Modelling rainfall interception losses of three plantations in the Loess Plateau

The forest canopy affects the water entering the forest ecosystem by intercepting rainfall. This is especially pertinent in forests that depend on rainfall for their ecological water needs, quantifying and simulating interception losses provide critical insights into their ecological hydrological processes. In the semi-arid areas of the Loess Plateau, afforestation has become an effective ecological restoration measure. However, the rainfall interception process of these plantations is still unclear. To quantify and model the canopy interception of these plantations, we conducted a two-year rainfall redistribution measurement experiment in three typical plantations, including a deciduous broadleaf plantation (Robinia pseudoacacia) and two evergreen coniferous plantations (Platycladus orientalis and Pinus tabuliformis). Based on this, the revised Gash model was used to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. The experiment clarified the rainfall redistribution in the three plantations, and the proportions of throughfall to gross rainfall in Robinia pseudoacacia, Platycladus orientalis, and Pinus tabuliformis were 84.8%, 70.4%, and 75.6%; corresponding, the stemflow proportions were 2.0%, 2.2%, and 1.8%; the interception losses were 13.2%, 27.4%, and 22.6%, respectively. The dominant rainfall pattern during the experiment was characterized by low-amounts, moderate-intensity, and short-duration, during which the highest interception proportions across the three plantations were observed. We used the Penman-Monteith equation and the regression method, respectively, to estimate the canopy average evaporation rate of the revised Gash model, finding that the latter provides a closer match to the measured cumulative interception (NSE >0.7). When simulating interception under the three rainfall patterns, the model with the regression method better simulated the cumulative interception and event-scale interception for Platycladus orientalis and Pinus tabuliformis plantations under the dominant rainfall pattern. The results contribute valuable information to assess the impact of forest rainfall interception on regional hydrologic processes.     

基于遗传算法优化的BP神经网络在密度界面反演中的应用

BP神经网络方法在二维密度界面的反演中取得了较好的效果,但在反演三维界面时,由于模型更复杂、参数更多,BP神经网络的收敛速度和反演精度都有一定程度的下降。为了改善反演效果,本文利用遗传算法对BP神经网络的权值、阈值选择过程进行优化,获得了更好的网络模型;并将此模型应用于密度界面模型的反演中,预测误差从上百米减小到数十米,同时迭代计算步数减少了近2/3,有效减少了计算时间,反演结果更准确。利用基于遗传算法优化的BP神经网络反演了法国某地区莫霍面深度,预测相对误差仅为1.8%,取得了较好的应用效果。基于遗传算法优化的BP神经网络在密度界面的反演中具有良好的应用价值和研究前景。     

频率慢度域自适应迭代反演算法压制海上倾斜缆鬼波方法及其应用

海上倾斜缆采集技术具有多样的陷波特征,通过去鬼波处理可获得宽频数据.针对海水面波浪起伏及缆深误差引起的鬼波延迟时间估计误差以及崎岖海底和目的层深度变化使得鬼波和一次反射波的振幅差异系数随偏移距的变化而难以给定一个固定值的问题,本文推导出频率慢度域中鬼波滤波算子以及自适应迭代反演求解上行波算法,该鬼波滤波算子与不同水平慢度对应的鬼波和一次反射波的振幅差异系数以及鬼波延迟时间有关.并基于计算出的理论下行波与实际下行波之间的平方误差最小理论实现自适应反演迭代最优计算该振幅差异系数和鬼波延迟时间.合成的及某海上采集的倾斜缆数据去鬼波处理结果表明,频率慢度域自适应迭代反演算法能较好地去除海上变深度缆鬼波,能达到拓宽地震记录频带目的.     

