Tsunamigenic Seismic Activity (Earthquakes) Prediction from III- Component Seismic Data

2021 ◽  
pp. 351-360
Author(s):  
Tammineni Gnananandarao ◽  
Rakesh Kumar Dutta ◽  
Vishwas Nandkishor Khatri
Keyword(s):  
Seismic Data ◽  
Seismic Activity

Seismicity of the Erguna Region (Northeastern China): Evidence for Local Stress Redistribution

2020 ◽  
Vol 110 (2) ◽  
pp. 803-815
Author(s):  
Alena I. Seredkina ◽  
Valentina I. Melnikova ◽  
Yan B. Radziminovich ◽  
Nadezhda A. Gileva
Keyword(s):  
Seismic Data ◽  
Seismic Activity ◽  
Local Stress ◽  
Point Of View ◽  
Stress Redistribution ◽  
P Wave ◽  
Small Scale ◽  
Amplitude Spectra ◽  
First Motion ◽  
Focal Parameters

ABSTRACT We consider the seismicity of the Erguna region in northeast China (48°–51° N, 117°–123° E) which is poorly studied from seismological point of view as it is characterized by a low level of seismic activity. We calculate focal parameters (focal mechanisms, scalar seismic moments, moment magnitudes, and hypocentral depths) for seven regional earthquakes with Mw 4.2–4.6 that occurred in 2000–2017 using global seismic data of Rayleigh- and Love-wave amplitude spectra and P-wave first-motion polarities recorded at regional stations. It has been shown that the study earthquakes are of small magnitudes (Mw 4.2–4.6), of various hypocentral depths (3–37 km), and are characterized by different kinematics in their sources (normal and thrust faults, strike slips). The different faulting mechanisms could reflect local stress redistribution in small-scale crustal blocks bordered by local short-length nonconnecting faults. The available geophysical and geological data evidence that the observed features of the seismic process in the Erguna region—low-seismic activity and inhomogeneity of the stress-strain field—are likely to be controlled by the structure of the crust and the upper mantle.

scholarly journals Crowdsourcing triggers rapid, reliable earthquake locations

2019 ◽  
Vol 5 (4) ◽  
pp. eaau9824 ◽  
Author(s):  
Robert J. Steed ◽  
Amaya Fuenzalida ◽  
Rémy Bossu ◽  
István Bondár ◽  
Andres Heinloo ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Activity ◽  
Low Cost ◽  
Global Scale ◽  
Cost Approach ◽  
Earthquake Locations ◽  
Seismic Location

In many cases, it takes several minutes after an earthquake to publish online a seismic location with confidence. Via monitoring for specific types of increased website, app, or Twitter usage, crowdsourced detection of seismic activity can be used to “seed” the search in the seismic data for an earthquake and reduce the risk of false detections, thereby accelerating the publication of locations for felt earthquakes. We demonstrate that this low-cost approach can work at the global scale to produce reliable and rapid results. The system was retroactively tested on a set of real crowdsourced detections of earthquakes made during 2016 and 2017, with 50% of successful locations found within 103 s, 76 s faster than GEOFON and 271 s faster than the European-Mediterranean Seismological Centre’s publication times, and 90% of successful locations found within 54 km of the final accepted epicenter.

Crustal stress crises and seismic activity in the Italian peninsula investigated by fractal analysis of acoustic emission, soil exhalation and seismic data

2006 ◽  
Vol 261 (1) ◽  
pp. 47-61 ◽  
Author(s):  
Gabriele Paparo ◽  
Giovanni P. Gregori ◽  
Maurizio Poscolieri ◽  
Iginio Marson ◽  
Francesco Angelucci ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fractal Analysis ◽  
Seismic Data ◽  
Seismic Activity ◽  
Crustal Stress ◽  
Italian Peninsula

Preservation and Reuse of Historical Seismic Data in Mexico: SISMOMex and the Online “National Seismogram Library”

2020 ◽  
Vol 91 (3) ◽  
pp. 1482-1487 ◽  
Author(s):  
Xyoli Pérez-Campos ◽  
Saul Armendáriz-Sánchez ◽  
Víctor H. Espíndola ◽  
Minerva Castro-Escamilla ◽  
Jesus Perez ◽  
...  
Keyword(s):  
Open Access ◽  
Seismic Data ◽  
Seismic Activity ◽  
Scientific Community ◽  
Institutional Repository ◽  
Online Search ◽  
Electronic Resources ◽  
The Future

