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采用启发式共轭梯度法,即随机爬山法 + 共轭梯度法,利用单斜介质中P波方位NMO速度椭圆轴向偏转角度接近于零这一特性,简化P波方位NMO速度公式,并利用多方位P波NMO速度,反演出某一初始CMP观测线与自然坐标系之间的夹角,作为进一步进行Thomsen各向异性参数反演的基础. 根据各向异性介质中方位NMO速度与Thomsen参数之间的关系,建立了利用三种波的多方位NMO速度及垂直传播速度反演单层单斜各向异性介质Thomsen各向异性参数的目标函数. 对计算的理论值添加具有一定标准差的正态分布的随机噪声,用以模拟实际观测存在的误差,通过对加噪后的数据进行多次反演的误差分析,表明了所建立的目标函数及选用的反演方法是有效可行的,而且相对稳定. 相似文献
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Onboard GRB trigger algorithms of SVOM-GRM 总被引:1,自引:0,他引:1
Dong-Hua Zhao Bo-Bing Wu Li-Ming Song Yong-Wei Dong Stephane Schanne Bertrand Cordier Jiang-Tao Liu 《天文和天体物理学研究(英文版)》2013,(11):1381-1396
The Gamma-Ray Monitor (GRM) is a high energy detector onboard the future Chinese-French satellite named the Space-based multi-band astronomical Variable Object Monitor which is dedicated to studies of gamma-ray bursts (GRBs). This paper presents an investigation of the algorithms that look for GRBs by searching for a significant increase in the photon count rate for the computer onboard GRM. The trigger threshold and trigger efficiency, which are based on a given sample of GRBs, are calculated with the algorithms. The trigger characteristics of onboard instruments GRM and ECLAIRs are also analyzed. In addition, the impact of solar flares on GRM is estimated, and a method to distinguish solar flares from GRBs is investigated. 相似文献
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For the Z-R relationship in radar-based rainfall estimation, the distribution of corresponding R values for a given Z value (or the corresponding Z value for a given R value) may be highly skewed. However, the traditional power-law model is physically deduced and fitted under the normal-distribution presumption of radar wave echoes associated with a rain rate value, and it may not be very appropriate. Considering this problem, the authors devised several generalized linear models with different forms and distribution presumptions to represent the Z-R relationship. Radar-reflectivity scans observed by a CINRAD/SC Doppler radar and 5-minute rainfall accumulation recorded by 10 ground gauges were used to fit these models. All data used in this study were collected during some large rainfalls of the period from 2005 to 2007. The radar and all gauges were installed in the catchment of the Yishu River, a branch of the Huaihe River in China. Three models based on normal distribution and a dBZ presumption of gamma distribution were fitted using maximum-likelihood techniques, which were resolved by genetic algorithms. Comparisons of estimated maximized likelihoods based on assumptions of gamma and normal distribution showed that all generalized linear models (GLMs) of presumed gamma distribution were better fitted than GLMs based on normal distribution. In a comparison of maximum-likelihood, the differences between these three models were small. Three error statistics were used to assess the agreement between radar estimated rainfall and gauge rainfall: relative bias (B), root mean square error (RMSE), and correlation coefficient (r). The results showed that no one model was excellent in all criteria. On the whole, the GLM-based models gave smaller relative bias than the traditional power-law model. It is suggested that validations conducted in many previous works should have been made against a specific criterion but overlooked others. 相似文献