TY - JOUR AU - Ilyés, Csaba AU - Mohammed, Musaab Adam Abbakar AU - Szabó, Norbert Péter AU - Szűcs, Péter TI - A hybrid approach to exploring the spatiotemporal patterns of precipitation in Sudan: Insights from neural network clustering and Fourier-wavelet transform analysis JF - Water Cycle J2 - Water Cycle VL - 7 PY - 2026 SP - 151 EP - 163 PG - 13 SN - 2666-4453 DO - 10.1016/j.watcyc.2025.07.004 UR - https://m2.mtmt.hu/api/publication/36281873 ID - 36281873 LA - English DB - MTMT ER - TY - JOUR AU - Elbalawy, Mohamed AU - Hemida, Mohamed Hamdy Eid AU - Badawi, Mohamed AU - Takács, Ernő AU - Velledits, Felicitász TI - From Lithology Prediction to Geothermal Energy Production: Leveraging Data-Driven and Financial Insights to Unlock Geothermal Potential of a Carbonate Reservoir, SE Hungary JF - RENEWABLE ENERGY J2 - RENEW ENERGY VL - 259 PY - 2026 PG - 14 SN - 0960-1481 DO - 10.1016/j.renene.2025.125031 UR - https://m2.mtmt.hu/api/publication/36519211 ID - 36519211 LA - English DB - MTMT ER - TY - JOUR AU - Turainé Vurom, Brigitta AU - Dobróka, Mihály TI - Multiexponenciális kőzetfizikai modell a szeizmikus/akusztikus P- és S-hullám jósági tényező nyomásfüggésének leírására JF - MAGYAR GEOFIZIKA J2 - MAGYAR GEOFIZIKA VL - 66 PY - 2026 IS - 3 SP - 94 EP - 101 PG - 8 SN - 0025-0120 UR - https://m2.mtmt.hu/api/publication/36858558 ID - 36858558 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Hemida, Mohamed Hamdy Eid AU - Jlaiel, Khouloud AU - Elbalawy, Mohamed AU - Flores Carpio, Yetzabbel Gerarda AU - Mohieldain, Ali Ahmed AU - Nassar, Tamer AU - Abukhadra, Mostafa R. AU - Alqhtani, Haifa A. AU - Kovács, Attila AU - Szűcs, Péter TI - Aquifer characterization and salinization origin using unsupervised machine learning and 3D gravity inversion modeling, Siwa Oasis, Egypt JF - GEOSCIENCE FRONTIERS J2 - GEOSCI FRONT VL - 2026 PY - 2026 PG - 45 SN - 1674-9871 DO - 10.1016/j.gsf.2026.102258 UR - https://m2.mtmt.hu/api/publication/36876209 ID - 36876209 LA - English DB - MTMT ER - TY - JOUR AU - Balassa, Csilla AU - Németh, Norbert AU - Kristály, Ferenc AU - Móricz, Ferenc AU - Bulátkó-Debus, Délia Henrietta TI - Heavy mineral sand from the vicinity of Tibolddaróc, Bükkalja region, NE Hungary JF - GEOSCIENCES AND ENGINEERING: A PUBLICATION OF THE UNIVERSITY OF MISKOLC J2 - GEOSCIENCES AND ENGINEERING VL - 13 PY - 2026 IS - 2 SP - 105 EP - 126 PG - 22 SN - 2063-6997 DO - 10.33030/geosciences.2025.02.008 UR - https://m2.mtmt.hu/api/publication/36878678 ID - 36878678 LA - English DB - MTMT ER - TY - JOUR AU - Valadez Vergara, Rafael AU - Szabó, Norbert Péter TI - Level of thermal maturity estimation in unconventional reservoirs using interval inversion and simulating annealing method JF - ACTA GEOPHYSICA J2 - ACTA GEOPHYS VL - 73 PY - 2025 IS - 2 SP - 1261 EP - 1280 PG - 20 SN - 1895-6572 DO - 10.1007/s11600-024-01413-4 UR - https://m2.mtmt.hu/api/publication/35172081 ID - 35172081 AB - This study presents a novel geophysical approach for estimating the level of thermal maturity (LOM) in unconventional hydrocarbon reservoirs using well log data. LOM is a crucial parameter for assessing the hydrocarbon generation potential of source rocks, but it traditionally relies on laboratory measurements of core samples, which can be time-consuming and costly. The proposed method combines two techniques: interval inversion for estimating total organic carbon (TOC) content from well logs and simulated annealing (SA) optimization for deriving LOM from the estimated TOC. The interval inversion method enables accurate TOC estimation by jointly interpreting multiple well logs over depth intervals, overcoming limitations of conventional point-by-point inversion. Using the estimated TOC, the SA algorithm optimizes an energy function related to Passey's empirical TOC-LOM relationship, iteratively finding the optimal LOM value that best fits the well log data. This approach provides a continuous in situ LOM profile along the borehole without requiring core measurements. The effectiveness of the method is demonstrated through case studies on datasets from the North Sea (Norway), the Pannonian Basin (Hungary), and the Kingak Formation (Alaska). The LOM estimates show good agreement with reported maturity levels and allow reliable reservoir characterization. Statistical analysis confirms the robustness and accuracy of the results. By reducing dependence on core data, this integrated inversion-optimization workflow streamlines the reservoir prospecting phase, enhancing operational efficiency. The method holds promising applications across diverse geological settings for cost-effective evaluation of unconventional hydrocarbon plays. LA - English DB - MTMT ER - TY - JOUR AU - Daoud, Abazar AU - Rady, Ali Shebl AU - Abdelkader, Mohamed M. AU - Mohieldain, Ali Ahmed AU - Csámer, Árpád AU - Satti, Albarra M.N. AU - Rózsa, Péter TI - Remote sensing and gravity investigations for barite detection in Neoproterozoic rocks in the Ariab area, Red Sea Hills, Sudan JF - REMOTE SENSING APPLICATIONS : SOCIETY AND ENVIRONMENT J2 - REMOTE SENS APPLIC SOC ENVIRON VL - 37 PY - 2025 PG - 27 SN - 2352-9385 DO - 10.1016/j.rsase.2024.101416 UR - https://m2.mtmt.hu/api/publication/35615531 ID - 35615531 LA - English DB - MTMT ER - TY - JOUR AU - Dörr, Wolfgang AU - Blau, Joachim AU - Marko, Linda AU - Petschick, Petra AU - Petschick, Rainer AU - Prinz-Grimm, Peter AU - Schastok, Janina AU - Velledits, Felicitász TI - Provenance analysis of the Rhenohercynian Basin and its southern collision zone: transition from a Silurian volcanic arc to the Rhenohercynian shelf (Central European Variscides) JF - INTERNATIONAL JOURNAL OF EARTH SCIENCES J2 - INT J EARTH SCI VL - 114 PY - 2025 IS - 1 SP - 55 EP - 74 PG - 20 SN - 1437-3254 DO - 10.1007/s00531-024-02480-0 UR - https://m2.mtmt.hu/api/publication/35663213 ID - 35663213 LA - English DB - MTMT ER - TY - JOUR AU - Elbalawy, Mohamed AU - Balash, Mohamed AU - Hemida, Mohamed Hamdy Eid AU - Takács, Ernő AU - Velledits, Felicitász TI - Innovative method integrates play fairway analysis supported with GIS and seismic modeling for geothermal potential evaluation in a basement reservoir JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 15 PY - 2025 IS - 1 PG - 20 SN - 2045-2322 DO - 10.1038/s41598-024-79943-6 UR - https://m2.mtmt.hu/api/publication/35672115 ID - 35672115 AB - The growing demand for clean and sustainable energy sources has prompted the investigation of numerous renewable and ecologically friendly options. Among these, geothermal energy is particularly noteworthy because of its widespread availability, compact size, and consistent, weather-independent power production. A geothermal play fairway analysis (GPFA) model was created for the study area, which is located in Békés county, southeastern Hungary. The GPFA model approach in the current study is the first model developed in Hungary to achieve three main goals. firstly, to quantitatively assess the geothermal potential, secondly, to identify the most favorable areas for geothermal exploration and development, and thirdly, to evaluate the corresponding risk levels in the study area. The study focuses on identifying and assessing three main risk components associated with exploitable geothermal systems in the study area. The risk parameters consist of the heat source, reservoir fracture permeability, and seal. Advanced 3D seismic interpretation, geographic information system (GIS), and 3DHIP (heat in place) calculator techniques are used to evaluate subsurface structural and thermal models. Two phases of seismic interpretation are used; conventional interpretation phase focused on conventional seismic data interpretation and advanced attribute generation phase where various seismic attribute cube volumes are generated. Common Risk Segment Maps (CRS) for each risk parameter are created by combining data from all the