Abstract The SISMOMex project represents more than 110 yr of seismological information from Mexico. Its objective is the preservation, search, recovery, systematization, reuse, and dissemination of data and information from the seismograms generated by the Servicio Sismológico Nacional (SSN, National Seismological Service of Mexico) and published material about earthquakes and seismology in Mexico. SISMOMex is the combination of resources in any format (print, electronic, multimedia, and so on) and their corresponding registry in a database and an institutional repository. The database and the repository have an interface for an online search, allowing access to open-access materials, through a link to the electronic resources generated or acquired. The project seeks to preserve for the future all the products generated by the SSN since the beginning of its operations, and organize the “National Seismogram Library” (Sismoteca Nacional en Línea), which is the physical place that stores the seismograms generated by the SSN. Similarly, it allows, under citation, the reuse of the data by interested researchers and students. These seismograms contain unique and unreproducible information. With the project presented here, support is provided mainly to the scientific community, by directing the search of historical and present data and information on the seismic activity of Mexico to a single place.

scholarly journals Quasi-static electric fields phenomena in the ionosphere associated with pre- and post earthquake effects

2008 ◽  
Vol 8 (1) ◽  
pp. 101-107 ◽  
Author(s):  
M. Gousheva ◽  
D. Danov ◽  
P. Hristov ◽  
M. Matova
Keyword(s):  
Pacific Ocean ◽  
Electric Field ◽  
Electric Fields ◽  
Seismic Data ◽  
Seismic Activity ◽  
Vertical Component ◽  
Satellite Orbits ◽  
Static Electric Field ◽  
Earthquake Zone ◽  
Earthquake Effects

Abstract. To prove a direct relationship between the quasi-static electric field disturbances and seismic activity is a difficult, but actual task of the modern ionosphere physics. This paper presents new results on the processing and analysis of the quasi-static electric field in the upper ionosphere (h=800–900 km) observed from the satellite INTERCOSMOS-BULGARIA-1300 over earthquakes' source regions (seismic data of World Data Center, Denver, Colorado, USA). Present research focuses on three main areas (i) development of methodology of satellite and seismic data selecting, (ii) data processing and observations of the quasi-static electric field (iii) study and accumulation of statistics of possible connection between anomalous vertical electric fields penetrating from the earthquake zone into the ionosphere, and seismic activity. The most appropriate data (for satellite orbits above sources of forthcoming or just happened seismic events) have been selected from more than 250 investigated cases.The increase of about 5-10-15 mV/m in the vertical component of the quasi-static electric field observed by INTERCOSMOS-BULGARIA-1300 during seismic activity over Southern Ocean, Greenland Sea, South-Weat Pacific Ocean, Indian Ocean, Central America, South-East Pacific Ocean, Malay Archipelago regions are presented. These anomalies, as phenomena accompanying the seismogenic process, can be considered eventually as possible pre-, co- (coeval to) and post-earthquake effects in the ionosphere.

scholarly journals Unification of data from various seismic catalogues to study seismic activity in the Carpathians Mountain arc

2019 ◽  
Vol 11 (1) ◽  
pp. 837-842
Author(s):  
Agnieszka Braclawska ◽  
Adam Filip Idziak
Keyword(s):  
Central Europe ◽  
Seismic Data ◽  
Seismic Activity ◽  
Earthquake Catalogue ◽  
Active Area ◽  
Seismic Events ◽  
Large Area ◽  
Carpathian Mountains ◽  
The Carpathians ◽  
Seismic Networks

Abstract The Carpathian Mountainsarc is the most seismically active area in Central Europe. Analysis of the seismicity of entire Carpathian arc requires data from each of the particular catalogues which have to be properly and uniformly entered, standardized and merged. For our study we first had to prepare a database of seismic events (ML ≥ 1.6) compiled from the data of earthquakes taken from individual national seismic networks as well as data from international seismic centers. However, a careful review of these catalogues has uncovered significant inconsistencies, particularly discrepancies in the description of the location, magnitude and completeness of seismic events. To address these inconsistencies, a newly created compound earthquake catalogue was compiled from the aforementioned seismic catalogues and included events that occurred in the Carpathian Mountains arc area between 1976 and 2017. This work is intended to point out some of the problems associated with collecting data from various seismic catalogues as well as the need for their very careful verification, in order to create a uniform set of seismic data across a large area spanning numerous countries. The results suggest that compiling a uniform and dependable earthquake catalogue is crucial for reliable seismic studies.

scholarly journals Analysis of swarm earthquakes around Mt. Agung Bali, Indonesia prior to November 2017 eruption using regional BMKG network

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohamad Taufik Gunawan ◽  
Ridwan Kusnandar ◽  
Pepen Supendi ◽  
Andri Dian Nugraha ◽  
Nanang T. Puspito ◽  
...  
Keyword(s):  
Focal Mechanism ◽  
Seismic Data ◽  
Seismic Activity ◽  
Strike Slip ◽  
Double Difference ◽  
S Wave ◽  
Focal Mechanism Solutions ◽  
Time Period ◽  
Swarm Earthquakes ◽  
Recent Eruption

