应用GPS观测青藏高原东北缘应力场变化

采用各向同性弹性地球模型推导了地面位移场速率与地壳内任意点应力场变化的边界积分关系,同时利用青藏高原东北缘1999～2001年观测的GPS资料对观测区地壳深度为5 km和25 km的主应力和最大剪应力进行了计算分析.结果表明,青藏高原东北缘的主应力变化主要集中在祁连山断裂、海原断裂等,在1920～1954年间历史上发生过多次震级为7.0～8.5级强震的断裂附近,并具有主应力变化沿断层走向分布、最大剪应力沿断层走向交替变化等特征.     

Influence of soft clays on the response spectra for structures in eastern Canada

The 1990 edition of the National Building Code of Canada (Associate Committee of the National Building Code, National Research Council, Ottawa, 1990) makes a clear distinction between eastern and western Canada in terms of seismic acceleration and velocity zones. While it is well established that ground motions can be amplified significantly through loose clay deposits, no results are available that take into consideration the typical high frequency content of ground motions in eastern Canada. This paper develops ground amplification curves for clays having depths between 10 and 70 m excited by typical eastern Canadian ground motions scaled to two different values of peak horizontal accelerations. Simplified free-field spectral design curves, which could be used by structural designers, are proposed. The curves show that maximum spectral accelerations occur for structural periods between 0.2 and 0.5 s. In addition, soil depth does not appear to be an important parameter controlling the response of typical clay deposits in eastern Canada.     

企鹅珍珠贝人工苗生长的初步观察

为了解企鹅珍珠贝生长规律，观察了壳高2.5～5.0mm的出池幼苗，按月测量其生长参数和成活率，以及环境因子。结果表明，企鹅珍珠贝生长最快的月份为7～11月和次年4～6月，壳长，壳高和壳宽月均增加量分别为3.8～13.0mm、4.7～11.2mm、2.3～3.8mm，月成活率97.8%～98.6%。企鹅珍珠贝生长最慢月份是11月至次年3月。     

The Statistical Analysis of Migration of Strong Earthquakes-Taking the North China Region as an Example

The migration of strong earthquakes is an important research topic because the migration phenomena reflect partly the seismic mechanism and involve the prediction of tendency of seismic activity. Research on migration of strong earthquakes has mostly focused on finding the phenomena. Some attempts on getting regularity were comparatively subjective. This paper suggests that there should be indices of migration in earthquake dataset and the indexes should have statistical meaning if there is regularity in the migration of strong earthquakes. In this study, three derivative attributes of migration, i.e., migration orientation, migration distance and migration time interval, were statistically analyzed. Results in the North China region show that the migration of strong earthquakes has statistical meaning. There is a dominant migration orientation (W by S to E by N), a dominant distance (≤100km and on the confines of 300～700km), and a dominant time interval (≤1a and on the confines of 3～4a). The results also show that the migration will differ slightly with different magnitude range or earthquake activity phase.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

Freely floating-body simulation by a 2D fully nonlinear numerical wave tank

Nonlinear interactions between large waves and freely floating bodies are investigated by a 2D fully nonlinear numerical wave tank (NWT). The fully nonlinear 2D NWT is developed based on the potential theory, MEL/material-node time-marching approach, and boundary element method (BEM). A robust and stable 4th-order Runge–Kutta fully updated time-integration scheme is used with regriding (every time step) and smoothing (every five steps). A special φ_n-η type numerical beach on the free surface is developed to minimize wave reflection from end-wall and wave maker. The acceleration-potential formulation and direct mode-decomposition method are used for calculating the time derivative of velocity potential. The indirect mode-decomposition method is also independently developed for cross-checking. The present fully nonlinear simulations for a 2D freely floating barge are compared with the corresponding linear results, Nojiri and Murayama’s (Trans. West-Jpn. Soc. Nav. Archit. 51 (1975)) experimental results, and Tanizawa and Minami’s (Abstract for the 6th Symposium on Nonlinear and Free-surface Flow, 1998) fully nonlinear simulation results. It is shown that the fully nonlinear results converge to the corresponding linear results as incident wave heights decrease. A noticeable discrepancy between linear and fully nonlinear simulations is observed near the resonance area, where the second and third harmonic sway forces are even bigger than the first harmonic component causing highly nonlinear features in sway time series. The surprisingly large second harmonic heave forces in short waves are also successfully reproduced. The fully updated time-marching scheme is found to be much more robust than the frozen-coefficient method in fully nonlinear simulations with floating bodies. To compare the role of free-surface and body-surface nonlinearities, the body-nonlinear-only case with linearized free-surface condition was separately developed and simulated.     

三疣梭子蟹全人工工厂化育苗技术

2000年笔者在日照市水产研究所利用1450m^3水进行梭子蟹全人工工厂化育苗生产，共出Ⅱ-Ⅲ期幼蟹512．7万只，创造了出苗量为3500只／m^3，总产值为150多万元的直接经济效益，同时对幼体培育的密度进行了初步试验，结果表明：从Z1至M期幼体培育密度10－15万／m^3为宜，M期以后密度以0．8－1．5万／m^3为宜。     

烧伤病人应用CHIMEHERB人工皮的临床评价

本研究初步评价CHIMEHERB人工皮的治疗效果.74例,105个创面应用人工皮:供皮区47个创面,愈合时间平均为9±1.94d(天),油纱布对照为11.7±1.93d(P<0.01);14个浅Ⅱ°新鲜创面,70％在3～5d内逐渐干燥与创面紧密粘附,平均愈合时间为9.8±3.09d;11个存留上皮组织的削痂深Ⅱ°创面,平均5.7±1.7d干燥,平均愈合时间为10.55±3.09d,不需植自体皮;8个削痂刨面至脂肪组织或切痂创面4～9d更植自体皮,其存活率近乎100％,2个创面因残留坏死组织而失败;人工皮应用于16个自体皮间隙创面,除三个网状植皮和一个条状植皮创面失败外,所植自体皮都在人工皮下扩展融合,未观察到占位现象;对后期残余小创面有良好的效果.本研究结果提示人工皮对创面愈合具有有利影响.主要适用于供皮区、浅Ⅱ°新鲜创面、深Ⅱ°脱痂和削痂后存留上皮组织较多的创面、自体皮间隙创面和后期残余小创面,不适宜于深Ⅲ°新鲜创面.     

人工神经网络在地质学判别分析中的应用

将现代人工神经网络方法应用于地质学的判别分析，通过实例计算表明，无论是对于两组判别分析还是多组判别分析，人工神经网络方法都是切实可行的。