Density Model Research Articles

127 Utilizing predictive models for bone mineral content and bone mineral density as a tool to assess dietary phosphorous

Abstract Dietary phosphorous is a necessary nutrient for bone development in pigs at all stages of its growing. Deficiencies in phosphorous decrease bone mineral content (BMC) and bone mineral density (BMD) and may be a causative of lameness. However, the normal range expected in whole body (WB) and individual bones for BMC and BMD in commercial pigs has been poorly defined. Therefore, our objectives were to 1) model normal WB bone density at multiple timepoints in wean to finish pigs, and 2) determine the extent to which individual bone (femur and 3rd metatarsal) BMC and BMD could predict WB BMC and BMD. Mixed sex pigs [Camborough (1050) x 337, PIC] were utilized and included freshly weaned ~21 d (n = 8), ~44 d of age (n = 24), ~84 d of age (n = 24) and ~154 d of age (n = 24). Pigs were individually penned. Excluding weaning age, at each age group pigs were randomly assigned to diets adequate, marginally deficient, or deficient in available phosphorus (avP) and fed for 3, 4, and 6 wk (age group 44, 84, 154, respectively). Within each age group, diets were formulated to meet nutrient and energy requirements except for avP and pigs fed ad libitum. On-test and off-test body weight (BW) and feed intake and gain to feed ratio were assessed for each age group. At the end of each feeding period, all pigs were euthanized and WB, femur (F) and 3rd metatarsal (M) BMC and BMD determined by dual X-ray absorptiometry (DXA). Data were analyzed using linear and generalized additive models that predicted response variables (BMC or BMD) as a function of BW or age. Only pigs fed adequate avP diets were used in the model fit. Pigs fed adequate avP diets increased BMD with age (0.22, 0.42, 0.84 and 1.10 g/cm2, days of age 21, 44, 84 and 154 respectively, P < 0.001). Within day of age 84, marginally and deficient avP diets reduced BMC and BMD by 21 to 41% (P = 0.002) and 12 to 25% (P = 0.006), respectively. Within day of age 154, marginally and deficient avP diets reduced BMC and BMD by 9% (P = 0.05) and 6 to 8% (P = 0.06), respectively. Body weight record and individual bone (F and M) BMC and BMD from pigs fed deficient avP diets were used to predict WB BMC and BMD and the mean square error of prediction was selected as a criterion to evaluate the ability of the models to detect these inadequately fed pigs. Using the selected models, pigs fed nutritionally deficient diets were detected due to 2- to 7-fold increase in MSE of predicted WBMD or WBMC compared with pigs fed adequate diets. These models can be used to assess bone development and strength in growing pigs and to infer the adequacy of avP level supplied in their diets.

Minimizing robust density power-based divergences for general parametric density models

Minimizing robust density power-based divergences for general parametric density models

Forecast cosmological constraints from the number counts of Gravitational Waves events

We present a forecast for the upcoming Einstein Telescope (ET) interferometer with two new methods to infer cosmological parameters. We consider the emission of Gravitational Waves (GWs) from compact binary coalescences, whose electromagnetic counterpart is missing, namely Dark Sirens events. Most of the methods used to infer cosmological information from GW observations rely on the availability of a redshift measurement, usually obtained with the help of external data, such as galaxy catalogues used to identify the most likely galaxy to host the emission of the observed GWs. Instead, our approach is based only on the GW survey itself and exploits the information on the distance of the GW rather than on its redshift. Since a large dataset spanning the whole distance interval is expected to fully represent the distribution, we applied our methods to the expected ET's far-reaching measuring capabilities. We simulate a dataset of observations with ET using the package darksirens, assuming an underlying ΛCDM cosmology, and including the possibility to choose between three possible Star Formation Rate density (SFR) models, also accounting for possible population III stars (PopIII). We test two independent statistical methods: one based on a likelihood approach on the theoretical expectation of observed events, and another applying the cut-and-count method, a simpler method to compare the observed number of events with the predicted counts. Both methods are consistent in their final results, and also show the potential to distinguish an incorrect SFR model from the data, but not the presence of a possible PopIII. Concerning the cosmological parameters, we find instead that ET observations by themselves would suffer from strong degeneracies, but have the potential to significantly contribute to parameter estimation if used in synergy with other surveys.

