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The identification and mapping of geochemical anomaly patterns have emerged as a more precise and efficient approach for mineral exploration, with deep learning algorithms being extensively employed in this realm. However, existing methodologies require further investigation regarding model interpretability and correlation with established mineral control factors. This paper proposes a regional geochemical anomaly identification method based on the memory-augmented autoencoder (MemAE), incorporating geological controlling factors. Firstly, the MemAE model is introduced to address the excessive generalization capability of the traditional autoencoder (AE) model. Secondly, utilizing multifractal singularity theory, a nonlinear functional relationship between faults and mineral deposits is established. This relationship reveals the controlling effect of faults on mineralization and it is incorporated as a constraint term in the MemAE's loss function. Finally, the constructed geochemical anomaly identification model is employed to delineate prospective mineralization areas, with comparative studies conducted on AE, MemAE, and geologically constrained MemAE models. The results demonstrate that the geologically constrained MemAE exhibits superior performance, achieving an AUC of 0.802. The eight delineated mineralization prospective areas show strong concordance with actual distributions. The proposed method, which considers geological controlling factors, effectively enhances model interpretability and demonstrates excellent geochemical anomaly identification capabilities. Consequently, this approach can be considered a viable methodology for mineral exploration.

This study presents the first comprehensive GIS-MCDM site suitability model for a Waste-to-Energy (WTE) facility in Upper Egypt. A sixteen-criterion analytical framework encompassing environmental protection, geological safety, infrastructure accessibility, and social proximity constraints was developed through a structured expert consultation process involving 42 specialists from academic, governmental, and environmental sectors. Criterion weights were derived using the Analytical Hierarchy Process (AHP) and validated with a Consistency Ratio of 2.6% (well below the 10% threshold). Spatial data layers were derived from Landsat-9 imagery (SVM classification), ASTER GDEM (30 m), ERA5-Land wind reanalysis, World population grids, OpenStreetMap infrastructure networks, and the Conoco–EGPC geological map of Egypt. Across the 10,698.5 km² study area, the integrated suitability map reveals that zones classified as high or very high suitability together constitute only 2.02% of the total area (59.5 km²; very high: 0.19%, 6.3 km²; high: 1.83%, 53.2 km²). The dominant land constraint, 69.3% classified as very low suitability, reflects strict environmental exclusion buffers around protected areas (PA; weight 11.4%), sensitive land uses (SU; 11.2%), surface water bodies (SW; 9.4%), and steep terrain (SP; 9.4%). Three candidate sites with high suitability scores were delineated, with the most favorable located east of Beni Suef city (coordinates: 29°01′ N, 31°07′ E; area: 22.75 km²), proximate to the governorate’s largest existing landfill (~ 2.6 km) and with favorable north-westerly wind alignment relative to populated zones. This study advances the GIS-MCDM literature by integrating geological (faults, lithology, soil bearing capacity) and environmental safety criteria within an arid-region planning context, an approach insufficiently addressed in prior Egypt-focused or MENA (WTE) siting studies. The resulting suitability model constitutes a reproducible, evidence-based decision-support tool for Egyptian environmental planners and aligns with Egypt’s Sustainable Development Strategy 2030 goals for renewable energy diversification and circular economy promotion. The selected site shows potential logistical and economic advantages due to its proximity to existing landfill infrastructure and regional road networks; however, these advantages represent spatial screening indicators and require further techno-economic and network-based transport assessment before implementation. Model validation using ROC–AUC analysis confirmed good discriminatory performance, with an AUC of 0.829, overall accuracy of 90.0%, and Kappa coefficient of 0.801.

