Topological analysis is a valuable tool for understanding the interactions, age relationships and kinematics of faults, and, hence,their role in making predictions about seismic hazard and subsurface fluid flow. Its application, however, requires caution. This paper examines what fault maps can reveal about the underlying topology of fault networks, highlighting the limitations imposed by data quality, mapping techniques and resolution. Three fault maps from central and southern Germany, representing different scales, are analysed to illustrate how topological methods can be used to assess map quality and resolution. Variations in geometry and topology, including differing proportions of I-, Y-, and X-nodes, are evident across maps of varying scale and resolution. The apparent connectivity of fault networks increases with resolution, as demonstrated using published maps clipped according to fault throw. Topology provides a means to assess, validate and compare fault maps, helping to identify issues such as improbable fault intersections and inconsistent abutting relationships. A high proportion of X-nodes, for example, may indicate problems with mapping or digitisation. Differences in network topology across adjacent areas may reflect variations in mapping techniques or strategies. Caution is advised when extrapolating fault network topology across scales or regions, particularly without accounting for differences in geological settings, lithologies and mapping methods. • Fault map topology is highly sensitive to resolution and interpretation style. • Higher resolution maps show greater fault connectivity and range of orientations. • Node type proportions (I, Y, X) reflect map resolution and mapping techniques. • Topological analysis can identify mapping inconsistencies and errors. • Fault maps must be critically evaluated before use in hazard or flow models.

Active fault reactivation poses significant hazards, and understanding their near-surface structure is crucial for mitigating seismic risk. Along the Balakot-Bagh fault (BBF), the source of the 2005 Mw 7.6 Kashmir earthquake, geomorphic evidence is gradually eroded and sedimented. Traditional Electrical Resistivity Tomography (ERT) often produces smoothly varying tomograms that obscure sharp structural boundaries. This study introduces an integrated interpretation framework that combines geological mapping, high-resolution ERT imaging, and machine learning (ML) k-means clustering to improve characterization of the BBF's shallow deformation zone at Sar Pain (S1) and Naushahra (S2), Pakistan. Geological surveys at S1 document numerous NW-SE-trending coseismic rupture strands with vertical displacements of 0.1-3m, defining an actively deforming damage zone, while at S2, no surface rupture is preserved due to thick alluvial cover. Inverted ERT models at both sites reveal low-resistivity anomalies associated with fractured, water-saturated materials and fault gouge; however, conventional inversions smooth sharp resistivity gradients, limiting structural interpretation. Applying k-means clustering as a post-inversion segmentation tool transforms continuous resistivity fields into discrete lithological and structural domains. The Elbow method is used to determine the optimal number of clusters to improve interpretability. The clustered models sharpen structural discontinuities, delineate fault cores and subsidiary strands at S1, and reveal concealed deformation at S2 where surface evidence is absent. This integrated interpretation framework significantly enhances the resolution and interpretability of near-surface fault architecture within a crustal-scale thrust system. The approach is particularly effective for imaging buried fault segments and has important implications for seismic hazard assessment and land-use planning.

Machine learning (ML) based approaches for rainwater harvesting (RWH) site suitability mapping often face limited data for training ML models. This study investigates whether the knowledge learned from data-rich catchments can be effectively transferred to the hydrologically and geomorphically similar and distinct basins. The transfer-learning (TL) framework is tested across three Indian catchments of different climatic conditions in the states of - Odisha, Maharashtra, and Tamil Nadu-using seven key influential predictors derived from LiDAR DEMs, geological and soil maps, and GR4J-simulated annual streamflow. A total of 18 experimental cases are designed, viz., intra, direct transfer, adaptation-based transfer, and multi-catchment combinations. Feature similarity between the source and target catchments is quantified using Kullback-Leibler (KL) divergence to systematically assess conditions for transfer feasibility. Four ML models-Support Vector Machine (SVM), Random Forest (RF), XGBoost (XGB), and KNN (K-Nearest Neighbour)-are trained over 100 randomized iterations using curated suitable and unsuitable RWH locations. Results show that performance of direct transfer declines sharply when dominant catchment features exhibit high divergence. RF and XGB are more resilient to cross-domain variability, while SVM and KNN are highly sensitive to feature mismatches. Incorporating a 20% target-domain sample substantially improves kappa and F1 scores by 25-40% in Odisha, 20-30% in Maharashtra, and 35-45% in Tamil Nadu, respectively, and stabilizes feature importance between the source and target domains. Multi-source training, which aggregates data from all three catchments, achieves the highest accuracy (>0.95) and kappa (>0.85). KL divergence analysis confirms that transferability depends on geomorphic similarity; that is, lower divergence enables effective transfer, whereas higher divergence reduces generalization. Overall, this study demonstrates that TL is feasible and beneficial for RWH site mapping, provides a practical and transferable tool for sustainable RWH planning in data-limited regions and also builds foundation for developing wide area scalable models by continuously updating the weights of influential thematic layers as new training data becomes available.

