利用福建爆破实验记录检验华南速度模型

基于2010~2012年"福建及台湾海峡地壳深部结构陆海联合探测"项目中18次爆破实验的准确发震时间、发震位置所确定的多震相走时数据,进行爆破震相特征分析,采用华南速度模型对单纯型、Hyposat、Hypo2000及Locsat等4种定位方法进行比较,得出最优定位方法。利用14炮爆破记录反演得到的速度模型与华南速度模型作比较,结果表明,反演模型的上地壳厚度小于华南模型的,下地壳厚度大于华南模型的,上地壳速度与华南模型的相当,下地壳速度大于华南模型的。利用本研究的反演结果对其他4次爆破进行重新定位,定位精度明显提高。     

Characterizing the heterogeneity of karst critical zone and its hydrological function: An integrated approach

Spatial heterogeneity in the subsurface of karst environments is high, as evidenced by the multiphase porosity of carbonate rocks and complex landform features that result in marked variability of hydrological processes in space and time. This includes complex exchange of various flows (e.g., fast conduit flows and slow fracture flows) in different locations. Here, we integrate various “state‐of‐the‐art” methods to understand the structure and function of this poorly constrained critical zone environment. Geophysical, hydrometric, and tracer tools are used to characterize the hydrological functions of the cockpit karst critical zone in the small catchment of Chenqi, Guizhou Province, China. Geophysical surveys, using electrical resistivity tomography (ERT), inferred the spatial heterogeneity of permeability in the epikarst and underlying aquifer. Water tables in depression wells in valley bottom areas, as well as discharge from springs on steeper hillslopes and at the catchment outlet, showed different hydrodynamic responses to storm event rainwater recharge and hillslope flows. Tracer studies using water temperatures and stable water isotopes (δD and δ¹⁸O) could be used alongside insights into aquifer permeability from ERT surveys to explain site‐ and depth‐dependent variability in the groundwater response in terms of the degree to which “new” water from storm rainfall recharges and mixes with “old” pre‐event water in karst aquifers. This integrated approach reveals spatial structure in the karst critical zone and provides a conceptual framework of hydrological functions across spatial and temporal scales.     

人工神经网络及其在地震预报中的应用

文概述了人工神经网络的原理和算法，利用１９８５－１９９２年全国年度趋势会商报告的资料来训练的检验神经网络。结果表明，网络经训练后具有较高的识别能力，在地震预报中有深入研究和进一步应用的价值。最后讨论了神经网络中几个重要参数的取值问题。     

Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate,upland catchments

Empirically based understanding of streamflow generation dynamics in a montane headwater catchment formed the basis for the development of simple, low‐parameterized, rainfall–runoff models. This study was based in the Girnock catchment in the Cairngorm Mountains of Scotland, where runoff generation is dominated by overland flow from peaty soils in valley bottom areas that are characterized by dynamic expansion and contraction of saturation zones. A stepwise procedure was used to select the level of model complexity that could be supported by field data. This facilitated the assessment of the way the dynamic process representation improved model performance. Model performance was evaluated using a multi‐criteria calibration procedure which applied a time series of hydrochemical tracers as an additional objective function. Flow simulations comparing a static against the dynamic saturation area model (SAM) substantially improved several evaluation criteria. Multi‐criteria evaluation using ensembles of performance measures provided a much more comprehensive assessment of the model performance than single efficiency statistics, which alone, could be misleading. Simulation of conservative source area tracers (Gran alkalinity) as part of the calibration procedure showed that a simple two‐storage model is the minimum complexity needed to capture the dominant processes governing catchment response. Additionally, calibration was improved by the integration of tracers into the flow model, which constrained model uncertainty and improved the hydrodynamics of simulations in a way that plausibly captured the contribution of different source areas to streamflow. This approach contributes to the quest for low‐parameter models that can achieve process‐based simulation of hydrological response. Copyright © 2009 John Wiley & Sons, Ltd.     

Impact of geometrical properties on permeability and fluid phase distribution in porous media

To predict fluid phase distribution in porous media, the effect of geometric properties on flow processes must be understood. In this study, we analyze the effect of volume, surface, curvature and connectivity (the four Minkowski functionals) on the hydraulic conductivity and the water retention curve. For that purpose, we generated 12 artificial structures with 800³ voxels (the units of a 3D image) and compared them with a scanned sand sample of the same size. The structures were generated with a Boolean model based on a random distribution of overlapping ellipsoids whose size and shape were chosen to fulfill the criteria of the measured functionals. The pore structure of sand material was mapped with X-rays from synchrotrons.     