elements contributing to that risk using GIS toolbox. The resulted CRS maps of the study area three risk parameters are summed to produce a Composite Common Risk Segment Map (CCRS) map. Based on the constructed CCRS map and the developed GPFA model, the study area holds valuable untapped geothermal potential, poses varying risk levels associated with geothermal exploration and development. The amount of risk resulting from the three risk components is not equal, and the reservoir fracture permeability is the main risk factor. The GPFA model is successfully narrowed down an expansive exploration area of around 350 km 2 to just 4 highly promising targets with high geothermal favorability and low risk as future drilling targets. The constructed 3D thermal capacity model indicates that the average heat content in the study area is estimated to be 65,450 Petajoules per square kilometer (PJ/km 2 ), with a recoverable heat energy of 6090 megawatt thermal per square kilometer (MWth/km 2 ). The recoverable heat for the four selected targets is estimated under different production scenarios: a 30-year plan, a 20-year plan, and a 10-year plan and it ranges from 7.5 to 32 MWth/km 2 , 11 to 48 MWth/km2, 22.2 to 96.8 MWth/km 2 respectively. The findings of this study have made important contributions to the field of geothermal exploration approaches and offer valuable insights for making well-informed decisions about sustainable energy development in the study area. LA - English DB - MTMT ER - TY - JOUR AU - Mohammed, Musaab Adam Abbakar AU - Szabó, Norbert Péter AU - Szűcs, Péter TI - High-resolution characterization of complex groundwater systems using wireline logs analyzed with machine learning classifiers and isometric mapping techniques JF - MODELING EARTH SYSTEMS AND ENVIRONMENT J2 - MESE VL - 11 PY - 2025 IS - 2 PG - 17 SN - 2363-6203 DO - 10.1007/s40808-024-02263-1 UR - https://m2.mtmt.hu/api/publication/35710705 ID - 35710705 AB - Characterizing the lithological and hydraulic behavior of heterogeneous groundwater systems presents a significant challenge in hydrogeology. Traditional methods often rely on sparse data points that lead to inaccurate representations of the complex systems. This study presents an innovative approach to the characterization of the heterogeneous groundwater systems using wireline logs analyzed by machine learning (ML) techniques to infer the lithological variations and estimate aquifer parameters within the Quaternary aquifer system in the Debrecen area, Eastern Hungary. Initially, Manhattan distance-based k-means analysis as an outliers-resistance clustering method is employed to identify distinct lithological clusters based on the well logs responses. The results of the k-means clustering were then used to train ML classifiers including linear discriminant analysis, gradient boosting, random forest, and support vector machine for automated mapping of the lithofacies distribution. Additionally, the study introduced the first application of isometric map (IsoMap) to estimate the shale content and hydraulic conductivity within the aquifer system. The IsoMapping extracts latent components that capture essential features of the wireline logs and correlate them to the aquifer parameters. The regression between the latent component and the deterministically estimated shale volume and hydraulic conductivity showed significant exponential relationships resulting in universal equations that can be used independently to estimate these parameters. For more robust estimation, genetic algorithm global optimization was applied to refine the regression parameters governing these relationships to overcome the limitations associated with linearized estimations. The proposed approach provided a fast, automated, and effective alternative for characterizing heterogeneous groundwater, offering reliable inputs for groundwater flow and contaminant transport models. LA - English DB - MTMT ER -