The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

Гитис, В. Г.; Derendyaev, A. B.

doi:10.3389/feart.2020.585317

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Ghosh et al [79] show that in the same EPZ, two VLF receiving locations show a sharp difference in the seismogenic anomalies. In statistical and case wise studies, it is obvious that a preparation zone will also tend to diminish the influence of an earthquake as one goes far from the epicenter [80][81][82]. A statistical analysis inside an EPZ may give a combined outcome of all the earthquakes that their EPZ overlaps with the EPZ of interest, and their occurrence is relatively close in time, having individual weightage depending on the distance of the overlap area from their epicenter, and how close in time they occur.…”

Section: Discussionmentioning

confidence: 99%

Preseismic Perturbations and their Inhomogeneity as Computed from Ground‐ and Space‐Based Investigation during the 2016 Fukushima Earthquake

Biswas

Kundu

Sasmal³

et al. 2023

Journal of Sensors

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We present the atmospheric anomalies instigated through seismogenic sources by a multichannel observation using ground- and satellite-based systems. This study emphasizes the seismic event which happened on the east coast of Japan, near the Fukushima Prefecture on November 21, 2016 (in UTC), with a magnitude of 6.9 and a depth of 11.4 km. We mainly focus on the atmospheric and ionospheric irregularities via acoustic and electromagnetic channels originating from earthquakes in the process of the lithosphere, atmosphere, and ionospheric coupling (LAIC) mechanism. In the acoustic channel, we study the seismogenic atmospheric gravity wave (AGW) which perturbs the local lower atmosphere. The observation of nighttime fluctuations in the very low frequency (VLF) signals and total electron content (TEC) is used to investigate the atmospheric perturbation through the electromagnetic channel. For the ground-based observations, a VLF signal network consisting of 5 receivers in Japan is used to study by recording the VLF amplitude transmitted from the Japanese transmitter JJI (22.2 kHz). VLF nighttime fluctuation is used to check the unusualities due to the earthquake. Preseismic wavelike structures having periods of AGW are observed in the nighttime signal. Direct investigation of such AGWs is done by computing the potential energy related to AGW from the sounding of the atmosphere using broadband emission radiometry (SABER) temperature profiles mounted on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. Ionospheric TEC inspection is done by using a ground-based global navigation satellite system (GNSS) receiver from the International GNSS Survey (IGS) station MIZU in Japan and observing anomalies in diurnal TEC around 6 and 10 days prior to the earthquake. We also obtain the wavelike structure of AGW from the small-scale fluctuation of TEC using wavelet analysis. All the parameters are found to be preseismic for this earthquake; the acoustics channel gives more consistent outcomes than the electromagnetic channel.

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Section: Discussionmentioning

confidence: 99%

Preseismic Perturbations and their Inhomogeneity as Computed from Ground‐ and Space‐Based Investigation during the 2016 Fukushima Earthquake

Biswas

Kundu

Sasmal³

et al. 2023

Journal of Sensors

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“…On the maps, target events fell into subdomains: for Japan, the proportion of the alarm area is about 15%, and for California, about 3%. In the modeling, we used the experience of our previous studies to assess the relationship between data on horizontal displacements of the Earth's surface with seismicity [19,52] and on the application of the method of the minimum area of alarm to earthquake prediction [50].…”

Section: Discussionmentioning

confidence: 99%

“…For Japan, the most informative were the fields S 9 and S 10 . Both of them previously proved to be the most effective in predicting earthquakes and their magnitudes in Kamchatka and the Aegean region [50]. The anomalous values of the S 9 field correspond to areas of the seismic process in which the density values of earthquake epicenters are quite high but significantly less than the average values of the density of epicenters in the interval from the beginning of the analysis to the start of training.…”

Section: Seismological Data Preprocessingmentioning

confidence: 99%

Analyzing the Performance of GPS Data for Earthquake Prediction

2021

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The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.

