2014年8月3日云南鲁甸MW6.1（MS6.5）地震破裂过程

2014年8月3日云南鲁甸（M_W6.1,M_S6.5）地震是一次规模不大、但灾害严重的走滑型地震事件.受走滑型地震辐射图型的影响,远震地震资料在特定方位上信噪比不高,给此次地震发震断层面的确定造成了一些干扰.本文概述了鲁甸地震发生后2.4小时发布的作为地震应急响应的破裂过程快速反演工作,以及随后对反演结果的修订工作.修订结果中,两个双力偶节面的反演都显示破裂方向朝地表和走向方向扩展.结合现有的烈度分布和余震精确定位结果,根据破裂方向和烈度与余震分布的优势方向的一致性,确定鲁甸地震是发生在走向162°,倾角86°的近乎垂直于地面的以左旋走滑为主的断层面上的一次破裂事件.根据破裂过程反演得到的震源时间函数,大部分地震矩在破裂开始后2~5 s内集中释放. 比较集中的地震矩释放过程可能是此次地震面波震级明显高于矩震级,且造成严重地震灾害的原因之一.     

波形拟合法在人工挖孔扩底桩承载力测试中的应用

从人工挖孔扩底桩的工作机理和波形拟合法的基本原理出发 ,结合工程实例阐述了波形拟合法在确定人工挖孔扩底桩承载力中的应用特点及可行性 .     

Identifying tropical dry forests extent and succession via the use of machine learning techniques

Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.     

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.     

A multi-subwaveform parametric retracker of the radar satellite altimetric waveform and recovery of gravity anomalies over coastal oceans

The quality of satellite radar altimetric data is very important in studies of geodesy,geophysics,and oceanography.Over coastal oceans,altimeter waveforms are contaminated by the terrain and physical environments so that the accuracy of altimeter data is lower than that over open oceans.Here we develop a new multi-subwaveform parametric retracker(MSPR) to improve the quality of altimeter data for the recovery of gravity anomaly in coastal oceans.The least squares collocation method is used to recover the residual gravity anomaly over the coastal water from altimetric data.The waveform data records from Geosat/GM around Taiwan Island are practically retracked with MSPR.When compared with the Taiwan geoid height,the results retracked by MSPR are more accurate than those retracked by the well-known β-5-parmeter method and from the geophysical data records(GDRs).The gravity anomalies over Taiwan coastal waters are calculated from the retracked altimeter data with the least squares collocation.When we compared gravity anomalies computed using altimeter GDRs with the ship-borne gravity data over Taiwan coastal ocean,we found that the results from retracked data are more accurate than those from GDRs.     

地震波形速度记录和位移记录分析比较

利用位移与速度的函数关系 ,讨论地震观测系统中速度记录与位移记录的波形特征。进一步分析速度记录在地震观测中的重要意义。     

CryoSat-2SARIn模式二级产品在内陆水域的偏差分析

对比青藏高原及天山地区所有湖泊内CryoSat-2基线C版本二级产品(SIR_SIN_L2I)和ICESat测高结果发现,它们之间存在一个多达几十米的系统性偏差,这种偏差限制了多源测高数据的联合研究,必须加以消除。从一级数据出发,利用经典的重跟踪方法重新获得湖面高程,能很好地消除这个偏差。研究发现,当前的二级产品在生成过程中使用了错误的窗口延迟参数,从而导致该系统性偏差。因此,CryoSat-2的SARIn模式二级产品不能直接用于多源数据的联合分析,需要对一级数据重跟踪或直接在二级数据上加一个偏差改正。
     

1998年1月10日河北省张北-尚义M_S6.2地震震源机制的初步研究

1998年1月10日在河北省张北—尚义地区发生Ms6．2级地震,研究本次地震的霞源机制对于研究震源区和华北地区的地震活动性、构造运动和地球动力过程都有重要意义,我们用地幔波波形拟合及P波初动符号估计了本次地震的震源机制,提供了若干条P波列宽频垂直向（BHZ）波形图,认为以逆冲为主的断裂引起本次地震．     

Jason-2卫星阈值重跟踪算法比较

选取Jason-2卫星在中国南海北纬15°~18.5°范围1 276个波形数据,依据Threshold算法和SeaIce算法对波形进行重跟踪,得到不同阈值水平下的距离改正结果。结果表明:当星下点轨迹靠近陆地时,距离改正值增大。然后,依据不同方法、不同阈值水平对应的距离改正值对卫星观测的海面高实施修正,得到相应的海面高结果。作为验证,选取法国国家太空研究中心(CNES)发布的海面高作为外部检核条件。将修正后的海面高与CNES公布的海面高进行较差比较,较差值的统计分析表明:SeaIce重跟踪算法在阈值水平为50%时,差值的标准差明显小于其他方法的计算结果。     

华北强震断层面解和震源深度特征

本文利用远震P波及SH波和区域地震P_(nl)波波形反演方法,测定了华北9次强震的断层面解、震源深度及地震矩.结合其他作者测定的7次地震的结果,分析了华北16次强震断层面解及华北地震震源深度特征.结果表明,华北多数强震为走滑型地震,也有个别正断层型及逆断层型地震;16次地震多数发生在地壳中部10—25km深度范围内.