Optimal Analysis of the proportion between Modified Lime Mud and Active Mineral Additive in Mortar: Insights from Particle Size Distribution and Packing Density Modeling

Optimal Analysis of the proportion between Modified Lime Mud and Active Mineral Additive in Mortar: Insights from Particle Size Distribution and Packing Density Modeling

Tomographic imaging of South Rewa basin, Central India: Implications of Gondwana rifting and Late Cretaceous volcanism

We have imaged thick (3.0–5.0 km) sub-trappean Gondwana sediments deposited in a typical rift-environment of the south Rewa basin in Central India masked by Deccan basalts. The basalts intrude through the basin forming several dykes, sills, and other intrusions along with large-scale undulations of the basement showing complex geology and tectonic settings due to Late Cretaceous volcanism (~65 Ma). To delineate complex subsurface geological structures and understand the tectonic settings, we use long-offset seismic reflection data along the N-S trending 127 km Kuvari-Shahdol profile of the basin. The conventional stack and pre-stack time migration techniques fail to image the complex subsurface structures dominated by steeply dipping faults. The steeply dipping faults with small-scale structures are influenced by strong lateral and vertical velocity variations that cause problems in imaging the basin. Hence, a robust tomographic inversion with pre-stack depth migration technique is adopted to image fine-scale subtle subsurface geological structures with smooth velocity variations. The tomographic velocity model and pre-stack depth migration seismic image could decipher basaltic trap thickness, numerous dyke intrusions, alternate horsts and grabens with deposition of thick hydrocarbon-bearing sub-trappean Gondwana rocks along with the basement configuration. The deep basinal faults with highly distorted basement structures represent the intense tectonic activity of this Gondwana rift-basin. The deep-seated hydrocarbon reservoir is discovered with an excellent trapping mechanism forming both pre-rift and syn-rift settings of the rift-basin. The seismic image is further constrained and corroborated by the inversion of residual Bouguer gravity anomaly data to obtain corresponding density model derived from tomographic velocity model. The large basement faults, horsts and grabens, several intrusives like dyke, sill, laccolith, and thick sub-trappean Gondwana formations with Deccan trap cover indicate complex geology and tectonic settings of south Rewa basin forming the Late Cretaceous Volcanic Province of India.

Three-dimensional density model of the mantle beneath the Ukrainian shield

Three-dimensional density model of the mantle beneath the Ukrainian shield

Simulation Method for the Impact of Atmospheric Wind Speed on Optical Signals in Satellite–Ground Laser Communication Links

To analyze the intensity of atmospheric turbulence in a satellite–ground laser communication link, it is important to consider the effect of increased atmospheric turbulence caused by wind speed. Atmospheric turbulence causes a change in the refractive index, which negatively impacts the quality and focusing ability of the laser beam by altering its phase front. To simulate the changes in amplitude and phase characteristics of laser beam propagation in atmospheric turbulence caused by wind speed, a transverse translation phase screen is used. To better understand and address the influence of atmospheric wind speed on the phase of optical signals in satellite–ground laser communication links, this paper proposes a Monte Carlo simulation method. This method utilizes the spatial and temporal variations in the refractive index in the atmosphere and integrates the principles of optical signal propagation in the atmosphere to simulate changes in the phase of optical signals under different wind speed conditions. By analyzing the variations in the received optical signal’s power, the Monte Carlo method is employed to simulate phase screens and logarithmic amplitude screens. Additionally, it models the probability density of the statistical behavior of received optical signal’s fluctuations, as well as the time autocorrelation coefficient of optical signals. This paper, under the coupling condition in satellite–ground laser communication links, conducted a Monte Carlo simulation experiment to analyze the characteristics of the optical signal’s fluctuations in the link and discovered that atmospheric wind speed affects the shape of the power spectral density model of the received optical signal. Increasing wind speed leads to a decrease in the time autocorrelation coefficient of the received optical signal and affects the coupling efficiency. The paper then used a cubic spline interpolation fitting method to verify the models of the power spectral density and the autocorrelation time coefficient of the optical signal. This provides a theoretical foundation and practical guidance for the optimization of satellite–ground laser communication systems.

Investigation of Acute Effects of Using Different Density Foam Roller Models on Hamstring Muscle Stiffness and Flexibility in Professional Soccer Players

It was aimed to examine the acute effects of the use of foam roller models of different densities on hamstring muscle stiffness and flexibility in professional soccer players. Hamstring muscle's stiffness were evaluated using the Myoton Pro device. The flexybility of the hamstring muscle were evaluated using with the unilateral sit-reach test on 21 male professional soccer players who played in Gaziantep Soccer Club at aged 18-40. Athletes were randomly divided into groups of seven to use soft, medium and hard foam rollers. The athletes applied the method consisting of two sets of one minute on the dominant hamstring muscle. Measurements were made twice, before the application (BA) and after the application (AA). The muscle stiffness measurement of the soccer players was determined BA mean of 16.26 N/m and AA mean of 16.17 N/m.. Flexibility measurement was determined BA mean of 28,36 cm and AA mean of 31,05 cm. It was found that the mean of pre-test and post-test did not differ statistically for muscle stiffness (p>0,05) according to measurement times, but statistically differed for flexibility (p0,05). It shows that foam roller application is important for increasing muscle flexibility, but there is no difference between foam roller models applied at different intensities in terms of removing muscle stiffness and increasing flexibility.

