三维多层介质重力-地震同步联合反演   总被引：1，自引：0，他引：1       下载免费PDF全文

吴招才  刘天佑  高金耀 《地球物理学进展》2009,24(4):1216-1224

联合反演是地球物理勘探的重要解释手段,能够提高模型参数的反演精度.本文在归纳和分析重力与地震资料联合反演的研究和应用现状的基础上,利用三维多层介质模型的地震走时和重力正演公式,推导了地震走时和重力异常对界面深度的雅可比矩阵,实现了三维重力-地震同步联合反演界面成像.最后进行了数值理论模型模拟和实例计算,结果表明地震走时和重力同步联合反演很好的重建了三维多层介质界面.  相似文献   

A hybrid neural network model for typhoon-rainfall forecasting   总被引：2，自引：0，他引：2  

Gwo-Fong Lin  Ming-Chang Wu 《Journal of Hydrology》2009,375(3-4):450-458

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.  相似文献   

雷达资料时间循环同化应用于闪电落区等研究初探     

杨艳蓉  王振会  张其林  郭凤霞  曾明剑  肖稳安 《热带气象学报》2012,28(4):533-540

为研究夏季雷暴天气条件下的对流、降水、闪电的发展过程,有效使用高时空分辨率的遥感手段,如多普勒雷达、闪电定位仪等,利用中尺度数值模式ARPS及敏感的数据同化系统ADAS将VCP21模式下每6 min一次的雷达基数据资料进行时间循环同化,模拟得到相对可靠的三维空间、每10 min输出一次的对流云初步性态描述信息;进而结合目前公认的感应及非感应起电机制,通过云中电场强度与同化得到的云内相态模拟资料之间粗略微分关系,设定放电阈值,初步得到放电主要落点等信息;把本次模拟结果与江苏省闪电定位系统(LLS)实测资料做了对比,发现二者具有一定的可比性:(1)实测30 min内共有78次闪电,感应起电机制下共模拟出40次,非感应起电下有76次且首发时间较早。这也说明了非感应起电机制更易于雷暴云内电荷的衍生;(2)30 min内两种机制下模拟得到雷电发生的主要位置差别不大,和实测基本一致。  相似文献   

Cumulative gain and lift charts for model performance assessment in mineral potential mapping     

WU Wei  CHEN Yongliang 《世界地质(英文版)》2017,20(2)

Model performance assessment is a key procedure for mineral potential mapping, but the corresponding research achievements are seldom reported in literature.Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer churn prediction for model performance assessment.In this paper, they are introduced into the field of mineral potential mapping for model performance assessment.These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets.A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction.A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random selection.As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression, restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China.The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly.Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.  相似文献   

分层抽取法在泥沙沉积过程中的应用研究     

何洪涛  朱伟  张春雷  王亮 《岩土力学》2011,32(8):2371-2378

悬浮泥沙到沉积土的形成过程非常复杂,涉及到泥沙的沉降和土的自重固结。物理模型试验需要实测含水率、密度、颗粒分布、超孔隙水应力和总应力,但目前尚无简单可行的方法。提出了一种改进的室内试验方法,通过与国内外现有试验方法相对比,改进后的方法不仅可以得到泥沙在沉积过程中的泥面下沉变化、含水率（密度）分布、超孔压消散剖面,而且还能得到沉积物在不同高度和时间的颗粒分布变化等性质。通过多个试验实测及一致性测试表明,实测数据合理、可信,一致性检验误差较小,验证了此方法的可行性。  相似文献   

基于案例挖掘的边坡稳定性智能评价系统研究     

谢全敏  陈立文  夏元友 《岩土力学》2008,29(1):145-148

虽然边坡灾害治理实践已经积累了大量的成功的或失败的边坡治理工程案例,但这些工程案例产生的大量数据信息未被充分利用与开发,造成了极大的资源浪费。为此,基于边坡工程案例,应用数据挖掘与知识发现和递归的自-组织模糊神经推理网络的方法,初步提出了一种基于案例挖掘的边坡稳定性智能评价系统,并通过案例挖掘的应用实例表明了该系统的有效性和可行性。  相似文献   

Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data   总被引：1，自引：0，他引：1  

Sophie Bertrand  Erich Díaz  Matthieu Lengaigne 《Progress in Oceanography》2008,79(2-4):379

Peruvian anchovy (Engraulis ringens) stock abundance is tightly driven by the high and unpredictable variability of the Humboldt Current Ecosystem. Management of the fishery therefore cannot rely on mid- or long-term management policy alone but needs to be adaptive at relatively short time scales. Regular acoustic surveys are performed on the stock at intervals of 2 to 4 times a year, but there is a need for more time continuous monitoring indicators to ensure that management can respond at suitable time scales. Existing literature suggests that spatially explicit data on the location of fishing activities could be used as a proxy for target stock distribution. Spatially explicit commercial fishing data could therefore guide adaptive management decisions at shorter time scales than is possible through scientific stock surveys. In this study we therefore aim to (1) estimate the position of fishing operations for the entire fleet of Peruvian anchovy purse–seiners using the Peruvian satellite vessel monitoring system (VMS), and (2) quantify the extent to which the distribution of purse–seine sets describes anchovy distribution. To estimate fishing set positions from vessel tracks derived from VMS data we developed a methodology based on artificial neural networks (ANN) trained on a sample of fishing trips with known fishing set positions (exact fishing positions are known for approximately 1.5% of the fleet from an at-sea observer program). The ANN correctly identified 83% of the real fishing sets and largely outperformed comparative linear models. This network is then used to forecast fishing operations for those trips where no observers were onboard. To quantify the extent to which fishing set distribution was correlated to stock distribution we compared three metrics describing features of the distributions (the mean distance to the coast, the total area of distribution, and a clustering index) for concomitant acoustic survey observations and fishing set positions identified from VMS. For two of these metrics (mean distance to the coast and clustering index), fishing and survey data were significantly correlated. We conclude that the location of purse–seine fishing sets yields significant and valuable information on the distribution of the Peruvian anchovy stock and ultimately on its vulnerability to the fishery. For example, a high concentration of sets in the near coastal zone could potentially be used as a warning signal of high levels of stock vulnerability and trigger appropriate management measures aimed at reducing fishing effort.  相似文献   

海域使用权立体分层确权及管理配套制度探讨          下载免费PDF全文

杨志浩  孙华烨  杨名名  张一帆 《海洋开发与管理》2022,39(3):79-83

由于社会经济的不断发展和科技的快速进步,我国对海域空间资源的开发利用已从二维平面转向三维立体空间开发,但随之也对我国海域空间范围界定和权属管理提出了更高的要求。文章从海域立体空间分层特性出发,对我国海域使用权立体分层确权的内涵、基本原则、考虑因素进行了深入剖析,并对我国海域三维立体开发利用中面临的困境和管理配套制度的设计进行了综合探讨。研究结果表明：海域空间可以分为水面上方、水面、水体、海床和底土5个部分,海域使用权立体分层确权则是在同一海域多层次利用中,对基于特定功能用途所占用的特定海域空间开展使用权确权的过程,在海域使用权管理的过程中,必须构建和完善海域空间三维产权法律制度体系,以确保海域使用权立体分层确权的实施。  相似文献   

Ocean wave forecasting using recurrent neural networks     

S. Mandal  N. Prabaharan 《Ocean Engineering》2006,33(10):1401-1410

The tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off Marmugao, west coast of India are used for this study. Here, the recurrent neural network of 3, 6 and 12 hourly wave forecasting yields the correlation coefficients of 0.95, 0.90 and 0.87, respectively. This shows that the wave forecasting using recurrent neural network yields better results than the previous neural network application.  相似文献   

Toponym matching through deep neural networks     

Rui Santos  Patricia Murrieta-Flores  Pável Calado  Bruno Martins 《International journal of geographical information science》2018,32(2):324-348

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.  相似文献