پديد آورندگان :
ندري، معصومه دانشگاه تهران - پرديس دانشكده هاي فني - دانشكده مهندسي نقشه برداري و اطلاعات مكاني , آخوندزاده هنزائي، مهدي دانشگاه تهران - پرديس دانشكده هاي فني - دانشكده مهندسي نقشه برداري و اطلاعات مكاني
كليدواژه :
زلزله , كلروفيل , جنگل تصادفي , موديس , ناهنجاري
چكيده فارسي :
زلزله يكي از مخربترين بلاياي طبيعي است كه به طور جامع پيش بيني آن تاكنون محقق نگشته است. تحقيقات پيشين نشان دادهاند به كمك داده هاي سنجش از دور مي توان به اطلاعاتي دسترسي پيدا كرد كه نشان دهنده رابطه اي معنادار بين وقوع نابهنجاري در تغييرات برخي پارامترهاي فيزيكي و شيميايي با وقوع زلزله هستند. اين پارامترهاي فيزيكي و شيميايي همان پيش نشانگرهاي زلزله هستند. يكي از پيش نشانگرهايي كه ارتباط آن با زلزله هاي ساحلي در سال هاي اخير مورد مطالعه برخي از پژوهشگران واقع شده، ميزان كلروفيل موجود در سطح آب است. پارامتر كلروفيل-آ از طريق روش هاي مختلفي از جمله روشهاي آزمايشگاهي طيف سنجي، اندازهگيري فلورسانس كلروفيل و يا از طريق داده هاي ماهوارهاي و با بكارگيري الگوريتم هاي شاخص رنگ (CI) و نسبت باندريلي (OCX) و غيره بدست مي آيد. با بررسي سريه اي زماني كلروفيل پنج زلزله بزرگ حاصل از محصولات سنجنده موديس (MODIS) بر روي سكوهاي آكوا (Aqua) و ترا (Terra) و با استفاده از الگوريتم جنگل تصادفي مشاهده شد كه انتشار انرژي حرارتي و گازهاي متصاعد زمين بر اثر فعاليت هاي صفحات تكتونيكي و يا ديگر فعاليت هاي فيزيكي و شيميايي پوسته زمين قبل، هنگام و بعد از وقوع زلزله هاي ساحلي و نزديك به ساحل مي تواند به تغيير در ميزان كلروفيل سطح آب منجر شود و اين پارامتر مي تواند به عنوان يك پيش نشانگر زلزله در تحقيقات بعدي مورد بررسي قرار گيرد.
چكيده لاتين :
Earthquake is one of the most devastating natural hazards which efforts to predict the time, location and magnitude of it have not been yet completely successful. Remote Sensing data is proved to be an effective source of information about lithospheric and atmospheric activities around the impending earthquakes which are referred to as earthquake precursors. The issue of detecting anomalies in these precursors has been interesting to many researchers. One of the precursors that has been taken into consideration by the researchers, is the chlorophyll-a (chl-a) concentration on the sea surface. Since, over %70 of the Earth's surface is covered by water and many seismic active faults are located in coastal belts of the continents, the behavior of oceanic earthquake-related parameters such as Sea Surface Temperature (SST), surface latent heat flux, upwelling index and chl-a, is of particular importance. Elastic strain in rocks, formation of micro-cracks, gas release and other chemical or physical activities in the Earth's crust before and during earthquakes has been reported to cause changes in oceanic parameters. Chl-a parameter is obtained through various methods including laboratory methods of spectroscopy, chlorophyll fluorescence measurement or through satellite data using Color Index (CI) and Raily band ratio (OCX) algorithms etc. Changes from time to time in plankton population in ocean surface and chl-which is the indicator of the primary productivity of phytoplankton biomass in the ocean, can be continuously monitored from space by Ocean Color sensors. In this study, MODIS on Aqua and Terra products were used to examine the pattern of variations of chl-a. By examining the chlorophyll time series of five large earthquakes produced by MODIS sensor products on Aqua and Terra platforms and using a random forest algorithm, it was observed that the release of thermal energy and ground gases due to the activity of Tectonic plates or other physical and chemical activities of the earth's crust before, during and after coastal and near-coastal earthquakes can lead to changes in the amount of chlorophyll in the water surface and this parameter can be used and investigated as an earthquake precursor in future research. The results showed that chlorophyll-a levels exceeded the permissible limits 51, 48, 46 and 28 days before the Gujarat earthquake by 85, 45, 15 and 35%, respectively. In the 2004 Sumatra earthquake in the 20 days before and 18 days after the earthquake, the percentage of chlorophyll-a parameter crossing the upper limit was 110 and 190, respectively. In the 2006 Java earthquake, 42 days before, 15 and 16 days after the earthquake, the amount of chlorophyll-a suddenly changed to 136.84, 52.63 and 107.89% of the allowable threshold. In two other studies, this amount is equal to 199.87, 25, 150 and 190% more than allowable limit, respectively, on the 44th and 34th days before, on the day of the earthquake and 13 days after the Chile earthquake, and 321.42, 50 and 160.71% more than allowable limit 7 and 4 days before and 17 days after the earthquake in Mexico. In addition, the clear superiority of the Random Forest (RF) algorithm in correct detection of anomalies showed that RF algorithm can be introduced as an effective tool in anomaly detection in time series.
