Application of spectral decomposition and neural networks to characterise deep turbidite systems in the outer fold and thrust belt of the Niger Delta

We have applied a wavelet‐based spectral decomposition scheme and a multi‐layered feed‐forward neural network to interpret turbidite depositional systems from three‐dimensional reflection seismic data and well logs for a prospective hydrocarbon zone in the outer fold and thrust belt of the Niger Delta. The goal was to overcome difficulties in interpreting depositional systems from deep sections of the Field, occasioned by loss of seismic resolution with depth and the sparse distribution of wells. The decomposition scheme allowed us to delineate multiple depositional systems not apparent on the conventional seismic amplitude display. These systems include linear channel systems with terminal splay lobes, a sinuous channel system and its abandoned meander loops, and sediment wave features in overbank areas. Delineated channel morphologies and transport directions varied both laterally and vertically and were possibly dependent upon the disposition of the pre‐thrusting paleo‐seafloor. Terminal splay lobes are fragmented and coincident with the locations of topographic lows, which are possibly related to the initial configurations of the oceanic basement below. Predicted porosity and resistivity distributions have morphologies that correlate well with the mapping provided by the spectral decomposition scheme. The property distributions indicate that reservoir prone systems in the Field and possibly within the outer fold and thrust belt are composed primarily of channel systems, both linear and sinuous, and their associated splay lobes. The channel systems appear vertically stacked, and this situation possibly increases the potential success rate for exploration wells in the region. Beyond channel limits, redistributive bottom currents varying rapidly in speed and direction apparently encouraged the dispersal of sand‐rich sediments to form sediment waves. Despite the limited well control, the methodology significantly aided our interpretation. It proved effective at revealing the distribution of reservoir prone facies within the Field and provided insight into the dominant factors that controlled deposition within the Field.     

Internet of Things‐based wireless networking for seismic applications

There is growing pressure from regulators on operators to adhere to increasingly stricter regulations related to the environment and safety. Hence, operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their licence to operate. A deeper understanding of production optimisation and production‐related risk requires strengthened knowledge of reservoir behaviour and overburden dynamics. To accomplish this, sufficient temporal and spatial resolution is required as well as an integration of various sources of measurements. At the same time, tremendous developments are taking place in sensors, networks, and data analysis technologies. Sensors and accompanying channels are getting smaller and cheaper, and yet they offer high fidelity. New ecosystems of ubiquitous wireless communications including Internet of Things nowadays allow anyone to affordably connect to the Internet at any time and anywhere. Recent advances in cloud storage and computing combined with data analytics allow fast and efficient solutions to handle considerable amounts of data. This paper is an effort to pave the way for exploiting these three fundamental advances to create Internet of Things‐based wireless networks of seismic sensors. To this aim, we propose to employ a recently developed Internet of Things‐based wireless technology, so‐called low‐power wide‐area networks, to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely operated minimum‐maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs, we show that neither the low data rate nor the low duty cycle of low‐power wide‐area networks imposes fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay tolerant. In order to confirm this claim, we cast our ideas into a practical large‐scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small‐scale field test in which we have employed our Internet of Things‐based wireless nodes for real‐time seismic quality control over clouds.     

Estimation of soil water content in watershed using artificial neural networks

Soil water content (SWC) is an important factor in transfer processes between soil and air, contributing to water and energy balances, and quantifying it remains a challenge. This study uses artificial neural networks (ANNs) to analyse spatial and temporal variation of SWC in a Brazilian watershed, based on climate information, soil physical properties and topographic variables. Thirty eight input variables were tested in 200 models. The outputs were compared with 650 gravimetric moisture measurements collected at 26 points (25 field studies). The results show that it is possible to estimate SWC efficiently (Nash-Sutcliffe statistic, NS = 0.77) using topographic data, soil physical properties and rainfall. If only climate information is considered, modelling is less efficient (NS = 0.28). Using many variables does not necessarily improve performance. Alternatively, SWC can be estimated by simplified models using rainfall and topographic maps information, although the performance is less good (NS = 0.65).     

