Paleoseismological investigations along Joggers Park Fault,Port Blair,South Andaman: Implications towards delineation of blind thrusting and related crustal deformation through ground penetrating radar (GPR) and electrical resistivity techniques (ERT and VES)

A comprehensive paleoseismological investigations was carried out along Joggers Park Fault (striking NW-SE) reveals pervasive traces of active compressional tectonics (blind thrusting) and shallow-surface recent tectonic signatures along the fault. The geometry and trends in the tectonic deformation of the blind zone (shallow-subsurface stratigraphy) were examined with GPR and electrical resistivity tomography (ERT) and vertical electrical sounding (VES), strongly demonstrates severe tectonic deformation (persistent tectonic style) from Quaternary to Holocene times in a compressive stress regime. The surface manifestation of deformation (topographic expression) along the fault can be marked as hanging wall uplift and can be visually observed in the exposed sections at nearly half kilometer distance from the table land of Joggers Park in the form of a well-defined disconnected high angle thrust fault scarp (reverse fault). The reverse fault kinematically and intimately linked with underlying fault geometry provides adduced evidence to evaluate strike displacement profiles (displacement length relationship) along the fault. In addition, deformed stacked colluvial wedge below the scarp represents a powerful basis to calibrate recent and long term slip rates of the underlying fault.     

A hybrid clustering-fusion methodology for land subsidence estimation

In many parts of Canada, limited data are available for hydrodynamic model inputs, and the ability to generate quality flood grids through 1D, 2D or 3D methods is nonviable. In this paper, the capability of simplified flood models, which rely solely on digital terrain models (DTMs), was explored to assess the quality and speed of their results. Results were validated against historic floods in two locations. Three non-physics-based simplified conceptual flood models were tested: (1) planar method, (2) inclined plane and (3) height above nearest drainage network (HAND) model. The accuracy and performance were evaluated using three criteria: inundation extent, water depth and computation time. Findings show that the HAND model is the best predictor of inundation extent, with Probability of Detection and Critical Success Index being higher than 0.90 in both study areas. Though the preprocessing time for the HAND model is lengthy, once completed, the time to simulate flooding at a variety of water levels is rapid, making this model the most suitable choice for web-based, on-demand flood inundation mapping. Knowledge of the fit of these flood models and associated uncertainty can be helpful to emergency managers such that they can better understand exposure and vulnerability while preparing flood response plans.     

Pressuremeter Modulus and Limit Pressure of Clayey Soils Using <Emphasis Type="Italic">GMDH</Emphasis>-Type Neural Network and Genetic Algorithms

Pressuremeter modulus (\(E_{M}\)) and limit pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.     

Using high-resolution satellite imagery to provide a relief priority map after earthquake

After the earthquake occurrence, collecting correct information about the extent of damage is essential for managing critical conditions and allocating limited resources. The prepared building damage maps sometimes bring about waste of time required for rescuing individuals under the rubble by wrongly conducting rescue teams toward regions with a lower rescue priority. In this research, an algorithm based on using a proposed standard at database level was developed to prioritize damaged buildings by considering five key elements of land use type, the degree of damage to buildings, the land use differentiation index, time of the highest population density in each land use, and time of disaster’s incidence. The steps of the proposed method which was implemented in the MATLAB environment include: detecting buildings on the pre- and post-event imagery, implementing texture features for each candidate building, choosing the optimal features by genetic algorithm, determining the degree of building damage in three classes of negligible damage, substantial damage, and heavy damage by using the difference between chosen features as inputs of the designed neurofuzzy inference system. Data collected from field observations were compared to the output obtained from the proposed algorithm. This comparison presented a general accuracy of 88% and Kappa coefficient of 79% in the classification of buildings into three damage classes. The proposed standard then was used for classifying damaged buildings into relief priorities of high, medium, and low. Findings revealed that the relief priority map could be a basis for correct guidance of relief and rescue teams during crucial times following earthquakes.     

