Co-seismic gravity changes in the Koyna-Warna region: Implications of mass redistribution

Koyna-Warna Region (KWR) is one of the known sites for reservoir triggered seismicity. The continued triggered seismicity over the five decades is restricted to a region of about 600–700 sq. km, which provides a unique opportunity to monitor geophysical anomalies likely to be associated with seismicity of the region. Present study confers temporal gravity changes recorded by gPhone and GRACE satellite and interprets observed changes in conjunction with seismological, geodetic (cGPS) observations and groundwater level measurements. GRACE data suggest that seasonal vertical deformation due to hydrological loading is ～ 2 cm, which corroborates with continuous GPS observations. Seasonal hydrological loading of the region, which is in a phase of reservoir loading, might be influencing the critically stressed KWR leading to the seasonal seismicity of the region. The gPhone gravity data distinctly show co-seismic gravity signals for eight earthquakes of M_w > 2 and gravity anomalies show positive correlation on a logarithmic scale with earthquake released energy. To investigate the cause of gravity changes, an estimate is made for 14^th April 2012 earthquake for M_w 4.8 using fault dislocation model. The recorded gravity changes of 189 μGal by gPhone located at a distance of 28 km from the hypocentre is much more than the estimate of ～0.1 μGal calculated for M_w 4.8 Koyna earthquake. Therefore, it is inferred that co-seismic gravity signals for eight earthquakes are primarily caused due to redistribution of mass at shallow depth.     

Isostatic response of the Laccadive Ridge from admittance analysis of gravity and bathymetry data

The Laccadive Ridge (L-R), trending roughly parallel to the west coast of India, is an intriguing segment of the northernmost Chagos-Laccadive Ridge (C-L-R) system. Although crustal nature and isostatic response of the southern C-L-R is well known, there are no similar studies on the L-R. In the present study, the isostatic response of the lithosphere beneath the L-R is estimated so as to characterize its crustal nature, total crustal as well as effective elastic plate thickness and mode of compensation. Twelve gravity and bathymetry profiles across the ridge were analyzed using linear transfer function and forward model techniques. The observed admittance function within the diagnostic waveband of 250 < λ > 80 km (0.025 < k > 0.080 km⁻¹) fits well with (i) the Airy model whose average crustal thickness (T_c) and density are 17 ± 2 km and 2.7 × 10³ kg m⁻³, respectively, and (ii) the thin plate flexure model of isostasy with an effective elastic plate thickness (T_e) of 2–3 km. The estimated average crustal thickness and density are in good agreement with published seismic refraction results over the ridge. The results of the present study support an Airy model of isostasy for the L-R. The low T_e value, in view of other published results in the study area, suggests stretched and loaded continental lithosphere of the L-R during the evolution of the western continental margin of India.     

Estimation of atmospheric surface layer parameters and numerical simulation using MM5 at Mangalore, West Coast of India

Atmospheric surface layer meteorological observations obtained from 20-m-high meteorological tower at Mangalore, situated along the west coast of India are used to estimate the surface layer scaling parameters of roughness length (z _o) and drag coefficient (C _D), surface layer fluxes of sensible heat and momentum. These parameters are computed using the simple flux–profile relationships under the framework of Monin–Obukhov (M–O) similarity theory. The estimated values of z _o are higher (1.35–1.54 m) than the values reported in the literature (>0.4–0.9 m) probably due to the undulating topography surrounding the location. The magnitude of C _D is high for low wind speed (<1.5 m s^?1) and found to be in the range 0.005–0.03. The variations of sensible heat fluxes (SHF) and momentum fluxes are also discussed. Relatively high fluxes of heat and momentum are observed during typical days on 26–27 February 2004 and 10–11 April 2004 due to the daytime unstable atmospheric conditions. Stable or near neutral conditions prevail after 1700 h IST with negative SHF. A mesoscale model PSU/NCAR MM5 is run using a high-resolution (1 km) grid over the study region to examine the influence of complex topography on the surface layer parameters and the simulated fluxes are compared with estimated values. Spatial variations of the frictional velocity (u _*), C _D, surface fluxes, planetary boundary layer (PBL) height and surface winds are noticed according to the topographic variations in the simulation.     

http://www.sciencedirect.com/science/article/pii/S1674987112000254

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters.In particular,the behavior of earth resembles the non-linearity applications.An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth.Artificial Neural Networks(ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used.The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network.The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results.Initially,certain synthetic data sets of all three-layer curves have been taken for training the network,and the network is validated by the Held datasets collected from Tuticorin Coastal Region(78°7′30″E and 8°48′45″N),Tamil Nadu.India.The interpretation has been done successfully using the corresponding learning algorithm in the present study.With proper training of back propagation networks,it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network.The network is trained with more Vertical Electrical Sounding(VES) data,and this trained network is demonstrated by the field data.Groundwater table depth also has been modeled.     

Seasonal variations in the sediment biogenic properties of a tropical mangrove environment,southwest coast of India

Temporal and spatial changes in the sediment properties of a mangrove ecosystem (Cochin, southwest coast India) are presented. The region was freshwater dominated during monsoon (June–September) and seawater dominated during other two seasons. The system remained eutrophic due to the high inputs of organic matter (OM) during most part of the year. The organic-rich sediments accumulated high amount of carbohydrates (22% of OM) and proteins (11% of OM) during non-monsoon months as compared to coastal environments. Principal component analysis showed that the biochemical properties are uniformly influenced by seasonal and spatial variations. Higher concentrations of sediment protein over carbohydrate indicate an efficient mineralization leading to the non-availability of aged OM in the system. The dominance of these labile components is generally indicative of the eutrophic condition of the system.     

A nonlinear data‐driven model for synthetic generation of annual streamflows

A hybrid model that blends two non‐linear data‐driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non‐parametric resampling technique MBB to obtain innovations, which are then added back to the ANN‐modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN‐based non‐linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN‐based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric‐based hybrid model (LPHM), owing to the effective capturing of the non‐linearities. Moreover, the ANNHM, being a completely data‐driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non‐linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright © 2007 John Wiley & Sons, Ltd.     

Environmental applications of geophysical and geochemical methods to map groundwater quality at Tuticorin, Tamilnadu, India

Coastal aquifers can become polluted due to natural and human activities, such as intrusion of saline water, discharge of effluents in industrial areas and chemical weathering of natural geological deposits. The present study is aimed mainly at understanding the geophysical and chemical characteristics of groundwater near Tuticorin, Tamilnadu, India by studying the electrical resistivity distribution of the subsurface groundwater by applying the Schlumberger vertical electrical sounding (VES) technique followed by chemical analysis of water samples. A total of 20 VES soundings were carried out to understand the resistivity distribution of the area and 21 water samples were collected to analyze the chemical quality. The interpretation and analysis of the results have identified different hydrogeologic behaviors, a highly saline coastal aquifer and freshwater locations. The results obtained from geophysical and geochemical sampling are in good agreement with each other. The approach shows the efficacy of the combination of geophysical and geochemical methods to map groundwater contamination zones in the study area.