Wavefield analysis of crosswell seismic data

Despite the numerous advantages of crosswell seismic data over surface seismic data, crosswell seismic geophysics is still underutilized and underdeveloped. The factors limiting the full utilization of crosswell data include the lack of standardized methods for processing and imaging the data. This is because crosswell data is not completely understood. To improve the understanding of crosswell data, we performed acoustic and elastic modeling of a west Texas carbonate oilfield data using finite difference methods and crosswell geometry. To account for the different wave modes in the field data, we decomposed the full data into its constituent wave modes. Results of the forward modeling show that elastic synthetic data is a better representation of crosswell field data than the popular acoustic synthetic data. Wavefield decomposition gave insight into the time-space kinematics behavior of the different wave modes that constitute the full data. Overall, the study improved our understanding of crosswell field data. The learning from this study has been utilized to perform data-driven reflection enhancement processing where the discerned characteristic of different seismic arrival is utilize to suppress unwanted and enhanced the desired wave modes. The processing reduced the complex data to only up-going P-P reflections that can be imaged to reveal the subtle geological structures of the oilfield.     

Temporal change of glaciers area and geomorphometric parameters in Parbati valley,Himachal Pradesh,India

Temporal change in the glacier coverage is analyzed for the period between 1962 and 2003 in Parbati valley, Himachal Pradesh. It is observed that the total glacier cover has been decreased by 17% ranging between 8 and 100% for individual glacier. The pattern of de-glaciation shows a high degree of shrinkage in outer zone of Parbati valley, while least shrinkage is observed in the inner valley. The present study is conducted to establish relationship between glacio-geomorphic parameters and glaciers shrinkage pattern to predict the future glacier cover in warming scenario. A systematic change is observed for glacio-geomorphic parameters associated with temporal change in glacier cover. It is observed that mean and minimum elevation, slope, relief and duration of insolation have changed substantially. Maximum elevation, plan/profile curvatures and aspect have shown less change from 1962 to 2003. A correlation matrix between glacio-geomorphic parameters for glaciers between 1962 and 2003 shows that the recent glaciers are much more controlled by terrain characteristics than that in the recent past.     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.     

Basin hydrogeological characterization using remote sensing,hydrogeochemical and isotope methods (the case of Baro-Akobo,Eastern Nile,Ethiopia)

Hydrogeochemical and isotopic signatures of the waters of the Baro-Akobo River Basin show deviation from signatures in other Ethiopian river basins. In this study, hydrogeochemical and isotope methods were employed to determine regional and local hydrogeology and characteristics of the basin. Optical, thermal and radar remote sensing products were used to update geological and structural maps of the basin and determine sampling points using the judgment sampling method. A total of 363 samples from wells, springs, rivers, lakes, swamps and rain were collected for this study, and an additional 270 water quality data sets were added from previous studies. These data were analyzed for their hydrogeochemical characteristics and isotope signatures. Analysis of the oxygen, deuterium and tritium isotopes shows the groundwater of the basin is modern water. Among all basins in Ethiopia, the Baro-Akobo Basin shows the highest enrichment. This indicates the proximity of the rainfall sources, which presumably are the Sudd and other wetlands in South Sudan. The hydrochemical properties of the waters show evapotranspiration is the dominant hydrologic process in the basin and explains the large amount of water that is lost in the lowland plain. Analysis of radon-222 shows no significant groundwater flux over the wetlands, which are part of Machar Marshes. This shows evaporation to be dominant hydrologic process in this zone. Results from all analyses help explain the limited holding capacity of the aquifers in the recharge zone and their vulnerability to anthropogenic impacts and climate variability. There is a trend of decreasing surface flow and rainfall and increasing water soil erosion.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.     

Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest

Biophysical and biochemical plant foliage parameters play a key role in assessing vegetation health. Those plant parameters determine the spectral reflectance and transmittance properties of vegetation; therefore, hyperspectral remote sensing, particularly imaging spectroscopy, can provide estimates of leaf and canopy chemical properties. Based on the relationship between spectral response and biochemical/biophysical properties of the leaves and canopies, the PROSPECT radiative transfer model simulates the interaction of light with leaves. In this study, more than 1100 leaf samples from the Amazon forest of Ecuador were collected at several study sites, some of which are affected by petroleum pollution, and across the vertical profile of the forest. For every sample, field spectroscopy at leaf level was conducted with a spectroradiometer. The goal of this study was to assess leaf optical properties of polluted and unpolluted rainforest canopies across the vertical profile and identify vegetation stress expressed in changes of biophysical and biochemical properties of vegetation. An ANOVA followed by Holme’s multiple comparisons of means and a principal component analysis showed that photosynthetic pigments, chlorophyll and carotenoids have significantly lower levels across the vertical profile of the forest, particularly in sites affected by petroleum pollution. On the other hand, foliar water content showed significantly higher levels in the polluted site. Those findings are symptoms of vegetation stress caused by reduced photosynthetic activity and consequently decreased transpiration and water-use efficiency of the plants. Cross-comparison between SPAD-502 chlorophyll content meter index and chlorophyll content showed strong positive correlation coefficients (r = 0.71 and r ² = 0.51) which suggests that using the SPAD-502 chlorophyll index itself is sensitive enough to detect vegetation stress in a multispecies tropical forest. Therefore, the SPAD-502 can be used to assess chlorophyll content of vegetation across polluted and non-polluted sites at different canopy layers. The results presented in this paper contribute to the very limited literature on field spectroscopy and radiative transfer models applied to the vertical profile of the Amazon forest.     

Regional modeling of dust storm of February 8, 2015 in the southwest of Iran

According to World Meteorological Organization report in 2015, the southwest of Iran has become one of the dust sources in the region. And the objective of this research is to study the dust storms originating in this region. For this purpose, based on the weather data of 14 stations, the dust storms of the region were investigated, and the dust storm of February 7, 2015, was selected due to its very high concentration of dust particles (66 times the normal values). For the analysis of source areas and storm paths, the FNL data was used. The regional models of Navy Aerosol Analysis and Prediction System (NAAPS), Barcelona Supercomputing Centre-Dust Regional Atmospheric Model 8b (DREAM 8b), Non-hydrostatic Multiscale Model Barcelona Supercomputing Center (NMMB/BSC), and hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) were used to study and analyze the selected storm. The results showed that the dust event in February 8, 2016, has been the result of the polar front jet stream (PJF) caused by western immigrant system that had been over the Sahara in Africa, the deserts of Iraq, Syria, Saudi Arabia, and finally southwest of Iran in making the extreme dust event. According to the Moderate Resolution Imaging Spectroradiometer data and point models of NAAPS, optical depth was very high. The DREAM 8b and NMMB/BSC models confirmed the impact of the local factors and closeness to the dust source regions. The backward tracking of the model with the HYSPLIT model showed three tracks transporting the dust particles to the region. This software also showed that the dust particles occupied an atmospheric tunnel of 1.5 km in diameter.     

The Effects of Integrated Marsh Management (IMM) on Salt Marsh Vegetation,Nekton, and Birds

An integrated marsh management (IMM) project in an urbanized watershed on Long Island, New York, USA, aimed to mitigate salt marsh degradation and to reduce mosquito production by an innovative combination of restoration and open marsh water management methods. The grid ditch network at two treatment marshes was replaced with naturalized tidal channels and ponds. Effects of the hydrologic alterations were monitored utilizing a before–after–control–impact approach. The treatment marshes experienced a number of beneficial outcomes including a fourfold reduction in the invasive Phragmites australis and increased native vegetation cover in the most degraded portions of the marsh, increased abundance and diversity of marsh killifish and estuarine nekton species, higher shorebird and waterfowl densities, and increased avian species diversity. The successful implementation of IMM concept led to improved marsh health and diminished mosquito production. Therefore, this study may serve as a template for similar large-scale integrated salt marsh restoration projects.     

A Top Pilot Tunnel Preconditioning Method for the Prevention of Extremely Intense Rockbursts in Deep Tunnels Excavated by TBMs

The headrace tunnels at the Jinping II Hydropower Station cross the Jinping Mountain with a maximum overburden depth of 2,525 m, where 80% of the strata along the tunnels consist of marble. A number of extremely intense rockbursts occurred during the excavation of the auxiliary tunnels and the drainage tunnel. In particular, a tunnel boring machine (TBM) was destroyed by an extremely intense rockburst in a 7.2-m-diameter drainage tunnel. Two of the four subsequent 12.4-m-diameter headrace tunnels will be excavated with larger size TBMs, where a high risk of extremely intense rockbursts exists. Herein, a top pilot tunnel preconditioning method is proposed to minimize this risk, in which a drilling and blasting method is first recommended for the top pilot tunnel excavation and support, and then the TBM excavation of the main tunnel is conducted. In order to evaluate the mechanical effectiveness of this method, numerical simulation analyses using the failure approaching index, energy release rate, and excess shear stress indices are carried out. Its construction feasibility is discussed as well. Moreover, a microseismic monitoring technique is used in the experimental tunnel section for the real-time monitoring of the microseismic activities of the rock mass in TBM excavation and for assessing the effect of the top pilot tunnel excavation in reducing the risk of rockbursts. This method is applied to two tunnel sections prone to extremely intense rockbursts and leads to a reduction in the risk of rockbursts in TBM excavation.