A spatial analysis for geothermal energy exploration using bivariate predictive modelling

Abstract

The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.

GIS-based multi-criteria decision models for barite exploration in Nigeria’s Benue Trough

“…No actual line of the boundary can be drawn to demarcate the specific portions, but key localities (towns/settlements) that establish the depocenter of the various sections have been well recognised and documented 26 , 28 , 29 .

Figure 1 Regional geology map of the Benue Trough showing the study location (Mid-Nigerian Benue Trough) 3 . Map was modified after Benkhelil 27 .

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“… Regional geology map of the Benue Trough showing the study location (Mid-Nigerian Benue Trough) 3 . Map was modified after Benkhelil 27 .…”

“…In most cases, a combination of existing geoscientific data such as geological, geophysical, geochemical, structural, and satellite data are indispensable requirements for an effective and thorough investigation of mineral resource occurrences 1 , 2 . Remote sensing (RS) and GIS-based techniques are a reliable platform for the integration of all these pools of geodata 3 , 4 . Sabins 5 described RS as the science of acquiring, processing, and interpreting images and related data, acquired from aircraft and satellites that record the interaction between matter and electromagnetic energy.…”

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

“…This is the reason why the geoengineering characteristics depend on the scale of the project are often approximated with different techniques and computer modelling approaches [14][15][16][17]. Producing 3D conceptual shallow geothermal potentials [18,19], utilizing GIS [20][21][22], spatial data analysis [23][24][25], applying numerical technique [26][27][28][29], integrated of different geophysical prospecting techniques such as magnetotelluric [30][31][32][33][34], gravity [33,35], seismic [31,33], and electrical resistivity [31,36,37], as well as evident geological characteristics [38,39] are some of the carried efforts in Spain, Chile, Pakistan, Iran, India, Nigeria, Indonesia, Denmark, China, Thailand, Italy, Taiwan, Finland and Japan. However, simulating the geothermal resources using numerical techniques due to complexity of the model preparation (natural state properties of the rocks and geothermal system), description of the realistic problem and evaluation of the results as well as inability in providing any insight into generalizations is a very time-consuming task that demands extensive experience.…”

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

“…A lot of GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], geographic information system (GIS) [20], statistical modeling [21] and conventional machine learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”