Traditional Inversion Research Articles

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

Nonlinear joint inversion of Rayleigh and Love wave dispersion curves based on Pearson correlation coefficient and thickness mean sharing

AbstractThe joint inversion of Rayleigh and Love waves plays a crucial role in mitigating the non‐uniqueness of surface wave inversion results and enhancing the stability of these inversions. Existing approaches to the joint inversion of Rayleigh and Love wave dispersion curves, which rely on conventional objective functions, often struggle with complex stratigraphic configurations and yield results of limited accuracy. This study introduces two novel nonlinear joint inversion techniques for Rayleigh and Love waves: Pearson correlation coefficient and thickness mean sharing. The Pearson correlation coefficient approach employs the Pearson correlation coefficient and alternating iterative objective functions to synchronize the shear wave velocity structures derived from Rayleigh and Love waves, thereby enhancing the accuracy of the joint inversion. Conversely, the thickness mean sharing method computes an average of the thickness values obtained in each iteration of the inversion, utilizing the traditional joint inversion objective function. Tests on three distinct stratigraphic structures—characterized by increasing velocity, high‐speed hard interlayers and low‐speed soft interlayers—as well as on measured data, demonstrate that the proposed methods significantly improve the stability and accuracy of nonlinear joint inversion.

Inversion Model for Permeability Coefficient Based on Random Forest–Secretary Bird Optimization Algorithm: Case Study of Lower Reservoir of C-Pumped Storage Power Station

The geological complexity of the karst regions presents significant challenges, with the permeability coefficient being a critical parameter for accurately analyzing seepage behavior in hydraulic engineering projects. To overcome the limitations of traditional inversion methods, which often exhibit low computational efficiency, poor accuracy, and instability, this study utilizes a finite-element forward model and orthogonal experimental design to establish a sample set for permeability-coefficient inversion. A surrogate model for seepage calculation based on the Random Forest (RF) algorithm is subsequently developed. Furthermore, the Secretary Bird Optimization Algorithm (SBOA) is incorporated to propose an intelligent RF–SBOA inversion method for permeability-coefficient estimation, which is validated through a case study of the C-pumped storage power station. The results demonstrate that the RF model’s predictions for water levels at four boreholes closely align with the measured data, outperforming models such as CART, BP, and SVR. The SBOA effectively identifies the optimal geological permeability coefficient, with the borehole water-level inversion achieving a maximum relative error of only 0.128%, which meets the accuracy requirements for engineering applications. Additionally, the computed distribution of the natural seepage field is consistent with the typical distribution patterns observed in mountain seepage systems. During the normal water-storage phase, both the calculated seepage flow and gradient comply with engineering standards, while the seepage-field distribution aligns with empirical observations. This inversion model provides a rapid and accurate method for estimating the permeability coefficient of strata in the project area, with potential applicability to permeability inversion in other engineering geology contexts, thus demonstrating considerable practical value for engineering applications.

Simultaneous quantification of triple pore types in carbonate reservoirs using well logs and seismic data

Accurate characterization of carbonate pore types is of great significance for seismic reservoir characterization. However, the existing pore-type inversion methods cannot characterize the coexistence of three main pore types (vuggy pores, matrix pores, and cracks) for each modeling point because they apply a simple iterative procedure without using S-wave velocity. To overcome this problem, we develop a simultaneous triple pore-type inversion (TPTI) strategy to estimate the porosities of different pore types from the P- and S-wave velocities using a global optimization technique known as very fast simulated annealing. Considering the rock composition variation and pore geometry complexity, we first establish an easy-to-implement multipore effective medium model for carbonate reservoirs based on the Keys-Xu model and Gassmann’s equation. Then, the applicability of this model is verified by two sets of experimental acoustic measurements for synthetic and actual carbonate samples. A good comparison of model predictions with laboratory data indicates that our model can effectively capture the elastic responses of carbonate rocks with different pore shapes, which provides a theoretical basis for quantifying multiple pore types. Based on the developed model, we further develop a TPTI method. We apply it to describe pore-type distribution from well logs and seismic data in a heterogeneous carbonate reservoir from the Sichuan Basin in southwest China. Real applications suggest that our method significantly improves the accuracy of porosity estimates for different pore types, outperforming the traditional dual pore-type inversion method. This advancement holds considerable promise for the seismic characterization of pore types in ultradeep carbonate reservoirs.