A rational three‐dimensional (3D) geological model with complex characteristics generated on a small amount of data is a crucial data infrastructure for scientific research and many applications. However, reconstructing structures with multi‐Z values on a single point caused by folding or overthrusting is still one of the bottlenecks in 3D geological modeling. Combined with the multi‐point statistics (MPS) method and fully connected neural networks (FCNs), this study presented a hybrid framework for 3D geological modeling. The loss functions of FCN and the conventional MPS method jointly form the kernel function of the proposed method, which is constrained by stratigraphic sequence and stratum thickness. The input and output parameters of the FCN are the coordinates and corresponding elevations of geological contacts, respectively. To solve the kernel function, the initial model, in which geological surfaces are generated by the FCNs, is generated using a sequential process. An iterative MPS process with an Expectation Maximization‐like (EM‐like) algorithm is carried out to illuminate the artifacts in the initial model. Ten orthogonal cross‐sections are extracted from the overthrust model created by SEG/EAGE as the modeling data source. The results illustrated that the geometry and spatial relationships of strata and faults are retained well with the geological constraints. The comparison of virtual boreholes from the results and the real model shows that the accuracy of the geological object reaches 75%. The presented method provides a new idea for simulating 3D structures with multi‐Z values, which overcomes the limitations of the conventional MPS‐based 3D modeling method. Key Points A hybrid framework based on MPS with FCNs for constructing multi‐Z values is proposed Stratigraphic sequence and strata thickness are used as constraints in the simulation The kernel function of FCN with the BP framework is built on contact elevations

Data sparsity has long been a problem in 3D geological modeling work. The geometric, topological, and attribute information of geological bodies in geological reports provide important constraint information during 3D geological modeling. However, manually extracting complex and diverse constraint knowledge from a large amount of textual data is a challenging and time-consuming task. The development of information extraction and text mining technology has made it possible to automatically extract textual constraint information. To this end, this study firstly summarized the textual description characteristics of geological body constraint information in geological reports, and used a span-based tagging scheme for data annotation; Secondly, a span-based joint entity and relation extraction framework was introduced to extract constraint information in geological 3D modeling, which improves the extraction capability of the geological modeling constraint information by obtaining deep semantic information of the characters through the BERT model, in addition, the model has the joint extraction capabilities of entity classification and relation classification on candidate entities; Finally, in the experiments study, a Chinese geological survey report was used as training data for evaluation, and we validated our method’s effectiveness through comparison of our results to those of different models. We further compared and analyzed the impact of different parameters and span representations on our model’s performance.

Geochemical anomaly detection is a critical stage in mineral exploration, playing a key role in predicting potential mineral targets. Traditional methodologies often struggle to integrate the spatial structure of geochemical data with underlying geological constraints effectively. To address this limitation, we propose GeoCLA, a geochemical anomaly detection framework that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM). This integrated spatial-attention architecture captures complex correlations among multiple features to improve anomaly identification. The method constructs spatial sequential samples from geochemical data. The CNNs extract local spatial patterns, the BiLSTM models sequential dependencies, and the AM enhances the representation of critical features. Anomaly scores are computed using the reconstruction error between the model output and the original data. In addition, a fault-distance weighting factor is incorporated to build a comprehensive anomaly evaluation index. The proposed model was applied to the Hatu gold district in Xinjiang, China. Both visual analysis and quantitative evaluation demonstrate effectiveness, achieving a ROC-AUC of 0.86 and a mineral occurrence coverage rate of 97% within moderate-to-high anomaly prospective areas, significantly outperforming baseline methods.

Key parameters for evaluating shale reservoirs include total organic carbon (TOC), thermal maturity, and hardness, the latter influencing fracture development and being crucial for managing ultralow permeability reservoirs. These parameters are often derived from costly, time-consuming core sample analyses and may be limited in availability. Recently, machine learning (ML) and deep learning (DL) have effectively predicted TOC and hardness from well logs but often require large datasets and lack integration with petrophysical and geological constraints. This study examines the impact of incorporating these constraints on prediction accuracy using four manually fine-tuned ML algorithms: Random Forest (RF), Support Vector Regression (SVR), XGBoost (XGB), and Artificial Neural Network (ANN). Data from five wells in the Horn River Basin (HRB) comprising 6366 data points were analyzed, with TOC and hardness values for 612 and 3492 points, respectively. Petrophysical constraints were derived from triple combo well logs (gamma ray, bulk density, neutron porosity), while geological constraints included stratigraphic data or spatial distance between training and target wells—petrophysical constraints most improved predictions, while stratigraphic and spatial constraints had progressively less impact. Our optimized models achieved R 2 (coefficient of determination) of 0.89 and RMSE (root-mean-square error) of 0.47 for TOC predictions and 0.90 and 34.8 for hardness predictions, reducing RMSE by up to 13.52% compared to the unconstrained model. The XGB algorithm emerged as the best choice, and integrating domain knowledge transforms a data-driven method into a scientifically driven one, enhancing prediction accuracy and aligning model predictions with petrophysical and geological intricacies. Article Highlights ML techniques—RF, SVR, XGB, and ANN—predict TOC and hardness in unconventional reservoirs using domain constraints. XGB excels in petrophysical and stratigraphic constraints, while ANN is best for vicinity; both are top ML algorithms. Petrophysical constraint reduces RMSE by 13.52%, stratigraphic by 13.03%, and vicinity by 3.25%. XGB performs the best.

