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REVOSIMA: a new automatic earthquake detection method

Since March 1st, 2021, Mayotte's volcanological and seismological monitoring network (REVOSIMA) has been using a new method for automatically detecting and locating earthquakes. This new, more efficient method will enable better real-time monitoring of seismicity and its potential variations.

REVOSIMA: a new automatic earthquake detection method

Publication date: 16/03/2021

Observatories, Press, Research

Related themes : Natural Hazards

Earthquakes are made up of 2 main waves: P waves and S waves. The old automatic detection method only used the first waves to arrive (P waves), whereas this new method also uses S waves.

This new automatic detection method will make it possible to

  • better detection of earthquakes, particularly during the day when seismic signals are strongly disrupted by human activity (any activity in the vicinity of our seismometers is recorded by them and disrupts the detection of earthquakes),
  • detect and locate earthquakes of lower magnitude,
  • locate earthquakes more accurately
  • better detect any variations in activity.

REVOSIMA manually records all earthquakes identified by operators, whether or not they are detected automatically. REVOSIMA only locates earthquakes that are detected automatically. The new method will therefore make it possible to increase the number of earthquakes detected and located each day, which can be consulted on the Renass portal and represented on REVOSIMA maps in its daily and monthly bulletins.

An increase in the number of earthquakes counted, directly and solely linked to this change of method from March 1st 2021, the date of its implementation, is therefore possible.
It should be noted, however, that earthquakes detected using this new method will continue to be validated and monitored by the on-call agents and the REVOSIMA location group.


> Find out more about the new procedure:

After a year of development as part of Revosima’s post-doctoral funding, REVOSIMA will be using an artificial intelligence earthquake detection method developed in California. This method, called PhaseNet, recognises and pinpoints P and S waves (the main waves generated by earthquakes) in the signals recorded by seismic stations. We have combined it with EarthWorm seismic data processing methods to recognise earthquakes on the basis of these P and S points. Until now, the algorithm used only used the first P arrivals to automatically detect and locate earthquakes, resulting in fewer and less reliable locations.

REVOSIMA will use a new velocity model (ALav) that was developed in 2020 as part of another REVOSIMA post-doctoral grant. It has been developed from the many earthquakes whose phases have been manually pinpointed on recordings from land stations and stations installed on the ocean floor (OBS), deployed and recorded during campaigns at sea.
ALav will make it possible to locate events more accurately and limit the bias between the locations routinely determined using only land-based stations and those determined using data from OBS.
However, the localisations using the points on the OBS (carried out after their recovery) will remain essential to properly delimit the seismically active zones, by adding points on the signals recorded by the OBS around the seismicity detected by the onshore stations.

In order to quantify more precisely the differences in the perception of seismicity that these changes (algorithm and velocity model) induce, we have already reworked the period from December 1, 2020 to January 31, 2021 under conditions close to operational conditions. The figure shows the number of earthquakes identified :

  • manually
  • by the old automatic method;
  • by the new automatic method.

The results show that, of the events identified manually by the operators, a much greater number are detected automatically using the new method (75% of the seismic events identified by the operators).
new method (75% of the VT seismicity currently identified manually, compared with around 20% with the old method).
In addition, 15% of the LP seismicity identified manually is detected with the new method, whereas the previous automatic method detected only a few events.

In the figure opposite, which shows a histogram of daily detections using the different methods, the orange rectangles highlight the sequences of manually identified LPs, only a small proportion of which are identified by the new automatic detection method.

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