Theoretical investigation of the excitation of leaky modes for multilayered models

SUMMARY The dispersion analysis with array-based methods enables us to detect and analyse the multimode dispersive waves with weak amplitudes. Over the past years, there has been widespread extraction of higher normal modes from earthquake records and ambient noise, which have been extensively employed in near-surface and crustal investigations. Notably, the frequent reporting of leaky modes in recent observations has gained significant attention. This is because leaky modes can provide more constraints on subsurface structures. Particularly, a subset of leaky modes is found to be sensitive to P-wave velocities, which has the potential to compensate for the limitations of traditional surface wave inversion. Nevertheless, due to the lack of knowledge of the excitation and practical application of leaky modes, it is urgent to have an effective guide in selecting certain leaky modes for practical inversion combined with other imaging methods. To this end, the theoretical investigation into the excitation of leaky modes for multilayered models is undertaken. By computing theoretical seismograms for various source mechanisms and source depths with the discrete wavenumber and the normal-mode summation methods, respectively, the qualitative evaluation of the leaky-mode contributions to the synthesized seismograms is conducted. Additionally, the impact of the source depth for the same source mechanism is meticulously examined by the dispersion spectra. Furthermore, with the accurate computation of leaky modes, we categorize the leaky modes into PL and OP (organ-pipe) modes according to their distinctive properties, that is, their attenuation and eigen-displacements, which may be used to explain very well the occurrence of certain leaky modes on the dispersion spectrum and be indicative of the reasonable choice of PL modes to put them into practical inversion for P-velocity structures.

The Main Themes, Motifs and Images Of A.Feinberg's Lyrics

Alexander Arkadyevich Feinberg is one of the first Russian-speaking national poet of Uzbekistan. The work of Alexander Feinberg continued the best traditions of classical poetry, and contributed to the widespread spread of the Russian language and literature, culture in our country. In this article highlights of the main themes, motifs and images of A.Feinberg's lyrics and its importance in youth artistic notions.

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

A convolutional neural network to optimize multi-mission satellite altimeter fusion for improving the marine gravity field

Satellite altimetry is the main tool for constructing global or regional marine gravity fields. To improve the accuracy and spatial resolution, it is necessary to fuse multi-mission altimeters. How to determine the weights of multi-mission altimeters is a crucial issue, making the conventional calculation process very complex. In addition, traditional satellite inversion methods are often independent of shipborne gravity, which is used only as validation data, thus not take full advantages of high accuracy and resolution of shipborne gravity. In this study, we introduce a convolutional neural network (CNN) to merge the vertical deflections (DOVs) obtained from multi-altimeter missions to construct a marine gravity model in the South China Sea. High-accuracy shipborne gravity and a dataset comprising DOVs and geo-locations are employed as input data for neural network training. For the validation of CNN method, the gravity model is also computed by conventional Inverse Vening Meinesz (IVM) method. Independent shipborne gravity measurements and SIO V32.1, DTU17 models are used as validation data. The evaluation results show that the CNN-derived model achieves a higher level of accuracy, yielding a standard deviation (STD) of 3.21 mGal, with an improvement of 36.56% compared to IVM-derived model. More than 92% of the differences between the CNN-derived model and shipborne gravity are less than 5 mGal. In addition, spectral analysis results further show that the CNN-derived model has stronger energy at short wavelengths (less than 25 km) compared to other models. These findings reveal that CNN method is feasible for marine gravity recovery and the CNN-derived model can achieve higher accuracy. The CNN method can improve the accuracy and spectral characteristics of the constructed gravity model by taking advantage of the high accuracy and high resolution of shipborne gravity.Graphical

One‐dimensional deep learning inversion of marine controlled‐source electromagnetic data