Abstract This study investigates the geological architecture and tectonic evolution of the Queih shear belt, located in the Central Eastern Desert of Egypt, within the Egyptian Nubian Shield. An integrated approach combining multi-sensor satellite remote sensing (Landsat-8, ASTER, Sentinel-1 A), aeromagnetic data analysis, and systematic field investigations was employed to delineate lithological units, structural features, and deformation phases. Advanced image processing and Enhanced Horizontal Gradient Amplitude (EHGA) techniques enabled precise mapping of lithological contacts, faults, folds, and shear zones. The results reveal that the Queih shear belt is a complex horst structure of Precambrian basement rocks, bounded by major normal faults and affected by a polyphase tectonic history. Four principal ductile deformation episodes (D1–D4), followed by Phanerozoic extensional tectonics (D5), are documented, involving NNW–SSE and ENE–WSW compression, transpressional shearing, and alternating dextral and sinistral strike-slip faulting related to the Najd Fault System. The spatial and temporal relationship between molasse sedimentation and phases of wrench faulting is highlighted, providing new insights into the interplay between sedimentation and tectonic reactivation during the late Neoproterozoic. This work refines geological maps of the region and advances understanding of crustal processes the Egyptian Nubian Shield.

Aeromagnetic mapping and geodynamic reconstruction of the Ediacaran Najd fault system in the Arabian Shield

Unconformity-Bounded Stratigraphic Units (UBSUs) can provide an invaluable approach to the geological mapping of Quaternary successions by delineating large-scale stratigraphic patterns that reflect changes in accommodation and sedimentation mostly dictated by glacio-eustatic variations. Through the example of the Po Basin, we document that a mosaic of 115 stratigraphic units, which reflect local nomenclature from 45 official geological maps, can be grouped within a markedly simpler and unambiguous framework of few regionally mappable UBSUs using the concepts of cyclostratigraphy. Synthems that developed in response to 100-kyr, eccentricity-driven glacio-eustatic fluctuations, with characteristic transgressive-regressive facies patterns and a diagnostic pollen signature, represent the building block of the Middle Pleistocene-Holocene subsurface stratigraphy and are here suggested to serve as a primary guide to geological mapping well beyond the regional scale. Synthem boundaries correspond to transgressive surfaces and correlative fluvial abandonment surfaces: they are marked by remarkable landward facies shifts that developed at the onset of interglacial periods in response to abrupt sea-level rise. Synthems may include internal unconformities of local to regional extent that reflect transition to glacial/cold periods and that are correlated to laterally extensive fluvial channel-belt sand/gravel bodies. Synthem boundaries can be tracked with good continuity through the depocenters; in contrast, they are correlated only tentatively into uplifted fluvial terraces and glacial/fluvioglacial successions buried or cropping out discontinuously at basin margins or across buried thrust fronts. Prominent key horizons demarcate the onset of the present (MIS1), last (MIS5e), and penultimate (MIS7) interglacials. Such surfaces offer the highest potential for global correlation. • We propose a simple framework for Quaternary geological mapping of the Po Basin. • 115 Quaternary stratigraphic units can be grouped into few regionally mappable UBSUs. • Synthem boundaries are key surfaces that formed at glacial/interglacial transitions. • Prominent key horizons demarcate the onset of MIS 1, MIS 5e, and MIS 7 interglacials. • UBSU that coincide with interglacial/glacial cycles offer high potential for global correlation.

