本文選題：地震滑坡　切入點：易發(fā)性評價　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

【摘要】：地震滑坡常見于地震災害鏈中,不僅帶來巨大的生命財產(chǎn)損失,還嚴重影響了震后救援工作。地震滑坡物理機制復雜,影響因素繁多,基于數(shù)據(jù)挖掘和GIS的地震滑坡易發(fā)性評價有效規(guī)避了基于物理模型評價存在的物理參數(shù)和監(jiān)測數(shù)據(jù)獲取困難的問題,成為地震滑坡易發(fā)性評價的重要手段。本文以2004年日本新o_中越地震為研究對象,本次地震主震震級為6.8級,其后陸續(xù)發(fā)生了 4次震級大于6.0級的余震,誘發(fā)了數(shù)以千計的滑坡,此次地震為隱伏斷層地震。本文采用了三種數(shù)據(jù)挖掘方法,同時為了彌補隱伏斷層地震缺乏地表破裂帶這一重要影響因子的不足,新增了同震地表變形作為影響因子,分別針對大面積區(qū)域和震中區(qū)域兩個尺度開展了基于數(shù)據(jù)挖掘和GIS的地震滑坡易發(fā)性評價工作。主要的研究工作和研究成果如下:(1)針對大面積研究區(qū)域,采用了邏輯回歸、支持向量機和人工神經(jīng)網(wǎng)絡等三個數(shù)據(jù)挖掘模型開展了地震滑坡易發(fā)性評價,并對研究區(qū)域進行了合理的易發(fā)性等級分區(qū)。結果表明三種模型方法都取得了較好的結果,ROC曲線下面積都達到了 80%以上,進行易發(fā)性分區(qū)后隨著易發(fā)性等級提升,區(qū)域內(nèi)滑坡比例呈較為明顯的梯度提升。綜合比較確定人工神經(jīng)網(wǎng)絡效果最好。(2)針對震中區(qū)域,在原有影響因子的基礎上新增同震地表變形作為影響因子,采用模擬效果最好的的人工神經(jīng)網(wǎng)絡進一步開展地震滑坡易發(fā)性評價,獲得了震中區(qū)域的精細化地震滑坡易發(fā)性等級分區(qū)圖。研究結果顯示考慮同震地表變形對結果有一定提升,其貢獻大于常見的坡向和距道路的距離等影響因子。因此,尤其是針對隱伏斷層地震影響因子資料不足的情況下,同震地表變形具備較大的應用價值。(3)多尺度區(qū)域評價結果對比分析表明,相較于大面積研究區(qū)域地震滑坡易發(fā)性評價,震中區(qū)域研究在同一區(qū)域取得更為精細的地震滑坡易發(fā)性分區(qū)圖,但模擬結果中ROC曲線下面積普遍較低。大面積區(qū)域和震中區(qū)域地震滑坡易發(fā)性評價相結合,實現(xiàn)了多尺度地震滑坡易發(fā)性分區(qū),滿足地震滑坡災害的不同層面的防控治理的需要。
[Abstract]:Earthquake landslide is common in earthquake disaster chain, which not only brings huge loss of life and property, but also seriously affects post-earthquake rescue work. The evaluation of seismic landslide vulnerability based on data mining and GIS effectively avoids the problems of obtaining physical parameters and monitoring data based on physical model evaluation. It has become an important means to evaluate the vulnerability of earthquake landslides. In this paper, the earthquake of 2004 in Japan was studied. The magnitude of the earthquake was 6.8, and four aftershocks with magnitude greater than 6.0 occurred one after another. Thousands of landslides have been induced, and the earthquake is a concealed fault earthquake. In this paper, three methods of data mining are used to make up for the deficiency of the hidden fault earthquake lacking the important factor of surface rupture zone. The coearthquake surface deformation is added as the influence factor, The evaluation of seismic landslide vulnerability based on data mining and GIS is carried out on two scales of large area area and epicenter area respectively. The main research work and research results are as follows: 1) for large area study area, logical regression is adopted. Three data mining models, support vector machine (SVM) and artificial neural network (Ann), are used to evaluate the vulnerability of earthquake and landslide. The results show that the area under the ROC curve is more than 80%, and the area increases with the grade of susceptibility. Compared with each other, artificial neural network has the best effect. Aiming at the epicentral area, the coseismic surface deformation is added as the influence factor on the basis of the original influence factors. The artificial neural network, which is the best simulation method, is used to further evaluate the vulnerability of seismic landslides. The fine zoning map of landslide susceptibility in epicentral area is obtained. The results show that considering the coseismic surface deformation, its contribution is greater than that of common influencing factors, such as slope direction and distance from road, and so on. Especially in view of the lack of data on the influencing factors of concealed faulted earthquakes, the results of multi-scale regional evaluation of coearthquake ground deformation have great application value. The results show that compared with the large area study area, seismic landslide susceptibility evaluation is better than that of coearthquake ground deformation. The regional study of epicenter obtained a more precise zoning map of seismic landslide susceptibility in the same area, but the area under the ROC curve is generally lower in the simulation results, and the large area is combined with the seismic landslide susceptibility evaluation in the epicenter region. The multi-scale seismic landslide prone zone is realized, which meets the need of prevention and control of different levels of earthquake landslide disaster.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P642.22

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