Role of crustal heterogeneity beneath Andaman–Nicobar Islands and its implications for coastal hazard

The Andaman–Nicobar (A–N) Islands region has attracted many geo-scientists because of its unique location and complex geotectonic settings. The recent occurrence of tsunamis due to the megathrust tsunamigenic north Sumatra earthquake (Mw 9.3) with a series of aftershocks in the A–N region caused severe damage to the coastal regions of India and Indonesia. Several pieces of evidence suggest that the occurrence of earthquakes in the A–N region is related to its complex geodynamical processes. In this study, it has been inferred that deep-seated structural heterogeneities related to dehydration of the subducting Indian plate beneath the Island could have induced the process of brittle failure through crustal weakening to contribute immensely to the coastal hazard in the region. The present study based on 3-D P-wave tomography of the entire rupture zone of the A–N region using the aftershocks of the 2004 Sumatra–Andaman earthquake (Mw 9.3) clearly demonstrates the role of crustal heterogeneity in seismogenesis and in causing the strong shakings and tsunamis. The nature and extent of the imaged crustal heterogeneity beneath the A–N region may have facilitated the degree of damage and extent of coastal hazards in the region. The 3-D velocity heterogeneities reflect asperities that manifest what type of seismogenic layers exist beneath the region to dictate the size of earthquakes and thereby they help to assess the extent of earthquake vulnerability in the coastal regions. The inference of this study may be used as one of the potential inputs for assessment of seismic vulnerability to the region, which may be considered for evolving earthquake hazard mitigation model for the coastal areas of the Andaman–Nicobar Islands region.     

Effects of errors and biases on the scaling of earthquake spatial pattern: application to the 2004 Sumatra–Andaman sequence

This study discusses the scaling properties of the spatial distribution of the December 26, 2004, Sumatra aftershocks. We estimate the spatial correlation dimension D ₂ of the epicentral distribution of aftershocks recorded by a local network operated by Geological Survey of India. We estimate the value of D ₂ for five blocks in the source area by using generalized correlation integral approach. We assess its bias due to finite data points, scaling range, effects of location errors, and boundary effects theoretically and apply it to real data sets. The correlation dimension was computed both for real as well as synthetic data sets that include randomly generated point sets obtained using uniform distributions and mimicking the number of events and outlines of the effective areas filled with epicenters. On comparing the results from the real data and random point sets from simulations, we found the lower limit of bias in D ₂ estimates from limited data sets to be 0.26. Thus, the spatial variation in correlation dimensions among different blocks using local data sets cannot be directly compared unless the influence of bias in the real aftershock data set is taken into account. They cannot also be used to infer the geometry of the faults. We also discuss the results in order to add constraints on the use of synthetic data and of different approaches for uncertainty analysis on spatial variation of D ₂. A difference in D ₂ values, rather than their absolute values, among small blocks is of interest to local data sets, which are correlated with their seismic b values. Taking into account the possible errors and biases, the average D ₂ values vary from 1.05 to 1.57 in the Andaman–Nicobar region. The relative change in D ₂ values can be interpreted in terms of clustering and diffuse seismic activity associated with the low and high D ₂ values, respectively. Overall, a relatively high D ₂ and low b value is consistent with high-magnitude, diffuse activity in space in the source region of the 2004 Sumatra earthquake.

     

Chemical weathering and gully erosion causing land degradation in a complex river basin of Eastern India: an integrated field,analytical and artificial intelligence approach

Hot and humid subtropical plateau regions are susceptible to land degradation in the form of weathering and gully erosion. Here, we investigate chemical weathering, gully erosion and cohesiveness through field-based measurements with a view to understand the controlling factors of potential land degradation, in complex river basin of the Chotanagpur plateau region in Eastern India. The layers of controlling factors of gully erosion were developed and prioritized considering boosted regression tree (BRT), alternative decision tree (ADT), particle swarm optimization (PSO) and random forest (RF) algorithms in the R software, and the results of these methods were also validated using receiver operating characteristic (ROC) curves. The spectroscopic analysis was carried out of collected soil samples to measure the degree of chemical weathering and cohesiveness. Furthermore, the climatic elements like temperature and rainfall were also considered for estimating the chemical weathering. The results of the gully erosion models (i.e., BRT, ADT, PSO and RF) show remarkable accuracy with ROC values of 0.93, 0.89, 0.91 and 0.84, respectively. An advanced decision tree model was integrated with the results of degree of chemical weathering and cohesiveness in geographical information system platform. The land degradation map developed from this approach shows that 10.53% of the study area is highly affected, whereas 17.36% area is moderately affected and the rest of the 73.85% area is less affected by land degradation. Our results provide essential information for policy makers in adopting measures for minimizing and controlling the land degradation. Our novel approach is significant to assess land degradation to a large scale.

     

Fluoride in groundwater: low-cost separation and stabilization by response surface optimization

A nanomembrane-based hybrid treatment system for separation of fluoride from contaminated groundwater and its subsequent stabilization in a solid matrix through chemical coagulation–precipitation process using response surface optimization for safe disposal were designed and investigated. The continuous flat-sheet cross-flow nanofiltration membrane module with well-screened commercial polyamide composite membrane succeeded in removing 99 % fluoride from water while yielding a pure water flux as high as 158–160 L/m²h of a transmembrane hydraulic pressure of only 14 bars. Such an operating pressure is much lower than that required in reverse osmosis for the same separation. The designed system for the first time provides a total solution to a complex problem in a very simple, compact, flexible, and novel design that ensures continuous, steady, and hassle-free long-term operation without the necessity for frequent replacement of membranes. The approximate cost for production of 1000 L of safe drinking water from fluoride-contaminated groundwater computes to only $ 1.4, indicating affordability in adopting the low-cost, high-flux water purification system by the affected people in many parts of the world.     

Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.     

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Natural Hazards - Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high...