Solar origin of geomagnetic storms and prediction of storms with the use of neural networks

This review deals with how the changes of the large-scale solar magnetic fields are related to the occurrence of solar phenomena, which are associated with geomagnetic storms. The review also describes how artificial neural networks have been used to forecast geomagnetic storms either from daily solar input data or from hourly solar wind data. With solar data as input predictions 1–3 days or a month in advance are possible, while using solar wind data as input predictions about an hour in advance are possible. The predictions one hour ahead of the geomagnetic storm indexD _st from only solar wind input data have reached such high accuracy, that they are of practical use in combination with real-time solar wind observations at L1. However, the predictions days and a month ahead need to be much improved in order to be of real practical use.     

用多变量时间序列相关模型预测矿井涌水量

运用多变量时间序列分析方法，建立了南桐煤矿二井的涌水量与降雨量之间的相关模型，并将其用于矿井涌水量的预测，结果表明，南桐二井涌水量与降雨量呈滞后一个月、三个月和三年的关系，预测结果与实测值对比分析表明，本模型的预测结果精度较高。     

Stage Predictions of Landslide and Subsidence from an Once-Through Cycle

In this paper both processes of landslide and subsidence are consid-ered to be limited systems.Each of these systems in nature might be re-garded as an organism.Generally their lifespan must develop with com-mon ecological characteristics,including several evolutional stages,such asinitiation,growth,maturation,decline and death.Among these stages,maturation is emphasized so as to find the occurring or thriving date ofboth systems.An once-through cycle of both landslide and subsidence isestablished and is accurately predicted by a developed,mathematic model of thePoisson cycle.The Weibull distribution is cited for a landslide example.Both fundamentals are discussed.Stage predictions of landslide and subsid-ence are performed for several examples.Back analysis of landslides thathave already happened are studied with the same model.And when com-pared with results from the biological mathematic model and with practicalresults,it is found that they correspond.Stage prediction of subsidences isalso researched     

长江源区冰川对气候变化的响应

长江源区是青藏高原冰川分布集中的地区之一,冰川总面积达1276.02km².研究表明,该区属于青藏高原升温幅度最大的地区之一,到2050年气温将比1961—1990年平均气温高出2.3~2.7℃,降水增加1%~33%.基于冰川编目资料,采用有关对长江源区未来50a内的气温和降水预测数据,应用冰川系统对气候响应的模型,对该区未来50a内冰川变化趋势进行预测.结果表明:到2010年、2030年、2050年该区冰川面积平均将减少3.2%、6.9%和11.6%;冰川径流平均将增加20.4%、26%和28.5%;零平衡线上升值为14m、30m和50m左右.最后,针对气候变化的不确定性,对预测结果的不确定性进行了探讨.     

A MATLAB implementation of TOPMODEL

The MATLAB SIMULINK programming language is applied to the TOPMODEL rainfall–runoff model. SIMULINK requires a good recognition of model dynamics, which has been achieved here in a version based on the first TOPMODEL (Beven and Kirkby, 1979). Introducing the topographic index distribution in a vector form allows the generalization and simplification of the SIMULINK structure. The SIMULINK version of TOPMODEL has a very easy to understand graphical representation, which shows, in a straightforward way, all the physical interactions that take place in the model. Moreover, owing to its modular structure it is easy to add new and/or develop old submodels, depending on the available data and the goal of the modelling. In the example given here TOPMODEL was extended by two submodels representing the soil moisture and evaporation distribution in the catchment. Preparation of the data and presentation of the results is done in MATLAB. Discharge predictions and spatial patterns of hydrological response are demonstrated for a separate validation period. © 1997 John Wiley & Sons, Ltd.     

分段Knothe函数优化及其动态求参

为了解决分段Knothe函数在开采沉陷动态预计中时间影响系数c、地表最大下沉速度对应的下沉值Wv以及最大下沉速度对应时间点t均为固定值的问题,对分段Knothe函数中各参数进行了理论和试验分析。结合概率积分法理论知识,推导出以时间为自变量的3个参数的函数关系,并在此基础上对分段Knothe函数进行了优化,得到一种预测精度更高、更具推广性的分段Knothe函数。试验结果表明:优化的Knothe函数解决了优化前Knothe函数中c、t、W_v为固定值的缺陷,弥补了利用相似地质条件拟合出经验公式的不足。通过对比分析优化前后模型的预测结果,得出优化模型中t的预计结果保持在实测最大下沉速度时间范围内。优化后模型最大均方误差和相对中误差分别为0.116 m、3.8%,较优化前模型分别提高了65.1%、51.9%,进一步证实了该优化模型具备一定的实用性和可靠性。     

Energetics prediction of frequency-dependent suspended sand transport rates on a macrotidal beach