AbstractThis paper explores the application of machine learning techniques, specifically deep learning, to the inverse problem of marine controlled‐source electromagnetic data. A novel approach is proposed that combines the convolutional neural network and recurrent neural network architectures to reconstruct layered electrical resistivity variation beneath the seafloor from marine controlled‐source electromagnetic data. The approach leverages the strengths of both convolutional neural network and recurrent neural network, where convolutional neural network is used for recognizing and classifying features in the data, and recurrent neural network is used to capture the contextual information in the sequential data. We have built a large synthetic dataset based on one‐dimensional forward modelling of a large number of resistivity models with different levels of electromagnetic structural complexity. The combined learning of convolutional neural network and recurrent neural network is used to construct the mapping relationship between the marine controlled‐source electromagnetic data and the resistivity model. The trained network is then used to predict the distribution of resistivity in the model by feeding it with marine controlled‐source electromagnetic responses. The accuracy of the proposed approach is examined using several synthetic scenarios and applied to a field dataset. We explore the sensitivity of deep learning inversion to different electromagnetic responses produced by resistive targets distributed at different depths and with varying levels of noise. Results from both numerical simulations and field data processing consistently demonstrate that deep learning inversions reliably reconstruct the subsurface resistivity structures. Moreover, the proposed method significantly improves the efficiency of electromagnetic inversion and offers significant performance advantages over traditional electromagnetic inversion methods.

Research on Digital Twin Method for Spaceborne Along-Track Interferometric Synthetic Aperture Radar Velocity Inversion of Ocean Surface Currents

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization.

Research and application of joint-constrained inversion of transient electromagnetic multivariate parameter

Due to the phenomena of stratigraphic inclination, complex structure, and lateral discontinuity of resistivity or layer thickness in most of the coal seams, the traditional one-dimensional transient electromagnetic inversion method has limitations in interpretation accuracy. In addition, two- and three-dimensional inversion and artificial intelligence inversion have problems of large computation and large sample size, respectively, which limit their application in small- and medium-sized engineering exploration. To improve the inversion effect, this study proposes a method of joint-constrained inversion of transient electromagnetic multivariate parameters. This method achieves the joint constraint inversion of the transient electromagnetic multi-parameter by making full use of the geological data and a priori information to construct the initial model and adding the constraints such as the resistivity, the thickness, and the layer interface of each layer in the inversion objective function, and at the same time, taking into account the spatial correlation of the stratigraphic structure between the neighboring measurement points, as well as the transverse and vertical constraints between the measurement points along the direction of the survey line and perpendicular to the survey line. First, a series of typical geoelectric models are established and numerically simulated, and the results are compared with those of the traditional inversion method to verify the applicability and effectiveness of the method. Then, the constrained inversion is carried out on the physical simulation and measured data, and the results are in good agreement with the actual geological conditions. The numerical simulation, physical simulation and measured data inversion results consistently prove that this method can effectively reduce the uncertainty of the inversion at the isolated measuring points, improve the spatial continuity of the formation boundary, and better reflect the actual geoelectric characteristics of the formation.

THE SOCIOLINGUISTIC POSITION OF PALEMBANG RELIGIOUS POETRY

This article examines the sociolinguistic position of Palembang religious poetry in the Palembang speech community in South Sumatra, Indonesia. The Palembang language is unique in that it is used as a language of wider communication and listed as level 3 on EGIDS, yet it does not have an official writing system. Until this study, no academic research on Palembang religious poetry had ever been conducted. This research examines the use of Palembang religious poetry by Palembang people. Ethnographic research methods were employed, and poets and poetry users from various backgrounds and ages were interviewed. Palembang traditional poetry called pantun is also discussed. Findings reveal the origins and the types of religious poetry. Research highlights five poems: three autogenic poems and two translated poems. Three poems are considered spoken poetry and two poems are sung. Poets and poetry users of Palembang religious poetry in this research consist of four men and three women ranging from 25-65 years of age. There are examples of Palembang religious poetry undergoing language shift but it is clear that poetry has strong connections to ethnic identity and language pride.

