GPU加速的水体辐射传输Monte Carlo模拟模型研究

水体辐射传输方程是复杂的微积分方程,只能利用数值方法求解,如Monte Carlo光线追踪法、不变嵌入法、离散坐标法等,其中,Monte Carlo方法是目前解决水体水下光场三维问题的唯一有效方法.根据辐射传输理论,开发了水下光场的Monte Carlo模拟模型,主要包含大气、水-气界面、层化水体和水底边界4个模块.实现了模拟任意太阳角度、不同水体固有光学属性和任意深度条件下,考虑大气、粗糙水面和水底边界的水下光场,能够获取辐亮度、辐照度等辐射量的空间分布.该模型暂不考虑Raman散射、偏振、内部光源的影响.实现了GPU加速水下光场Monte Carlo模拟,并用Mobley等提出的海洋光学标准问题中的问题1~6进行验证.在两种计算环境下,通过对不同边界条件下的CPU、GPU运行时间及加速比的对比,发现GPU计算可以达到几百至上千倍的加速比.     

基于Landsat 8 OLI影像反演的湖北大冶湖水体光学衰减特性

水体光学衰减特性直接影响湖泊的清澈程度和沉水植被的生存,利用遥感技术获取湖泊光学衰减分布特性能极大提高效率.基于2017-2019年的原位调查数据,利用Landsat 8 OLI影像开发了大冶湖水体光学衰减系数(Kd)的遥感反演模型,并分析大冶湖水体Kd的多年时空分布特性与驱动机制,以期为大冶湖流域的修复与管理提供参考...     

基于TM影像的水体透明度反演模型——以鄱阳湖国家自然保护区为例

以江西鄱阳湖国家自然保护区为例,研究基于Landsat TM 5影像的水体透明度反演模型.结合6个时期的影像与对应的13个实测塞氏盘深度(SDD)数据建立了SDD的自然对数变换值与蓝、红波段的自然对数变换值的线性组合之间的回归模型,即ln(SDD)=-4.016-0.722ln(blue)-0.587ln(red).此模型能够解释88%的水体透明度变化.利用另外12个样点进行模型的检验.检验结果显示实际量测值与模型反演值之间的相关系数为0.93,误差标准差等于0.22 m.因此我们认为此模型获得了可以接受的结果.     

基于水体反射光谱和组分浓度的固有光学特性反演算法

吸收系数和后向散射系数是水体的固有光学特性参数,在探测水体中各组分浓度的过程中起着至关重要的作用.但通常组分固有光学特性的测定是一个操作复杂且费时费力的过程.提出了一种利用已知组分浓度和相应的反射光谱的水体样本来推定水体中各组分的吸收和后向散射系数的算法,并利用生物光学模型产生的模拟光谱数据对算法的合理性进行了验证.结果表明,在理想的实验条件下(反射光谱满足机理模型,且选取的训练样本相互独立),可以高精度地反演出各组分的固有光学特性,从而证实了本算法在理论卜的合理性.     

基于多角度热红外遥感的混合像元组分温度演化反演方法

研究了多年来热红外多通道遥感反演陆面温度的成果后指出, 由于通道间信息高度相关, 以及不能直接反演混合像元组分温度, 所以它的反演精度及应用价值都受到极大的限制. 在建立非同温混合像元热辐射方向性模型基础上, 指出热红外多角度遥感提供了直接反演组分温度的可能性, 但这是一个多参数的同步反演问题. 通过数值模拟和实验验证表明, 演化算法是一个行之有效的多参数同步反演方法. 热红外多角度遥感与演化反演算法结合有望实现组分温度反演精度达到1 K以内的目的.     

基于三维模拟的海洋CSEM资料处理

海洋可控源电磁法已经成为海洋油气勘探一个重要工具,但是其资料处理和解释还处于定性和一维模拟阶段.在积分方程三维模拟的基础上对Troll油田实测数据进行了处理,采用人机交互三维模拟寻找背景模型和异常体初始模型,最后对异常体电阻率采用准线性近似快速反演,取得了定量的结果.同时,说明对于二维测线和二维模型依然可以用三维来模拟,其结果优于二维反演.在电子计算机技术快速发展的今天,可以预计三维反演将成为资料处理解释的主流.     

基于FFT-MA谱模拟的快速随机反演方法研究

虽然基于地质统计学的随机反演方法能够有效融合测井资料中的高频信息,但计算效率低,占用内存大,限制了它在实际资料中的应用.本文在保留传统随机反演方法优点的基础上,创造性地引入傅里叶滑动平均(Fast Fourier Transform-Moving Average, FFT-MA)谱模拟进行频率域的地质统计模拟,并利用逐步变形算法(Gradual Deformation Method,GDM)确保模拟结果与实际地震数据的匹配,构建了基于FFT-MA谱模拟的新的快速随机反演方法.与常规随机反演相比,新方法不仅分辨率高,而且能够使反演解得到快速收敛,有效提高计算效率,减少内存占用.模型试算获得了与理论模型吻合度较好的高分辨率反演结果.实际资料分析也表明新方法所得到的高分辨率反演结果能够对薄互储层进行良好的展示,为薄储层的识别提供高效可靠的技术支持.     

一种基于温度历史和瞬态地温场模拟的剥露历史反演方法

根据低温热年代学数据,提取岩石从深部剥露到地表的信息,对理解诸多地质问题（如造山带演化、地表过程及其相互作用等）具有重要意义.本文提出一种基于岩石温度历史（可利用古温标、热年代计等方法制约）,并考虑剥露过程对地温场扰动的剥露历史反演计算方法.基于假定的与真实数据的正反演模拟和参数敏感性分析表明：热扩散率的变化对剥蚀量计算影响不大,在常规岩石热扩散率变化范围内（20~35 km²/Ma）,总剥蚀量变化小于10%;传统计算方法低估了剥蚀总量,对于现今地温梯度小于20℃/km、冷却速率大于2~3℃/Ma,或现今地温梯度大于30℃/km、冷却速率大于5~10℃/Ma的地区,需要考虑热平流对剥蚀量计算的影响;匀速冷却的热历史指示剥蚀速率持续减小,而非匀速剥蚀.本文将该方法应用到龙门山南段和四川盆地,反演计算显示龙门山南段15 Ma以来的剥蚀总量为8 km,四川盆地中部80 Ma以来剥蚀总量为约3 km、东部约5 km.

     

基于MODTRAN模型对高分五号卫星断层逸出气体探测能力的辐射模拟

地下流体监测数据和地表断层调查都显示构造活动强烈期和大地震前后活动断裂带会伴有大量气体逸出。 中国即将发射的高分五号(GF-5)卫星搭载的大气环境红外甚高光谱分辨率探测仪及全谱段光谱成像仪两个传感器, 主要以大气气体的探测为应用目标。 本文基于两个传感器的参数设置, 使用大气辐射传输模型, 对断层逸出气体中的水汽、 CH₄和CO₂三种气体在大气中的含量变化对卫星传感器的辐射影响进行了仿真模拟, 分析了两个传感器对水汽、 CH₄和CO₂气体异常的探测能力。 结果表明, GF-5卫星两个红外传感器特定的光谱通道对大气水汽、 CH₄和CO₂气体异常变化均有不同程度的敏感性, 可以期待发展具有较高精度的相关气体遥感反演模型, 用于地震的监测及预测。     

基于Landsat 8光学影像的巴基斯坦Awaran M_w7.7地震形变监测及参数反演研究

2013年9月24日巴基斯坦俾路支省(Balochistan)境内的阿瓦兰县(Awaran)发生了Mw7.7级地震.本文利用覆盖该地区的Landsat 8数据,基于影像配准的方法获取了该次地震的同震形变场,并运用地统计的方法对形变结果进行精度评定.针对传统四叉树算法中近场和远场中采样密度的不均匀性,以及噪音区域对数据降采样和反演结果收敛性的影响,本文提出了改进的四叉树算法对点的密度和形变梯度进行合理兼顾.最后利用光学影像获取的形变结果和数据的精度水平,基于Okada弹性半空间形变模型反演了该地震的震源参数和断层滑动分布.结果表明,地震断层北倾47°,滑动以左旋走滑为主,断层的西南部兼具少量的倾滑运动分量,断层滑动主要集中分布在断层面0~15km深度范围,最大滑动量达10m.反演获得的地震标量矩为4.68×1020 N·m,震级约为Mw7.75级.本文的研究结果可以为该地区的地壳应力变化研究和地震灾害评估提供依据,同时为Landsat 8光学影像应用于地震的形变研究提供参考.     

