Timing analysis of the black hole candidate EXO 1846-031 with insight-HXMT monitoring

We present the observational results from a detailed timing analysis of the black hole candidate EXO 1846-031 during its outburst in 2019 with the observations ...     

江苏大丰潮滩潮流边界层特征研究

2003年7月中小潮期间使用MIDAS-400用户化数据采集系统在江苏大丰潮滩上进行了垂向多层位同步的流速和悬沙浓度观测。根据观测资料研究了潮流作用下的潮滩底部边界层过程,结果表明,观测地点的潮汐不对称现象十分明显,落潮流速、历时和输水输沙量皆明显大于涨潮;在潮周期内的多数时间里流速剖面符合对数分布,但在风力较强、水流快速增加和流速缓慢的情况下,流速剖面常偏离对数分布;悬沙颗粒垂向混合均匀,悬沙浓度剖面符合Rouse公式;落潮时的底部切应力和摩阻流速明显大于涨潮,摩阻流速与各水层流速通常有较好的线性关系;滩面糙度主要与沙纹形态和推移质运动强度有关,它与摩阻流速存在密切关系;悬沙浓度具有明显的减阻效应,受其影响,摩阻流速和底部切应力分别减小了28%~41%和40%~62%。     

Fluid Resonance Between Twin Floating Barges with Roll Motion Under Wave Action

China Ocean Engineering - The wave-induced fluid resonance between twin side-by-side rectangular barges coupled with the roll motion of the twin barges is investigated by both numerical simulation...     

应用KDD技术分析气象地质灾害与降水的关系

应用人工智能技术——数据库中知识发现(KDD)技术,分析研究发生在云南省境内造成重大损失的气象地质灾害的发生与当日及前期降水的关系。结果表明,绝大多数气象地质灾害都是由强降水诱发的,云南气象地质灾害与发生当日及前期降水的关系主要有3种类型,即暴雨诱发型、多日中大雨诱发型和连阴雨诱发型。所得到的云南气象地质灾害与当日及前期降水的定量关系,为云南地质灾害的气象监测、预报模式的建立提供了依据。     

面向复杂山地的机载LiDAR与无人机影像融合应用

本文针对LiDAR点云与无人机影像数据特征的优缺点,利用LiDAR点云与无人机DOM影像融合,将影像数据光谱信息赋给LiDAR点云数据,使其不仅具备精准的空问结构信息,还能得到清晰的纹理信息.为验证融合数据应用的可行性与数据提取的准确性,对融合前后的点云数据进行地面点提取与DEM构建.试验表明:将无人机影像的光谱信息赋...     

瞬变电磁法在彬州市某煤矿采空区勘查中的应用

采用地面瞬变电磁法对拟建搬迁安置小区是否存在煤矿采空区进行勘查,运用未充水采空区、充水采空区分别在视电阻率断面图上呈现相对高阻、低阻异常的地球物理标志,结合地面地质灾害调查成果,较为准确的确定了彬州市某煤矿采空区具体位置,并推断塌陷过渡带范围。     

彬长矿区水文地质特征及其对煤层气的控制作用

对彬长矿区水文地质特征进行了详细的论述,结合煤层实测含气性特征,探讨了水文地质条件对该区煤层气成藏的控制作用。研究表明:研究区构造简单,延安组煤系含水层与其他含水层不具明显水力联系,有利于煤层气富集成藏,同时使得煤系含水层地下水补给只能来源于区外侧向径流补给;矿区划分为3个水动力强弱不同的区域:侧向径流补给区、弱径流区和相对滞流区,其中位于矿区东南部的相对滞流区,也是水势低洼带煤层气汇集区,有利于煤层的富集成藏。同时,东南部煤层的含气量最高,说明该区水文地质条件确实对煤层气成藏起着关键作用。     

长江口水下三角洲表层沉积物有机质分布特征

根据对长江口水下三角洲31个表层样沉积物粒度、总有机碳(TOC)、总氮(TN)的测定,得出了粒度及有机质的分布特征,利用C/N比分析有机质来源及不同来源的贡献率。研究结果表明: 1)长江口水下三角洲表层沉积物平均粒径介于3~8Φ之间,存在由陆向海变细的趋势; 2)TOC含量在0.1% ~1.2% 之间,平均值为0.52%; TN含量介于0.02% ~0.08% 之间,平均值为0.057% 。TOC和TN含量的分布特征相似,具有自陆向海、由低至高的变化趋势,这主要是受到沉积物粒径的影响,但是TOC在崇明岛东侧和九段沙东侧有两个高值点,可能与人类活动或局地因素有关; 粒度校正之后,TOC含量在0.40% ~1.67% 之间,平均值为0.68%; TN的含量介于0.05% ~0.10% 之间,平均值为0.07% 。TOC含量在近岸地区略高,TN含量是北部地区略高于南部地区,并且TOC和TN都在崇明岛东侧和九段沙东侧有两个高值点; 3)C/N比值在6~16之间,平均值为9.6; 利用C/N比值估算得到的陆源有机碳含量在35% ~90% 之间,平均值为60% 。C/N比和陆源有机碳含量在长江口南支外围邻近海域表层沉积物中较高,表明南支带来了大量的陆源有机质入海,并呈舌状向东北方向凸出; 而在长江北支附近其值相对较低,表明北支径流影响相对减弱,潮流作用相对增强。研究表明,长江南支是主要的陆源有机质入海通道,而进入水下三角洲之后陆源有机碳分布受到了河口混合过程的显著影响。     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.     

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China

Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring–summer and higher in autumn–winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.