地震地质最新文献

{"title":"Wavelet Maxima Method for Identifying Singularities in Electromagnetic Signal","authors":"Bing Han, Ji Tang, Guoze Zhao, Y. Bi, Lifeng Wang, Yuanzhi Cheng","doi":"10.3969/J.ISN.0253-4967.2015.03.008","DOIUrl":"https://doi.org/10.3969/J.ISN.0253-4967.2015.03.008","url":null,"abstract":"Wavelet maxima method as a kind of data mining method has been applied to earthquake science research,which gives us a direct way to identify the singularities of different time and frequencies in the long time observations. This paper introduces how to identify the electromagnetic anomalies using the wavelet maxima,i.e.,the wavelet coefficients are calculated by using continuous wavelet transform and then calculate the maximum value of wavelet coefficients in each scale and identify the singularities associated with the earthquake. The identified singularities are further examined by Lipschitz-exponent α. The proposed method has been employed using the 35 days' data of the electromagnetic field recorded in Baosheng station in Sichuan after the Lushan MS7. 0 earthquake,and three electromagnetic anomalies are collected,then,the relationships between the electromagnetic anomalies and the earthquakes are discussed. This method cannot give a certain relationship between the electromagnetic anomaly and earthquake,but it proves the method's effectiveness in extracting the electromagnetic anomaly in continuous observation data.","PeriodicalId":35696,"journal":{"name":"Dizhen Dizhi","volume":"37 1","pages":"765-779"},"PeriodicalIF":0.0,"publicationDate":"2015-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70064697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACCURACY ANALYSIS OF TERRAIN POINT CLOUD ACQUIRED BY “STRUCTURE FROM MOTION”USING AERIAL PHOTOS","authors":"Zhanyu Wei, R. Arrowsmith, Honglin He, Wei Gao","doi":"10.3969/J.ISSN.0253-4967.2015.02.024","DOIUrl":"https://doi.org/10.3969/J.ISSN.0253-4967.2015.02.024","url":null,"abstract":"The need to acquire high-quality digital topographic data is evident throughout geoscience research. The use of these data elevates the research level of geosciences. Airborne and terrestrial light detection and ranging( Li DAR) are currently the most prevalent techniques for generating such data,but the high costs and complex post processing of these laser-based techniques restrict their availability. In the past few years, a new stereoscopic photogrammetry mapping method called Structure from Motion( Sf M) has been applied in geoscience,in which the 3D digital topography is reconstructed using feature matching algorithms from overlapping photographs of multiple viewpoints.Sf M only needs a series of overlapping images with no special requirements about the camera positions,orientations and lens parameters,making it possible to use images collected from an affordable Sf M platform to rapidly generate high-quality 3D digital topography. This paper summarizes the basic principles and the Sf M workflow,and shows that Sf M is a low-cost,effective tool for geoscience applications compared to Li DAR. We use a series of digital aerial photos with ~ 70%overlap collected at one-thousand-meter height to produce a textured( color) Sf M point cloud with point density of 25. 5 / m2. Such a high density point cloud allows us to generate a DEM with grid size of0. 2m. Compared with Li DAR point cloud,statistical analysis shows that 58. 3% of Li DAR points deviate vertically from the closed Sf M point by 0. 1m and 88. 3% by 0. 2m. There is different Sf M accuracy in different landforms. The Sf M accuracy is higher in low dips and subdued landforms than in steep landforms. In consideration of relative vertical error of 0. 12 m in Li DAR data,Sf M has a higher measuring accuracy compared with Li DAR.","PeriodicalId":35696,"journal":{"name":"Dizhen Dizhi","volume":"37 1","pages":"636-648"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70065907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing detection of volcanic ash cloud using independent component analysis","authors":"Chengfan Li, Yang-Yang Dai, Junjuan Zhao, Jingyuan Yin, Shi-Qiang Zhou","doi":"10.3969/J.ISSN.0253-4967.2014.01.011","DOIUrl":"https://doi.org/10.3969/J.ISSN.0253-4967.2014.01.011","url":null,"abstract":"The volcanic ash cloud is mainly composed of volcanic ash debris and gases. The adequate mixture of the two can form acidic aerosols. It not only causes the major global climate and environmental changes,but also seriously threatens the aviation safety. Remote sensing can quickly and accurately obtain the information of the surface's and the atmosphere's changes; therefore it is playing an important role in the monitoring of volcanic activity. In recent years,with the advancement of sensor technology,the thermal infrared remote sensing technology has become an important means of detecting the volcanic ash cloud. However,due to the large amount of spectral bands and data,the remote sensing data have pretty strong band correlation and obvious information redundancy problem, all of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Therefore,it is necessary to introduce new data processing methods into the volcanic ash cloud remote sensing detection field. Principal component analysis(PCA)can compress a large number of complex information effectively into a few principal components; as a result,it is widely applied in the data compression and hyperspectral remote sensing field. Independent component analysis(ICA)is a recently developed new data processing method which can linearly decompose the observed data into mutually dependent components,and achieve the decorrelation and redundancy elimination of remote sensing data; so it has certain potential in volcanic ash cloud detection. A remote sensing detecting algorithm of volcanic ash cloud,which uses ICA method,is proposed after the exploration of the physics and chemical properties of volcanic ash cloud. This paper takes the MODIS remote sensing image of Iceland's Eyjafjallajokull volcanic ash cloud on April 19,2010 as data source. It uses ICA in volcanic ash cloud detection on the basis of the principal component analysis(PCA)processing of MODIS image,and gives comparison among these following parties: the detected results,the relevant research results, United States Geological Survey(USGS)standard spectral database and SO2 concentration distribution. The results show that: ICA can successfully obtain the information of the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the USGS standard spectral database and the SO2concentration distribution,thus,it can obtain pretty good detection results.","PeriodicalId":35696,"journal":{"name":"Dizhen Dizhi","volume":"36 1","pages":"137-147"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70065828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time Series Analysis on the Ratio for Pixels with Abnormal Brightness Temperature Increase and Its Variation Before Some Earthquakes with Ms ≥5.0 in the Taiwan Area","authors":"Liu, Fang, Xin, Hua, Zhang, Tiebao, Lu, Qian, Ren, Yuexia","doi":"10.21611/qirt.2012.312","DOIUrl":"https://doi.org/10.21611/qirt.2012.312","url":null,"abstract":"In the study of application of MODIS satellite remote sensing data to earthquake prediction,the paper put forward for the first time a quantificational method for the ratio of the pixels with abnormal brightness temperature(BT)increasing and a preliminary scheme for cloud removal.The principle is that firstly,the cloudless data observed by the same satellite at the same period of time but in different days(usually 1 to 3 days)are mosaiched to get high cloudless rate data,and then the brightness temperature variation curve and mean variance of each pixel are calculated with the data from the covered area to determine daily whether the brightness temperature data of the day is normal or not at certain pixel by using twice of the mean variance as criterion.The ratio of the pixels with abnormal BT increasing can be calculated by dividing the total number of abnormal pixels with the total pixels of the whole area.Analysis on a series of recent earthquakes in Taiwan area shows that the ratio of pixels with abnormal BT increasing,which normally undulates around zero,had a sudden jump 1 to 20 days before the medium-strong earthquakes.It is expected that a new method for identifying earthquake auspice could be found through special studies in regions with frequent seismic activity by analyzing the change of ratio of the pixels with abnormal BT increasing from MODIS satellite remote sensing infrared information on which the effect of cloud has been removed to a certain extent.","PeriodicalId":35696,"journal":{"name":"Dizhen Dizhi","volume":"21 1","pages":"437-444"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68566444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}