Forward Model Research Articles

A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks

A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This paper established a fast computing model (ANN-HASFCM) for high-spectral-resolution absorption spectra by using artificial neural networks and PCA (principal component analysis) spectral reconstruction technology. This paper chose the line-by-line radiative transfer model (LBLRTM) as the comparative model and simulated training spectral data in the oxygen A-band (12,900–13,200 cm−1). Subsequently, ANN-HASFCM was applied to the retrieval of the atmospheric density profile with the data of the Global Ozone Monitoring by an Occultation of Stars (GOMOS) instrument. The results show that the relative error between the optical depth spectra calculated by LBLRTM and ANN-HASFCM is within 0.03–0.65%. In the process of using the global-fitting algorithm to invert GOMOS-measured atmospheric samples, the inversion results using Fast-LBLRTM and ANN-HASFCM as forward models are consistent, and the retrieval speed of ANN-HASFCM is more than 200 times faster than that of Fast-LBLRTM (reduced from 226.7 s to 0.834 s). The analysis shows the brilliant application prospects of ANN-HASFCM in limb remote sensing.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

High-Precision Forward Modeling of Controlled Source Electromagnetic Method Based on Weighted Average Extrapolation Method

High-Precision Forward Modeling of Controlled Source Electromagnetic Method Based on Weighted Average Extrapolation Method

Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by model complexity. Increased model complexity decreases prediction bias, increases variance, and increases overfitting possibilities. Overfitting is a significantly smaller training prediction error relative to the trained model prediction error for an independent validation set. Uncertain data generate risks for AI–ML because they increase overfitting and limit generalization ability. Specious confidence in predictions from overfit models with limited generalization ability, leading to misguided water resource management, is the uncertainty-related negative consequence. Improved data is the way to improve AI–ML models. With uncertain water resource data sets, like stream discharge, there is no quick way to generate improved data. Data assimilation (DA) provides mitigation for uncertainty risks, describes data- and model-related uncertainty, and propagates uncertainty to results using observation error models. A DA-derived mitigation example is provided using a common-sense baseline, derived from an observation error model, for the confirmation of generalization ability and a threshold identifying overfitting. AI–ML models can also be incorporated into DA to provide additional observations for assimilation or as a forward model for prediction and inverse-style calibration or training. The mitigation of uncertain data risks using DA involves a modified bias–variance tradeoff that focuses on increasing solution variability at the expense of increased model bias. Increased variability portrays data and model uncertainty. Uncertainty propagation produces an ensemble of models and a range of predictions.

Understanding the Effects of Spacecraft Trajectories through Solar Coronal Mass Ejection Flux Ropes Using 3DCOREweb

This study investigates the impact of spacecraft positioning and trajectory on in situ signatures of coronal mass ejections (CMEs). Employing the 3DCORE model, a 3D flux rope model that can generate in situ profiles for any given point in space and time, we conduct forward modeling to analyze such signatures for various latitudinal and longitudinal positions, with respect to the flux rope apex, at 0.8 au. Using this approach, we explore the appearance of the resulting in situ profiles for different flux rope types, with different handedness and inclination angles, for both high- and low-twist CMEs. Our findings reveal that CMEs exhibit distinct differences in signatures between apex hits and flank encounters, with the latter displaying elongated profiles with reduced rotation. Notably, constant, nonrotating in situ signatures are only observed for flank encounters of low-twist CMEs, suggesting the existence of untwisted magnetic field lines within CME legs. Additionally, our study confirms the unambiguous appearance of different flux rope types in in situ signatures in all of the cases, barring some indistinguishable cases, contributing to the broader understanding and interpretation of observational data. Given the model assumptions, this may refute trajectory effects as the cause for mismatching flux rope types as identified in solar signatures. While acknowledging limitations inherent in our model, such as the assumption of constant twist and a nondeformable torus-like shape, we still draw relevant conclusions within the context of the global magnetic field structures of CMEs and the potential for distinguishing flux rope types based on in situ observations.

