A late Cenozoic Earth's crust and climate dynamics record from Lake Baikal

The sedimentary record from Lake Baikal (Siberia, Russia) has been an important source of information about paleoclimatic variability in the northern hemisphere and dynamics of continental rift development. A lack of reliable chronology has, however, been a major obstacle to fully utilizing the Baikal archive for time scales beyond about 4-5 Myr. In this paper we use the distribution of ¹⁰Be to establish a new chronology for the longest core drilled in Lake Baikal so far. The ¹⁰Be-based chronology spans the last 8 Myr and provides better constraints on sedimentation rates and consequently on an east-west tectonic extension in the lake, which has been apparently coeval with other rifts in Asia that are related to the Tibetan plateau uplift. Our data also show higher ¹⁰Be flux in the sediment section older then 5 Myr compared with the younger period. This can be explained partly by warm and humid climatic conditions of the Miocene and partly by a high cosmic ray flux to the Earth resulting from possible low geomagnetic field intensity during that time.     

Predicting blast-induced ground vibration using various types of neural networks

Prediction of vibration is very important in mining operations as well as civil engineering projects. In this paper, multi layer perceptron neural network (MLPNN), radial basis function neural network (RBFNN) and general regression neural network (GRNN) were utilized to predict ground vibration level in a Sarcheshmeh copper mine, Iran. It was observed that the MLPNN gives the best results. For this technique root mean square error and coefficient of correlation were found 0.03 and 0.954, respectively. Sensitivity analysis showed that distance from the blast, number of holes per delay and maximum charge per delay are the most effective parameters in making ground vibration in the blasting operation.     

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca²⁺, and Mg²⁺. Therefore, the first dominant factor was hardness. In the second PC, Cl^-, SAR, and Na⁺ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.