NDLI: Principal component analysis method in the detection of total electron content anomalies in the 24 hrs prior to large earthquakes

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Principal component analysis method in the detection of total electron content anomalies in the 24 hrs prior to large earthquakes

Content Provider	Springer Nature Link
Author	Lin, Jyh Woei
Copyright Year	2010
Abstract	The goal of this research is to examine one-dimensional total electron content (TEC) data using principal component analysis (PCA) to search for total electron content (TEC) anomalies associated with large earthquakes in 24 h prior to nucleation. The characteristics of principal eigenvalues generated for TEC prior to 24 earthquakes of magnitude scale M ≥ 5.0 and 6 lesser earthquakes of magnitude scale M < 5 that occurred in Taiwan from 01 January 2000 to 31 December 2001 are examined. In an earlier paper, I was able to confirm the statistical findings of Liu et al. (J Geophys Res 111, 2006) that sparse earthquake-associated TEC anomalies existed in 5 days prior to the 12 large earthquakes they examined (Lin, Terr Atm Ocean Sci, 2010). In this paper, I wish to examine the subtlety of principal component analysis in detecting earthquake-associated TEC anomalies by examining if such precursors can be detected in 24 h prior to large earthquakes. Of the earthquakes examined, TEC anomalies given by clear extreme principal eigenvalues were evident within 24 h of nucleation for 21 of the 24 earthquakes of M ≥ 5.0. After making allowance for the general status of background TEC, it is clear that these extreme principal eigenvalues are representative of earthquake-associated anomalies. For the smaller earthquakes (M < 5), it was not possible to differentiate earthquake-associated anomalies from background effects on TEC status. These new findings confirm the validity of PCA in searching for earthquake-associated TEC anomalies and show that it is subtle enough to detect TEC anomalies within 24 h leading to a large earthquake. If this approach continues to prove successful, it could theoretically be used in real-time prediction of large earthquakes through early detection of earthquake-associated TEC anomalies.هدف هذا البحث هو فحص بيانات المحتوى الاليكتروني الإجمالي أحادي البعد باستخدام تحليل العنصر الرئيسي للبحث عن شاذات الTEC المصاحبة للزلازل الكبيرة في الساعات ال24 السابقة لبداية الزلزال. تم فحص خصائص القيم الذاتية (eigenvalues) الناتجة للTEC التي تسبق الزلازل في 24 زلازل ذات قدر زلزالي 5 و6 زلازل ذات قدر زلزالي اقل من 5 والتي وقعت في تايوان من 1 يناير عام 200 إلى 31 ديسمبر عام 2001. في بحث سابق استطعت تأكيد نتائج الاحصاءات كما في Liu وآخرين (2006) أن شاذات الTEC المصاحبة للزلازل المتناثرة وجدت في ال5 أيام السابقة لل12 زلزال كبير تم فحصهم (Lin 2010). في هذا البحث ، آمل فحص قدرة تحليل المركبة الرئيسية في رصد شاذات الTEC المصاحبة للزلازل الكبيرة بالنظر إذا كانت هذه السوابق يمكن رصدها في ال24 ساعة السابقة للزلازل الكبيرة. في الزلازل التي فحصت ، شاذات الTEC الناتجة عن القيم الذاتية الرئيسية المتناهية الوضوح قد أثبتت خلال 24 ساعة من بدء التنوي (nucleation) ل 21 زلزال من ال24 ذو قدر زلزالي 5.0. بعد تقييم السماحية للحالة العامة لخلفية الTEC ، يتضح أن القيم الذاتية الأساسية القصوى هي تعبير عن الشاذات المصاحبة للزلزال. بالنسبة للزلازل الأقل قدر زلزالي (M < 5) ، لم نتمكن من التفريق بين الشاذات المصاحبة للزلزال من التأثيرات الخلفية على حالة الTEC. هذه الاستنتاجات الجديدة تؤكد قابلية الPCA في البحث عن شاذات ال TEC المصاحبة للزلزال وتبين أنه يكفي براعة إكتشاف شاذات الTEC خلال ال24 ساعة السابقة لزلزالي كبير. إذا استمر هذا الاتجاه في تحقيق نجاح فإنه ، نظريا ، يمكن استخدامه في توقع الزلازل الكبيرة في الزمن الحقيقي(real-time) من خلال الاكتشاف المبكر لشاذات الTEC المصاحبة للزلزال.
Starting Page	575
Ending Page	593
Page Count	19
File Format	PDF
ISSN	18667511
Journal	Arabian Journal of Geosciences
Volume Number	4
Issue Number	3-4
e-ISSN	18667538
Language	Arabic
Publisher	Springer-Verlag
Publisher Date	2010-10-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	One-dimensional total electron content (TEC) Principal component analysis (PCA) Precursors Real-time prediction Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science

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