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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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原子吸收光谱法和等离子光谱法分别研究了潮滩盐沼植物翅碱蓬(Suaedaheterop-tera)根际与非根际(根上部和根下部)沉积物中Cu、Zn、Pb和Cd的总量和化学形态。结果表明,从总量来看,不同潮滩沉积物中4种重金属次序均为Zn>Pb>Cu>Cd,但同一元素随潮滩位置变化明显,尤以中潮滩差异最显著,重金属总量明显大于其他两个潮滩(低潮滩和高潮滩),特别是根际沉积物中的重金属总量远高于非根际沉积物总量,其比值分别为Cu3·4—4·2倍,Zn2·2—2·7倍,Pb3·2—3·3倍。同一潮滩均表现为根际沉积物>根上部>根下部。Cd含量相对较低,其变化不明显。从化学形态看,沉积物重金属表现为环境直接影响态(交换态和有机结合态)、环境间接影响态(碳酸盐态和铁锰氧化物态)和稳定态(残渣态)。与非根际沉积物相比,根际沉积物中重金属的化学形态发生了显著的变化,Cu和Pb以稳定态为主,其次为环境间接影响态,环境直接影响态最低;Zn与上述2种金属不同,以环境直接影响态含量最高(可交换态是有机结合态的7倍),其次分别为环境间接影响态(主要是碳酸盐结合态)和稳定态。实验结果表明,由于特异根圈效应,一方面该植物使可迁移形态的Cu和Pb在根际逐步得到矿化,使其生物可利用性降低;另一方面使Zn的生物可利用性提高,促进了植物对Zn的吸收利用。 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
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