Molecular evidence for life in the 3.5 billion year old Warrawoona chert

The biological origin of organic matter in the oldest siliceous sediments (cherts) is still debated. To address this issue, the insoluble organic matter (kerogen) was isolated from a chert of the Warrawoona group. The chemical structure of the kerogen was investigated through a combination of analytical techniques including solid-state ¹³C nuclear magnetic resonance and pyrolysis. Although dominated by aromatic hydrocarbons, the pyrolysate comprises a homologous series of long chain aliphatic hydrocarbons characterized by odd-over-even carbon number predominance. This distribution is only consistent with a biological origin. As kerogen must be contemporaneous of the solidification of the chert, this observation should be regarded as an evidence for the presence of life on Earth, 3.5 By ago.     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.     

Dinarides东南部跨界岩溶含水层的特性

Dinarides南部是世界上岩溶发育最强烈的地区之一,同时也是欧洲降水量最大的地区。区内碳酸盐岩沉积厚度大于300m,平均岩溶发育深度从250m 至350m,沿断裂发育的岩溶,甚至更深。地下水平均流速变化较大,从0.002 cm/s至55.2cm/s。地下水滞留时间很短,水位变化非常迅速,变幅高达312m。地下水位对强降雨的响应小于4小时。岩溶大泉的主要特征是管道出口深,最大最小流量变化大。区内发育的落水洞有成千上百个,其中一些落水洞排水能力超过100m3/s,规模巨大并被洪水暂时淹没的岩溶坡立谷闻名世界各地。泉水天然流量和野生动物生存环境的改变,特别是当地特有物种的破坏,已经成为新近许多岩溶工程项目的焦点议题。由于该区极其复杂的水文地理和政治环境,不同国度间的跨界含水层似乎是一个非常敏感的问题。      

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

地震和爆破时频域能量分布特征的对比分析

运用小波分析方法计算和分析了CDSN乌鲁木齐台记录的天然地震和人工爆破在时频域的能量分布特征,分析结果表明,在震中距大致相同的范围内,同一台站记录的不同当量的人工爆破具有相似的时频变化规律,而天然地震能量分布却存在较大差别。天然地震与人工爆破的时频域能量分布谱图存在比较明显的差异。     

杭州城市生活质量的定量评价

以乡镇、街道为基本空间统计单元,选取人口密度、住宅基准地价、大专以上学历人口比重为社会经济指标,建设用地比重、NDVI、地表温度为生物物理指标,采用主成分分析和GIS叠加法,定量评价了杭州城市生活质量的空间分布状况。结果表明：NDVI和地表温度 (相关系数r = -0.63)、城镇用地比重 (相关系数r = -0.83) 呈明显的负相关,地表温度和城镇建设用地是影响杭州“绿色环境”两个的最重要因素。NDVI和住宅基准地价 (r = -0.66)、大学毕业生比例 (相关系数r = -0.59)、人口密度 (相关系数r = -0.29) 也呈较明显的负相关,“绿色环境”并未成为高学历人群和高收入家庭的主要决定因素,较繁华或较便利地段仍是高学历人群和高收入家庭选择居住环境的首要选择。高生活质量值 (前20%) 区域主要位于分布于城市中心区,即杭州上城区、下城区及西湖区的部分单元,而郊区的生活质量相对较低。     

Local government, local economic development and quality of life in Poland

The establishment of local self-government was a key part of the post-1989 transformation in East and Central Europe. Local government in both Western and East and Central Europe has increasingly been expected to play a role in local economic development (LED). Local government is one important agent in the complex processes of building 'institutional thickness' to ensure the development of local economies and the quality of life of inhabitants. This paper presents the results of a national postal questionnaire survey of the LED role of the lowest level of local self-government in Poland, the gmina or commune. The paper establishes a baseline of knowledge regarding: the local economic problems faced by communes; their attitudinal, strategic and organisational responses; and the main factors which are hindering the communes' LED role. This revised version was published online in July 2006 with corrections to the Cover Date.