Hydraulic Significance of Fracture Correlated Lineaments in Precambrian Rocks in Purulia District, West Bengal

Abstract

Filter analysis of lineaments in Precambrian metamorphic rocks was used to delineate fracture-correlated lineaments and hydraulically significant fractures. The unfiltered analysis technique fails to show correlation between major lineaments and fractures. Domain-based and discrete filtering techniques successfully identify fracture-correlated lineaments within the brittle-ductile shear zone in conjunction with fractures characterized by high fracture frequencies (>10/m). The locales of hydraulically significant fractures can thus be assessed if the geological controls governing the spatial distribution of fracture frequencies are computed using structural domain approach. The concurrence of fracture-correlated lineaments and hydraulically significant fractures within the brittle-ductile shear zone is evident.

Geo-investigation on groundwater control in some parts of Ogun state using data from Shuttle Radar Topography Mission and vertical electrical soundings

“…In this investigative study, nineteen (19) resistivity soundings were carried out both in NE -SW and NW -SE orientations, where lineaments were concentrated and interconnected (Lattman and Parizek, 1964;Acharya et al, 2012) as shown in Figures 5a and b. ABEM 4000 series equipment was used and this computes and displays mean apparent resistivity for the electrode configurations of choice.…”

“…In addition, extracted lineaments from satellite imagery will aid the choice of VES point selection within the study area, such that these fracture traces will be the prospects for hydrogeophysical mapping in Iju -Ota community. Although some studies have reported significant deviations between the traced lineaments and the major fracture systems in some regions (Acharya et al, 2012;Acharya and Mallik, 2011;De'gnan and Clark, 2002), integration of geophysical method (such as electrical resistivity method) with satellite imagery for groundwater exploration will assist one to locate the correct point that is suitable for groundwater exploitation (Omeje et al, 2014;Dafalla et al, 2015;Anbazhagan and Jothibasu, 2016). This present study, therefore, employed the integration ofSRTM and VES with the aim to accurately investigate groundwater potential zones in an inter-bedded area (conglomerates of sedimentary and shallow basement rocks) such as Iju -Ota community in Ogun State, in order to address the inadequate water supply being experienced in the area.…”

“…It is however essential to understand the stratigraphic structures of a terrain before its groundwater resource can be optimally harnessed, either through dug wells or boreholes (Olasehinde, 2010). Various methods have been employed in groundwater exploration globally, ranging from traditional methods such as fracture trace method and borehole drilling (Lattman and Parizek, 1964;Acharya et al, 2012) to the use of geophysical methods such as electrical resistivity, magnetics, electromagnetics, seismic, radiometric techniques and so on (McNeill, 1991;Bernard and Legchenko, 2003;Goldman and Neubauer, 1994;Hewaidy et al, 2015;Shishaye and Abdi, 2016;Helaly, 2017;Muhammad and Khalid, 2017;Muthamilselvan et al, 2017;Poongothai and Sridhar, 2017;Umar et al, 2017;Gao et al, 2018). In addition, Sunmonu et al (2016), Adagunodo et al (2018) and Joel et al (2019a) had established that geophysical methods are capable to map the depth togroundwater table.…”

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

“…The distance from lineaments in the Hesare-No Basin was provisioned from the Landsat-8 OLI image as shown in Figure 5c. Another factor is lineament density, which is utilized to distinguish areas with great lineament concentrations that are correlated with permeability and GW potential [57]. Lineament density illustrates the total length of lineaments in a unit area, which is calculated by a line density function.…”

Mapping of groundwater potential zones in hard rock terrain using geoinformatics: a case of Kumari watershed in western part of West Bengal

“…1c). Northern portion of the study area is a part of the CGGC, consisting varieties of granite gneisses, such as quartz-biotite granite gneiss and porphyroblastic granite gneiss while the Singbhum Group (SG) rocks exposing at the south belongs to Singhbhum orogenic belt and comprises chiefly of mica schist and phyllite (Acharya et al 2012). The average rainfall of this region is 1330.8 mm/year (IMD), and the availability of surface water of the river basin as surface flow and storage in reservoirs has been calculated as 653 and 24 million cubic metres (mcm) (Kundu 2004).…”