本文選題：地震預(yù)測(cè)　切入點(diǎn)：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(zhǎng),每年有近2億人深受此類災(zāi)難。我國(guó)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(yán)重遭受自然災(zāi)害的威脅,因此開展地震預(yù)測(cè)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(cè)地震已經(jīng)取得了一些成就,但傳統(tǒng)的預(yù)測(cè)方法存在一定的局限性,一是無(wú)法準(zhǔn)確預(yù)測(cè)震中位置,二是都是人工或者半人工的實(shí)現(xiàn)預(yù)測(cè)。為了解決這兩個(gè)問題,本文提出了一種跟蹤異常云團(tuán)出現(xiàn)位置和頻率的熱紅外異常云識(shí)別地震預(yù)測(cè)方法。根據(jù)熱紅外異常云團(tuán)地震預(yù)測(cè)理論,該方法主要分為熱紅外異常云的識(shí)別和跟蹤兩個(gè)部分。對(duì)于識(shí)別部分,分為樣本訓(xùn)練和分類識(shí)別兩個(gè)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(fù)樣本,計(jì)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(duì)每張輸入云圖做分類識(shí)別,先對(duì)輸入熱紅外云圖進(jìn)行預(yù)處理,對(duì)可疑區(qū)域進(jìn)行圖像增強(qiáng),并過濾掉非云區(qū),再計(jì)算出云圖中每個(gè)點(diǎn)周圍的紋理特征向量并作為分類器的輸入來(lái)對(duì)每個(gè)像素點(diǎn)進(jìn)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(duì)于跟蹤部分,通過跟蹤一段時(shí)間內(nèi)的熱紅外云圖,如果某個(gè)區(qū)域異常云復(fù)現(xiàn)頻率較高,則可以認(rèn)為這個(gè)位置有發(fā)生地震的可能,并根據(jù)異常云團(tuán)中心的演變位置估計(jì)地震震中位置。論文通過地震反演實(shí)驗(yàn)結(jié)果證明熱紅外異常云團(tuán)識(shí)別算法可有效實(shí)現(xiàn)自動(dòng)地震預(yù)測(cè)。該方法不僅能準(zhǔn)確預(yù)測(cè)地震中心,而且對(duì)震級(jí)和發(fā)震時(shí)間也有一定的預(yù)測(cè)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(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：P315.7;TP391.41

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