Geology,geochemistry, sulfur isotope composition,and fluid inclusion data of Farsesh barite deposit,Lorestan Province,Iran

Farsesh barite in the central part of Iranian Sanandaj-Sirjan zone is a sample of epigenetic hydrothermal mineralization in dolomitized limestone, which provides appropriate chemicophysical conditions making the passage of mineral-bearing fluids possible. Barite veins may range from a few centimeters to 2 m in thickness that increases downward. The microthermometry measurements obtained from more than 30 fluid inclusions show relative homogenization temperatures ranging from 125 to 200 °C with an average of 110 °C for Farsesh barite deposits. The mean salinity measured proves 16 times as much as weight percentage of NaCl for barite. Coexistence of liquid- and vapor-rich fluid inclusions in barite minerals may provide an evidence of boiling in ore veins. Moreover, occurrence of bladed calcite, high-grade ore zones, and presence of hydrothermal breccia are all consistent with boiling. Thermometric studies indicate that homogenization temperatures (Th) for primary and pseudosecondary fluid inclusions in barite range from 125 to 200 °C with an average of 1,100 °C. The δ³⁴S values of barite also lie between 8.88 and 16.6 %. The relatively narrow spread in δ³⁴S values may suggest uniform environmental conditions throughout the mineralization field. Thus, δ³⁴S values are lower than those of contemporaneous seawater, which indicates a contribution of magmatic sulfur to the ore-forming solution. Barite is marked by total amounts of rare Earth elements (REEs) (6.25–17.39 ppm). Moreover, chondrite-normalized REE patterns of barite indicate a fractionation of light REEs (i.e., LREEs) from La to Sm, similar to those for barite from different origins. The La_CN/Lu_CN ratios and chondrite-normalized REE patterns reveal that barite in Farsesh deposit is enriched in LREEs compared with heavy rare Earth elements (HREEs). Similarity between Ce/La ratios in barite samples and those found in deep-sea barite supports its marine origin. Lanthanum and Gd exhibit positive anomalies, which are common features of chemical marine sediments. Cerium shows a negative anomaly in most samples inherited from the negative Ce anomaly of hydrothermal fluid that is mixed with seawater at barite precipitation. The available data including tectonic setting, host rock characteristics, REE geochemistry, and sulfur isotopic compositions may support a hydrothermal submarine origin for Farsesh barite deposit.     

An application of parametric statistical tests to well-yield data from carbonates of central Pennsylvania

Variation in productivity, yield in gallons per minute per foot of drawndown per foot of saturated thickness, of 80 water wells located in folded and faulted carbonate rocks and shales of Cambro-Ordovician age in central Pennsylvania, was related to six hydroecologic factors. Parametric and nonparametric statistical techniques were applied and results of One Factor Analysis of Variance and Student t-test are presented. Productivity values were transformed to common logarithms, and it was assumed that a log-normal model would reasonably describe the variation in productivity especially as the sample size was increased. These tests showed that the variations in number of fracture traces, rock type, dip of bedrock strata and topography were significant and variation in depth to water table was not significant in accounting for variation in well yield. Student t-test showed that anticlinal wells were significantly different from synclinal wells. However, wells in the same rock type but different structural settings were not significantly different. This shows that variations in rock type and number of fracture traces are more important than other structural variations. Both parametric and nonparametric tests gave identical results, which justifies the use of parametric tests which require normally distributed data.     

Application of GIS-based data-driven models for groundwater potential mapping in Kuhdasht region of Iran

Water shortage and population growth in Iran rapidly diminish groundwater supplies. Thus, finding the techniques such as GIS that can be used as powerful tools in groundwater management, and predicting groundwater potential is required. The main objective of this study is to evaluate the efficiency of the statistical index (SI), frequency ratio (FR) weights of evidence (WoE) and evidential belief function (EBF) models for groundwater potential mapping at Kuhdasht region, Lorestan province, Iran. For this purpose, 12 groundwater influencing factors were considered in this investigation. From 171 available wells in the study area, 114 wells (67%) and 57 wells (33%) were used based on random selection in SI, FR, WoE and EBF models as training and validation data-sets, respectively. The area under the ROC curve (AUC) for SI, FR, WoE and EBF models was calculated as 91.8, 91, 93.6 and 93.3%, respectively. These curve values indicated that all four models have reasonably good accuracy in spatially predicting groundwater potential in this area.     

Seismic modelling for reservoir studies: a comparison between convolutional and full-waveform methods for a deep-water turbidite sandstone reservoir

Two seismic modelling approaches, that is, two-dimensional pre-stack elastic finite-difference and one-dimensional convolution methods, are compared in a modelling exercise over the fluid-flow simulation model of a producing deep-water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one-dimensional convolution is used, the differences in three-dimensional and four-dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common-depth points and their associated angles of incidence. Conventional seismic images generated by the one-dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite-difference seismic images are very similar. Although transmission effects, internal multiples and P-to-S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root-mean-square) amplitude attributes and waveform kinematics indicates that the finite-difference approach does not offer any tangible benefit in our target-oriented seismic modelling case study, and the potential errors from one-dimensional convolution modelling are comparatively much smaller than the production-induced time-lapse changes.     

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...     

Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

Natural Resources Research - Low-salinity waterflooding (LSWF) has, in the past decade, attained a lot of attention to enhance oil recovery. In LSWF, diluted water is injected into an oil reservoir...