Artificial neural network forward modelling and inversion of electrokinetic logging data     

N. Ardjmandpour  C. Pain  J. Singer  J. Saunders  E. Aristodemou  J. Carter 《Geophysical Prospecting》2011,59(4):721-748

An artificial neural network method is proposed as a computationally economic alternative to numerical simulation by the Biot theory for predicting borehole seismoelectric measurements given a set of formation properties. Borehole seismoelectric measurements are simulated using a finite element forward model, which solves the Biot equations together with an equation for the streaming potential. The results show that the neural network method successfully predicts the streaming potentials at each detector, even when the input pressures are contaminated with 10% Gaussian noise. A fast inversion methodology is subsequently developed in order to predict subsurface material properties such as porosity and permeability from streaming potential measurements. The predicted permeability and porosity results indicate that the method predictions are more accurate for the permeability predictions, with the inverted permeabilities being in excellent agreement with the actual permeabilities. This approach was finally verified by using data from a field experiment. The predicted permeability results seem to predict the basic trends in permeabilities from a packer test. As expected from synthetic results, the predicted porosity is less accurate. Investigations are also carried out to predict the zeta potential. The predicted zeta potentials are in agreement with values obtained through experimental self potential measurements.  相似文献   

基于MATLAB神经网络方法的多层砖房震害预测   总被引：1，自引：0，他引：1       下载免费PDF全文

姜伟  马令勇  刘功良 《地震工程学报》2011,33(2):155-158

提出利用MATLAB人工神经网络工具箱建立基于贝叶斯正则算法的BP神经网络模型,以地震区多层砖房震害调查数据为因子的震害预测方法.神经网络模型输入震害因子包括建筑的层数、施工质量、房屋整体性等,输出值为建筑物在地震作用下的破坏程度.结果表明,本方法可以对多层砖房的震害样本进行预测并达到较理想的效果.  相似文献   

聊古1井在不同取水模式下气体观测效果的对比分析     

李月强  王华  刘保华  连凯旋  崔娜  孙丕锋  冯恩国  赵杰锋 《中国地震》2011,27(2):192-198

为解决聊古1井断流问题,聊城地震水化试验站先后引进、开发了人工激发引流观测技术和潜水泵变频稳流抽水技术.对天然自流观测系统、人工激发引流观测系统和潜水泵变频稳流抽水观测系统等3种不同取水模式下产出的气体观测数据进行一致性分析.结果表明,人工激发引流观测系统下气体观测动态特征年变比较明显,潜水泵变频稳流抽水观测系统下气体观测动态特征与天然自流状态下的动态特征基本一致.  相似文献   

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation     

Dhaval Vyas  N.S.R. Krishnayya  K.R. Manjunath  S.S. Ray  Sushma Panigrahy 《International Journal of Applied Earth Observation and Geoinformation》2011

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.  相似文献   

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine     

Maarit Middleton  Paavo Nrhi  Raimo Sutinen 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):287-297

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.  相似文献   

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach     

Jian Zhao  Yanguo Fan  Yuxiang Mu 《Marine Geodesy》2019,42(4):344-366

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.  相似文献   

Modeling of Dissolved Oxygen Concentration and Its Hysteresis Behavior in Rivers Using Wavelet Transform‐Based Hybrid Models     

《洁净——土壤、空气、水》2017,45(2)

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基于遗传—神经网络方法的广西台风灾害评估模型研究     

李艳兰  金龙  史旭明  陈丹 《气象与环境学报》2021,37(3):139-144

选取1981—2018年影响广西且灾情记录比较完整的86个台风样本,基于台风灾害伤亡人数、直接经济损失划分灾情等级,选取致灾因子,利用遗传算法与神经网络相结合的方法建立广西台风灾害评估模型。结果表明：选取的台风灾害致灾因子与台风灾情等级之间具有显著的相关性,构建的遗传—神经网络集合预报模型对台风灾情预估效果较好,训练样本拟合一致率为86.1%,测试样本预报准确率为71.4%,其中严重和较重的台风灾情等级预报结果与实况基本一致,较轻等级的预报准确率达83.3%。  相似文献   

Response-based method for determining the extreme behaviour of floating offshore platforms     

Said Mazaheri  Martine J. Downie 《Ocean Engineering》2005,32(3-4):363-393

The accurate prediction of extreme excursion and mooring force of floating offshore structures due to multi-variete environmental conditions which requires the joint probability analysis of environmental conditions for the worst case situation is still impractical as the processing of large amount of met-ocean data is required. On the other hand, the simplified multiple design criteria (e.g. the N-year wave with associated winds and currents) recommended by API known as traditional method does lead neither to the N-year platform response nor to the N-year mooring force. Therefore, in order to reduce the level of conservatism as well as uncertainties involved in the traditional method the response-based method can be used as a reliable alternative approach. In this paper this method is described. In order to perform the calculations faster using large databases of sea states, Artificial Neural Networks (ANN) is designed and employed. In the paper the response-based method is applied to a 200,000 tdw FPSO and the results are discussed.  相似文献   

单体褶牡蛎(Crassostrea sp.)的研究     

王昭萍  王如才  徐从先  仇宝志  姜福正  张祝绪 《中国海洋大学学报(自然科学版)》1992,(2)

采用肾上腺素(EPI)和去甲肾上腺素(NE)诱导、颗粒固着基和先固着后脱基三种方法,对褶牡蛎的眼点幼虫进行处理,产生单体蛎苗。EPI和NE诱导的最适浓度为10~(-4)M,最适处理时间为3h,幼虫的不固着变态率分别达47.7％和46.6％。处理时间延长,变态率增加不明显。EPI和NE的诱导效果差别不明显,其诱导作用对稚贝的生长无明显副作用。颗粒固着基以贝壳粉为佳,最适颗粒的规格为幼虫的壳长。幼虫先固着后脱基而形成单体蛎苗,以可弯曲的灰色塑料板效果最好,固着的小贝可通过来回弯曲塑料板而从上面脱落下来,小贝长至1～2cm大小时,脱基最容易。  相似文献