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R2 of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R2 of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs.

Deep learning outperforms traditional interpolation methods in 3D geological modeling due to its ability to model nonlinear relationships and its flexibility in incorporating diverse geological data. However, acquiring geological data for practical applications is challenging, and the quality of the data can vary significantly, which limits the effectiveness of purely data-driven deep learning models in 3D geological modeling. To address this challenge, this paper introduces GeoSAE, a geoconstraint-driven 3D geological modeling method. GeoSAE improves potential field prediction by employing a stacked autoencoder network (SAE) and incorporating geological constraints as a loss function during model training. This approach generates a geologically consistent, smooth, and continuous 3D stratigraphic model. To validate the method, this study applies it to a 60-square-kilometer region in Jiangdong new district, Haikou city, China. Stratigraphic interface points were utilized to predict the 3D potential field, with PyVista (version 0.44.2) enabling the accurate extraction of stratigraphic interfaces. Model quality was evaluated through comprehensive assessments of loss function analysis, data fitting, and the verification of stratigraphic smoothness constraints. Results indicate that the stratigraphic model generated by GeoSAE closely aligns with the actual data, accurately capturing stratigraphic geometry. Additionally, incorporating smoothness constraints enhances model smoothness, minimizes irregular stratigraphic fluctuations, and produces a more natural and continuous stratigraphic morphology.

Stratigraphic correlation plays a crucial role in reservoir characterization. However, it is often time-consuming and heavily dependent on geological expertise. To address this issue, we propose a novel method called CMT-enhanced Hiformer, which integrates convolutional neural networks meet vision transformers (CMT) and hierarchical multi-scale representations using transformers (Hiformer). First, the architecture of CMT-enhanced Hiformer fuses the advantages of convolutional neural networks and transformers, effectively extracting complex features from well logs and capturing both local and global dependencies via a well-designed attention mechanism. Next, a geological constraint with regularization parameters is incorporated into the loss function. The new loss function promotes the accuracy of stratigraphic boundaries. The proposed method was validated using data from the Shuanghe oil field in central China. Specifically, the model achieved a maximum F1 score of 0.8857 and a precision of 0.8865 on the blind test dataset, demonstrating its robustness and high classification accuracy. Moreover, we conducted ablation studies and performed a detailed comparison with state-of-the-art deep learning models. The results demonstrate that the proposed method significantly improves the accuracy and efficiency of stratigraphic correlation.

Geophysical inversion employs various methods to minimize the misfit between geophysical datasets and three-dimensional petrophysical distributions. Inversion techniques rely on many subjective inputs to provide a solution to a non-unique underdetermined problem, including the use of a priori model elements (i.e. a contiguous volume of the same litho-stratigraphic package), the a priori input model itself or inversion constraints. In some cases, inversion may produce a result that perfectly matches the observed geophysical data, but can still misrepresent the geological system. A workflow is presented here that offers objective methods to provide inputs to inversion: (1) simulations are performed to create a model suite that contains a range of geologically possible models; (2) stratigraphic variability is determined via uncertainty analysis to identify low certainty model regions and elements; (3) geodiversity analysis is then conducted to determine geometrical and geophysical extremes and commonalities within the model space; (4) geodiversity metrics are simultaneously analysed using principal component analysis to identify the contribution of different model elements toward overall model suite uncertainty; (5) principal component analysis also determines which models exhibit diverse or common geological and geophysical characteristics which (6) facilitate the selection of models as inputs to geophysical inversion. This workflow is applied to a three-dimensional model of the Ashanti Greenstone Belt, southwestern Ghana in West Africa in order to reduce the subjectivity incurred during decision making, explore the range of geologically possible models and provide geological constraints to the inversion process to produce geologically and geophysically robust suites of models. Results further suggest that three-dimensional uncertainty grids can optimize inversion processes and assist in finding geologically reasonable solutions.