عنوان مقاله [English]
An earthquake is a natural disaster that a lot of human and financial losses are results of that, and its prediction has not been materialized comprehensively. This damage can be prevented with earthquake prediction. The first successful prediction of large earthquakes was in 1975 that scientists could predict strong Haicheng earthquake in China.
To date, different algorithms have been suggested to predict earthquakes, but there is not a unique algorithm that could be able to predict each earthquake around the world. Remote sensing data can be used to access information that is closely related to an earthquake called earthquake’s precursors. To date, various precursors were studied. The unusual variations of lithosphere, atmosphere and ionosphere parameters before the main earthquakes are considered as earthquake precursors.
This paper examines one of the parameters which can be derived from satellite imagery. The mentioned parameter is Aerosol Optical Depth (AOD), and this article reviews its relationship with an earthquake.
Aerosol Optical Depth (AOD) is one of the aerosol parameters that can give useful information about aerosols. Aerosols are small (sub-micron to several microns) suspend particles in the solid or liquid phase in the atmosphere. The main origins of aerosols are natural and anthropogenic. AOD in nature can be calculated by measuring the absorption of light at specific wavelengths of the visible spectrum. To use a wide variety of AOD, absorption at wavelength of 550 nm is recommended. Another way to obtain it is implementing different methods on satellite images such as AVHRR, MODIS, MISR, Sea WIFS, POLDER, TOMS, and MISR. However, it is a difficult task to achieve it, because solar lights are reflected by the atmosphere and the whole solar lights do not hit the ground.
The most famous methods used to derive aerosol parameters are Dark Dense Vegetation (DDV) and SYNergy of Terra and Aqua MODIS (SYNTAM). DDV, which was presented by Kaufman in 1997, today is one of the most important algorithms for processing AOD. This algorithm has shown well performance for MODIS data. It determines dark pixels in the mid-infrared band and then estimates its reflectivity, after that, reaches the AOD. However, this method has limitations. The algorithm was related to dark pixels, these pixels can be found in wet areas or areas with vegetation and water and ice. SYNTAM approach can remove limitations in deriving AOD by combining data from two sensors of MODIS of TERRA and AQUA satellites and this method gives the right results.
In this study, aerosol variations have been analyzed using one of the atmosphere daily global 1 degree products deduced from MODIS Terra and Aqua daily level-3 data. It has been shown that by analyzing AOD's time series for five major earthquakes of Iran, this parameter has unusual behaviors before and after the studied earthquakes.
In this paper, the median/interquartile method has been implemented for anomaly detection.
Before strong earthquakes, the value of AOD increases due to the emanation of gaseous molecules after the pre-seismic changes. Moreover, the aftershocks lead to a significant change in AOD due to the emanation of gaseous molecules and dust. This behavior suggests that there is a close relationship between earthquakes and unusual variations of AOD parameter. Therefore, the unusual AOD variations around the time of earthquakes can be introduced as an earthquake precursor.
It should be noted that the mechanism of the earthquake precursors is complicated and unknown. It is necessary to incorporate geophysics to assess geophysical and geochemical variations in lithosphere and atmosphere and their relations with AOD unusual variations. The magnitude, focal depth, seismic history, geomorphology structure, geographic location and other geophysical and geochemical parameters should be considered to acknowledge the different lead times.