CAJ | 학술논문

利用卫星热红外异常判别技术进行地震预测的方法都是纯手工或半手工的，在分析处理海量遥感数据时具有局限性，并且传统方法对地震三要素的预测准确率不高，尤其是震中位置的预测。针对上述问题，提出一种综合震象云颜色、纹理以及浮现频率等特征来自动预测地震的方法。利用灰度共生矩阵对热红外数据进行纹理特征提取，使用BP神经网络模型训练目标神经网络，将纹理特征输入目标神经网络进行识别，提取疑似目标，同时滤掉非目标并跟踪，将疑似目标浮现频率超过5次的区域精确定位为目标出现的位置，从而实现智能化和自动化的地震预测。反演实验验证结果表明，该方法是一种震中位置预测较为准确的中短期地震预测方法。
이용위성열홍외이상판별기술진행지진예측적방법도시순수공혹반수공적，재분석처리해량요감수거시구유국한성，병차전통방법대지진삼요소적예측준학솔불고，우기시진중위치적예측。침대상술문제，제출일충종합진상운안색、문리이급부현빈솔등특정래자동예측지진적방법。이용회도공생구진대열홍외수거진행문리특정제취，사용BP신경망락모형훈련목표신경망락，장문리특정수입목표신경망락진행식별，제취의사목표，동시려도비목표병근종，장의사목표부현빈솔초과5차적구역정학정위위목표출현적위치，종이실현지능화화자동화적지진예측。반연실험험증결과표명，해방법시일충진중위치예측교위준학적중단기지진예측방법。
The earthquake prediction research based on interpretation technique of satellite thermal anomaly has a history of over 20 years. Previous studies are pure manual or semi-manual with many shortages in processing huge quantity remote data. Meanwhile, the traditional methods cannot give an accurate estimation on three elements of earthquakes, especially on epicenter location. In order to solve the above-mentioned problems, this paper puts forward a method based on image recognition with considering the color, texture and occurrence frequency of quake-trace cloud. An earthquake can be predicted intelligently and automatically by using automatic target detection in artificial intelligence. The entire procedure is as follows. It gets the texture features from thermal infrared data by using gray level co-occurrence, trains a target neural network by making use of BP neural network model, inputs texture features into target neural network and gets the suspected targets, filters suspected target which is undersized or oversize, tracks the remaining suspected targets, acquires the certain target by its occurrence frequency which is larger than 5, and predicts an earthquake. Experimental result shows that it is a short term earthquake prediction method with more accurate epicenter location prediction.