ABSTRACT The southeast Seoul region was the early center of Baekje, an ancient kingdom that emerged in the central part of the Korean Peninsula. However, extensive urban development has significantly altered the original topography, imposing limitations on understanding the ancient landscape and archeological remains. This study aims to identify the geoenvironmental conditions of the area prior to urbanization and examine how they influenced the settlement location and architectural activities of early Baekje. Using topographic, geological, and landform maps, the study reconstructs the pre-urbanization geoenvironment. In addition, drilling data were employed to reproduce the spatial distribution of soil. The findings indicate that terraces, natural levees, and clay layers were key geoenvironmental elements. Among these, terraces and levees were preferred for residential sites in early Baekje. Clay plains were used for wet rice cultivation, and locally available clay resources appear to have been actively utilized as building materials. These results suggest that early Baekje communities adapted to and actively utilized their environmental conditions to support their development. This study enhances the understanding of land-use patterns in early Baekje and provides a foundational reference for predicting the distribution of buried cultural heritage and designating conservation areas, offering practical implications for urban archeology.

This study focuses on lithological identification in the Wasegi Indah area, Prafi District, Manokwari Regency, West Papua, using Digital Elevation Model (DEM) and Landsat 8 imagery. Geological surveys traditionally rely on costly and time-consuming fieldwork, but remote sensing technologies, especially DEM and satellite imagery, offer efficient alternatives for geological mapping. This research aimed to process and analyze DEM data alongside Landsat 8 imagery to identify lithological characteristics in the region and compile a lithological boundary map. The study utilized a combination of primary data, including Landsat 8 imagery and DEMNAS data, along with secondary data from geological maps to support geological and lithological interpretation. The results demonstrate that combining Landsat 8 composite images with DEMNAS imagery provides valuable insights into the lithological distribution, aiding in the identification of geological formations such as granite, diorite, and limestone. The findings indicate the potential of remote sensing in geological mapping, although the study also highlights the need for further image processing techniques to improve accuracy. In conclusion, while the lithological maps generated offer regional insights, more detailed mapping is necessary for specific applications like resource exploration and geological hazard management.

This study examines the grain-size distribution of the Tutut Formation sandstone using granulometric analysis. The purpose of this granulometric observation is to determine the depositional environment and its transportation mechanisms. In addition to using granulometric methods, this study also includes geological mapping to observe the rock lithology in the field. The results show that the study location consists of sandstone and mudstone units of the Tutut Formation, Meulaboh sand deposit units, and alluvial deposit units. The characteristics of Tutut Formation sandstone are gray-brown, reddish-brown due to weathering, coarse to fine sand grain size, medium to slightly rounded roundness, and does not react with HCl. Based on granulometric analysis, Tutut Formation sandstone has an average grain size of 0.214-0.327 ϕ, which is classified as coarse sand. The degree of sorting is 0.321 ϕ to 0.417 ϕ, with an average of 0.35 ϕ, so it is classified as good. Skewness values range from 0.107 to 0.263, with an average of 0.1775 ϕ, indicating fine skewness. The 0.1775 ϕ of the average skewness value indicates that the flow in this depositional environment is turbulent and a river depositional environment. Meanwhile, the kurtosis value ranges from 0.060 to 0.11, indicating a platykurtic to highly platykurtic distribution, suggesting a fairly diverse and even grain-size distribution from fine sizes in the matrix to coarse sizes. The diverse grain size implies that the matrix fills the pores between coarse grains. Thus, porosity is not available and has no potential as a reservoir rock.

Multi-element geochemical data derived from systematic surveys from a variety of media within a geospatial continuum contain information that provides insight into processes that reflect mineralogy, paragenesis, alteration and economic mineralization. The relationships of the elements and corresponding attributes using a range of data analytical methods, including univariate and multivariate statistical methods along with machine learning, provide the framework for building knowledge from which extended methods of artificial intelligence can enhance model building and mineral systems discovery. This contribution provides the elements for the design of workflows that meet the requirements of enhancing knowledge from geochemical and mineralogical surveys for the purposes of geologic mapping, mineral resource prediction, or environmental management. Further, it outlines a framework for a systematic evaluation of geochemical data plus attributes that enable the discovery of processes from which models can be constructed and tested using machine learning methods. Methods are described for “process discovery” followed by additional methods for “process validation/prediction”. Six case studies are presented that highlight different approaches to the discovery of processes that assist in mineral exploration and geologic mapping at various scales. The workflow includes caveats and flags potential issues or limitations on what can be discovered and validated.

Reconstructing the landscape evolution of the Apennines peri-Adriatic belt requires separating climatic and tectonic signals within its fluvial terrace record, a task so far limited by sparse chronological constraints. Here, we mapped terrace features from three rivers draining the peri-Adriatic belt by combining semiautomatic extraction of tread surfaces from high-resolution digital terrain models with detailed mapping of associated basal strath surfaces using the 1:10,000 geological map. Nine luminescence and radiocarbon ages, integrated with sedimentological data and published chronologies, refine the timing of terrace formation for the region. Although age uncertainties are substantial, cumulative probability distributions indicate that many terrace deposits broadly coincide with late-interglacial cooling and glacial conditions, whereas older terraces show more dispersed ages. Age-elevation modeling reveals spatially variable uplift histories since ~1 million years ago, consistent with differential crustal uplift or long-strike variations in anticline growth. Overall, these results provide new constraints on the interplay between climate and tectonics shaping the peri-Adriatic landscape.

The Srikakulam district in eastern India frequently experiences water stress due to rainfall variability, inadequate storage facilities, and increasing agricultural and domestic demand. This study aims to identify regionally suitable sites for dams and barrages along the Nagavali and Vamsadhara rivers to support integrated water resources planning in a data-limited semi-arid basin. Remote sensing and Geographic Information System (GIS) techniques were integrated with the Analytical Hierarchy Process (AHP) to assess regional suitability. Sixteen thematic layers derived from Landsat imagery, ASTER DEM, published geological maps, and long-term rainfall data were used to represent topographic, geological, geomorphological, land surface, and hydrological factors. Criteria weights were assigned through expert-based pairwise comparison and validated using consistency ratios. A composite suitability map was produced using a weighted overlay approach. The results classify zones of high, moderate, and low suitability for water-harvesting structures. Validation against the spatial distribution of existing dams and barrages showed strong agreement, confirming the reliability of the proposed screening framework. This research report offers a generalisable and economical GIS–AHP procedure of preliminary screening of potential dam and barrage locations in the data-deficient semi-arid basins, and can be applied in other hydro-climatic environments.

The ocean environment is a highly complex acoustic space where Sound Navigation and Ranging (SONAR) systems play a crucial role in detecting, classifying, and interpreting underwater signals for defense, geological exploration, and environmental monitoring. In recent years, over 70% of underwater monitoring data has been identified as acoustically rich but noisy, with more than 60% of manually analyzed samples showing inconsistency due to signal overlap and human bias. The need for automated and accurate sound classification and regression modeling arises in applications such as submarine detection, marine geological structure mapping, and industrial underwater noise assessment, where realtime and precise sound source identification is essential. Traditional manual classification methods suffer from high subjectivity, delayed analysis time, and inefficiency in handling large multivariate datasets with overlapping frequency domains. To overcome these limitations, this study introduces a Hybrid Hydro-Acoustics framework that combines regression and classification through an ensemble learning approach. The framework first preprocesses multivariate SONAR data—such as frequency, amplitude, and power spectral density—and then utilizes the ensemble mechanism to integrate decision boundaries and regression estimates from multiple learners. The existing algorithms, including Support Vector Classifier (SVC), Support Vector Regression (SVR) models and Gradient boosting (GB) CART as baseline learners for both regression and classification tasks, while enhancing prediction robustness using an Ensemble Extra Decision Tree (Ensemble EDT) strategy. The proposed Ensemble EDT for Classification effectively captures nonlinear separations in acoustic features, while Ensemble EDT for Regression provides improved prediction accuracy and stability for continuous parameters like sound intensity and frequency response. This hybrid ensemble framework demonstrates superior adaptability and generalization, enabling efficient modeling of complex underwater sound environments with higher accuracy, reduced overfitting, and enhanced computational efficiency.

