Lateral tidal mixing in the Antarctic marginal seas

Tidal mixing plays an important role in the modification of dense water masses around the Antarctic continent. In addition to the vertical (diapycnal) mixing in the near-bottom layers, lateral mixing can also be of relevance in some areas. A numerical tide simulation shows that lateral tidal mixing is not uniformly distributed along the shelf break. In particular, strong mixing occurs all along the Ross Sea and Southern Weddell Sea shelf breaks, while other regions (e.g., the western Weddell Sea) are relatively quiet. The latter regions correspond surprisingly well to areas where indications for cross-shelf exchange of dense water masses have been found. The results suggest that lateral tidal mixing may account for the relatively small contribution of Ross Sea dense water masses to Antarctic Bottom Water.     

广西近海灾害地质因素及声学反射特征

利用高分辨率地球物理资料及相关历史资料,对北部湾北部的广西近海灾害地质因素进行了地质地球物理解译,识别出海底沙波、海底沙脊、塌陷洼地、冲刷沟槽、浅层气、残积砂体、地层间断、埋藏丘、埋藏古河道等多种灾害地质因素.根据它们出现的频率、规模及声学特征可知:浅层气和埋藏古河道是广西近海最为发育的灾害地质因素,其对海洋工程的施工与建设有较大的危害性.     

基于贝叶斯正则化BP神经网络的DEM趋势面逼近

趋势面从宏观上揭示了研究对象的特性,在各领域发挥着重要作用。BP神经网络可以对复杂系统进行无限逼近,进而进行预测。建立了基于贝叶斯正则化BP神经网络的数字高程模型趋势面,与二次多项式建立的数字高程模型趋势面进行比较分析,证明了该方法的可行性和有效性。     

Spectral classification methods in monitoring small local events by the Israel seismic network

We use the dense Israel Seismic Network (ISN) to discriminate between low magnitude earthquakes and explosions in the Middle East region. This issue is important for CTBT monitoring, especially when considering small nuclear tests which may be conducted under evasive conditions. We explore the performance of efficient discriminants based on spectral features of seismograms using waveforms of 50 earthquakes and 114 quarry and underwater blasts with magnitudes 1.0–2.8, recorded by ISN short-period stations at distances up to 200 km. The single-station spectral ratio of the low and high-frequency seismic energy shows an overlap between explosions and earthquakes. After averaging over a subnet of stations, the resolving power is enhanced and the two classes of events are separated. Different frequency bands were tested; the (1–3 Hz)/(6–8 Hz) ratio provided the best discriminant performance. We also estimated normalized r.m.s. spectral amplitudes in several sequential equal frequency windows within the 1–12 Hz band and applied multiparametric automatic classification procedures (Linear Discrimination Function and Artificial Neural Network) to the amplitudes averaged over a subnetwork. A leave-one-out test showed a low rate of error for the multiparametric procedures. An innovative multi-station discriminant is proposed, based on spectral modulation associated with ripple-firing in quarry blasts and with the bubbling effect in underwater explosions. It utilizes a distinct azimuth-invariant coherency of spectral shapes for different stations in the frequency range (1–12 Hz). The coherency is measured by semblance statistics commonly used in seismic prospecting for phase correlation in the time domain. After modification, the statistics applied to the network spectra provided event separation. A new feature of all the above mentioned procedures is that they are based on smoothed (0.5 Hz window), instrument-corrected FFT spectra of the whole signal; they are robust to the accuracy of onset time estimation and, thus well suited to automatic event identification.     

基于遥感与GIS的20世纪90年代中国城镇用地时空特征

文章利用20世纪90年代初期、中期和末期全国1:100000土地利用动态变化数据提取城镇用地动态变化数据,利用单元自动机和人工神经网络模型对全国城镇用地进行了区划.在此基础上,研究了90年代两个阶段中国城镇用地时空格局.研究表明:90年代前5年东部沿海地区受经济高速发展和开放政策的影响,城镇用地扩展迅速,中西部地区城镇用地扩展较慢;90年代后5年国家加大了耕地资源保护力度,在政府宏观调控政策和耕地资源保护条例的影响下,东部沿海地区城镇用地扩展大幅回落,中部地区城镇扩展也有较大幅度回落,西部地区随着经济发展加快,城镇用地扩展回落较小.     

Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used.     

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

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.     

潮汐对瞬态气候响应模拟的影响研究

In this study, the impact of oceanic processes on the sensitivity of transient climate change is investigated using two sets of coupled experiments with and without tidal forcing, which are termed Exp_Tide and Exp_Control,respectively. After introducing tidal forcing, the transient climate response(TCR) decreases from 2.32 K to 1.90 K,and the surface air temperature warming at high latitudes decreases by 29%. Large ocean heat uptake efficiency and heat storage can explain the low TCR in Exp_Tide. Approximately 21% more heat is stored in the ocean in Exp_Tide(1.10×10~(24) J) than in Exp_Control(0.91×10~(24) J). Most of the large ocean warming occurs in the upper 1 000 m between 60°S and 60°N, primarily in the Atlantic and Southern Oceans. This ocean warming is closely related to the Atlantic Meridional Overturning Circulation(AMOC). The initial transport at mid-and high latitudes and the decline in the AMOC observed in Exp_Tide are both larger than those observed in Exp_Control. The spatial structures of AMOC are also different with and without tidal forcing in present experiments. The AMOC in Exp_Tide has a large northward extension. We also investigated the relationship between AMOC and TCR suggested by previous studies using the present experiments.     

太阳黑子和ENSO对日本吉野川流域水文要素影响

剖析"太阳-气候-水资源"体系作用机制对区域水资源科学管理具有重要科学意义。利用相关分析、主成分分析和小波分析方法,探究太阳黑子运动和厄尔尼诺（ENSO）对日本吉野川流域降雨、地表径流和地下水位影响。研究结果表明:太阳黑子活动和ENSO对研究区域水文过程显著影响分别发生在11 a和2~7 a周期上;太阳黑子运动能量以ENSO为"媒介"作用于流域降水和河川径流,但对地下水位波动影响不明显;太阳黑子活动在不同时频域对研究区域水文过程产生"直接"和"间接"影响,太阳黑子的"直接"影响可能通过调制ENSO外的气候模态来实现,其"间接"作用则通过"ENSO-西太平洋副高-东亚环流-水汽运动"系统作用实现。     

Prediction of the dynamic response of a slender marine structure under an irregular ocean wave using the NARX-based quadratic Volterra series

The time series of the dynamic response of a slender marine structure was predicted in approximate sense using a truncated quadratic Volterra series. The wave-structure interaction system was identified using the NARX (Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through supervised training using prepared datasets. The dataset used for network training was obtained by nonlinear finite element analysis of the slender marine structure under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of the relative velocity between the water particle and structure in the Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of the response of the structure was predicted using the quadratic Volterra series. To check the applicability of the method, the response of a slender marine structure under a realistic ocean wave environment with a given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. The predicted time series of the response of structure with quadratic Volterra series successfully captured the slowly varying response with reasonably good accuracy. This method can be used to predict the response of the slender offshore structure exposed to a Morison type load without relying on the computationally expensive time domain analysis, especially for screening purposes.