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Abstract This study interprets aeromagnetic data from the Tizi n'Test area in the High Atlas massif of Morocco, aiming to gain insights into its litho-structural architecture and implications for mineral exploration and mining. We employed six different analytical techniques to the residual magnetic field data, including reduction to the pole (RTP), upward continuation, total horizontal derivative, Tilt angle, Centre for Exploration Targeting (CET) analysis, and Euler deconvolution. Our analyses differentiated the study area into three magnetic domains: the eastern Ouzellarh block, characterized by positive anomalies, a central domain characterized by a negative magnetic signature demarcating the transitional zone between the Anti-Atlas and the High Atlas separated by the Ouchden fault: and the western domain, represented by the Tichka massif. The application of total horizontal derivative, tilt angle, and a combination of filters in ternary image formats (Tilt angle, upward continuation 1000 + Tilt angle and upward continuation 3000 + Tilt angle) revealed both known and previously unidentified geological lineaments, mapping structural complexity across various orientations (NE–SW, NNE–SSE, E–W, NW–SE, and N–S). The CET grid analysis method unveiled the structural complexity, highlighting the geodynamic evolution of the region. Particularly, the Ouchden fault delineates a magnetic domain divide between the ancient High Atlas and the Ouzellarh block (Anti-Atlas). Furthermore, Euler deconvolution indicated magnetic source depths ranging from 52 m in the western domain of the Tichka massif to 6560 m in the Ouzellarh block. A comprehensive structural scheme, classified by C-A fractal analysis, identified zones favourable for exploration and mining, particularly along the Ouchden fault, Tizi n'Test, NE-SW trending lineaments in the northwestern domain, as well as along the Tichka granite’s margin.

Groundwater potential in Morocco’s Souss-Massa mountainous basin (SMMB) is being identified using geospatial tools and geological data. We deployed four mathematical models, namely Data-Driven Multi-index Overlay (DMIO), Geometric Average (GA), Support Vector Machine (SVM), and Logistic Regression (LR), to establish data-driven patterns among the nine influencing factors, primarily drainage density, permeability, slope, distance to rivers, elevation, lineament density, distance to lineaments, intersection node density, and rainfall. Based on the Concentration-Area (C-A) fractal approach, the findings of the four models were developed and classified into five levels of potentiality ranging from very low to very high. The regions designated as having high and very high potentialities for the DMIO, GA, SVM, and LR models, respectively, account for 22.44 %, 9.80 %, 19.36 %, and 26.77 % of the overall basin. We validated the models by calculating each model's area under the ROC curve (AUC). The estimated AUC values are more than 70 %, suggesting the model performs well. The four models' performance was compared, revealing that the SVM model outperforms the others. Gravimetric data shows that possible groundwater zones closely coincide with gravimetric lineaments. The findings of this study can provide valuable insights to decision-makers, allowing them to improve decision-making processes and develop holistic groundwater resource management in the Souss-Massa mountainous basin (SMMB).