Multivariate statistical analysis approach for prediction of blast-induced ground vibration

Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits.     

Automated mapping of snow/ice surface temperature using Landsat-8 data in Beas River basin,India, and validation with wireless sensor network data

In this paper, an automated method for retrieval of snow surface temperature (SST) in Beas River Basin, India, using Landsat-8 thermal data is proposed. Digital number (DN) values of thermal data were converted into Top of Atmospheric (TOA) radiance. Surface radiance has been estimated from TOA radiance using a single channel method. The estimated surface radiance was then converted into SST. Cloud free Landsat-8 data for January and February 2017 has been used to estimate SST. Snow and Avalanche Study Establishment (SASE) has established a wireless sensor network (WSN) in an avalanche prone slope in Beas River Basin, India. Landsat-8 retrieved SST has been compared and validated with recorded SST at WSN stations. The retrieved SST using proposed algorithm was in good agreement with SST recorded on ground by sensor network. The mean absolute error (MAE) and root-mean-square error (RMSE) between estimated and recorded SST has been observed as ~?1.1 K and ~?1.5 K for 23 January 2017 and ~?0.7 and ~?1.6 K for 24 February 2017. Algorithm has shown a potential for automated mapping of snow and ice surface temperature using Landsat-8 data for snow cover and glaciers in Himalaya.     

Relative role of sea surface temperature and snow on Indian summer monsoon seasonal simulation using a GCM

Indian summer monsoon is a global scale phenomenon controlled by different land, ocean, and atmospheric parameters. Sea surface temperature (SST) and snow are two of the major parameters, which may alter the spatial and temporal patterns of circulation and rainfall during Indian summer monsoon. In the current paper, we study the monsoon variability using long integrations (20 years) of the Indian Institute of Technology Delhi (IITD) Spectral model at T80L18 resolution with observed and climatological SST and snow. Study shows response of IITD GCM in simulating the Indian summer monsoon rainfall and circulation relative to the snow and SST as boundary conditions. The model’s response to SST and snow is examined by conducting four types of experiments by varying observed and climatological values of snow and SST. This paper discusses the seasonal total rainfall for country as a whole and 850 and 200 hPa wind for the period of 20 years starting from 1985 to 2004. The model has been integrated in the ensemble mode with five different initial conditions from the last week of April and first week of May. The model is able to capture the climatological patterns of seasonal total rainfall and averaged wind at lower and upper levels. Observed snow in the presence of climatological SST as a boundary condition shows much impact on rainfall and circulation than observed SST in the presence of climatological snow. Model performance is good in simulating the normal and excess monsoon conditions; it shows poor skill in capturing deficit monsoon years.     

A multi-scale particle-tracking framework for dispersive solute transport modeling

Particle-tracking simulation offers a fast and robust alternative to conventional numerical discretization techniques for modeling solute transport in subsurface formations. A common challenge is that the modeling scale is typically much larger than the volume scale over which measurements of rock properties are made, and the scale-up of measurements have to be made accounting for the pattern of spatial heterogeneity exhibited at different scales. In this paper, a statistical scale-up procedure developed in our previous work is adopted to estimate coarse-scale (effective) transition time functions for transport modeling, while two significant improvements are proposed: considering the effects of non-stationarity (trend), as well as unresolved (residual) heterogeneity below the fine-scale model. Rock property is modeled as a multivariate random function, which is decomposed into the sum of a trend (which is defined at the same resolution of the transport modeling scale) and a residual (representing all heterogeneities below the transport modeling scale). To construct realizations of a given rock property at the transport modeling scale, multiple realizations of the residual components are sampled. Next, a flow-based technique is adopted to compute the effective transport parameters: firstly, it is assumed that additional unresolved heterogeneities occurring below the fine scale can be described by a probabilistic transit time distribution; secondly, multiple realizations of the rock property, with the same physical size as the transport modeling scale, are generated; thirdly, each realization is subjected to particle-tracking simulation; finally, probability distributions of effective transition time function are estimated by matching the corresponding effluent history for each realization with an equivalent medium consisting of averaged homogeneous rock properties and aggregating results from all realizations. The proposed method is flexible that it does not invoke any explicit assumption regarding the multivariate distribution of the heterogeneity.     

Archaeological Evidence for Modern Coastal Uplift at Diu,Saurashtra Peninsula,India

There is a tank hewn into coastal Pleistocene limestone near Diu city on the Saurashtra Peninsula of western India. Site survey and a review of similar structures worldwide provide evidence that this tank could have been used for holding fish or Murex snails. The approximately 5 × 5 m tank is connected to the sea by a 1‐m‐deep canal; today it would be impossible to use the tank, given that not even the high spring tides can fill it. It is suggested that the Diu coast was uplifted by ∼0.5 m after the tank was hewn in the coastal platform. Since that time, the carved surfaces have been modified by coastal karst dissolution and have developed deep gouge marks. Uplift of the Diu coast raises the possibility of a major seismic event in Diu during the latter part of the last millennium.     

Implications of urban expansion on land use and land cover: towards sustainable development of Mega Manila,Philippines

GeoJournal - Planning of land use and infrastructure in advance for a population that is projected to grow rapidly is highly important for its sustainable development. A correlative approach of...     

Slope Stability Analysis Using Rf,Gbm, Cart,Bt and Xgboost

Slopes in geotechnical and mining engineering are the most crucial geo-structure. Predicting or forecasting the stability or instability of the slope and then classifying the slope accordingly helps in mitigating the risks and enhancing the design by maximizing the safety. Computing techniques have overpowered the analytical and statistical models used for predicting the stability of the slopes. To reduce the uncertainties and ambiguity of the previously used models, lately, researchers have come up with the novel techniques for Slope Stability Classification (SSC) which are Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, Boosted Trees and Classification and Regression Trees. These computational algorithms are employed in this research paper and the slope details are taken from a literature i.e. 221 input datasets are used and slopes are classified accordingly using the mentioned models. The relation between the inputs such as height (H), slope angle (β), cohesion (c), pore water pressure ratio (ru), unit weight (γ), angle of internal friction (φ) and slope stability (output) is established and slopes are categorized according to their failure and stability. Performance analysis is done thereafter to analyses and compare different models and let the readers and researchers know that which model sufficed and fitted best to the study.

     

Rainfall-Runoff Modeling using Clustering and Regression Analysis for the River Brahmaputra Basin

In this research, k-means, agglomerative hierarchical clustering and regression analysis have been applied in hydrological real time series in the form of patterns and models, which gives the fruitful results of data analysis, pattern discovery and forecasting of hydrological runoff of the catchment. The present study compares with the actual field data, predicted value and validation of statistical yields obtained from cluster analysis, regression analysis with ARIMA model. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models is investigated for monthly runoff forecasting. The different parameters have been analyzed for the validation of results with casual effects. The comparison of model results obtained by K-means & AHC have very close similarities. Result of models is compared with casual effects in the same scenario and it is found that the developed model is more suitable for the runoff forecasting. The average value of R² determined is 0.92 for eight ARIMA models. This shows more accuracy of developed ARIMA model under these processes. The developed rainfall runoff models are highly useful for water resources planning and development.