Signatures in flowing fluid electric conductivity logs   总被引：1，自引：0，他引：1  

Christine Doughty  Chin-Fu Tsang 《Journal of Hydrology》2005,310(1-4):157-180

Flowing fluid electric conductivity logging provides a means to determine hydrologic properties of fractures, fracture zones, or other permeable layers intersecting a borehole in saturated rock. The method involves analyzing the time-evolution of fluid electric conductivity (FEC) logs obtained while the well is being pumped and yields information on the location, hydraulic transmissivity, and salinity of permeable layers. The original analysis method was restricted to the case in which flows from the permeable layers or fractures were directed into the borehole (inflow). Recently, the method was adapted to permit treatment of both inflow and outflow, including analysis of natural regional flow in the permeable layer. A numerical model simulates flow and transport in the wellbore during flowing FEC logging, and fracture properties are determined by optimizing the match between simulation results and observed FEC logs. This can be a laborious trial-and-error procedure, especially when both inflow and outflow points are present. Improved analyses methods are needed. One possible tactic would be to develop an automated inverse method, but this paper takes a more elementary approach and focuses on identifying the signatures that various inflow and outflow features create in flowing FEC logs. The physical insight obtained provides a basis for more efficient analysis of these logs, both for the present trial and error approach and for a potential future automated inverse approach. Inflow points produce distinctive signatures in the FEC logs themselves, enabling the determination of location, inflow rate, and ion concentration. Identifying outflow locations and flow rates typically requires a more complicated integral method, which is also presented in this paper.  相似文献   

Statistical palaeomagnetic field modelling and symmetry considerations     

G. Hulot  C. Bouligand 《Geophysical Journal International》2005,161(3):591-602

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Statistical palaeomagnetic field modelling and dynamo numerical simulation     

C. Bouligand  G. Hulot  A. Khokhlov  G. A. Glatzmaier 《Geophysical Journal International》2005,161(3):603-626

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Double-couple earthquake focal mechanism: random rotation and display     

Yan Y. Kagan 《Geophysical Journal International》2005,163(3):1065-1072

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The future of applied tracers in hydrogeology     

Craig E. Divine  Jeffrey J. McDonnell 《Hydrogeology Journal》2005,13(1):255-258

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The Basic Shape Classification of Space Debris with Light Curves     

LU Yao  ZHAO Chang-yin 《Chinese Astronomy and Astrophysics》2021,45(2):190-208

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.  相似文献   

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches          下载免费PDF全文

Sebastiano Piccolroaz  Elisa Calamita  Bruno Majone  Aurélien Gallice  Annunziato Siviglia  Marco Toffolon 《水文研究》2016,30(21):3901-3917

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data‐demanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low‐land natural rivers, lake outlets, snow‐fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross‐validation conducted over a 30‐year period indicates that they can be suitably adopted for long‐term analyses. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Assessing regression‐based statistical approaches for downscaling precipitation over North America          下载免费PDF全文

Jie Chen  François P. Brissette  Robert Leconte 《水文研究》2014,28(9):3482-3504

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010          下载免费PDF全文

Ali A. Assani  Raphaëlle Landry  Stacey Biron  Jean‐Jacques Frenette 《水文研究》2014,28(13):4011-4022

We compared the interannual variability of annual daily maximum and minimum extreme water levels in Lake Ontario and the St Lawrence River (Sorel station) from 1918 to 2010, using several statistical tests. The interannual variability of annual daily maximum extreme water levels in Lake Ontario is characterized by a positive long‐term trend showing two shifts in mean (1929–1930 and 1942–1943) and a single shift in variance (in 1958–1959). In contrast, for the St Lawrence River, this interannual variability is characterized by a negative long‐term trend with a single shift in mean, which occurred in 1955–1956. As for annual daily minimum extreme water levels, their interannual variability shows no significant long‐term change in trend. However, for Lake Ontario, the interannual variability of these water levels shows two shifts in mean, which are synchronous with those for maximum water levels, and a single shift in variance, which occurred in 1965–1966. These changes in trend and stationarity (mean and variance) are thought to be due to factors both climatic (the Great Drought of the 1930s) and human (digging of the Seaway and construction of several dams and locks during the 1950s). Despite this change in means and variance, the four series are clearly described by the generalized extreme value distribution. Finally, annual daily maximum and minimum extreme water levels in the St Lawrence and Lake Ontario are negatively correlated with Atlantic multidecadal oscillation over the period from 1918 to 2010. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Neptune Trojans: a Revisit and a New Membertwo     

Wu Rui-jun  Zhou Lei  Zhou Li-yong 《Chinese Astronomy and Astrophysics》2019,43(2):262-277

Up to now, 17 Neptune Trojan asteroids have been detected with their orbits being well determined by continuous observations. This paper analyzes systematically their orbital dynamics. Our results show that except for two temporary members with relatively short lifespans on Trojan orbits, the vast majority of Neptune Trojans located within their orbital uncertainties may survive in the solar system age. The escaping probability of Neptune Trojans, through slow diffusion in the orbital element space in 4.5 billion years, is estimated to be ～50%. The asteroid 2012 UW177 classified as a Centaur asteroid by the IAU Minor Planet Center currently is in fact a Neptune Trojan. Numerical simulations indicate that it is librating on the tadpole-shaped orbit around the Neptune's L₄ point. It was captured into the current orbit approximately 0.23 million years ago, and will stay there for at least another 1.3 million years in the future. Its high inclination of i ≈ 54^° not only makes it the most inclined Neptune Trojan, but also makes it exhibit the complicated and interesting co-orbital transitions between the leading and trailing Trojans via the quasi-satellite orbit phase.  相似文献