Experimental study of identification and control of structures using neural network. Part 2: control

Experimental verifications of a recently developed active structural control method using neural networks are presented in this paper. The experiments were performed on the earthquake simulator at the University of Illinois at Urbana—Champaign. The test specimen was a 1/4 scale model of a three-storey building. The control system consisted of a tendon/pulley system controlled by a single hydraulic actuator at the base. The control mechanism was implemented through four active pre-tensioned tendons connected to the hydraulic actuator at the first floor. The structure modelling and system identification has been presented in a companion paper. (Earthquake Engng. Struct. Dyn. 28 , 995–1018 (1999)). This paper presents the controller design and implementation. Three controllers were developed and designed: two neurocontrollers, one with a single sensor feedback and the other with three sensor feedback, and one optimal controller with acceleration feedback. The experimental design of the neurocontrollers is accomplished in three steps: system identification, multiple emulator neural networks training and finally the neurocontrollers training with the aid of multiple emulator neural networks. The effectiveness of both neurocontrollers are demonstrated from experimental results. The robustness and the relative stability are presented and discussed. The experimental results of the optimal controller performance is presented and assessed. Comparison between the optimal controller and neurocontrollers is presented and discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

Dense RFB和LSTM遥感图像舰船目标检测

针对当前遥感图像舰船目标检测精度不佳问题,本文构建舰船目标数据集STAR,提出基于Dense RFB和LSTM多尺度舰船目标检测算法。该算法首先在SSD网络基础上设计了浅层特征增强模块,基于人眼视点图采用Dense RFB特征复用和膨胀卷积增大感受野的尺度和种类,增强浅层网络对细节特征的提取能力;其次设计了深层多尺度特征金字塔融合模块,采用FPN和LSTM思想,基于反卷积和残差网络对深层不同尺度特征进行融合,增强网络结构非线性和特征层的表征能力;最后加入聚焦分类损失函数进行联合训练,有效避免了正负样本失衡问题。在遥感图像舰船数据集上实验,本文所提舰船目标检测算法精度均值达到81.98%,检测速度达到29.6帧/s。此外,遥感图像中成像模糊、被遮挡、部分被裁剪等舰船目标的检测效果也优于原有经典算法,实验结果表明该算法对遥感图像舰船目标检测的泛化能力较强,有效地提高了遥感图像舰船目标检测的精度。     

基于生成对抗网络模型的热带和亚热带海洋中尺度涡预报研究

中尺度涡蕴含海洋超过90%的动能,显著影响海洋物质能量循环。对中尺度涡的预报是目前物理海洋学研究的热点和难点。文章基于卫星高度计观测的近30年海表面高度异常数据(sea level anomaly, SLA),采用基于博弈思想的生成对抗网络方法(generative adversarial networks, GAN),构建了中尺度涡预报模型,进行了28天预报,并采用独立样本分析了预报涡旋的空间分布、时间分布、能量强度等特征参数,探讨影响预报结果准确性和时效性的主要因素。结果表明,半径为100～200km的涡旋在15天左右的预报时长仍能保持较好的准确性及时效性,误差在20%以内。该区域的平均涡动能约为0.875m²·s^–2,其预报的均方根误差(root mean square error, RMSE)普遍介于0.02～0.04m²·s^–2。且涡旋预报结果受异常天气影响较小,在正常天气条件和台风娜基莉条件下具有相似的预报能力。这些结果对进一步理解并应用生成对抗网络这一新方法预报海洋中尺度涡提供了参考。     

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km²). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.     

社会网络分析在国外旅游研究中的应用进展

近年来社会网络分析在旅游相关研究中表现出超强的适应性,成为国内外旅游研究的流行范式之一。论文从研究视角和方法、研究内容以及中层理论的应用3个方面分析了国际旅游研究的十大权威期刊2009—2018年发表的社会网络分析相关文献67篇。在全面、系统、深入地梳理与分析的基础上,与10 a前相关文献综述进行对比,总结出近10 a社会网络分析在国外旅游研究中最新的进展。研究发现：① 近10 a相关成果的研究视角进一步完善,表现为自我中心社会网视角以及与整体网结合的双重视角的研究成果显著增加;② 研究方法多样化,关系数据的来源更加丰富,融合相关性分析等各类统计分析技术的定量研究成为主流,扎根理论等定性研究方法也开始应用;③ 研究对象从传统的目的地合作网、政策网等扩展至居民关系网、超链接网、口碑分享网、旅游形象网等;④ 研究内容从旅游社会关系网络结构的描述与分析转向关系网络的影响因素和效应分析,社会关系网络的形成、发展以及演化研究等;⑤ 社会网络分析相关中层理论的应用也有所拓展,其中社会资本理论对旅游相关议题的解释力进一步凸显,这体现了社会网络分析在旅游研究中的应用由变量测度到测度解释的转向。最后,文章总结了社会网络分析在国外旅游研究中应用存在的问题与难点,并展望了未来相关研究的方向。研究成果有助于全面把握旅游社会网络分析的最新研究动态与趋势,为进一步拓展和深化社会网络分析在旅游研究中的应用奠定基础。     

Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches

In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws.