A Novel Technique for Earthquakes Magnitude Estimation
2021 (English)In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper (Refereed)
Abstract [en]
Earthquakes are natural phenomena whose consequences can be catastrophic. The introduction of new technologies such as intelligent transducers, which, being equipped with a communication interface, allow the exchange of information with other devices connected to the network, prompted researchers to study systems able to exploit data related to the first moments of the seismic event in an attempt to predict its characteristics. This idea is the base of Earthquake Early Warning (EEW) systems that aim to estimate the upcoming event's fundamental parameters to issue an alarm signal in case of danger, allowing the necessary precautions to be taken. There are many problems concerning parameters' estimation representing an indication of the phenomenon's danger in this context. In particular, the magnitude estimation and the compromise between the processing of reliable results and the time window's brevity in which the system algorithm must acquire and process the data to produce such results represent challenging aspects. This paper present new results of an approach, proposed by the authors that use a quotient of iterative filters. By choosing the right filter parameters, this quotient correlates with the earthquake magnitude by processing the incoming wave's first three seconds. © 2021 IEEE.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021.
Keywords [en]
Earthquake magnitude, EEW, geophone, seismograph, αMCT, Alarm systems, Iterative methods, Communication interface, Earthquake early warning, Earthquake magnitudes, Exchange of information, Magnitude estimation, Natural phenomena, Novel techniques, Reliable results, Earthquakes
Identifiers
URN: urn:nbn:se:miun:diva-43068DOI: 10.1109/I2MTC50364.2021.9459799Scopus ID: 2-s2.0-85113716740ISBN: 9781728195391 (print)OAI: oai:DiVA.org:miun-43068DiVA, id: diva2:1595682
Note
Export Date: 20 September 2021; Conference Paper; CODEN: CRIIE
2021-09-202021-09-202021-09-20Bibliographically approved