地震活动增强方式及其复杂性

分析了地震活动增强特征参数W1与相应其它参数的关系以及在一些6级左右地震前W1值的动态图像变化。结果表明中强以上地震前的地震活动“增强”通常表现为在强度上的增高、时间和空间上的丛集以及强度增高与时空丛集同时出现3种方式。一些6级左右地震前W1值动态图像变化表明地震过程往往表现为多应力集中区相互作用和影响的演变过程。     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.     

An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements

The present paper looks at algorithms to be implemented in the software of bottom pressure recorders (BPRs) for the automatic, real-time detection of a tsunami within recorded signals. The structure of an algorithm based on the use of an artificial neural network (ANN) is presented and compared to the one developed under the Deep-ocean Assessment and Reporting of Tsunamis (DART) program run by the U.S. National Oceanic and Atmospheric Administration (NOAA). The performance and efficiency of the two algorithms are compared using both synthetic and actually measured time series. Results show that an improvement in detection performance can be obtained by using the ANN algorithm.     

A virtual laboratory for stability tests of rubble-mound breakwaters

The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project.     

The impact of the 2005 hurricane season on the Louisiana Artificial Reef Program

The 2005 hurricane season in the Gulf of Mexico was the worst in the history of offshore production, with Hurricanes Katrina and Rita destroying 110 oil and gas structures and eight mobile offshore drilling units. Infrastructure destroyed by accident or natural catastrophe are decommissioned according to the same federal regulations that guide normal decommissioning operations, but depending on the nature of the destruction and the market conditions in the months following the event, special conditions and delays may occur. Historically, offshore infrastructure destroyed by hurricanes or other unusual circumstances have been considered for inclusion in the Louisiana Artificial Reef Program (LARP) under the Special Artificial Reef Site (SARS) category. The purpose of this paper is to review the impact of the 2005 hurricane season on the LARP and the current status of the SARS program. We examine the criteria employed in project evaluation and approval as well as aggregate program statistics. The characteristics and risks associated with decommissioning destroyed infrastructure are also described. At the end of 2006, 10 projects representing 35 platforms destroyed in the 2005 hurricane season have been approved as SARS in the Gulf of Mexico, effectively doubling the number of sites and structures classified as SARS.     

Temporal and spatial variations of radiocarbon in Japan Sea bottom water

In 1995 and 2000, the radiocarbon ratio (Δ¹⁴C) of total dissolved inorganic carbon was measured in the Japan Sea where deep and bottom waters are formed within the sea itself. We found that (1) since 1979, the Δ¹⁴C in bottom water below about 2000-m depth in the western Japan Basin (WJB) had increased by about 30‰ by 1995, and (2) the bottom Δ¹⁴C in the WJB did not change between 1995 and 2000. The former finding was due to penetration of surface bomb-produced radiocarbon into the bottom water owing to bottom ventilation, whereas the latter was caused by stagnation of the bottom ventilation there. In the eastern Japan Basin (EJB), the bottom Δ¹⁴C also increased by about 30‰ between 1979 and 2002. Recent stagnation of the bottom ventilation in the EJB is also suggested from analyses of constant bomb-produced tritium between 1984 and 1999. The temporal variations of Δ¹⁴C, tritium, and dissolved oxygen in the bottom waters indicate that: (1) new bottom water is formed south of Vladivostok in the WJB only in severe winters; and (2) the new bottom water then follows the path of a cyclonic abyssal circulation of the Japan Sea, which results in the increases in dissolved oxygen and the transient tracers in the bottom waters in the EJB and Yamato Basin with an approximate 3-to 6-year time lag. This process is consistent with the spatial variations of Δ¹⁴C, bomb-produced ¹³⁷Cs, and chlorofluorocarbon-11 in the bottom waters of the Japan Sea.     

The shipboard analysis of trace levels of sulfur hexafluoride, chlorofluorocarbon-11 and chlorofluorocarbon-12 in seawater

Methods are described for the rapid (11 min) automated shipboard analysis of dissolved sulfur hexafluoride (SF₆) in small volume (200 cm³) seawater samples. Estimated precision for the SF₆ measurements is 2% or 0.02 fmol kg⁻¹ (whichever is greater). The method also allows for the simultaneous measurement of chlorofluorocarbon-11 (CFC11) and chlorofluorocarbon-12 (CFC12) on the same water sample, with significantly improved sensitivity over previous analytical methods.     

远海梭子蟹幼体饵料初步研究

分别选用轮虫、轮虫 卤虫、轮虫 卤虫 扁藻、卤虫、扁藻为饵料 ,将刚孵化的远海梭子蟹氵蚤状幼体培育至幼蟹第二期。结果发现 :轮虫 卤虫 扁藻的效果最好 ,幼蟹第二期的成活率为 15% ,轮虫 卤虫的成活率为 11% ,卤虫的成活率为 10 %。而轮虫组仅能培育至 氵蚤状幼体第三期 ,没有氵蚤状幼体第四期幼体出现 ;扁藻最差 ,仅能培育至氵蚤状幼体第一期 ,没有氵蚤状幼体第二期个体出现。同时发现 ,轮虫 卤虫组与卤虫组培育时间均比轮虫卤虫 扁藻组长 ;各期幼体的阶段成活率比较而言 ,氵蚤状幼体第一期最高 ,而氵蚤状幼体幼体第五期最低     

赤潮预报的人工神经网络方法

基于渤海湾赤潮观测数据(2003年5月~9月),对藻类浓度与环境因子进行了相关性分析,选择出影响藻类浓度的主要环境因子,然后对主要环境因子作主成分分析,在主成分分析的基础上,建立了一个用于赤潮预报的人工神经网络模型。     

基于人工神经网络的赤潮预测模型

本文利用非线性时间序列预测模型,将海洋预报和人工神经网络BP算法相结合,提出了基于神经网络的海洋预报模型;运用改进的三层BP(Back Propagation)神经网络模型对海洋气象进行赤潮灾害监测和预报;同时针对仿真结果进行分析,结果表明该模型具有较好的预测能力。