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1.
Kunde Yang Yuanliang Ma Chao Sun Miller J.H. Potty G.R. 《Oceanic Engineering, IEEE Journal of》2004,29(4):964-972
This paper discusses the results of geoacoustic inversion carried out using explosive charge data from the Asian Seas International Acoustic Experiment (ASIAEX) East China Sea (ECS) Experiment. A multifrequency incoherent matched-field inversion processor and a genetic algorithm (GA) are used for the inversion. A multistep matched field inversion approach is presented, which makes use of the varying sensitivities of wave fields at various frequencies to reduce the inversion problem into a sequence of smaller inversions with fewer unknowns to estimate at each stage. Different parameters are estimated using data at different frequencies according to their sensitivities. Inversion results for different areas in the ECS region are summarized and compared with core data. 相似文献
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在浅层工程折射地震测量中 ,通常采用相遇观测系统采集数据和相应的解释方法进行解释 ;对于单边观测采集方法由于与其相应的解释方法较少而少被采用。本文提出 1种方法简单、精度可靠、可直接利用同一点相邻 2排列 2次观测的初至折射时间并适合于海洋走航式单边连续观测系统的折射初至资料解释方法。其精度基本与同观测段的采用相遇观测的差异时距方法解释的精度十分接近。该方法即适合于人工解释又适合于计算机自动解释。 相似文献
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基于AHP和BP神经网络的深部地热水可持续开发能力评价 总被引:1,自引:0,他引:1
采用资料完整性、开采潜力、回灌量、平均水压下降速率、地面沉降速率、水温、水质、地热井布局8项指标构建天津地热可持续开发能力评价指标体系;运用层次分析法确定了各项指标的权重,建立起评价因素集和评语集,给出了归一化数值;建立了天津地热可持续开发能力的BP神经网络模型,以层次分析法得出的结果作为样本,对BP网络进行了训练和测试,实例评价结果表明了AHP和BP神经网络方法的可行性,为地热资源的可持续开发能力评价提供了一种新的评价方法。 相似文献
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A one-dimensional model for thinning of the plasma sheet is developed on the basis of launching a fast mode MHD rarefaction wave propagating in the tailward direction along the plasma sheet. Behind the rarefaction wave the pressure is reduced, leading to thinning of the plasma sheet and also to an Earthward plasma flow with a speed on the order of the sound speed a0. The plasma sheet thickness is reduced by a factor of 2 if an Earthward plasma flow speed of 0.8a0 is induced. The predictions of the model are in reasonable agreement with observations. 相似文献
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Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems. 相似文献