Abstract Mt. Agung, located in Karangasem-Bali, Indonesia, had a significant increase of swarm earthquakes from September 2017 until the recent eruption in November 2017. To analyze the seismic swarm and its correlation with the magmatic movement, we worked on the regional seismic data recorded by Agency for Meteorology, Climatology and Geophysics of Indonesia (BMKG) between September 14 to October 20, 2017. P-and S-wave phases of the swarm events had been manually picked. In total, 804 events in the time period of September 14 to October 20, 2017 were successfully determined. To improve the location precision, the double-difference relocation method was performed. We identified most of the events as Volcano-Tectonic type A (VT-A) earthquakes and located between Mt. Batur and Mt. Agung. Those events form a cluster striking in NE-SW direction at a depth between 2 and 20 km. Focal mechanism solutions for selected events below Mt. Agung show a thrust and strike-slip faulting regime. Interestingly, a trend of event propagation toward the summit of Mt. Agung was observed. The frequency of VT-A event occurrences is significantly increased at the later stage of the swarms. We concluded that the increased seismic activity in Mt. Agung was due to the migration of magma from the deep chamber to the shallow reservoir.

Heterogeneous Parallel Processing Enabled Deep Learning Pattern Recognition of Seismic Big Data in Syrna and Kandelioussa

2021 ◽  
Author(s):  
Antonios Konstantaras ◽  
Theofanis Frantzeskakis ◽  
Emmanouel Maravelakis ◽  
Alexandra Moshou ◽  
Panagiotis Argyrakis
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Pattern Recognition ◽  
Big Data ◽  
Deep Learning ◽  
Parallel Processing ◽  
Seismic Data ◽  
Seismic Activity ◽  
Point Cloud ◽  
Deep Learning Neural Network

<p>This research aims to depict ontological findings related to topical seismic phenomena within the Hellenic-Seismic-Arc via deep-data-mining of the existing big-seismological-dataset, encompassing a deep-learning neural network model for pattern recognition along with heterogeneous parallel processing-enabled interactive big data visualization. Using software that utilizes the R language, seismic data were 3D plotted on a 3D Cartesian plane point cloud viewer for further investigation of the formed three-dimensional morphology. As a means of mining information from seismic big data, a deep neural network was trained and refined for pattern recognition and occurrence manifestation attributes of seismic data of magnitudes greater than Ms 4.0. The deep learning neural network comprises of an input layer with six input neurons for the insertion of year, month, day, latitude, longitude and depth, followed by six hidden layers with a hundred neurons each, and one output layer of the estimated magnitude level. This approach was conceptualised to investigate for topical patterns in time yielding minor, interim and strong seismic activity, such as the one depicted by the deep learning neural network, observed in the past ten years on the region between Syrna and Kandelioussa. This area’s coordinates are around 36,4 degrees in latitude and 26,7 degrees in longitude, with the deep learning neural network achieving low error rates, possibly depicting a pattern in seismic activity.</p><p>References</p><p>Axaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.</p><p>Konstantaras A. Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone. Informatics, 7 (4), 39, 2020.</p><p>Konstantaras A.J. Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100, 2016.</p><p>Konstantaras A.J. Classification of distinct seismic regions and regional temporal modelling of seismicity in the vicinity of the Hellenic seismic arc. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6 (4), 1857-1863, 2012.</p><p>Konstantaras A., F. Vallianatos, M.R. Varley, J.P. Makris. Soft-Computing modelling of seismicity in the southern Hellenic Arc. IEEE Geoscience and Remote Sensing Letters, 5 (3), 323-327, 2008.</p><p>Konstantaras A., M.R. Varley, F. Vallianatos, G. Collins and P. Holifield. Recognition of electric earthquake precursors using neuro-fuzzy methods: methodology and simulation results. Proc. IASTED Int. Conf. Signal Processing, Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 303-308, 2002.</p><p>Maravelakis E., Konstantaras A., Kilty J., Karapidakis E. and Katsifarakis E. Automatic building identification and features extraction from aerial images: Application on the historic 1866 square of Chania Greece. 2014 International Symposium on Fundamentals of Electrical Engineering (ISFEE), Bucharest, 1-6, 2014. doi: 10.1109/ISFEE.2014.7050594.</p><p>Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.</p><p>Maravelakis E., Bilalis N., Mantzorou I., Konstantaras A. and Antoniadis A. 3D modelling of the oldest olive tree of the world. International Journal Of Computational Engineering Research. 2 (2), 340-347, 2012.</p>

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