Testing the volume integrals of travel-time sensitivity kernels for flows

Context. Helioseismic inversions largely rely on sensitivity kernels, in which 3D spatial functions describe how the changes in the solar interior translate into the change in helioseismic observables. These sensitivity kernels in most cases come from the forward modelling that is used in the most advanced solar models. Aims. We aim to test the sensitivity kernels by comparing their volume integrals with measured values from helioseismic travel times. Methods. By manipulating the tracking rate, we mimicked the additional zonal velocity in the Dopplergram datacubes. These datacubes were then processed by a standard travel-time measurements pipeline. We investigated the dependence of the east-west travel time averaged over a box around the disc centre on the implanted tracking velocity. The slope of this dependence is directly proportional to the total volume integral of the sensitivity kernel that corresponds to the travel-time geometry that is used. Results. The agreement between measurements and models for travel times that are computed with a ridge filtering is very good to acceptable. The dependence we sought to determine indeed resembles a linear function, and its slope agrees with the expected volume integral from the forward-modelled sensitivity kernel. The agreement is poorer for the phase-speed filtered datacubes. The disagreement is particularly strong for the slowest phase speeds (filters td1–td4). For the higher phase speeds, our result indicates that the measured kernel integrals are systematically larger than expected from the forward modelling. We admit our testing procedure may not be appropriate for high phase speeds and higher radial modes.

The Strength of Bisymmetric Modes in SDSS-IV/MaNGA Barred Galaxy Kinematics

The Sloan Digital Sky Survey-IV/Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey data provide an unprecedented opportunity to study the internal motions of galaxies and, in particular, represent the largest sample of barred galaxy kinematic maps obtained to date. We present results from Nirvana, our nonaxisymmetric kinematic modeling code built with a physically motivated Bayesian forward modeling approach, which decomposes MaNGA velocity fields into first- and second-order radial and tangential rotational modes in a generalized and minimally supervised fashion. We use Nirvana to produce models and rotation curves for 1263 unique barred MaNGA galaxies and a matched unbarred control sample. We present our modeling approach, tests of its efficacy, and validation against existing visual bar classifications. Nirvana finds elevated noncircular motions in galaxies identified as bars in imaging, and bar position angles that agree well with visual measurements. The Nirvana-MaNGA barred and control samples provide a new opportunity for studying the influence of nonaxisymmetric internal disk kinematics in a large statistical sample.

Building a General Algorithm for Seismic Hazard Analysis in the Sunda Arc through Geodynamic Simulations

Abstract Because of its well-documented subduction zone and outer island arc, Sumatra provides a unique setting for studying and forecasting earthquakes within the seismically active Sunda Arc. This study builds on previous research that utilized Global Positioning System data and the Akaike information criterion to analyze probabilistic seismic hazard functions. However, this study replaces surface displacement rate data with a forward model derived from previous fault modeling results to create a more broadly applicable earthquake forecasting algorithm. Although the best-fit model patterns generated by this new algorithm are consistent with past studies, the forward model demonstrates a lower degree of fit compared to models utilizing natural surface displacement data. This discrepancy highlights the need to refine the fault parameter models to estimate surface displacement rates. Despite this limitation, the study makes a valuable contribution by developing a general algorithm applicable to other subduction zones within the Sunda Arc region. With further refinement and incorporation of more accurate fault modeling and data, this algorithm has the potential to formulate the best-fit earthquake spatial forecast models. This approach could be applied to other seismically active areas, particularly those near subduction zones.

Three-dimensional Thermodynamic Structures of the Intracluster Medium across Edges in the X-Ray Surface Brightness of Massive, Bright, Dynamically Active Galaxy Clusters

We present a detailed study of three-dimensional (3D) thermodynamic structures of the intracluster medium (ICM) across edges in the X-ray surface brightness of four massive, bright, dynamically active galaxy clusters (A3667, A2319, A520, and A2146), with the Chandra X-ray Observatory. Based on a forward modeling approach developed in previous work, we extend this approach with more generalized ICM density and temperature profiles, allowing us to apply uniformly to the observed X-ray surface brightness profiles to detect edges and measure the 3D thermodynamic profiles of the ICM simultaneously and self-consistently. With the forward modeling analysis, we find, in agreement with previous works, that the obtained 3D thermodynamic structures of the ICM across the edges in A3667 and A2319 are consistent with the characteristics of cold fronts, whereas those in A520 and A2146 are consistent with the nature of shock fronts. We find that the azimuthal distribution of the pressure ratio at the cold front in A3667 shows a different trend from that in A2319. For the shock fronts in A520 and A2146, the observed 3D temperature profiles of the ICM indicate that the temperature is highest at the position of the shock front. In the case of the sector exhibiting M=2.4 in A520, the ICM temperature appears isothermal with a temperature of ∼10 keV until ∼300 kpc away from the shock front in the post-shock region, being consistent with the hypothesis of the instant-equilibration model for shock heating.

