Developing a Fuzzy Inference System Based on Multi-Sensor Data to Predict Powerful Earthquake Parameters

Abstract

Predicting the parameters of upcoming earthquakes has always been one of the most challenging topics in studies related to earthquake precursors. Increasing the number of sensors and satellites and consequently incrementing the number of observable possible earthquake precursors in different layers of the lithosphere, atmosphere, and ionosphere of the Earth has opened the possibility of using data fusion methods to estimate and predict earthquake parameters with low uncertainty. In this study, a Mamdani fuzzy inference system (FIS) was proposed and implemented in five case studies. In particular, the magnitude of Ecuador (16 April 2016), Iran (12 November 2017), Papua New Guinea (14 May 2019), Japan (13 February 2021), and Haiti (14 August 2021) earthquakes were estimated by FIS. The results showed that in most cases, the highest number of anomalies was usually observed in the period of about one month before the earthquake and the predicted magnitude of the earthquake in these periods was slightly different from the actual magnitude value. Therefore, based on the results of this study, it could be concluded that if a significant number of anomalies are observed in the time series of different precursors, it is likely that an earthquake of the magnitude predicted by the proposed FIS system within the Dobrovolsky area of the studied location will happen during the next month.

Observation of the Preparation Phase Associated with Mw = 7.2 Haiti Earthquake on 14 August 2021 from a Geophysical Data Point of View

“…A previous work on this earthquake was conducted by Akhoondzadeh [57], which analysed 75 days before up to 15 days after the Mw = 7.2 Haiti 2021 earthquake using CSES-01, Swarm Alpha Bravo and Charlie satellites and atmospheric data from the Giovanni web-portal. He identified an increase in anomalies in the last 20 days before the earthquake, similar to the result of the recent catastrophic earthquake in Turkey [58]. Another work by Khan et al [59] used a machine learning technique to analyse satellite atmospheric data 30 days before and up to 15 days after the mainshock.…”

“…In this paper, we applied several methods to investigate the lithosphere, atmosphere and ionosphere, following the approach we had successfully used before several earthquakes and some volcano eruptions. These cases include among all, the Mw = 6.7 Lushan (China) 2013 [69], Mw = 7.8 Ecuador 2016 [70], Mw = 6.0 and Mw = 6.5 Amatrice-Norcia 2016 [71], Mw = 7.5 Indonesia 2018 [72], Mw = 7.2 Kermadec Islands (New Zealand) 2019 [73] and Mw = 7.8 and Mw 7.5 Turkey 2023 [58] earthquakes. In all of these mentioned cases, a possible lithosphere-atmosphere and ionosphere coupling (LAIC) has been identified in the form of a chain of anomalies and, in some cases (as Lushan 2013), even more possible couplings with different physical mechanisms have been proposed by Zhang et al [69].…”

Atmospheric and Ionospheric Effects of La Palma Volcano 2021 Eruption

“…Such an approach was derived from another very similar algorithm, CAPRI [60], with the main difference being the data source: MEANS investigate the climatological archive of MERRA-2 from NASA [61] (the one used in this paper) and CAPRI ERA-Interim and operational archive from ECMWF. Such algorithms have been successfully applied to several earthquakes in the world as well as volcano eruptions [39,54,59,60,62,63].…”

Quick Report of the ML=3.3 on 1 January 2023 Guidonia (Rome, Italy) Earthquake: Evidence of a Seismic Acceleration

“…With the experience of previous investigated earthquake, the MEANS software has been used with the remotion of a linear trend to take into account the possible effect of the global warming as done in De Santis et al [12] for Ridgecrest, US, 2019 earthquake analysis. The MEANS algorithm has been used the first time by Piscini et al [13] to investigate volcano eruptions and after applied to several other volcano eruptions and earthquakes [12,[14][15][16][17][18]. The algorithm essentially estimates the typical value of the atmospheric parameter for the day and region constructing an historical time series and in particular the mean and standard deviation of the parameter for the area under study.…”