Integration of geographic features and bathymetric inversion in the Yangtze River's Nantong Channel using gradient boosting machine algorithm with ZY-1E satellite and multibeam data

This study investigates the integration of geographic features from ZY-1E satellite data with advanced machine learning techniques to enhance water depth inversion in the Yangtze River's Nantong Channel. Utilizing the Gradient Boosting Machine (GBM) and its geospatially enhanced version, GBM-Lon./Lat., significant improvements in modeling precision were observed, as reflected by lower RMSE and higher R² values compared to traditional depth inversion methods. The research underscores the benefits of incorporating geospatial data, which allows for a more nuanced understanding of the hydrological dynamics and facilitates more accurate predictions in the turbid waters of the channel. Challenges such as atmospheric effects, water turbidity, and data acquisition issues under variable weather conditions were identified. The study proposes further optimization of these models to handle diverse environmental conditions and enhance the accuracy of bathymetric mapping. The integration of machine learning with remote sensing not only supports navigational safety and efficient waterway management but also contributes significantly to environmental monitoring and sustainable riverine infrastructure development.

Jansenist Education and Racine’s Intertextuality in European Context: Can an Early Modern Translation of Andromaque Help Revisit an Old Concept?

Abstract The article deals with the intertextual dimensions of Jean Racine’s tragedy Andromaque and its rendering by the seventeenth–century Polish translator Stanisław Morsztyn. The analysis is based on an old hypothesis concerning the link between Racine’s intertextual literary practice and his erudite education at the Petites écoles de Port-Royal. The translator, although he had not received Jansenist education, was able to identify correctly and develop the original strategy of quotations and allusions. The article attempts to seek the factors that helped the Polish translator render the intertextuality of Andromaque in another cultural context. Three such factors have been proposed—the tradition of neo-Latin poetry developed both in France and in Poland, the Italian Petrarchism (along with the idea of sprezzatura), perceived not as a store of topics but as a pattern of free operation with Ancient allusions. The similarity between Racine’s education and that of Morsztyn, which combined the influence of the Polish Brethren with homeschooling, could have been the third factor. The article closes with a question concerning the perspectives of cultural studies in intertextual strategies of poets connected with various religious circles of the seventeenth century.

Analysis Of Meaning and Moral Education Values of Al Barzanji's Poetry in The Community Environment Cianjur District

The tradition of reading Barzanji's poems in Cianjur District seems to be decreasing and decreasing. The causal factors include 1) the lack of socialization and training from Barzanji speakers towards the younger generation, 2) the lack of interest and attention from the younger generation to learn the recitation of Barzanji's poems, 3) the absence of a specific institution to deepen Islamic cultural art in terms of this is the preservation of the tradition of reading Barzanji's poetry, 4) the rapid flow of infiltration of non-Islamic culture towards the Muslim community so that the Muslim community knows and applies non-Islamic cultures more than the Islamic culture itself. Furthermore, the author conveys that this research will describe the results of data analysis on the meaning and values ​​of Moral Education in the tradition of reading Barzanji's Poems in the community of Cianjur Regency. This method is descriptive. An effort to preserve the Barzanji tradition in the community of Cianjur Regency includes: 1) Improving the learning and training process for the Barzanji Tradition at Majlis Ta'lim and Islamic boarding schools so that the Barzanji tradition can be passed down from generation to generation, 2) Organizing a Barzanji festival periodically with involving religious institutions and schools, especially during religious holidays, 3) making or incorporating Barzanji poetry into teaching materials in Islamic Religious Education or Indonesian language subjects in schools from elementary to tertiary level, 4 ) Carrying out intensive outreach regarding the historicity and meaning of Barzanji's poetry to the wider community both in urban and rural areas.

Stylistic codification of traditional forms of poetic text (based on the material of the ancient Chinese theoretical study of poetry “Shi-Ping” (诗品, “Categories of Poems”)

Abstract The study of the main works that codified the classical forms of the Chinese text is important for analysing the ways of development of poetic works and determining the formation of their stylistic norms, mechanisms, and thematic concepts. The research aims to analyse the peculiarities of the stylistic codification of ancient Chinese traditional verse forms based on the theoretical material of Zhong Rong’s “Shi-Ping” (诗品). The following methods were used: deductive, content analysis, and comparative. The research results determined that ancient Chinese poetry is known for its unique depth, sophistication, and richness of images. Zhong Rong’s treatise “Shi-Ping” influenced the formation of stylistic features of classical Chinese five-line poetry. The analysis of its textual data has shown that this anthology emphasises the importance of the poetic form, especially rhythm and melody, and provides specific recommendations for the use of iambic pentameter, which contributed to its popularisation and development. The practical significance lies in the use of the information by scholars in the fields of philology, linguistics, cultural studies, linguistics, as well as specialists who study the traditional forms of Chinese poetic text.