Combining traditional and novel techniques to increase our understanding of the lock-in depth of atmospheric gases in polar ice cores – results from the EastGRIP region

Abstract. We investigate the lock-in zone (LIZ) of the East Greenland Ice Core Project (EastGRIP) region, northeastern Greenland, in detail. We present results from the firn air-pumping campaign of the S6 borehole, forward modeling, and a novel technique for finding the lock-in depth (LID, the top of the LIZ) based on the visual stratigraphy of the EastGRIP ice core. The findings in this work help to deepen our knowledge of how atmospheric gases are trapped in ice cores. CO2, δ15N, and CH4 data suggest that the LID lies around 58 to 61 m depth. With the pixel value intensity and bright-spot analysis based on visual stratigraphy, we can pinpoint a change in ice properties to exactly 58.3 m depth, which we define as the optical lock-in depth (OLID). This visual change in ice properties is caused by the formation of rounded and enclosed air bubbles that alter the measured refraction of the light pathways. The results for the LID and OLID agree accurately on the depth. We furthermore use the visual stratigraphy images to obtain information on the sharpness of the open- to closed-porosity transition. Combining traditional methods with the independent optical method presented here, we can now better constrain the bubble closure processes in polar firn.

Trajectory planning and automatic docking of LNG five-axis loading arm

PurposeIn response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.Design/methodology/approachAn automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.FindingsThe docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.Originality/valueThe proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

The SG‐Climbot: An Adaptable, Efficient, Inspection and Repair Robot for Steam Generator Heat Transfer Tube Sheet

ABSTRACTThe steam generator plays a crucial role as a safety component and protective barrier in nuclear power plants. Regular inspection and repair of heat transfer tubes, which operate in high‐temperature, high‐pressure, corrosive, and abrasive environments for extended periods, are essential. Inspection and repair robots move inside the steam generator to conduct comprehensive inspections and evaluations of the heat transfer tubes. However, existing robots have relatively simple execution devices and fixed movement methods, resulting in poor adaptability to different specifications of tube sheets and limited flexibility in handling maintenance tasks. In response to these limitations, the SG‐Climbot, a quadruped crawling tube sheet inspection and repair robot, has been proposed. This robot is designed to adapt to full‐specification tube sheets and operate efficiently by conducting inspections while moving. By comparing different motion modes, a robot configuration with a quadruped parallel structure was suggested. Kinematic and static analyses were conducted, resulting in the development of both forward and inverse kinematic models, along with the identification of conditions leading to tipping. Using parametric modeling and optimization design, optimized design parameters were obtained. Finite element simulation analysis was performed on the overall structure and main components, leading to an optimized structural model. Finally, based on the design model and computational analysis, a prototype was developed and tested. The experimental results indicate that the robot has achieved the expected functionality and performance targets. Its efficient maintenance operation mode of conducting inspections while moving provides a basis for the autonomous and intelligent development of steam generators.

Trajectory tracking control of a four-bar linkage prosthetic knee with magnetorheological damper

In order to improve biomimicry, a four-bar linkage prosthetic knee with magnetorheological (MR) damper was developed in this paper. The principle and structure of four-bar linkage MR prosthetic knee was introduced and its kinetic model was established. A MR damper matching the proposed prosthetic knee was designed, fabricated and tested, and the forward and inverse mechanical model were conducted. In addition, to solve the problems of complex model, poor bionics and low trajectory tracking accuracy in the MR prosthetic knee, a PD-type iterative learning control (ILC) controller was developed. The simulated and experimental test results show that the positive maximum error of the knee joint swing trajectory based on PD-type ILC controller are 2.2°(speed of 0.5 m/s), 4.1°(speed of 1.0 m/s), and 1.9°(speed of 1.5 m/s), respectively, and the negative maximum error of that are −1.4°(speed of 0.5 m/s), −6.7°(speed of 1.0 m/s), and −5.5°(speed of 1.5 m/s), respectively, which effectively tracks the reference swing trajectory.

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data.

