本文選題：地震預(yù)測　切入點：熱紅外遙感　出處：《華中科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：全球自然災(zāi)害的發(fā)生數(shù)量逐步增長,每年有近2億人深受此類災(zāi)難。我國自然災(zāi)害類型多,分布不均勻,70%以上的人口、80%以上的城市和工農(nóng)業(yè)嚴重遭受自然災(zāi)害的威脅,因此開展地震預(yù)測的研究十分迫切。在過去的幾十年里,雖然使用遙感圖像預(yù)測地震已經(jīng)取得了一些成就,但傳統(tǒng)的預(yù)測方法存在一定的局限性,一是無法準確預(yù)測震中位置,二是都是人工或者半人工的實現(xiàn)預(yù)測。為了解決這兩個問題,本文提出了一種跟蹤異常云團出現(xiàn)位置和頻率的熱紅外異常云識別地震預(yù)測方法。根據(jù)熱紅外異常云團地震預(yù)測理論,該方法主要分為熱紅外異常云的識別和跟蹤兩個部分。對于識別部分,分為樣本訓(xùn)練和分類識別兩個步驟。首先訓(xùn)練樣本,選擇確定的異常云作為正樣本,非異常云作為負樣本,計算其紋理特征向量,將紋理特征向量作為輸入,是否是異常云作為輸出,訓(xùn)練得到異常云的神經(jīng)網(wǎng)絡(luò)分類器。其次對每張輸入云圖做分類識別,先對輸入熱紅外云圖進行預(yù)處理,對可疑區(qū)域進行圖像增強,并過濾掉非云區(qū),再計算出云圖中每個點周圍的紋理特征向量并作為分類器的輸入來對每個像素點進行分類,將分類結(jié)果聚類并過濾后提取出疑似異常云區(qū)域。對于跟蹤部分,通過跟蹤一段時間內(nèi)的熱紅外云圖,如果某個區(qū)域異常云復(fù)現(xiàn)頻率較高,則可以認為這個位置有發(fā)生地震的可能,并根據(jù)異常云團中心的演變位置估計地震震中位置。論文通過地震反演實驗結(jié)果證明熱紅外異常云團識別算法可有效實現(xiàn)自動地震預(yù)測。該方法不僅能準確預(yù)測地震中心,而且對震級和發(fā)震時間也有一定的預(yù)測作用。
[Abstract]:The number of natural disasters in the world is increasing step by step, and nearly 200 million people are affected by such disasters every year. In China, there are many types of natural disasters, and more than 80% of the population with uneven distribution are seriously threatened by natural disasters. Therefore, it is very urgent to carry out the research of earthquake prediction. In the past few decades, although some achievements have been made in using remote sensing images to predict earthquakes, there are some limitations in the traditional prediction methods. One is that the epicenter location cannot be accurately predicted. Second, both are artificial or semi-artificial predictions. In order to solve these two problems, In this paper, a method of seismic prediction based on thermal infrared anomaly cloud identification is proposed, which can track the location and frequency of abnormal cloud cluster, according to the theory of earthquake prediction of thermal infrared abnormal cloud cluster. The method is mainly divided into two parts: recognition and tracking of thermal infrared anomaly cloud. For the recognition part, it is divided into two steps: sample training and classification recognition. First, the sample is trained, and the determined abnormal cloud is selected as positive sample. The non-abnormal cloud is used as a negative sample to calculate its texture feature vector, the texture feature vector is taken as input, and whether the abnormal cloud is output is trained to obtain the neural network classifier of abnormal cloud. Secondly, every input cloud image is classified and recognized. The input thermal infrared cloud image is preprocessed, the suspicious area is enhanced, and the non-cloud region is filtered out. Then the texture feature vector around each point in the cloud image is calculated and classified as the input of the classifier. Clustering the classification results and filtering to extract the suspected abnormal cloud area. For the tracking part, by tracking the thermal infrared cloud image for a period of time, if the frequency of abnormal cloud reappearance in a region is higher, Then it can be considered that there is a possibility of an earthquake occurring in this location. The seismic epicenter location is estimated according to the evolution position of abnormal cloud cluster center. The experimental results of seismic inversion prove that the thermal infrared anomaly cloud cluster identification algorithm can effectively realize automatic earthquake prediction, and this method can not only accurately predict the seismic center, but also predict the seismic center accurately. Moreover, it can predict the magnitude and the time of earthquake occurrence.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：P315.7;TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 鐘美嬌;張元生;張璇;;祁連山地震帶中強地震前熱紅外異常研究[J];地震工程學(xué)報;2015年04期

2 李青梅;張元生;呂俊強;任家琪;張麗峰;張璇;;2014年10月7日云南景谷M_S6.6地震熱紅外異常[J];地震工程學(xué)報;2015年04期

3 陳順云;馬瑾;劉培洵;劉力強;扈小燕;任雅瓊;;利用衛(wèi)星遙感熱場信息探索現(xiàn)今構(gòu)造活動:以汶川地震為例[J];地震地質(zhì);2014年03期

4 張璇;張元生;魏從信;田秀豐;湯倩;高見;;四川蘆山7.0級地震衛(wèi)星熱紅外異常解析[J];地震工程學(xué)報;2013年02期

5 張璇;張元生;魏從信;田秀豐;馮紅武;;云南彝良5.7級地震前衛(wèi)星熱紅外異常[J];地震工程學(xué)報;2013年01期

6 郭曉;張元生;鐘美嬌;沈文榮;魏從信;;提取地震熱異常信息的功率譜相對變化法及震例分析[J];地球物理學(xué)報;2010年11期

7 馬瑾;陳順云;扈小燕;劉培洵;劉力強;;大陸地表溫度場的時空變化與現(xiàn)今構(gòu)造活動[J];地學(xué)前緣;2010年04期

8 ;Wenchuan earthquake:Brightness temperature changes from satellite infrared information[J];Chinese Science Bulletin;2010年18期

9 張騰飛;李云;;基于粗糙-神經(jīng)網(wǎng)絡(luò)的非線性系統(tǒng)逆模型控制[J];儀器儀表學(xué)報;2009年08期

10 何靈敏;沈掌泉;孔繁勝;劉震科;;SVM在多源遙感圖像分類中的應(yīng)用研究[J];中國圖象圖形學(xué)報;2007年04期

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