基于MatLab的神经网络在江苏矿山地质环境评估中的应用

神经网络作为一种新的方法体系,具有分布并行处理、非线性映射、自适应学习和鲁棒容错等特性,在模式识别、控制优化和智能信息处理等方面有着广泛的应用。利用MatLab的神经网络工具箱,建立了江苏矿山地质环境质量的评估模型,评估结果经过实际验证,具有较高的可信度和实用性。     

个旧老-卡岩体接触-凹陷带锡-铜多金属矿地质特征及成因分析

个旧老—卡岩体接触—凹陷带是个旧矿集区最重要的锡-铜多金属成矿构造带,该带中锡、铜矿产资源十分丰富。通过对凹陷带内矿体地质特征、围岩地层含矿性、微量元素、稀土元素、稳定同位素等的研究,结合大地构造环境,结果认为,凹陷带矿体是多种成矿物质来源,经历多种地质作用、多个大地构造演化阶段的多因复成矿床,接触-凹陷带锡-铜矿体形成主要与燕山中晚期花岗岩岩浆作用有关,其次与印支期海底火山-沉积与海底（火山）喷流-热水沉积成矿作用有关。     

大井铜-锡多金属矿床地质特征及其成因

通过对大井铜-锡多金属矿床成矿地质环境、矿床地质特征以及对岩（矿）石同位素、稀土元素、成矿温度等地球化学和成矿物理化学环境的研究,认为,大井铜-锡多金属矿床为一典型的岩浆热液充填型矿床。与成矿有关的隐伏岩体受深部隐伏的深大断裂控制,可能与黄岗梁矽卡型铁-锡矿床同属一构造岩浆成矿带上不同成矿环境下形成的矿床。并认为,在该成矿带上及大井矿区深部仍有较大的找矿潜力。     

桂西岩溶堆积型铝土矿地质异常分析及基于GIS的找矿有利地段圈定

桂西岩溶堆积型铝土矿床受地层岩性、构造、岩溶地貌、气候与水文等因素控制。通过对岩溶堆积型铝土矿床地质异常的分析和识别,利用G IS技术圈定铝土矿找矿的有利地段,并预测其资源潜力。     

Coastal processes and environmental hazards: the Buenos Aires (Argentina) and Venetian (Italy) littorals

The Buenos Aires (Argentina) and Venice (Italy) coastlands have experienced significant saltwater contamination of the phreatic aquifer, coastal erosion, hydrodynamic changes and relative sea level rise processes due to natural and man-induced factors. These factors expose coastal areas to morpho-hydro-geological hazards, such as soil desertification, frequency and degree of flooding, littoral erosion, and the silting of river mouths and channels. Man-made interventions and actions, such as beach mining, construction of coastal structures and exploitation of aquifers without an adequate knowledge of the hydrology setting and an adequate management program, worsen these natural hazards. Uncontrolled human activity induces environmental damage to the overall coastal plains. The coastal plains play an important role in the social/economic development of the two regions based on land use, such as agriculture, horticulture, breeding, and tourism, as well as industry. Results of investigations on saltwater contamination, sea level rise and morphological changes recently performed in these two coastal areas are presented here.     

Time independent seismic hazard analysis in Alborz and surrounding area

The Bayesian probability estimation seems to have efficiencies that make it suitable for calculating different parameters of seismicity. Generally this method is able to combine prior information on seismicity while at the same time including statistical uncertainty associated with the estimation of the parameters used to quantify seismicity, in addition to the probabilistic uncertainties associated with the inherent randomness of earthquake occurrence. In this article a time-independent Bayesian approach, which yields the probability that a certain cut-off magnitude will be exceeded at certain time intervals is examined for the region of Alborz, Iran, in order to consider the following consequences for the city of Tehran. This area is located within the Alpine-Himalayan active mountain belt. Many active faults affect the Alborz, most of which are parallel to the range and accommodate the present day oblique convergence across it. Tehran, the capital of Iran, with millions of inhabitants is located near the foothills of the southern Central Alborz. This region has been affected several times by historical and recent earthquakes that confirm the importance of seismic hazard assessment through it. As the first step in this study an updated earthquake catalog is compiled for the Alborz. Then, by assuming a Poisson distribution for the number of earthquakes which occur at a certain time interval, the probabilistic earthquake occurrence is computed by the Bayesian approach. The highest probabilities are found for zone AA and the lowest probabilities for zones KD and CA, meanwhile the overall probability is high.     

Seismic Hazard Analysis for the Bangalore Region

Indian peninsular shield, which was once considered to be seismically stable, is experiencing many earthquakes recently. As part of the national level microzonation programme, Department of Science and Technology, Govt. of India has initiated microzonation of greater Bangalore region. The seismic hazard analysis of Bangalore region is carried out as part of this project. The paper presents the determination of maximum credible earthquake (MCE) and generation of synthetic acceleration time history plot for the Bangalore region. MCE has been determined by considering the regional seismotectonic activity in about 350 km radius around Bangalore city. The seismotectonic map has been prepared by considering the faults, lineaments, shear zones in the area and historic earthquake events of more than 150 events. Shortest distance from the Bangalore to the different sources is measured and then peak ground acceleration (PGA) is calculated for the different source and moment magnitude. Maximum credible earthquake found in terms of moment magnitude is 5.1 with PGA value of 0.146 g at city centre with assuming the hypo central distance of 15.88 km from the focal point. Also, correlations for the fault length with historic earthquake in terms of moment magnitude, yields (taking the rupture fault length as 5% of the total fault length) a PGA value of 0.159 g. Acceleration time history (ground motion) and a response acceleration spectrum for the corresponding magnitude has been generated using synthetic earthquake model considering the regional seismotectonic parameters. The maximum spectral acceleration obtained is 0.332 g for predominant period of 0.06 s. The PGA value and synthetic earthquake ground motion data from the identified vulnerable source using seismotectonic map will be useful for the PGA mapping and microzonation of the area.     

Determination of earthquake early warning parameters, τc and Pd, for southern California

We explore a practical approach to earthquake early warning in southern California by determining a ground-motion period parameter  τ _c   and a high-pass filtered displacement amplitude parameter Pd from the initial 3 s of the P waveforms recorded at the Southern California Seismic Network stations for earthquakes with M > 4.0. At a given site, we estimate the magnitude of an event from  τ _c   and the peak ground-motion velocity ( PGV ) from Pd . The incoming three-component signals are recursively converted to ground acceleration, velocity and displacement. The displacements are recursively filtered with a one-way Butterworth high-pass filter with a cut-off frequency of 0.075 Hz, and a P -wave trigger is constantly monitored. When a trigger occurs,  τ _c   and Pd are computed. We found the relationship between  τ _c   and magnitude ( M ) for southern California, and between Pd and PGV for both southern California and Taiwan. These two relationships can be used to detect the occurrence of a major earthquake and provide onsite warning in the area around the station where onset of strong ground motion is expected within seconds after the arrival of the P wave. When the station density is high, the methods can be applied to multistation data to increase the robustness of onsite early warning and to add the regional warning approach. In an ideal situation, such warnings would be available within 10 s of the origin time of a large earthquake whose subsequent ground motion may last for tens of seconds.