Amelioration of marine farming impact on the benthic environment using artificial reefs as biofilters

An in situ monitoring of the sediment characteristics and macrobenthic communities was undertaken at a marine fish culture site in subtropical waters of Hong Kong before and after the deployment of biofilters which were made of cement concrete artificial reef (AR) structures. According to the distance to the boundary of the fish cages, 6 points were selected as sampling stations: 2 at the fish cages, 2 near the boundary of the fish culture area, and 2 reference sites further away from the culture area. Bimonthly sediment samples were collected for analysis of silt-clay fraction (SCF), moisture content (MC), total organic carbon (TOC), total Kjeldahl nitrogen (TKN) and total phosphorus (TP). The macrobenthos (>0.5mm) present in the sediment were sorted, identified and enumerated. TOC, TKN and TP levels at the fish cage stations were consistently higher than those at the reference stations over the 1-year pre-AR and 2-year post-AR deployment monitoring period. The diversity of macrofauna was significantly reduced at the fish cage stations relative to the reference sites. The intermediary stations near the fish culture area showed a transitional state of disturbance. Over the 2-year post-AR deployment period, TOC, TKN and TP showed a decreasing trend at the fish cage and intermediary stations. More diverse macrofaunal communities were recorded at the fish cage stations, with species diversity H'increasing from 0-1 at the beginning of the AR deployment to H'>2 at the end of the study. The present results demonstrated that artificial reefs can improve the benthic abiotic environment and biotic conditions beneath fish rafts which are deteriorated due to farming activities.     

Using an Artificial Intelligence Method to Recognize Seismic Precursory Anomaly (SPA)

In this work,we seek for a new way to recognize and analyze the Seismic Precursory Anomaly(SPA)quantitatively.For further study of the mechanism of SPA,an artificial intelligence method,the Machine Discovery(MD)method,is proposed to process the Seismic Precursor Observation(SPO)data and to fit an analytic function to them.The analytic function is used as a criterion to identify the SPA.The algorithm of MD is worked out based on the theory of Recursive Function(RF).The data of Amplitude Factor of Solid Tide(7 value)are processed by the proposed method.We find that the subsequent data fit to the extrapolation of the discovered function during the quiet period,whereas they deviate from it before a strong earthquake.Finally,the feasibility of quantitative analysis of the SPA is testified.     

Ergodicity and the ‘scale effect’

Solute plume spreading in an aquifer exhibits a ‘scale effect’ if the second spatial concentration moment of a plume has a non-constant time-derivative. Stochastic approaches to modeling this scale effect often rely on the critical assumption that ensemble averages can be equated to spatial averages measured in a single field experiment. This ergodicity assumption should properly be evaluated in a strictly dynamical context, and this is done in the present paper. For the important case of trace plume convection by steady groundwater flow in an isotropic, heterogeneous aquifer, ergodicity does not obtain because of the existence of an invariant function on stream surfaces that is not uniform throughout the aquifer. The implications of this result for stochastic models of solute transport are discussed. © 1997 Elsevier Science Ltd. All rights reserved     

Headwaters drive streamflow and lowland tracer export in a large-scale humid tropical catchment

Headwaters are generally assumed to contribute the majority of water to downstream users, but how much water, of what quality and where it is generated are rarely known in the humid tropics. Here, using monthly monitoring in the data scarce (2,370 km²) San Carlos catchment in northeastern Costa Rica, we determined runoff-area relationships linked to geochemical and isotope tracers. We established 46 monitoring sites covering the full range of climatic, land use and geological gradients in the catchment. Regression and cluster analysis revealed unique spatial patterns and hydrologically functional landscape units. These units were used for seasonal and annual Bayesian tracer mixing models to assess spatial water source contributions to the outlet. Generally, the Bayesian mixing analysis showed that the chemical and isotopic imprint at the outlet is throughout the year dominated by the adjacent lowland catchments (68%) with much less tracer influence from the headwaters. However, the headwater catchments contributed the bulk of water and tracers to the outlet during the dry season (>50%) despite covering less than half of the total catchment area. Additionally, flow volumes seemed to be linearly scaled by area maintaining a link between the headwaters and the outlet particularly during high flows of the rainy season. Stable isotopes indicated mean recharge elevations above the mean catchment altitude, which further supports that headwaters were the primary source of downstream water. Our spatially detailed “snap-shot” sampling enabled a viable alternative source of large-scale hydrological process knowledge in the humid tropics with limited data availability.     

Optimization and Modeling of the Photocatalytic Degradation of the Insect Repellent DEET in Aqueous TiO2 Suspensions

Response surface methodology (RSM) and artificial neural networks (ANNs) based on a multivariate central composite design (CCD) were applied to model and optimize the photocatalytic degradation of N,N‐diethyl‐m‐toluamide (DEET). The individual and interaction effects of three main operating factors (mass of TiO₂, initial DEET concentration, and irradiation intensity) on process efficiency were estimated, proving their important effect on % DEET removal. Among the independent variables, TiO₂ concentration displayed the highest effect on DEET degradation followed by initial DEET concentration and UV intensity. The optimization and prediction capabilities of ANNs and RSM were compared on the basis of root mean squared error, mean absolute error, absolute average deviation, and correlation coefficient values. Results proved the usefulness and capability of the experimental design strategy for successful investigation and modeling of the photocatalytic process. Moreover, the selected ANN gave better estimation capabilities throughout the range of variables than RSM. Based on the models and the related experimental conditions, the optimal values of each parameter were determined. Under optimum conditions, DEET and total organic carbon (TOC) followed pseudo‐first order kinetics. Nearly complete degradation of DEET took place within 15 min whereas high TOC removal percentages (>85%) was achieved after 90 min irradiation time.