High-resolution fMRI at 7T is challenged by suboptimal alignment quality between functional data and structural scans. This study aims to develop a rapid acquisition method that provides distortion-matched, artifact-mitigated structural reference data. We introduce an efficient sequence protocol termed T1234, which offers adjustable distortions. This approach involves a T1-weighted 2-inversion 3D-EPI sequence with four spatial encoding directions optimized for high-resolution fMRI. A forward Bloch model was used for T1 quantification and protocol optimization. Twenty participants were scanned at 7T using both structural and functional protocols to evaluate the utility of T1234. Results from two protocols are presented. A fast distortion-free protocol reliably produced whole-brain segmentations at 0.8mm isotropic resolution within 3:00-3:40 minutes. It demonstrates robustness across sessions, participants, and three different 7T SIEMENS scanners. For a protocol with geometric distortions that matched functional data, T1234 facilitates layer-specific fMRI signal analysis with enhanced laminar precision. This structural mapping approach enables precise registration with fMRI data. T1234 has been successfully implemented, validated, and tested, and is now available to users at our center and at over 50 centers worldwide.

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

An Efficient Algorithm for Gravity Forward Modeling (GFM) with Masses of Arbitrary Shapes and Density Distributions

Summary Currently, gravimetric forward modelling of mass density structures with arbitrary geometries and density distributions typically involves subdividing the mass body into individual geometric elements (such as rectangular prisms), calculating their gravitational contributions that are then summed up to obtain the gravitational attraction of the whole body. To achieve a more accurate approximation of the true geometric shape and density distribution, this rectangular prism model requires fine dividing, which significantly increases computational load and reduces numerical efficiency. To address this issue, we propose the algorithm for gravimetric forward modeling of arbitrary geometric shapes and density distributions in spectral domain that significantly improves numerical efficiency while preserves computational accuracy. The novelty of our proposed algorithm lies in dividing the masses into multiple layers of equal thickness in the vertical direction, providing constant upper and lower bounds. This allows to extended Parker's formulas and apply the Fast Fourier Transform (FFT) to increase numerical efficiency. The algorithm is tested using synthetic models and then used to compute gravitational effects of topography and sediments using real data from Tibet. Results show high accuracy and numerical efficiency than rectangular prism approach.

Insights into the crustal and the magmatic feeding structure at the Payunia Volcanic Province highlighted by geophysical methods, in the retroarc of the Southern Central Andes

The Payunia Volcanic Province is a Quaternary volcanic plateau emplaced in the retroarc area in the northern Neuquén Mesozoic Basin, associated with a hydrocarbon system. At deeper levels, this basin is linked to different intrusive systems that developed in the retroarc region at different times, during the Jurassic, Cretaceous, Eocene, Miocene, and even the Quaternary which influenced the hydrocarbon system maturity. We analyzed the Moho structure through this retroarc region, as well as the crustal structure affected by different stages of regional extension. From continuous seismic noise data, we calculated the autocorrelograms to obtain the reflection response below each seismological station. This allowed imaging the surface of primary crustal reflectors and in a few stations the top of an asthenospheric anomaly (SWAP) found by magnetotelluric survey and in concordance with satellite magnetic data. The crustal reflectors were identified in all stations at a mean two-way travel time of about ∼8.5 s and ∼12.5 s using frequency bands of about 1.0–2.4 Hz. Therefore, this is the first geophysical research that estimates the depth of the magmatic system, hosted at the top of the lower crust and the Moho discontinuity. The deepest reflector, only recognized in 4 stations, was observed with a two-way travel time of 17.2 s to 19.6 s. We used a mean one-dimensional Vp model to obtain the corresponding reflector depths which constrain the two-dimensional forward gravity model that fits with the observed regional anomaly for the region. We finally established a relationship between the shallowest sublithospheric electrical conductivity anomalies determined in previous researches and the strong deep reflections observed in some of the seismological stations. This information may help to constrain geochemical and petrological models and re-evaluate the hydrocarbon system maturity of the northern Neuquén basin.

Ultrasound biomicroscopy in dogs suggests postoperative ocular hypertension may be associated with ciliary cleft changes related to cataract surgery.