龙门山中段中央断裂和前山断裂的晚新生代垂向活动性差异及其构造意义

2008汶川地震之后,多个研究组对龙门山的新生代剥蚀历史进行了研究,但是在龙门山推覆构造带中段,剥蚀历史研究主要集中在彭灌杂岩,而彭灌杂岩东侧(即中央断裂下盘)的热年代学资料相对缺乏,其剥蚀历史还比较模糊.对于彭灌杂岩东侧岩体的新生代剥蚀历史研究,不仅可以了解龙门山推覆构造带的新生代断层活动历史,而且对于青藏高原东缘的新生代隆升机制具有重要约束作用.在前人热年代学研究基础上,在龙门山推覆构造带中段中央断裂和前山断裂附近补充了一些裂变径迹样品.采用外探测器法(external detector method)对样品进行裂变径迹分析,实验测试在台湾中正大学裂变径迹实验室完成.实验获得了6个锆石裂变径迹和6个磷灰石裂变径迹年龄.前山断裂上盘,AFT(磷灰石裂变径迹)年龄以小鱼洞断裂为界存在明显的差异,其中小鱼洞断裂以南的样品AFT年龄为39Ma,小鱼洞断裂以北的4个AFT年龄介于6—8 Ma之间.研究揭示出中央断裂和前山断裂的新生代活动性以NW向小鱼洞断裂为界存在较大差异:距今8Ma以来,小鱼洞断裂以北,中央断裂和前山断裂的平均垂向滑动速率分别为约0.1mm·a-1和约0.55mm·a-1;小鱼洞断裂以南,平均垂向滑动速率则分别为约0.55mm·a-1和约0.1mm·a-1.低温热年代学方法获得的断层新生代垂向滑动速率与汶川地震断层垂向同震位移分布基本一致.前山断裂(小鱼洞断裂以北)距今8 Ma以来北西-南东向水平缩短量达到8~12km,表明地壳缩短是造成龙门山抬升和剥蚀的重要因素之一.本研究结论不支持下地壳增厚模型对于龙门山隆升的解释.     

岩石单轴压缩下能量与损伤演化规律研究

能量变化是材料各参数变化的本质特征。通过颗粒流模拟岩石单轴压缩试验,从细观力学角度分析能量转化、裂纹扩展及损伤演化规律。颗粒流利用细观颗粒的运动克服了宏观力学理论不易实现试件多裂纹破坏形态的缺点,以此研究能量变化更加合理。应力-应变各阶段能量与微裂纹及损伤存在着相互对应的发展关系。通过3种工况论证了宏-细观力学参数对应关系,采用最小二乘法拟合得出微裂纹与轴向应变呈幂次函数关系。采用割线模量定义损伤变量,取模量加速下降处（对应裂纹加速扩展）为损伤门槛,对应损伤阈值为0.158。3种工况下微裂纹数量与损伤呈线性发展关系,为损伤演化的深入研究提供参考。     

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.     

资源三号卫星不同空间分辨率图像斑状植被检测比较研究

黄河三角洲滨海区域大量分布着由柽柳、芦苇、翅碱蓬、二色补血草、白茅等等组成的斑状植被,多呈棋盘状规则分布,胜利油田地震勘探被认为是产生这一现象的原因之一。深入研究其分布格局并探讨其形成机理,对于区域湿地生态系统植被恢复至关重要。为此,首先就需要对斑状植被进行有效地检测和制图。本文评价了我国首颗民用高分辨率光学传输型立体测图卫星（资源三号卫星）不同空间分辨率图像检测黄河三角洲斑状植被的能力,结果表明：在利用霍夫曼圆检测方法下,前视、后视、正视全色图像和正视多光谱图像都能很好地检测斑状植被,空间分辨率高的图像有更高的检测精度。     

High-Frequency Rayleigh-Wave Method

High-frequency (≥2 Hz) Rayleigh-wave data acquired with a multichannei recording sys-tem have been utilized to determine shear (S)-wave velocities in near-surface geophysics since the early 1980s. This overview article discusses the main research results of high-frequency surface-wave tech-niques achieved by research groups at the Kansas Geological Survey and China University of Geosciences in the last 15 years. The multichannel analysis of surface wave (MASW) method is a nou-iuvasive acoustic approach to estimate near-surface S-wave velocity. The differences between MASW results and direct borehole measurements are approximately 15% or less and random. Studies show that simultaneous inversion with higher modes and the fundamental mode can increase model resolution and an investigation depth. The other important seismic property, quality factor (Q), can also be estimated with the MASW method by inverting attenuation coefficients of Rayleigh waves. An inverted model (S-wave velocity or Q) obtained using a damped least-squares method can be assessed by an optimal damping vector in a vicinity of the inverted model determined by an objective function, which is the trace of a weighted sum of model-resolution and model-covariance matrices. Current developments include modeling high-frequency Rayleigh-waves in near-surface media, which builds a foundation for shallow seismic or Rayleigh-wave inversion in the time-offset domain; imaging dispersive energy with high resolution in the frequency-velocity domain and possibly with data in an arbitrary acquisition geometry, which opens a door for 3D surface-wave techniques; and successfully separating surface-wave modes, which provides a valuable tool to perform S-wave velocity profiling with high-horizontal resolution.