Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone,south Iran

Chromite deposits in Iran are located in the ophiolite complexes, which have mostly podiform types and irregular in their settings. Exploration for podiform chromite deposits associated with ophiolite complexes has been a challenge for the prospectors due to tectonic disturbance and their distribution patterns. Most of Iranian ophiolitic zones are located in mountainous and inaccessible regions. Remote sensing approach could be applicable tool for choromite prospecting in Iranian ophiolitic zones with intensely rugged topography, where systematic sampling and conventional geological mapping are limited. In this study, Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for chromite prospecting and lithological mapping in the Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio and principal component analysis (PCA) were applied to Landsat TM and ASTER data sets for lithological mapping at regional scale. The RGB decorrelated image of Landsat TM spectral bands 7, 5, and 4, and the principal components PC1, PC2 and PC3 image of ASTER SWIR spectral bands efficiently showed the occurrence of major lithological units in the study area at regional scale. The band ratios of 5/3, 5/1, 7/5 applied on ASTER VNIR‐SWIR bands were very useful for discriminating most of rock units in the study area and delineation of the transition zone and mantle harzburgite in the Neyriz ophiolitic complex. Spectral Angle Mapper (SAM) technique was implemented to ASTER VNIR‐SWIR spectral bands for detecting minerals of rock units and especially delineation of the transition zone and mantle harzburgite as potential zones with high chromite mineralization in the Neyriz ophiolitic complex. The integration of information extracted from the image processing algorithms used in this study mapped most of lithological units of the Neyriz ophiolitic complex and identified potential areas of high chromite mineralization (transition zone and mantle harzburgite) for chromite prospecting targets in the future. Furthermore, image processing results were verified by comprehensive fieldwork and laboratory analysis in the study area. Accordingly, result of this investigation indicate that the integration of information extracted from the image processing algorithms using Landsat TM and ASTER data sets could be broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such Iranian ophiolitic zones.     

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.     

Application of rock mass index (RMi) to the rock mass excavatability assessment in open face excavations

Earthwork and surface excavation activities play an important role in construction projects. Selecting the best technique to loosen the overburden material within the surface excavation in open mining and geotechnical projects is of great importance from economical and technical viewpoints. Surface excavation includes direct digging, ripping and blasting. To select the most effective method and plan for excavation, geotechnical investigation is very important. It is also a big help in avoiding conflict between contractors and clients when they do not reach mutual agreement regarding the price of rock and soil excavation. There are many engineering classification systems used to assess rock masses for excavation purposes. All of these systems consider several geotechnical parameters to assess the earth masses. This study reviews these systems and then offers a new categorization based on the Rock Mass index (RMi) classification system and block volume to assess excavation in rock masses. The original dataset was obtained from the literature review as well as the surface excavation in Upper Gotvand dam and Hydro Power Plant (HPP). The offered system was also validated through the data extracted from the surface excavation in Sardasht dam and HPP in Iran.     

Assessing the influence of watershed characteristics on soil erosion susceptibility of Jhelum basin in Kashmir Himalayas

Soil erosion, a serious environmental problem, is a global challenge. Once a portion of a fertile soil is lost, it is very difficult to replace it, and this leads to decrease in crop production, damage to drainage networks, and siltation of dams and reservoirs. Human practices like intensive agriculture, overgrazing, and deforestation have intensified the rate of soil erosion all over the world. The Jhelum basin which forms the north-western part of the complex mountain system Himalayas is not only highly vulnerable to natural hazards like earthquakes, landslides, and floods but is also highly susceptible to soil erosion. There is an immediate need to device strategies to reduce adverse impacts of soil erosion and to conserve natural resources like soil, water, and forests by means of proper watershed management programs in the Himalayan region. The present study is carried out for eight upper watersheds of Jhelum basin, an area which are facing serious issues like boulder extraction, deforestation, and unplanned urbanization. The present work demonstrates the use of morphometry, land use, and slope coupled with the multicriteria analytical (MCA) framework to estimate the soil erosion susceptibility of these watersheds using Remote Sensing and Geographical Information System techniques. The present study revealed that out of eight upper watersheds, Arapal, Lidder, and Bringi fall in high priority and need immediate attention and measures to reduce soil erosion in the area. Sandran, Rembiara, and Romshii fall in medium priority. Kuthar and Vishav fall in the low-priority category and are least susceptible to soil erosion.     

Modeling and assessing the effects of land use changes on runoff generation with the CLUE-s and WetSpa models

Theoretical and Applied Climatology - Land use change is an important determinant of hydrological processes and is known to affect hydrological parameters such as runoff volume, flood frequency,...     

Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND

Abstract

The main objective of this study is to assess the relative contribution of the state-of-the-art topo-hydrological factor, known as height above the nearest drainage (HAND), to landslide susceptibility modellling using three novel statistical models: weights-of-evidence (WofE), index of entropy and certainty factor. In total, 12 landslide conditioning factors that affect the landslide incidence were used as input to the models in the Ziarat Watershed, Golestan Province, Iran. Landslide inventory was randomly divided into a ratio of 70:30 for training and validating the results of the models. The optimum combination of conditioning factors was identified using the principal components analysis (PCA) method. The results demonstrated that HAND is the defining factor among hydrological and topographical factors in the study area. Additionally, the WofE model had the highest prediction capability (AUPRC = 74.31%). Therefore, HAND was found to be a promising factor for landslide susceptibility mapping.     

Interpretation of magnetic and gravity gradient tensor data using normalized source strength – A case study from McFaulds Lake,Northern Ontario,Canada

In this paper, we present a case study on the use of the normalized source strength (NSS) for interpretation of magnetic and gravity gradient tensors data. This application arises in exploration of nickel, copper and platinum group element (Ni‐Cu‐PGE) deposits in the McFaulds Lake area, Northern Ontario, Canada. In this study, we have used the normalized source strength function derived from recent high resolution aeromagnetic and gravity gradiometry data for locating geological bodies. In our algorithm, we use maxima of the normalized source strength for estimating the horizontal location of the causative body. Then we estimate depth to the source and structural index at that point using the ratio between the normalized source strength and its vertical derivative calculated at two levels; the measurement level and a height h above the measurement level. To discriminate more reliable solutions from spurious ones, we reject solutions with unreasonable estimated structural indices. This method uses an upward continuation filter which reduces the effect of high frequency noise. In the magnetic case, the advantage is that, in general, the normalized magnetic source strength is relatively insensitive to magnetization direction, thus it provides more reliable information than standard techniques when geologic bodies carry remanent magnetization. For dipping gravity sources, the calculated normalized source strength yields a reliable estimate of the source location by peaking right above the top surface. Application of the method on aeromagnetic and gravity gradient tensor data sets from McFaulds Lake area indicates that most of the gravity and magnetic sources are located just beneath a 20 m thick (on average) overburden and delineated magnetic and gravity sources which can be probably approximated by geological contacts and thin dikes, come up to the overburden.