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“…This model suggests that the earthquake of a particular magnitude (M) in a region during a period of time can be approximately 1 http://www.world-stress-map.org/casmo/ considered as a Poisson process (Ogata, 1988). In addition, the method of the minimum area of alarm for earthquake magnitude prediction (Gitis and Derendyaev, 2020) and a method for earthquake predictions based on alarms (Zechar and Jordan, 2008) have all been suggested and evaluated.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Forecasting of Strong Earthquakes in North America, South America, Japan, Southern China and Northern India With Machine Learning

et al. 2022

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Strong earthquakes (magnitude ≥7) occur worldwide affecting different cities and countries while causing great human, ecological and economic losses. The ability to forecast strong earthquakes on the long-term basis is essential to minimize the risks and vulnerabilities of people living in highly active seismic areas. We have studied seismic activities in North America, South America, Japan, Southern China and Northern India in search for patterns in strong earthquakes on each of these active seismic zones between 1900 and 2021 with the powerful mathematical tool of wavelet transform. We found that the primary seismic activity patterns for M ≥ 7 earthquakes are 55, 3.7, 7.7, and 8.6 years, for seismic zones of the southwestern United States and northern Mexico, southwestern Mexico, South American, and Southern China-Northern India, respectively. In the case of Japan, the most important seismic pattern for earthquakes with magnitude 7 ≤ M< 8 is 4.1 years and for strong earthquakes with M ≥ 8, it is 40 years. Every seismic pattern obtained clusters the earthquakes in historical intervals/episodes with and without strong earthquakes in the individually analyzed seismic zones. We want to clarify that the intervals where no strong earthquakes do not imply the total absence of seismic activity because earthquakes can occur with lesser magnitude within this same interval. From the information and pattern we obtained from the wavelet analyses, we created a probabilistic, long-term earthquake prediction model for each seismic zone using the Bayesian Machine Learning method. We propose that the periods of occurrence of earthquakes in each seismic zone analyzed could be interpreted as the period in which the stress builds up on different planes of a fault, until this energy releases through the rupture along faults and fractures near the plate tectonic boundaries. Then a series of earthquakes can occur along the fault until the stress subsides and a new cycle begins. Our machine learning models predict a new period of strong earthquakes between 2040 ± 5 and 2057 ± 5, 2024 ± 1 and 2026 ± 1, 2026 ± 2 and 2031 ± 2, 2024 ± 2 and 2029 ± 2, and 2022 ± 1 and 2028 ± 2 for the five active seismic zones of United States, Mexico, South America, Japan, and Southern China and Northern India, respectively. In additon, our methodology can be applied in areas where moderate earthquakes occur, as for the case of the Parkfield section of the San Andreas fault (California, United States). Our methodology explains why a moderate earthquake could never occur in 1988 ± 5 as proposed and why the long-awaited Parkfield earthquake event occurred in 2004. Furthermore, our model predicts that possible seismic events may occur between 2019 and 2031, with a high probability of earthquake events at Parkfield around 2025 ± 2 years.

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The Method of the Minimum Area of Alarm for Earthquake Magnitude Prediction

Abstract: An approach for the systematic forecasting of earthquake magnitudes is considered. To solve this problem, we use the minimum area of alarm method. Testing the approach for Kamchatka and the Aegean Region shows a satisfactory quality of the forecast of earthquakes and their magnitudes.

Cited by 7 publications

References 31 publications

Preseismic Perturbations and their Inhomogeneity as Computed from Ground‐ and Space‐Based Investigation during the 2016 Fukushima Earthquake

Preseismic Perturbations and their Inhomogeneity as Computed from Ground‐ and Space‐Based Investigation during the 2016 Fukushima Earthquake

Analyzing the Performance of GPS Data for Earthquake Prediction

Long-Term Forecasting of Strong Earthquakes in North America, South America, Japan, Southern China and Northern India With Machine Learning

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