Groundwater characterization in arid regions using seismic and gravity attributes: Al Jaww Plain, UAE

This paper discusses a multivariate approach aimed at geophysical characterization of subsurface rocks for groundwater exploration in arid environments. We integrate several findings obtained from a single-source, single-sensor seismic profile with gravity data to detail the tectonic settings and characterize the subsurface, water-bearing formations of the Al Jaww Plain, one of the most important groundwater reserves of the United Arab Emirates. The seismic data have been acquired in the framework of the first EAGE Middle-East Bootcamp. Edge-detection techniques applied to the Bouguer anomaly map of the Al Jaww Plain allowed us to infer the presence of unreported ENE trending tectonic lineaments. The prestack depth migrated seismic image shows a clear and previously unreported east-verging thrust affecting seismic wave propagation and consistent with gravity data which was interpreted as an evidence of a detachment fold and thrust above the bottom of Fiqa Formation. Using the first-arrival picking from the common shot gathers, we tested a coupled tomographic inversion of P-wave velocity and Q factor which was aimed at achieving a qualitative characterization of rock petrophysical properties. K-means clustering was used to classify and map subsurface areas characterized by similar observations of P- and Q-wave velocity and therefore to outline shallow zones (<600 m) that are consistent with geophysical properties which are typical for water accumulations and within the reach of water groundwater exploration.

Automated summary

This paper presents a multivariate approach for geophysical characterization of subsurface rocks for groundwater exploration in arid environments. Integrating seismic profiles and gravity data, the study details the tectonic settings and water-bearing formations of Al Jaww Plain in the UAE. Edge-detection techniques reveal unreported tectonic lineaments, and tomographic inversion is used to characterize rock petrophysical properties. K-means clustering is employed to classify subsurface areas with similar geophysical properties for groundwater exploration.