The Ruby metamorphic core complex domain encompasses an ∼10,000 km2 area that includes the Ruby Mountains, East Humboldt Range, Wood Hills, Windermere Hills, and Pequop Mountains in northeast Nevada, USA. The domain contains metamorphic and mylonitized mid-crustal rocks that were exhumed by an intricate array of poorly understood Late Cretaceous−Cenozoic normal faults. This study establishes the first holistic structural architecture and sequence of normal faulting in the Ruby metamorphic core complex domain. The architecture and sequence of faulting are established by synthesizing new and published geologic mapping, and geochronology and sedimentology of synextensional basin fill. Our synthesis shows that after Mesozoic thrust faulting via the Windermere-Angel Lake and Independence thrusts, including a period of Barrovian metamorphism, the core complex sustained four phases of exhumation accommodated by normal faulting. The first phase of normal faulting produced the top-to-the-west to -northwest Pequop fault and was active at some time between 84 Ma and 41 Ma. The second phase began between 38 Ma and 35 Ma and was accommodated by the newly recognized, top-to-the-northwest Ruby-East Humboldt (REH)-Holborn fault. The REH-Holborn fault was a rolling-hinge-style, ductile-to-brittle normal fault that created a synextensional basin filled with sediment of the Clover Creek formation from at least 35−17 Ma. Basin filling was followed by the extinction of the fault between 17 Ma and 15 Ma. The third phase of exhumation was accomplished by N-striking horst-and-graben−style normal faults of the 16 Ma to >3 Ma east-dipping Thousand Springs and west-dipping Knoll-Ruby fault systems, whose synextensional basins filled with sediment of the Humboldt Formation. The modern range-bounding normal faults are accomplishing the fourth and ongoing phase of exhumation. This study has implications for previous work that attributes high pressures recorded by metamorphic rocks to tectonic overpressure in that it shows that significant thrust faulting was a prominent contributor to the development of high pressures. Moreover, this study shows that significant Eocene−Oligocene exhumation of mid-crustal rocks−−previously attributed to diapirism in the absence of regional extension−−occurred during extension and was accommodated by the REH-Holborn normal fault. This indicates that diapirism was not the primary mode of exhumation.

Remote sensing offers distinct advantages for lithological mapping, but its ability to detect underlying bedrock is limited in covered areas, whereas geochemical data are constrained by sparse sampling and low spatial resolution. To address these challenges, this study proposes a texture-guided adaptive data fusion framework combined with a Multi-scale Convolutional Neural Network (MCNN) for lithological mapping, using the Guyang area in Inner Mongolia as a case study. First, the non-linear relationships between geochemical components and remote sensing spatial textures are modeled to achieve complementary integration of heterogeneous multi-source data. Second, an MCNN model is constructed to extract multi-scale geological features, enabling improved discrimination of lithological units and more effective inference of concealed bedrock beneath Quaternary cover. Experimental results show that the proposed method overcomes the limitations of single data sources and achieves an overall accuracy (OA) of 0.95 on the fused dataset. Ablation experiments further demonstrate that the texture-guided fusion strategy significantly improves lithological identification performance. This study provides an effective framework for intelligent geological mapping and confirms the feasibility of inferring underlying bedrock in covered areas using multi-source surface information.

ABSTRACT In 3D geological modeling of long-distance lines, building a model for the entire area is often a lengthy process. Moreover, the reliability of the model is sometimes uncertain. These problems arise because the line covers a large region, involves complex strata, shows strong stratigraphic variations, and passes through areas with large elevation changes. A practical solution is to divide the line into smaller segments for modeling. This study introduces an adaptive segmentation algorithm that integrates topographic data, geological maps, and borehole stratigraphy. The algorithm enhances segmentation by developing a cost function that incorporates topographic roughness, stratigraphic variations, and geological boundary density. This further improves the results by minimizing the cost of inconsistency. It applies dynamic programming to calculate the optimal segmentation points. Through dynamic programming, the model identifies the best segmentation points, ensuring adaptive segmentation. In addition, this paper develops a comprehensive evaluation system for segmentation. The system includes three indicators: the segmentation quality score, modeling efficiency gain, and model accuracy. These indicators allow assessment from three perspectives: the original data used, the benefits for modeling efficiency, and the accuracy of the segmented 3D model. The paper also introduces a formula to measure borehole stratigraphic similarity by combining thickness and category factors. This approach enables a quantitative evaluation of the model’s accuracy. The accuracy is measured by comparing the similarity between the virtual boreholes in the model and the real borehole data. The approach was validated via empirical data from the Xinjiang-Xizang Railway project. The results show that adaptive segmentation reduces the modeling time by approximately 40%, and the model’s accuracy has been improved by at least 5% compared with other segmentation methods. Overall, the proposed framework offers a reliable solution for 3D geological modeling of large-scale linear projects.