To determine whether novel pre- and postsurgical ultrasound biomicroscopy (UBM) measurements of the canine ciliary cleft (CC) are associated with postoperative ocular hypertension (POH) following cataract surgery and to explore the relationship between intraocular pressure and CC UBM measurements. Following pharmacologic mydriasis, UBM images were obtained from 31 client-owned dogs before elective cataract surgery, immediately postsurgery, and 4 to 6 hours following surgery or while experiencing POH ≥ 25 mm Hg. Presurgery and the pre- to postsurgery change in CC measurements were assessed for association with POH using individual mixed-effects logistic regression models and forward variable selection models. Linear mixed-effects models were used to evaluate the relationship of intraocular pressure to UBM measurements within the same eye across multiple time points. Presurgical measurements were not predictive of POH development. An increase in pectinate ligament distance and CC area from presurgical baseline to immediate postsurgical measurement was associated with reduced odds of developing POH, while increasing CC length (from apex to mid point on the pectinate ligament) from pre- to postsurgery and immature cataracts was associated with increased odds of POH. The change in CC morphology following cataract surgery appears more impactful in the development of POH than individual variations in presurgery CC measurements. Several changes in the CC dimensions following surgery appear associated with POH risk. This understanding of a potential mechanism of POH development opens new avenues for researching preventative measures associated with modifying surgical techniques to influence CC morphology following cataract surgery.

Characterising the contribution of dust-obscured star formation at ≳ 5 using 18 serendipitously identified [C ii] emitters

ABSTRACT We present a new method to determine the star formation rate density (SFRD) of the Universe at $z \gtrsim 5$ that includes the contribution of dust-obscured star formation. For this purpose, we use a [C ii] (158 $\mu$m) selected sample of galaxies serendipitously identified in the fields of known $z\gtrsim 4.5$ objects to characterise the fraction of obscured star formation rate (SFR). The advantage of a [C ii] selection is that our sample is SFR-selected, in contrast to an ultraviolet (UV)-selection that would be biased towards unobscured star formation. We obtain a sample of 23 [C ii] emitters near star-forming (SF) galaxies and quasi-stellar objects (QSOs) – three of which we identify for the first time – using previous literature and archival Atacama Large Millimeter/submillimeter Array data. 18 of these serendipitously identified galaxies have sufficiently deep rest-UV data and are used to characterise the obscured fraction of the star formation in galaxies with SFRs $\gtrsim 30\ \text{M}_{\odot } \ \text{yr}^{-1}$. We find that [C ii] emitters identified around SF galaxies have $\approx$63 per cent of their SFR obscured, while [C ii] emitters around QSOs have $\approx$93 per cent of their SFR obscured. By forward modelling existing wide-area UV luminosity function (LF) determinations, we derive the intrinsic UV LF using our characterisation of the obscured SFR. Integrating the intrinsic LF to $M_{\mathrm{ UV}}$ = $-$20, we find that the obscured SFRD contributes to $\gt 3~{{\ \rm per\ cent}}$ and $\gt 10~{{\ \rm per\ cent}}$ of the total SFRD at $z \sim 5$ and $z \sim 6$ based on our sample of companions galaxies near SF galaxies and QSOs, respectively. Our results suggest that dust obscuration is not negligible at $z\gtrsim 5$, further underlining the importance of far-infrared observations of the $z\gtrsim 5$ Universe.

Effective application of geological process modeling for unravelling carbonate build-up complexity: A case study from the EX-Carbonate build-up in central Luconia Province, Malaysia

In the dynamic field of carbonate reservoir Exploration, the need for specialized models is vital, assisting as a key tool to facilitate interdisciplinary collaboration and enhance strategies for predicting reservoir behavior. This study examines the use of geological process (GPM), a standard tool to simulate forward stratigraphic modelling, particularly focusing on the Central Luconia Province in the South China Sea. Central Luconia Province stands out for its extensive carbonate build-ups and is recognized as a prolific hydrocarbon reservoir. Forward Stratigraphic Modelling (FSM) leads to a paradigmatic change in geological modeling. Unlike geostatistical models controlled by geometrical parameters, FSM relies on mathematical representations of the physical rules determining erosion, transport, and sedimentation in carbonate growth. This tool, supported by various researchers, has proven its ability in quantitative sedimentary system analysis across diverse temporal and spatial scales. This study located in EX Field showcases the ability of stratigraphic forward modeling in replicating sedimentary complexities and resulting stratigraphic architecture of the EX isolated carbonate platform. The methodology links parameters from existing literature with multiscale data. Additionally, carbonate production laws, contingent on water depth, play a pivotal role in the meticulous modeling process. This work illustrates the efficacy of geological process modelling as a transformative approach for unraveling the complexities of carbonate systems, particularly in regions with prolific hydrocarbon reservoirs like Central Luconia. The incorporation of literature-derived parameters and multiscale data serves as evidence of the model's ability to offer nuanced understandings of the dynamic evolution of carbonate platforms, thereby advancing our understanding of reservoir prediction and future management strategies.