Assessing Lateral Continuity within the Yammama Reservoir in the Foroozan Oilfield, Offshore Iran: An Integrated Study

Located in Iranian sector of the Persian Gulf, Foroozan Oilfield has been producing hydrocarbons via seven different reservoirs since the 1970s. However, understanding fluid interactions and horizontal continuity within each reservoir has proved complicated in this field. This study aims to determine the degree of intra-reservoir compartmentalization using gas geochemistry, light hydrocarbon components, and petroleum bulk properties, comparing the results with those obtained from reservoir engineering indicators. For this purpose, a total of 11 samples of oil and associated gas taken from different producing wells in from the Yammama Reservoir were selected. Clear distinctions, in terms of gas isotopic signature and composition, between the wells located in northern and southern parts of the reservoir (i.e. lighter δ13C1, lower methane concentration, and negative sulfur isotope in the southern part) and light hydrocarbon ratios (e.g. nC7/toluene, 2,6-dmC7/1,1,3-tmcyC5 and m-xylene/4-mC8) in different oil samples indicated two separate compartments. Gradual variations in a number of petroleum bulk properties (API gravity, V/Ni ratios and asphaltene concentration) provided additional evidence on the reservoir-filling direction, signifying that a horizontal equilibrium between reservoir fluids across the Yammama Reservoir is yet to be achieved. Finally, differences in water-oil contacts and reservoir types further confirmed the compartmentalization of the reservoir into two separate compartments.     

Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

Soil erosion is considered as the most widespread form of soil degradation which causes serious environmental problems. This study investigates the performance of the maximum entropy (ME) in mapping rill erosion susceptibility in the Golgol watershed, Ilam province, Iran. To this end, ten rill erosion conditioning factors were selected to be employed in the modelling process based on an investigation of the literature. These layers are: elevation, slope percent, aspect, stream power index, topographic wetness index, distance from streams, plan curvature, lithology, land use, and soil. Then, a training dataset of rill erosion locations was used for modelling this phenomenon. The area under receiver operating characteristics curve was used for evaluating the performance of the ME model. In addition, Modified Pacific South-West Inter Agency Committee (MPSIAC) framework was applied and sediment yield was determined for different hydrological units in the study area. At last, Jackknife test was implemented to show the contribution of the factors in the modelling process. The results depicted that area under ROC curve for training and validation datasets were 0.867, and 0.794, respectively. Therefore, this conclusion can be achieved that ME worked well and could be a good tool for generating rill erosion susceptibility maps and its output could be employed for soil conservation in similar areas.     

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.     

Field evidence of Quaternary seismites in the Mostaganem-Relizane (western Algeria) region: seismotectonic implication

Northwestern Algeria, Tell Atlas chain, belongs to the converging Africa-Eurasia plate boundary. Several active faults have been previously identified and several earthquakes occurred in the past. In the present study, seismites are observed in the Quaternary deposits. The identified seismites include injection sand dykes, pillar structures, pillow structures, load-cast structures, water escape structures, sismoslumps, thixotropic wedges, and thixotropic bowls. The following arguments support their seismic origin: (i) presence of active faults able of producing strong earthquakes, (ii) the granulometric characteristics of the deposits are favorable to liquefaction, (iii) the observed features, mainly those related to water escape structures, are comparable to those observed in modern earthquakes. Therefore, such features are evidence of the occurrence of earthquakes of M?>?5.5 magnitude in this study area, which may occur in the future.     

Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis

Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (V_p), dry density (γ_d), porosity, and water absorption (A_b) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.     

Skin friction behavior of pile fully embedded in limestone

Pile foundation system in limestone rock layers is commonly used in Riyadh area, Saudi Arabia, for high-rise building, bridges, and other structures. Especially in Riyadh region, bored piles are used for bridges and underpasses not only because of bearing capacity but mainly because of limited spaces for using shallow foundations. In addition, piles are used for bridges over wadis to avoid scouring effects. The loads are transferred by the pile from a superstructure to the rock strata through side friction and end-bearing resistance. However, there are no studies conducted in Riyadh area to quantify the skin friction capacity of pile embedded in limestone rock. Accordingly, this experimental study describes in details the behavior of load transfer mechanism through side friction only on a reinforced concrete pile (75 mm diameter and 150 mm long) constructed on hard limestone rock sample. Soft material (Styrofoam) was placed at the bottom of the pile to eliminate the effect of end-bearing resistance. Unconfined compression test was conducted on intact rock sample to find out the properties of the rock used. The result of the ultimate side friction obtained from the test was compared with values predicted by other researcher methods mentioned in the literature.