基于神经网络的滑移隔震结构智能半主动控制

考虑上部结构的刚度和阻尼,使用神经网络控制算法计算基底摩擦力的大小,研究了滑移隔震结构的半主动控制。对计算实例的分析表明,通过半主动控制的滑移隔震结构不但具有较好的隔震效果,且能有效地减小基底的最大滑移量及残余位移。为对比各种控制方法的控制效果,文中还利用Bang-Bang控制和瞬时最优控制算法对滑移隔震结构进行了半主动控制。对比分析表明,基于神经网络控制算法的控制效果优于其它控制算法,具有反馈量少,稳健性强等特点。     

应用人工神经元网络方法自动检测地震事件

利用人工神经元网络方法,提出了一种从连续的地震数据中检测出地震事件的方法。该方法分两步,首先,低阈值的STA／LTA算法从连续的波形中检测出类似地震事件;其次利用神经元网络方法,区分事件是地震事件还是噪声事件。通过对数据检测结果比较,找出了适合地震检测的神经元网络训练方法和神经元传递函数。在对天山流动台阵其中两个台的检测结果表明,在连续约两个月数据中,39RLS台检测出地震75个,30RNA台检测出地震95个,证明该方法对地震事件检测来说是一种有效的方法。     

人工神经网络在潜在地震危险区估计中的应用

利用环境剪应力较高的地区较容易发生中强地震这一原理,提出了将人工神经网络应用于估计潜在地震危险区的方法。由于该方法是通过人工神经网络技术提前1~2年预测某一地区未来环境应力值的变化来估计潜在地震危险区,因而可大大提高估计的准确性。该方法今后在多震地区的预报工作中值得一试。     

Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan

Abstract

Evaporation is an important reference for managers of water resources. This study proposes a hybrid model (BD) that combines back-propagation neural networks (BPNN) and dynamic factor analysis (DFA) to simultaneously precisely estimate pan evaporation at multiple meteorological stations in northern Taiwan through incorporating a large number of meteorological data sets into the estimation process. The DFA is first used to extract key meteorological factors that are highly related to pan evaporation and to establish the common trend of pan evaporation among meteorological stations. The BPNN is then trained to estimate pan evaporation with the inputs of the key meteorological factors and evaporation estimates given by the DFA. The BD model successfully inherits the advantages from the DFA and BPNN, and effectively enhances its generalization ability and estimation accuracy. The results demonstrate that the proposed BD model has good reliability and applicability in simultaneously estimating pan evaporation for multiple meteorological stations.

Citation Chang, F.J., Sun, W., and Chung, C.H., 2013. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan. Hydrological Sciences Journal, 58 (4), 813–825.     

Geoeffectiveness of XRA events associated with RSP II and/or RSP IV estimated using the artificial neural network

A forecasting scheme of geomagnetic activity is presented, based on the analysis of the geoeffectiveness of X-ray flares, accompanied by Type II and/or Type IV radio bursts (RSP) observed on the solar disc in the years 1996–2004. The neural network was used to construct this scheme enabling us to determine the probability, with which flares will be followed by a geomagnetic response of a particular intensity. The successfulness of forecasts produced after the fact depended on the flare class and on the combination of radio-burst types. In the case of RSP IV, 58% of the geomagnetic responses of X-ray flares of at least B class were successful. If only RSP II was observed, the forecast was successful only for flares of the X class (67% of successful forecasts). In the second step, a strong geomagnetic response was correctly forecast after geoeffective flares in 58% of the cases. The results are in a good agreement with recent papers based on physical modelling. fridrich@geomag.sk ph@ig.cas.cz, jboch@ig.cas.cz     

人工神经网络系统在接收函数反演中的应用

为使接收函数的反演更为简便,本文提出了一种基于人工神经网络误差反传(BP)算法的接收函数反演新方法,该方法采用人工神经网络反演系统,避免了接收函数反演过程中复杂的地震响应计算及耗时的雅可比矩阵计算,只需经过学习训练就能够解决复杂的实际问题,而且具有记忆功能,这使接收函数的反演工作具有延续性和可继承性.理论数据的反演计算结果表明,该方法是切实可行的.     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

基于遗传神经网络的砂土液化判别模型

针对BP人工神经网络具有易陷入局部极小等缺陷，本文提出了将遗传算法与神经网络相结合，同时优化网络结构与权值、阈值的思想。根据地震液化的实测资料，建立了砂土液化判别的遗传神经网络模型，比较计算结果证明了该模型的科学性、高效性。文中并进行主成分分析，提出液化影响的主要因素。     

Artificial neural network models for estimating regional reference evapotranspiration based on climate factors

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET₀) calculation was investigated. ANNs were trained and tested for arid (west), semi‐arid (middle) and sub‐humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET₀ and the ANNs simulated ET₀ better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0·130 mm d^?1, RE of 2·7% and R² of 0·986) in the semi‐arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub‐humid area (with RMSE of 0·21 mm d^?1, RE of 5·2% and R² of 0·961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.     

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.