fa تشخیص آنامولی های TEC قبل از وقوع زلزله های بزرگ با استفاده از شبکه عصبی مصنوعی ژئودزی و هیدروگرافی Geo&Hydro پژوهشي Research <p dir="RTL" style="text-align: justify;">وقوع زلزله علاوه بر تغییر در هندسه و فیزیک پوسته زمین تأثیرات دیگری را نیز به همراه دارد. از آن جمله، تأثیر بر لایه یونسفر می<span dir="LTR">&zwj;</span>باشد که خود را به‌صورت تغییر در میزان الکترون، چگالی یون‌ها، میدان‌های الکتریکی و مغناطیسی این لایه نشان می<span dir="LTR">‌</span>دهد. هر پارامتر ژئوفیزیکی و ژئوشیمیایی در لایه‌های لیتوسفر، اتمسفر و یونسفر زمین که قبل از وقوع زلزله تغییراتی در آن پدید آید به‌عنوان پیش<span dir="LTR">‌</span>نشانگر شناخته می<span dir="LTR">‌</span>شود.</p> <p dir="RTL" style="text-align: justify;"><span dir="RTL">با پردازش داده</span>‌<span dir="RTL">های </span>GPS<span dir="RTL"> می</span>‌<span dir="RTL">توان به میزان محتوای کل الکترون (</span>Total Electron Content<span dir="RTL">) لایه یونسفر دست‌یافت. در سیستم</span>‌<span dir="RTL">های پیچیده و غیرخطی استفاده از روش</span>‌<span dir="RTL">های کلاسیک مانند میانگین، برای بازشناسی الگو و پیش‌بینی سری</span>‌<span dir="RTL">های زمانی بسیار دشوار است، به همین دلیل در این مقاله سعی گردیده است از روش</span>‌<span dir="RTL">های هوش مصنوعی همچون شبکه عصبی مصنوعی برای تشخیص و بازسازی الگوی تغییرات </span>TEC<span dir="RTL"> استفاده گردد. در همین راستا زلزله اهر آذربایجان شرقی (۲۱ مرداد ۱۳۹۱) و زلزله کاکی بوشهر (</span><span dir="RTL">20فروردین‌ماه 1392</span><span dir="RTL">) </span><span dir="RTL">مورد بررسی قرارگرفته است.</span> <span dir="RTL">ابتدا </span><span dir="RTL">با استفاده از نرم</span>‌<span dir="RTL">افزار </span>Bernese<span dir="RTL"> و به روش </span>PPP(Precise Point Positioning)<span dir="RTL"> مختصات ایستگاه</span>‌<span dir="RTL">ها محاسبه گردید و سپس با استفاده از مدل جهانی مقادیر </span>TEC<span dir="RTL"> به دست آمد. نتایج حاصل، ناهنجاری</span>‌<span dir="RTL">هایی را چند روز قبل و بعد از زلزله نشان می‌دهد که بیانگر آن است که الگوریتم شبکه عصبی مصنوعی به‌خوبی توانسته آنامولی‌های موجود را آشکارسازی نماید. همچنین مقایسه مقادیر </span>TEC<span dir="RTL"> به‌دست‌آمده از ایستگاه‌های زمینی با مدل استاندارد جهانی از همبستگی بالایی برخوردار می‌باشند.</span></p> <div align="left"> <p style="text-align: justify;">Discussion about earthquake to reduce its casualties and damages is very important, especially in the Seismicity area like Iran that the occurrence of this natural phenomenon is seen annually<strong><span dir="RTL">. </span></strong>Iran has an approximate area of 1648000 square kilometers with geographical coordinates 25 to 40 degrees north latitude and 44 to 64 degrees east longitude that located in the middle of Alpine-Himalayan seismic belt. In this erea there are many active faults that their movement continues and the final balance has not been established. The occurrence of severe earthquakes as Buin Zahra earthquake (1962), Tabas (1978), Rudbar (1990), Bojnoord (1997), Bam (2003) and other numerous earthquakes prove this subject. While most natural disasters are out of human control, but it seems that Success in prediction of temporal and local of them can dramatically control damages and casualties. Earthquake occurrence in addition to changes of geometry and physics of the earth crust has many other effects. Some of its effects is in the ionosphere layer that are indicated as changes in the electrons values, ions density and electromagnetic field. Anomalies detection before earthquake is an important role for earthquake prediction. Each geophysical and geochemical parameter of the lithosphere, atmosphere and ionosphere layers that unusually changes before earthquake are known as earthquake precursor. Ionosphere changes that recognition by remote measurements (such as using Global Positioning System (GPS)) are known as earthquake ionospheric precursor<strong>.</strong></p> <p style="text-align: justify;">TEC (Total Electron Content) of the ionosphere can be achieved by GPS data processing. Classic methods such as mean are unable to detect non linear pattern and therefore in complex and nonlinear systems they are not suitable for recognition and prediction of time series. Because of the nonlinear behavior TEC&nbsp; and land surface changes in order to detect changes, in this paper an attempt is done using an artificial intelligence method including ANN (Artificial Neural Network) and multilayer Perceptron (MLP) for pattern recognition and prediction of TEC variations. Because ionospheric fluctuations usually do not have a normal distribution and do not follow Gaussian curve, in order to detect seismic anomalies, the mean and interquartile range is used to determine the lower and upper bounds. In this study several data sets from the ionospheric&nbsp; total electron content (TEC) derived from the GPS data processing by Bernese softwares. In this way earthquakes of Ahar located in east Azerbaijan (2012/08/11) and Bushehr (2013/4/9) have been studied and the results were compared with data from global stations. First the stations coordinates were calculated using Bernese software with PPP (Precise Point Positioning) method. Then TEC values were obtained using GIM (Global Ionosphere Model). By analyzing the causes of ionospheric anomalies such as the geomagnetic field and solar activity and remove them from the process<strong>, </strong>results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms could be useful for the prediction of nonlinear time series and outstanding anomalies ocurr some days before and after earthquake. It can be concluded that ANN algorithm has been able to detect TEC anomalies well. Also TEC values are obtained from ground stations have a high correlation with the results of global standard model.</p> </div> زلزله, یونوسفر, آنومالی, TEC, شبکه عصبی مصنوعی Earthquake, Ionosphere, Anomaly, TEC, Artificial Neural Network 49 58 http://jgst.issgeac.ir/browse.php?a_code=A-10-315-1&slc_lang=fa&sid=1 M. Shamshiri منیره شمشیری m.shamshiri@ut.ac.ir 10031947532846003181 10031947532846003181 Yes University of Tehran دانشگاه تهران M. Akhoondzadeh Hanzaei مهدی آخوندزاده هنزائی makhonz@ut.ac.ir 10031947532846003182 10031947532846003182 No University of Tehran دانشگاه تهران