The use of peritoneum in female genital gender-affirming surgery: A systematic review

Genital gender-affirming surgery (GAS) plays a crucial role in alleviating psychological discomfort and assisting transgender patients in achieving their transition goals. One of the most common procedures for primary genital reconstruction in transwomen is penile inversion vaginoplasty. But it presents a risk of vaginal stenosis and limited depth, particularly in cases with inadequate penile and scrotal tissue. This limitation becomes more relevant as transgender individuals increasingly seek early hormone therapy and puberty blockade. We conducted a literature review focusing on studies published between 2000 and 2022 to explore the application of peritoneal vaginoplasty, a technique primarily used in cisgender women with congenital vaginal agenesis, to address these challenges in transwomen.Data were extracted from 13 selected articles reporting 313 patients who underwent peritoneal vaginoplasty.Peritoneal vaginoplasty was found to offer advantages in terms of neovaginal depth and width, with an average depth of 13.8 cm and width of 3.5 cm. Patient reporting satisfaction rates for sexual intercourse and dilatation achievement were high, with 96.2% and 94.4% respectively. The technique did not result in an increased rate of complications compared to traditional penile inversion vaginoplasty.This review suggests that peritoneal vaginoplasty could be consider first for transwomen genital GAS, especially in cases with limited penile tissue. The well-vascularized peritoneum provides tissue with regenerative capacities and reduces the risk of stenosis. Moreover, peritoneum remains unaffected by hormonal treatments and allows deep enough neovaginal canals. This method is safe and does not lead to increased complications compared to traditional techniques.

Refined Bathymetric Prediction Based on Feature Extraction of Gravity Field Signals: BATHY‐FE

AbstractThe resolution of altimetry‐derived gravity signals renders traditional bathymetry inversion methods inadequate for detecting short‐wavelength bathymetric features. This study presents a new global seafloor topography model by merging regional models constructed via a deep learning approach recently developed by the authors. Feature extraction techniques were used to refine each regional bathymetric model, resulting in sharpened features that are fuzzily revealed (or not seen) in global bathymetric models such as GEBCO_2023 and SRTM15+V2, especially in areas close to the poles. This is proof that, with regard to current gravity field products, conventional bathymetry inversion methods are weaker at generalizing to uncharted locations of the global ocean floor. At test points, the global seafloor model has mean error and error standard deviation of 1.75 ± 81.15 m. The refined seafloor topography can be used to supplement existing seafloor maps, especially in the polar regions. It is useful for studying seafloor geography, and also for studies in which seafloor ruggedness is required.

Multifaceted anomaly detection framework for leachate monitoring in landfills

The imperative to preserve environmental resources has transcended traditional conservation efforts, becoming a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgency. Contaminants such as leachate from landfills are increasingly threatening groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced detection and monitoring solutions to effectively safeguard our groundwater resources. To address this pressing need, we introduce the Multifaceted Anomaly Detection Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models—Isolation Forest (IF), One-Class Support Vector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and analyzes ERT data, employing these hybrid machine learning models to identify and quantify anomaly signals accurately via the majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvements in detection capability. The framework enhanced the precision of anomaly detection, evidenced by higher Youden Index values (≈ 6.216%), with a 30% increase in sensitivity and a 25% reduction in false positives compared to traditional ERT inversion methods. Indeed, these enhancements are crucial for effective environmental monitoring, where the cost of missing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive. These results underscore MADF's potential as a robust tool for proactive environmental management, offering a scalable and adaptable solution for comprehensive landfill monitoring and pollution prevention across varied environmental settings.