A lateral variation in the heterolithic platform facies is present on the southern flank of the Askrigg Block where it transitions to mudrock-dominated basinal facies of the Craven Basin during the Brigantian substage. Revised geological mapping and new micropalaeontological assessment are used to correlate Transition Zone strata with the mixed carbonate and deltaic (Yoredale) cyclic facies of the Alston Formation of the Askrigg Block. This shows the Hawes Limestone to be entirely early Brigantian in age, with a consistent thickness across the study area. The overlying Gayle and Hardraw Scar cycles show marked variations consistent with displacement on the North Craven Fault, whereas the subsequent Simonstone and Middle Limestone cycles are absent within the Transition Zone. The Transition Zone includes younger Brigantian strata than found on the southern Askrigg Block. The Transition Zone is demonstrated to be: (1) affected by early Brigantian regional extension leading to transtensional oblique-slip displacement on the North Craven Fault and northward tilting of the Askrigg Block; (2) development of an erosive palaeo-slope in the Transition Zone during the early to late Brigantian extending across the North Craven Fault in response to transpressional structural inversion, uplift and erosion; (3) onlap of the late Brigantian Alston Formation and subsequently Bowland Shale Formation onto the incised Alston Formation during post-rift regional subsidence; and (4) ultimate northward tilting of the Askrigg Block during the early Pendleian in response to transpressional oblique-slip on the North Craven Fault, prior to deposition of the Millstone Grit Group.

Refers to: Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasília Belt, Central Brazil. Journal of the Geological Survey of Brazil, Volume 8 (1), April 2025, pages 51-63. View pdf Following a request from an author/reader regarding the lack of proper attribution to his work in our article, we apologize for the unintentional error and have added the correct credit as follows: 1) Page 53 (caption of Figure 1): Figure 1: a) Location of the Tocantins Province in Central Brazil; b) Study area in the central portion of the Brasília Belt; c) Simplified Geological Map of the Goias Tin Province and the placement of the granites from the Tocantins and Paranã Sub-Provinces (modified from Costa Filho 2020). 2) Page 53 (last paragraph): This is similar to the ages of 571 ± 24 Ma (Teixeira 2002) and 546 ± 11 Ma (Costa-Filho 2020), both using the U-Pb method in monazite and to 530 Ma reported by Hasui and Almeida (1970), in biotite from the Serra da Mesa granite. Guilherme Ferreira da Silva and co-authors. Reference: Costa-Filho D.S. 2020. Caracterização mineralógica e proveniência de Monazita-(Ce), Xenotima-(Y) e Zircão de Placer na Província Estanífera de Goiás : estão estes minerais relacionados com o granito tipo-A Serra Dourada? Dissertation. Instituto de Geociências, Universidade de Brasília, Brasília, Distrito Federal, 48 p. http://repositorio.unb.br/handle/10482/39482

ABSTRACT The 1:30,000 scale geological map of the Viù Valley (∼200 km²) depicts the tectonic architecture of the Susa and Lanzo Valleys Ophiolites (SLVO), a slice of the Jurassic Ligurian-Piedmont oceanic lithosphere stacked in the Western Alps. The SLVO successions (i.e. serpentinite, metagabbro, metabasalt and metasediments) were subducted, metamorphosed under high-pressure conditions, and exhumed during the Alpine orogeny. Detailed mapping and structural analysis highlight that the SLVO underwent four deformation phases and consist of six tectonic units bounded by two first-order shear zones (i.e. Susa and Viù-Locana shear zones) and crosscut by a network of second-order shear zones (i.e. Usseglio, Margone, Lemie, Ovarda, and Cugni shear zones). The current tectonic framework was mainly established during the long-lasting exhumation-related D2 phase, characterized by a switch from early top-to-W to late top-to-E kinematics. This map details the SLVO lithostratigraphic and structural setting, improving knowledge about the ophiolites of the Alpine-Apennine subduction-exhumation system.

ABSTRACT A new geological map of the northern sector of the Southern Main Ethiopian Rift is presented, providing unique insights into the processes driving continental rifting (ca. 45 Ma to the present). The map includes a cross-section through the rift valley and records multiple phases of rift evolution. This region, shaped by ongoing extension between the Nubian and Somalian lithospheric plates, preserves a tectonomagmatic history typical of active continental rifts. Evolution begins with a ‘pre-rift’ phase (Eocene-Oligocene), characterized by extensive flood basalt volcanism dominated by tholeiitic to alkaline basalt and trachybasalt flows, driven by mantle plume upwelling. The ‘early-rift’ phase (Miocene) features fault-controlled bimodal volcanism and the initial development of the rift valley. The current ‘late-rift’ phase (Pliocene-Holocene) is dominated by magma-assisted extension with bimodal volcanism, including alternating alkaline basalts and alkaline to subalkaline rhyolite-trachyte flows. This phase also features lava domes, cinder cones, calderas, and voluminous felsic pyroclastic deposits.