽  
Earthquake Parameters ◽  
Warning Messages ◽  
Earthquake Source Parameters ◽  
Hypocenter Depth ◽  
Epicenter Location

Abstract. In this paper we present and discuss the performance of the procedure for earthquake location and characterization implemented in the Italian Candidate Tsunami Service Provider at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in Rome. Following the ICG/NEAMTWS guidelines, the first tsunami warning messages are based only on seismic information, i.e., epicenter location, hypocenter depth, and magnitude, which are automatically computed by the software Early-est. Early-est is a package for rapid location and seismic/tsunamigenic characterization of earthquakes. The Early-est software package operates using offline-event or continuous-real-time seismic waveform data to perform trace processing and picking, and, at a regular report interval, phase association, event detection, hypocenter location, and event characterization. Early-est also provides mb, Mwp, and Mwpd magnitude estimations. mb magnitudes are preferred for events with Mwp &amp;lesssim; 5.8, while Mwpd estimations are valid for events with Mwp &amp;gtrsim; 7.2. In this paper we present the earthquake parameters computed by Early-est between the beginning of March 2012 and the end of December 2014 on a global scale for events with magnitude M &amp;geq; 5.5, and we also present the detection timeline. We compare the earthquake parameters automatically computed by Early-est with the same parameters listed in reference catalogs. Such reference catalogs are manually revised/verified by scientists. The goal of this work is to test the accuracy and reliability of the fully automatic locations provided by Early-est. In our analysis, the epicenter location, hypocenter depth and magnitude parameters do not differ significantly from the values in the reference catalogs. Both mb and Mwp magnitudes show differences to the reference catalogs. We thus derived correction functions in order to minimize the differences and correct biases between our values and the ones from the reference catalogs. Correction of the Mwp distance dependency is particularly relevant, since this magnitude refers to the larger and probably tsunamigenic earthquakes. Mwp values at stations with epicentral distance Δ &amp;lesssim; 30° are significantly overestimated with respect to the CMT-global solutions, whereas Mwp values at stations with epicentral distance Δ &amp;gtrsim; 90° are slightly underestimated. After applying such distance correction the Mwp provided by Early-est differs from CMT-global catalog values of about δ Mwp &amp;approx; 0.0 &amp;mp; 0.2. Early-est continuously acquires time-series data and updates the earthquake source parameters. Our analysis shows that the epicenter coordinates and the magnitude values converge within less than 10 min (5 min in the Mediterranean region) toward the stable values. Our analysis shows that we can compute Mwp magnitudes that do not display short epicentral distance dependency overestimation, and we can provide robust and reliable earthquake source parameters to compile tsunami warning messages within less than 15 min after the event origin time.

Earthworm-based automatic system for real-time calculation of local earthquake source parameters

2017 ◽  
Vol 53 (4) ◽  
pp. 267-279 ◽  
Author(s):  
A. A. Stepnov ◽  
A. V. Konovalov ◽  
A. V. Gavrilov ◽  
K. A. Manaychev
Keyword(s):  
Real Time ◽  
Automatic System ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Source Parameters ◽  
Time Calculation ◽  
Local Earthquake

scholarly journals Appraising the Early-est earthquake monitoring system for tsunami alerting at the Italian candidate Tsunami Service Provider

2015 ◽  
Vol 3 (4) ◽  
pp. 2913-2952 ◽  
Author(s):  
F. Bernardi ◽  
A. Lomax ◽  
A. Michelini ◽  
V. Lauciani ◽  
A. Piatanesi ◽  
...  
Keyword(s):  
Service Provider ◽  
Epicentral Distance ◽  
Source Parameters ◽  
Earthquake Source ◽  
Tsunami Warning ◽  
Earthquake Parameters ◽  
Earthquake Source Parameters ◽  
Hypocenter Depth ◽  
Epicenter Location ◽  
Distance Correction

Abstract. In this paper we present the procedure for earthquake location and characterization implemented in the Italian candidate Tsunami Service Provider at INGV in Roma. Following the ICG/NEAMTWS guidelines, the first tsunami warning messages are based only on seismic information, i.e. epicenter location, hypocenter depth and magnitude, which are automatically computed by the software Early-est. Early-est is a package for rapid location and seismic/tsunamigenic characterization of earthquakes. The Early-est software package operates on offline-event or continuous-realtime seismic waveform data to perform trace processing and picking, and, at a regular report interval, phase association, event detection, hypocenter location, and event characterization. In this paper we present the earthquake parameters computed by Early-est from the beginning of 2012 till the end of December 2014 at global scale for events with magnitude M &amp;geq; 5.5, and the detection timeline. The earthquake parameters computed automatically by Early-est are compared with reference manually revised/verified catalogs. From our analysis the epicenter location and hypocenter depth parameters do not differ significantly from the values in the reference catalogs. The epicenter coordinates generally differ less than 20 &amp;mp; 20 km from the reference epicenter coordinates; focal depths are less well constrained and differ generally less than 0 &amp;mp; 30 km. Early-est also provides mb, Mwp and Mwpd magnitude estimations. mb magnitudes are preferred for events with Mwp &amp;lesssim; 5.8, while Mwpd are valid for events with Mwp &amp;gtrsim; 7.2. The magnitude mb show wide differences with respect to the reference catalogs, we thus apply a linear correction mbcorr = mb · 0.52 + 2.46, such correction results into δmb ≈ 0.0 &amp;mp; 0.2 uncertainty with respect the reference catalogs. As expected the Mwp show distance dependency. Mwp values at stations with epicentral distance Δ &amp;lesssim; 30° are significantly overestimated with respect the CMT-global solutions, whereas Mwp values at stations with epicentral distance Δ &amp;gtrsim; 90° are slightly underestimated. We thus apply a 3rd degree polynomial distance correction. After applying the distance correction, the Mwp provided by Early-est differs from CMT-global catalog values of about δ Mwp ≈ 0.0 &amp;mp; 0.2. Early-est continuously acquires time series data and updates the earthquake source parameters. Our analysis shows that the epicenter coordinates and the magnitude values converge rather quickly toward the final values. Generally we can provide robust and reliable earthquake source parameters to compile tsunami warning message within less than about 15 min after event origin time.

scholarly journals Earthquake source parameters from GPS-measured static displacements with potential for real-time application

2013 ◽  
Vol 40 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Thomas B. O'Toole ◽  
Andrew P. Valentine ◽  
John H. Woodhouse
Keyword(s):  
Real Time ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Source Parameters ◽  
Real Time Application
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