The on–offshore (cross-shore) transport of sand on beaches is highly time-variable, which has made it difficult to model or predict. In this paper, simple energetics modelling is used to compare velocity moment predictions with field observations of suspended sand transport rates. Separate consideration is given to transport associated with the three main frequency-dependent cross-shore transport processes: that associated with the short (incident) waves, that due to the long (infragravity) waves, and transport associated with the mean flow. Direct comparison between the depth-averaged model predictions, and the in-situ point measurements was facilitated by making the first order assumption that the time-averaged suspension profile is exponential and the wave velocity profile is vertically uniform. An appropriate rippled bed roughness was used to provide the drag coefficient in the energetics model and the vertical length scale of the exponential suspension profile. Despite these simple assumptions, comparison of the velocity moment predictions with the field observations of suspended sand fluxes reveals that this approach has the capacity to predict transport magnitudes due to short wave, long wave, and mean flow components to within about one order of magnitude. However, owing to the limitations of the model, the transport direction of the short wave component could not, on occasion, be correctly determined, probably due to ‘reverse’ transport over ripples. © 1998 John Wiley & Sons, Ltd.     

Coal seam development characteristics and distribution predictions in marginal sea basins: Oligocene Yacheng Formation coal measures,Qiongdongnan Basin,northern region of the South China Sea

Abstract

Coal measures located in marginal sea basins are important hydrocarbon source rocks. For the purpose of effectively guiding future oil and gas exploration, the characteristics and distribution patterns of coal seams in coal measures of a marginal sea basin are systematically outlined. Coal measures in marginal sea basins can have large thicknesses, but the individual coal seams can be very thin and lack lateral continuity. In the study area, the organic micro-components of the coal are dominated by vitrinite, with very low amounts of inertinite and liptinite. The amount of inorganic microscopic components is large, but with limited drilling results, few cores and thin coal seams, which are easily overlooked during logging activities, a comprehensive analysis of the logging data may improve efficiency in coal-seam identification and thickness determination. The development and distribution of coal seams in marginal sea basins are controlled by various factors, including (1) paleoclimatic and paleobotanic conditions, which could fundamentally limit coal formation, (2) coal-forming sedimentary environmental conditions that may limit the scope of coal-seam development from a macroscopic perspective, and (3) paleotectonic and paleotopographic conditions that define the coal-forming structures. Therefore, the descending and rising cycles of base-levels, along with changes in the growth rates of the accommodation spaces, can be used to determine the horizons that are potentially favourable for coal formation and can also indicate the migration trends of coal-forming environments on the structural plane. Seismic wave impedance inversion methods could be utilised for semi-quantitative assistance for prediction of coal seams. In summary, for models of coal-seam development in marginal sea basins, the grades should be divided according to reliability, and the different reliability levels should be predicted separately.

1. The characteristics of coal seams developed in marginal sea basins are described.

2. The macerals of coals developed in marginal sea basins have been ascertained.

3. A development model and distribution prediction method for coal seams are assessed according to the control factors.

4. A model for the prediction of coal-seam distribution is presented.

     

Estimation of infragravity waves at intermediate water depth

The accuracy of nearshore infragravity wave height model predictions has been investigated using a combination of the spectral short wave evolution model SWAN and a linear 1D SurfBeat model (IDSB). Data recorded by a wave rider located approximately 3.5 km from the coast at 18 m water depth have been used to construct the short wave frequency-directional spectra that are subsequently translated to approximately 8 m water depth with the third generation short wave model SWAN. Next the SWAN-computed frequency-directional spectra are used as input for IDSB to compute the infragravity response in the 0.01 Hz–0.05 Hz frequency range, generated by the transformation of the grouped short waves through the surf zone including bound long waves, leaky waves and edge waves at this depth. Comparison of the computed and measured infragravity waves in 8 m water depth shows an average skill of approximately 80%. Using data from a directional buoy located approximately 70 km offshore as input for the SWAN model results in an average infragravity prediction skill of 47%. This difference in skill is in a large part related to the under prediction of the short wave directional spreading by SWAN. Accounting for the spreading mismatch increases the skill to 70%. Directional analyses of the infragravity waves shows that outgoing infragravity wave heights at 8 m depth are generally over predicted during storm conditions suggesting that dissipation mechanisms in addition to bottom friction such as non-linear energy transfer and long wave breaking may be important. Provided that the infragravity wave reflection at the beach is close to unity and tidal water level modulations are modest, a relatively small computational effort allows for the generation of long-term infragravity data sets at intermediate water depths. These data can subsequently be analyzed to establish infragravity wave height design criteria for engineering facilities exposed to the open ocean, such as nearshore tanker offloading terminals at coastal locations.