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3D Interpretation of Resistivity Data for Groundwater Potential Assessment of Pakhli Plain, Mansehra District, Pakistan


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The present research describes a method of combining geostatistical analysis with geophysical inversion of electrical resistivity data conducted in Pakhli Plain, northwestern Himalayas, Pakistan. The raw data has been collected from the Technical Report VII-I on Ground Water Resources in Pakhli Plain, Mansehra District. Subsequently, the data has been deciphered and broadened from one dimensional resistivity data into a 2D model that can be entirely visualized and deduced in a spatial sense. Interpretation and calibration of the electrical resistivity curves with the lithologies and geophysical logs of boreholes suggests possible identification of distinctive sedimentary accumulations occurring within the Pakhli Plain. The 2D and 3D gridding and visualization is imperative to map the extents of the alluvial deposits within the Pakhli Plain formed during the periods of extreme tectonic activity. The coarser sediments are associated with lower levels of resistivity as measured in the electrical surveys, whereas the finer sediments exhibit characteristically lower resistivities. Therefore, the zones of low and high resistivity values are indicative of particles associated with coarser and finer sediments, respectively. It has been mentioned that the Pakhli Plain has remained a lacustrine zone during some time in the geological past as indicated by low resistivities representing finer sediments in the middle of the Plain. Consequently, the overall transmissivity of the sediments is low, which imply poor conditions for commercial groundwater production in the Pakhli Plain. Moreover, high resistivity zones of coarse material could be further investigated for groundwater potential areas. In particular, the prime objectives of the present study include 3D modeling of underground resistivity and its exploration in terms of groundwater potential on the basis of distribution of low resistivity zones.

eISSN:
2521-2923
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Geosciences, Geography, Geophysics, Remote Sensing, other