Exploring Heat Sources Using Gravimetric Data: A Case Study of Magadi Geothermal Prospect, Kenya

Understanding the location of the subsurface heat sources is crucial for efficient geothermal resource exploration and exploitation. This study aimed to investigate the faults and the depth to heat sources for a geothermal system in Magadi, southern Rift Valley, through the integration of gravity mapping, 3D Euler deconvolution, and spectral analysis. Gravity mapping is a powerful geophysical method widely used to infer subsurface density variations, which are indicative of geological structures and volcanic intrusions that can be potential heat sources. The Volcano-Tectonic and Fluvial-Deltaic Sedimentation process of the Kenyan rift which encompasses the Magadi basin are responsible for geomorphic and geologic processes in the area. Alkali lava sheets of Magadi plateau trachytes covered with lacustrine sediments characterize 80% of the area. Deeper is the Tanzanian craton basement, overlain by Pliocene to Miocene volcanic and sedimentary rocks. A gravity survey with a data density of 2.375 stations/km2 produced high-resolution anomaly and total horizontal derivative maps showing gravity highs between −180 mGals to −174 mGals along the eastern zone of the study area. A buried major fault trending N-S was delineated in the mid-upper region of the area by Euler solutions at an average depth of 350 meters. Deeper features associated with possible volcanic dykes and sills gave Euler depth ranges of 0.7 km to 2.2 km. Radial average spectral analysis showed depth to the top of shallow and deep features at 2.4694 km and 5.827 km respectively. The correlation between gravity anomalies, geological structures, and present hot springs supports the hypothesis that volcanic processes have played a significant role in the development of the geothermal system in the study area.

Spectral indices derived,non-parametric Decision Tree Classification approach to lithological mapping in the Lake Magadi area,Kenya

Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya.The work involves the collection of rock and soil samples in the field,their analyses using reflectance and emittance spectroscopy,and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method.The latter method is strictly non-parametric,flexible and simple which does not require assumptions regarding the distributions of the input data.It has been successfully used in a wide range of classification problems.The DTC method successfully mapped the chert and trachyte series rocks,including clay minerals and evaporites of the area with higher overall accuracy(86%).Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data.Moreover,the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately,which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction.