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Earthquake prediction assessment using VLF radio signal sounding and space-based ULF emission observation

Mahmoudian Alireza, Safari Mohsen, Rezapour Mehdi

Acta Geophysica

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2022

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Vol. 70, no 3

1269--1284

EN

The present paper aims at earthquake prediction assessment in Iran using VLF radio signal sounding and space-based ULF emission observation. VLF subionospheric data using the Denizkoy transmitter in Turkey at 26.7 kHz and a receiving station in Tehran are incorporated. Three earthquake events during 2019 located at different distances to the signal propagation path are used in this study. The short-term variations in the VLF signal for the 5 days averaged amplitude, as well as the deviation of the VLF signal against the 30 days averaged signal to monitor the alternation in the trend, are employed in this study to perform earthquake prediction assessment using VLF radio signal sounding. Several characteristic parameters of the VLF signal such fall-time, minimum 1 and minimum 2 throughout the day are used. A threshold over the standard deviation of the signal is used to determine the signal anomaly. The signal anomalies associated with three selected events and the correlation of prediction with the distance are discussed. A decision-making procedure for the detection of EQ-related anomalies based on the assessment of the proposed approach is introduced. The ULF emissions recorded by the China Seismo-Electromagnetic Satellite (CSES) are provided and associated with the two analyzed earthquakes using the VLF data due to lack of time coverage for the third case. The space-based detected ULF signals are presented and discussed. The proximity of the detected ULF emission with respect to the earthquake epicenter is discussed.

2

Study of local ionospheric plasma perturbation induced by pre-seismic activities

Mahmoudian A., Saravani Ghayour A.

Acta Geophysica

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2021

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Vol. 69, no. 5

1585--1595

EN

The pre-seismic ionospheric anomalies coupled through well-proposed lithosphere–atmosphere–ionosphere processes are known to cause pre-seismic ionospheric disturbances (PIDs). The present paper investigates the regional variation of ionospheric densities in the Iran area with the purpose of anomalous ionospheric detection. The ionospheric reference model (IRI) is employed to examine the accuracy of such empirical models for typical TEC (total electron content) values in Iran to determine any deviation from the normal ionospheric state. Two strong consecutive earthquakes with a magnitude of larger than 6 in the northeast of Iran were selected along with the GPS data from 5 ground stations within 50 km of the epicenter. The local ionospheric plasma density mapping using the GPS signal for earthquake prediction is studied. The results show a very promising temporal variation of local ionospheric plasma between the stations from 4 weeks to almost a week leading to the event. The deviation of TEC from the mean value between the fve stations shows no enhancement or suppression of local plasma rather than a plasma motion. A very quiet PID signature could validate the lithosphere– atmosphere–ionosphere coupling (LAIC) process associated with pre-earthquake geochemical and dynamical mechanisms. To validate that the analyzed time period falls on the minimum of solar activity, the observed positive anomalies in the regional TEC correspond to fuctuations to pre-earthquake activity and not to geomagnetic activity, and the Kp and Dst indices are taken into consideration.

3

Possibility of an earthquake prediction based on monitoring crustal deformation anomalies and thermal anomalies at the epicenter of earthquakes with oblique thrust faulting

Alizadeh Zakaria Zahra, Farnood Ahmadi Farshid

Acta Geophysica

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2020

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Vol. 68, no. 1

51--73

EN

Is it possible to predict location, time and magnitude of earthquakes through identifying their precursors based on remotely sensed data? Earthquakes are usually preceded by unusual natural incidents that are considered as earthquake precursors. With the recent advances in remote sensing techniques which have made it possible monitoring the earth’s surface with diferent sensors, scientists are now able to better study earthquake precursors. Thus, the present study aims at developing the algorithm of classic PS-InSAR processing for obtaining crustal deformation values at the epicenter of earthquakes with magnitude larger than 5.0 on the Richter scale and with oblique thrust faulting and then after calculating temperature values using remotely sensed thermal imagery at the epicenter of same earthquakes; thermal and crustal deformation anomalies were calculated using data mining techniques before earthquake occurrence. In the next stage, taking the correlation between thermal anomalies and crustal deformation anomalies at the epicenter of the study earthquakes into account, an integrated technique was proposed to predict probable magnitude and time of oblique thrust earthquakes occurrence over the earthquake-prone areas. Eventually, the validity of the proposed algorithm was evaluated for an earthquake with a diferent focal mechanism. The analysis results of the thermal anomalies and crustal deformation anomalies at the epicenter of April 16, 2016, Japan-Kumamoto earthquake of magnitude 7.0 with strike-slip faulting, showed completely diferent trends than the suggested patterns by the proposed algorithm.

4

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Asim K. M., Awais M., Martinez-Alvarez F., Iqbal T.

Acta Geophysica

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2017

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Vol. 65, no. 5

919--930

EN

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.