Permeability upscaling of fault zones in the Aztec Sandstone,Valley of Fire State Park,Nevada, with a focus on slip surfaces and slip bands

The effect of open and filled slip surfaces on the upscaled permeability of two fault zones with 6 and 14 m strike-slip in an eolian Aztec Sandstone, Nevada, USA is evaluated. Each fault zone is composed of several fault components: a fault core, bounded by filled through-going slip surfaces referred to as slip bands, and a surrounding damage zone that contains joints and deformation bands. Slip band geometry, composition, and petrophysical properties are characterized. Measurements and modeling show that slip band permeabilities can vary over 12 orders of magnitude depending on the degree of fill within the slip bands. The slip bands along with other fault zone components are represented in finite volume numerical calculations and the impact of various slip-band representations on upscaled fault zone permeability is tested. The results show 2 orders of magnitude variation in upscaled fault zone permeability in the fault-normal direction and a factor of 2 variation in the fault-parallel direction. The numerical results presented here are compared to the earlier numerical results in which structured Cartesian grids were used for the numerical simulations, and are in qualitative agreement with earlier calculations but use about a factor of 250–400 fewer numerical cells.     

Development of stochastic dynamic Nash game model for reservoir operation. I. The symmetric stochastic model with perfect information

Increasing water demands, higher standards of living, depletion of resources of acceptable quality and excessive water pollution due to agricultural and industrial expansions have caused intense social and political predicaments, and conflicting issues among water consumers. The available techniques commonly used in reservoir optimization/operation do not consider interaction, behavior and preferences of water users, reservoir operator and their associated modeling procedures, within the stochastic modeling framework. In this paper, game theory is used to present the associated conflicts among different consumers due to limited water. Considering the game theory fundamentals, the Stochastic Dynamic Nash Game with perfect information (PSDNG) model is developed, which assumes that the decision maker has sufficient (perfect) information regarding the associated randomness of reservoir operation parameters. The simulated annealing approach (SA) is applied as a part of the proposed stochastic framework, which makes the PSDNG solution conceivable. As a case study, the proposed model is applied to the Zayandeh-Rud river basin in Iran with conflicting demands. The results are compared with alternative reservoir operation models, i.e., Bayesian stochastic dynamic programming (BSDP), sequential genetic algorithm (SGA) and classical dynamic programming regression (DPR). Results show that the proposed model has the ability to generate reservoir operating policies, considering interactions of water users, reservoir operator and their preferences.     

Rock joint modeling using a visco-plastic multilaminate model at constant normal load condition

Rock joints play an important role in the behavior of rock masses under normal and shear loading conditions. Numerical simulation of the behavior of jointed rock masses is not an easy task due to complexities involved in the problem such as joint roughness, joint shear strength, hardening and softening phenomenon and mesh dependency. In this study for modeling purposes, a visco-plastic multilaminate model considering hardening and softening effects has been employed. For providing the necessary data for numerical simulation, a series of laboratory experiments have been carried out on regular tooth-shape asperities made by gypsum, under constant normal load conditions. Shear stress–shear displacement and normal displacement–shear displacement of artificial joint specimens are simulated using the proposed numerical model at constant normal load condition (CNL). The results indicate the capability of the model for simulating rock joints behavior in both strength and deformation field. Although the numerical model has been developed for simulating the behavior of artificial joints, the concept of the method can also be used for natural rock joints.     

Seasonal and Interannual Sea Surface Temperature Variability in the Coastal Cities of Arabian Sea and Bay of Bengal

The sea surface temperature (SST) variations play a veryimportant role in the genesis and maintenance of meteorological and oceanographic processessuch as monsoon depressions and subsequent floods, large-scale sea level fluctuationsand genesis of tropical cyclones. Many low lying coastal regions of South Asia are adjacentto river deltas and have large population. The dense population, poor economy and severalother socio-economic factors make these areas most vulnerable to the impact of climate change.Variability of sea surface temperature (SST) is importantas the duration and intensity of SST provide the basis for studies related to climatic changescenario. In this study an attempt has been made to estimate the recent SST trends in the coastalwaters of some cities, which lie on the Arabian Sea and Bay of Bengal. The annual andinterannual variability has also been studied. The SST variations have then been linkedwith the El Nino and La Nina events.The NOAA-NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST fields from 1985-1998, created in the Jet Propulsion Laboratory(JPL), USA are used in this study. Here the quality of data is an important factor toobtain reliable estimates of Sea Surface Temperature (SST) trends and other related parameters.However, this is not possible with the conventional type data, due to low quality as wellas sparse data in the region. Though the satellite based SST climatologies have shorterobservation lengths, they can provide reliable estimates of recent SST variability overa large oceanic areas with sparse or no data.Increasing trend of SST is observed throughout all theseasons in the northern Arabian Sea extending from Oman to Karachi and Mumbai and furthersouth to Salalah and Colombo. However, in coastal islands stations further south ofIndia such as at Colombo the increment is not significant. Though the increasing trend in SSTduring winter is not significant, nevertheless it shows the increasing influence of coldspells on this Island. An interesting situation has been observed in the Bay of Bengal. On anaverage, increasing trends in the annual SST were observed in Visakhaputnam. But at thestations located in the northeastern part of Bay of Bengal, namely Hiron Point and Cox'sBazar reverse conditions are observed. In the Southern Bay of Bengal variations in SST isnot significant which reflects in the SST analysis of Chennai and Port Blair stations. Locationof these stations at lower latitudes (near by equator) probably is the reason for this insignificantchange. It has been found that the interannual mode of SST variations dominate the linear SSTtrends which is characterized by the El Nino Southern Oscillations (ENSO) scale cycle.     

T-phases from Atlantic earthquakes

Summary Underwater sound waves from earthquakes or so-calledT-waves are investigated for the Atlantic-Arctic area for the years 1953–1968, mainly from Swedish seismograph records, and for comparison also from an earthquake in the equatorial Atlantic withT-waves recorded on both sides of the ocean. The waves travel as sound waves through water, and asPg, Sg andRg over the land path. The North Atlantic source area of theT-waves, recorded at Scandinavian stations, is very well limited in extent with a strong concentration northeast of Jan Mayen, probably due to favourable bottom topography. The calculated sound velocities in water are 1.43 km/sec for the Arctic case and 1.52 km/sec for the equatorial one. TheT-waves exhibit a clear inverse dispersion. The dispersion explains their long duration. The duration of theT-phase increases logarithmically with the maximum amplitudes within theT-wave group and decreases linearly with distance over the land path, corresponding to a quality factor of about 700. Propagation across the ocean by multiple reflections between surface and bottom appears as the most probable mechanism. The particle motion ofT Sg is dominantly transverse horizontal, which is explained by refraction when the waves are transmitted to land. TheT-wave spectra at two different localities show clear relations, depending upon the ocean depths.     

Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables

ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (T_dew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting T_dew values.     

Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions

ABSTRACT

In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions.