Modeling geothermal energy potential zones in the Bertoua region and surroundings with machine learning using descent gradient based on linear-regression model (Eastern Cameroon)摘要
Renewable geothermal energy exploration is common to African countries as a reliable and vital solution to meet energy challenges. Cameroon, heavily involved in the exploration of mining and natural resources, has made geothermal energy one of the pillars of the country’s development. The present study aims to access geothermal potential zones in the region of Bertoua and surroundings. The methodological approach consisted of applying spectral analysis to aeromagnetic data and predicting the radial power spectrum from the descent gradient technique based on linear regression. The various maps of Curie depth, geothermal gradient and geothermal potential model from the magnetic sources were obtained using the cubic interpolation method in the Python environment language. The results show a curie depth ranging between 0 and 2100 m, with a mean depth of 1036 m. In addition, the mean value of the calculated geothermal gradient and the estimated heat flux are 1.46 °C/m and 3.6 mW/m2. The superficial curie depth and the high values of the geothermal gradient as well as the heat flow in the study area are mainly due to the magmatic intrusions that abound in the Bertoua region. According to the results, favorable localities for geothermal exploration are located in the northern part of Koundi, in Gamboula and in the southwestern part of Petit Bello as they present significant geothermal parameters variations ranges between 100 m and 2100 m, 3 °C/m2 and 14 °C/m2, and, 3 mW/m2 and 32 mW/m2 respectively. The proposed results in the present research study allow to have preconceived ideas for any geothermal exploration project in the region.
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