印度尼西亚两次特大地震引发海啸和不引发海啸的比较研究

2004年12月26日苏门答腊岛安达曼海附近海域发生的9．0级地震和2005年3月28日苏门答腊岛明打威群岛北附近海域的8．7级地震,在构造环境、震级、震源深度、地震类型都相似的情况下,为何前者引发海啸,后者不引发的海啸？对此进行了对比分析,认为9．0级地震发生时,在其震源体附近的两板块相交的海沟两侧陡坡蕴育着滑坡体或和崩塌体（或者两者都有）,9．0级地震发生时,强烈的地震波,促使滑坡体的滑动或崩塌体的崩塌,推压和扰动海水,引发诲啸。而8．7级地震发生时、在其震源体附近的两板块相交的海沟两侧陡坡无滑坡体或和崩塌体,或先存滑坡体或崩塌体在9．0级地震发生时已滑坡或崩塌殆尽,当8.7地震发生时,无滑坡体滑动或崩塌体崩塌,不可能对海水有较大的扰动,故不可能引发海啸。     

Decline of the Jakarta Bay molluscan fauna linked to human impact

In 1937/38 representative mollusc collections were made in Jakarta Bay (West Java, Indonesia). New data from here and the adjacent offshore Thousand Islands archipelago (Kepulauan Seribu) became available in 2005. Although collecting efforts and sampling methods differed, a comparison of the molluscan fauna of Jakarta Bay between 1937/38 and 2005 reveals a distinct deterioration. From 1937 to 2005, Jakarta Bay received increasing amounts of sewage from the greater Jakarta area, as well as increased sediment input from the deforested West Java hinterland. Predatory gastropods and numerous mollusc species associated with carbonate (reef) substrate have vanished from Jakarta Bay, among which many edible species.     

基于物理试验的海底滑坡涌浪研究

灾难性海啸有时由海底滑坡运动造成,海底滑坡的涌浪产生过程研究是海啸研究的关键。海啸形成主要受控于滑体的几何尺寸、滑动速度、水深、滑动角度等因素。基于这些因素,文章设计并实施了水下刚性滑块正交物理实验。试验中滑块的厚长比在0.035~0.180之间,斜坡滑道的倾角在10°~16°之间,Froude数处于0.18~0.70之间。试验分析表明初始涌浪形成早于滑块停止;初始波谷（最大波谷）是在滑动最大速度时产生的,波谷位置也是速度最大时滑块的质心位置。初始涌浪波的典型特征是大的波谷与小的波峰。利用试验的输入输出数据,回归推导形成了最大波谷和最大波峰计算公式。试验分析结果深化了对海底滑坡产生海啸的认识,为海底滑坡海啸预测提供了基础和技术支撑。     

联合星载高光谱影像和堆栈集成学习回归算法的红树林冠层叶绿素含量遥感反演

红树林是世界上生产力最高、价值最高的湿地生态系统之一。冠层叶绿素含量CCC（Canopy Chlorophyll Content）作为红树林重要的生物物理参量,是估算其生产力和评价其健康状况的重要指标。本文利用珠海一号高光谱卫星（OHS）影像与Sentinel-2A多光谱数据计算传统植被指数与组合植被指数并构建了高维数据集,综合利用正态分布检验、最大相关系数法与变量重要性评价进行数据降维和变量优选;分别基于单一线性回归算法、机器学习回归算法和堆栈集成学习回归算法构建了红树林CCC遥感反演模型,探明北部湾红树林CCC的最佳遥感反演模型,验证OHS高光谱影像与Sentinel-2A数据反演红树林CCC的精度差异,评估SNAP-SL2P算法反演红树林CCC的适用性。研究结果表明：（1）通过数据降维和变量选择处理,从高维度OHS数据集选取了8个特征变量,其中RSI_（12,17）、DSI_（12,18）和NDSI_（6,12）组合植被指数对红树林CCC反演精度的贡献率较高;（2）联合OHS数据和最优堆栈GBRT集成学习回归模型（Score=0.999,RMSE=0.963 μg/cm²）的训练精度优于最优RF机器学习回归模型（RMSE降低了7.531 μg/cm²）,明显优于最优Lasso线性回归模型（RMSE降低了19.383 μg/cm²）;（3）在最优堆栈集成学习回归模型下,OHS数据反演红树林CCC的精度（R²=0.761,RMSE=16.738 μg/cm²）高于Sentinel-2A影像（R²=0.615,RMSE=20.701 μg/cm²）;（4）联合OHS和Sentinel-2A数据的最优堆栈集成学习回归模型反演红树林CCC的精度都明显优于SNAP-SL2P算法（R²=0.356,RMSE=49.419 μg/cm²）。研究结果论证了正态分布检验、最大相关系数法和基于XGBoost的特征选择方法有效降低了高维数据集的维度,并得到了最优特征变量;OHS数据的最优堆栈GBRT集成学习回归模型训练精度最高,是估算红树林CCC的最优反演模型;OHS和Sentinel-2A数据都能有效反演红树林CCC（R²均大于0.61）,而OHS数据的估算精度更高（R²大于0.75）;SNAP-SL2P算法不能有效反演红树林CCC（R²小于0.4）,且对红树林CCC数值存在系统性低估。     

Using GIS to develop a mobile communications network for disaster-damaged areas

Communications network damage resulting from a large disaster causes difficulties in the ability to rapidly understand the current situation and thus make appropriate decisions towards mitigating problems, such as where to send and dispense emergency supplies. The research outlined in this paper focuses on the rapid construction of a network after a disaster occurs. This study suggests ZigBee and geographic information systems (GIS) technologies to resolve these problems and provide an effective communication system. The experimental results of the ZigBee network system are presented, examples are provided of the mapping and analysis undertaken using GIS for the disaster-stricken area of Tsukuba City, Japan, and the communications node arrangements are determined for this region. These results demonstrate the effectiveness of establishing such a communications system for supporting efforts to relieve disaster-damaged areas.     

Characterization of solid bitumens originating from thermal chemical alteration and thermochemical sulfate reduction

Solid bitumen can arise from several reservoir processes acting on migrated petroleum. Insoluble solid organic residues can form by oxidative processes associated with thermochemical sulfate reduction (TSR) as well as by thermal chemical alteration (TCA) of petroleum. TCA may follow non-thermal processes, such as biodegradation and asphaltene precipitation, that produce viscous fluids enriched in polar compounds that are then altered into solid bitumens. It is difficult to distinguish solid bitumen formed by TCA from TSR since both processes occur under relatively high temperatures. The focus of the present work is to characterize solid bitumen samples associated with TSR- or TCA-processes using a combination of solid-state X-ray Photoelectron Spectroscopy (XPS), Sulfur X-ray Absorption Near Edge Structure Spectroscopy (S-XANES), and ¹³C NMR. Naturally occurring solid bitumens from three locations, Nisku Formation, Brazeau River area (TSR-related); La Barge Field, Madison Formation (TSR-related); and, the Alaskan North Slope, Brooks Range (TCA-related), are compared to solid bitumens generated in laboratory simulations of TSR and TCA.The chemical nature of solid bitumens with respect to organic nitrogen and sulfur can be understood in terms of (1) the nature of hydrocarbon precursor molecules, (2) the mode of sulfur incorporation, and (3) their concentration during thermal stress. TSR-solid bitumen is highly aromatic, sulfur-rich, and nitrogen-poor. These heteroatom distributions are attributed to the ability of TSR to incorporate copious amounts of inorganic sulfur (S/C atomic ratio >0.035) into aromatic structures and to initial low levels of nitrogen in the unaltered petroleum. In contrast, TCA-solid bitumen is derived from polar materials that are initially rich in sulfur and nitrogen. Aromaticity and nitrogen increase as thermal stress cleaves aliphatic moieties and condensation reactions take place. TCA-bitumens from the Brooks Range have <75% aromatic carbon. TCA-bitumens exposed to greater thermal stress can have a higher aromaticity, like that observed in TSR-bitumens. Organic sulfur in TCA-organic solids remains relatively constant with increasing maturation (S/C atomic ratio <0.035) due to offsetting preservation and H₂S elimination reactions. Although S-XANES and ¹³C NMR provide information needed to understand changes in structure and reactivity that occur in the formation of petroleum solids, in some cases XPS analysis is sufficient to determine whether a solid bitumen is formed by TCA or TSR.     

基于多时相主被动遥感的漓江水面监测与水质参数反演(2016-2020年)

以漓江流域为研究区域,以2016-2020年Landsat 8 OLI、GF-1、Sentinel-2A及Sentinel-1A逐月影像为数据源,选用归一化水指数(NDWI)、改进型归一化水指数(MNDWI)、增强型水体指数(EWI)、归一化差值池指数(NDPI)、后向散射系数(S)与主被动遥感加权指数(JQ)提取漓江...