Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to pr...Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.展开更多
在复杂监测环境下,全球卫星导航定位(GNSS)信号极易受环境干扰产生多路径误差,观测数据中包含大量较差的观测值,导致GNSS监测精度降低甚至不可用。考虑到监测网中的基准站通常布设于开阔无遮挡环境,基准站和监测站跟踪的卫星信息相关性...在复杂监测环境下,全球卫星导航定位(GNSS)信号极易受环境干扰产生多路径误差,观测数据中包含大量较差的观测值,导致GNSS监测精度降低甚至不可用。考虑到监测网中的基准站通常布设于开阔无遮挡环境,基准站和监测站跟踪的卫星信息相关性较强,提出一种基于基准站信噪比先验信息的GNSS观测数据多路径误差识别方法。该方法利用卫星信噪比观测值与多路径误差的强相关性,通过对信噪比观测值作站间差分来识别受多路径影响严重的较差数据,并对其进行剔除处理,以抵御复杂监测环境的多路径影响。以遮挡严重的河南三门峡地区某滑坡监测环境为例,基于实测数据验证表明,提出的新方法能够对受山体、植被、人工设施等多路径影响严重的较差观测值进行有效识别,环境自适应能力更强,显著提升了模糊度固定率及定位精度。提出的新方法模糊度固定率结果相比传统固定截止高度角(TFC)模型平均提高39.6%,相比自适应截止高度角(ADEM)模型平均提高28.6%;固定解定位精度在E、N方向优于4 mm, U方向优于9 mm。展开更多
文摘Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.
文摘在复杂监测环境下,全球卫星导航定位(GNSS)信号极易受环境干扰产生多路径误差,观测数据中包含大量较差的观测值,导致GNSS监测精度降低甚至不可用。考虑到监测网中的基准站通常布设于开阔无遮挡环境,基准站和监测站跟踪的卫星信息相关性较强,提出一种基于基准站信噪比先验信息的GNSS观测数据多路径误差识别方法。该方法利用卫星信噪比观测值与多路径误差的强相关性,通过对信噪比观测值作站间差分来识别受多路径影响严重的较差数据,并对其进行剔除处理,以抵御复杂监测环境的多路径影响。以遮挡严重的河南三门峡地区某滑坡监测环境为例,基于实测数据验证表明,提出的新方法能够对受山体、植被、人工设施等多路径影响严重的较差观测值进行有效识别,环境自适应能力更强,显著提升了模糊度固定率及定位精度。提出的新方法模糊度固定率结果相比传统固定截止高度角(TFC)模型平均提高39.6%,相比自适应截止高度角(ADEM)模型平均提高28.6%;固定解定位精度在E、N方向优于4 mm, U方向优于9 mm。