X-ray counterpart detection and γ-ray analysis of the supernova remnant G279.0+01.1 with eROSITA and Fermi-LAT

A thorough inspection of known Galactic supernova remnants (SNRs) along the Galactic plane with SRG/eROSITA yielded the detection of the X-ray counterpart of the SNR G279.0+01.1. The SNR is located just 1.5° above the Galactic plane. Its X-ray emission emerges as an incomplete, partial shell of ~3° angular extension. It is strongly correlated to the fragmented shell-type morphology of its radio continuum emission. The X-ray spatial morphology of the SNR seems to be linked to the presence of dust clouds in the surroundings. The majority of its X-ray emission is soft (exhibiting strong O, Ne, and Mg lines), and it occurs in a narrow range of energies between 0.3 and 1.5 keV. Above 2.0 keV the remnant remains undetected. The remnant’s X-ray spectrum is purely of a thermal nature. Constraining the X-ray absorption column to values which are consistent with optical extinction data from the remnant’s location favors nonequilibrium over equilibrium models. A nonequilibrium two-temperature plasma model of kT ~ 0.3 keV and kT ~ 0.6 keV, as well as an absorption column density of NH ~ 0.3 cm−2 describe the spectrum of the entire remnant well. Significant temperature variations across the remnant have been detected. Employing 14.5 yr of Fermi-LAT data, we carried out a comprehensive study of the extended giga-electronvolt source 4FGL J1000.0-5312e. By refining and properly modeling the giga-electronvolt excess originating from the location of the remnant, we conclude that the emission is likely related to the remnant itself rather than being colocated by chance. The remnant’s properties as determined by the X-ray spectra are consistent with the ~2.5 kpc distance estimates from the literature, which implies a source diameter of ~140 pc and an old age of >7 × 105 yr. However, if the source is associated with any of the pulsars previously considered to be associated with the SNR, then the updated nearby pulsar distance estimates from the YMW16 electron density model rather place the SNR at a distance of ~0.4 kpc. This would correspond to a ~20 pc linear size and a younger age of 104− < 7 × 105 yr, which would be more in line with the nonequilibrium state of the plasma.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Magnetically driven relativistic jet in the high-redshift blazar OH 471

Understanding the mechanisms that launch and shape powerful relativistic jets from supermassive black holes (SMBHs) in high-redshift active galactic nuclei (AGNs) is crucial for probing the co-evolution of SMBHs and galaxies over cosmic time. We focus on the high-redshift ($z=3.396$) blazar OH 471 to explore the jet launching mechanism in the early Universe. Using multi-frequency radio monitoring observations and high-resolution Very Long Baseline Interferometry (VLBI) imaging over three decades, we studied the milliarcsecond structure and long-term variability of OH 471. Our spectral modeling of the radio flux densities revealed a synchrotron self-absorbed spectrum, indicating strong magnetic fields within the compact core. By applying the flux freezing approximation, we estimated the magnetic flux carried by the jet. We found that it reaches or exceeds theoretical predictions for jets powered by black hole spin energy via the Blandford-Znajek mechanism. This implies that OH 471 is in a magnetically arrested disk (MAD) state, where the magnetic flux accumulated near the horizon regulates the accretion flow, allowing for an efficient extraction of black hole rotational energy. Our study demonstrates the dominance of MAD accretion in powering the prominent radio flares and relativistic jets observed in the radio-loud AGN named OH 471. Statistical studies of larger samples of high-redshift AGNs will shed light on the role of MAD accretion in launching and accelerating the earliest relativistic jets.

Prediction of Density in Standing Trees of Various Wood Species in Natural Forests Using Near-infrared Spectroscopy

Density plays an important role as basic information for applying wood as construction materials. Recent years, the application of near-infrared spectroscopy as non-destructive testing (NDT) has been promising. Density prediction for standing trees in huge variation trees and species of natural forest needs to be investigated using NDT as of eco-green harvesting. The combination of density information and near-infrared spectroscopy is enabled to build a prediction model. This research applied increment cores sampling for density prediction analysis using near-infrared spectroscopy method. The research combined increment cores samples from multiple wood species to be analyzed in one chemometrics analysis of cross-validation partial least squares regression (CV-PLSR) to build a prediction model of density.  The research resulted coefficient of determination for cross-validation (R2CV) of 0.76 with number of latent variable (LV) 10 from the 1st derivative with 13 smoothing-point spectra and wavelength of 1200 – 1800 nm as the best prediction model. The result seemed sufficient enough with those number of LV for this small tube wood sampling of increment cores from multiple wood species. This research proved that building a prediction model for multiple wood species is possible to be done.

Objective QC for diffusion MRI data: Artefact detection using normative modelling

Abstract Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions, or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work, we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging-derived phenotypes from the UK Biobank dataset. The considered features comprised four microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion, and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods’ ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants.

Process Optimization of Pellet Manufacturing from Mixed Materials in Ultrasonic Vibration-Assisted Pelleting

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided.

Machine Learning Based Modeling of Thermospheric Mass Density

AbstractIn this study, we propose a machine learning based approach to construct an empirical model of thermospheric mass densities, based on the MultiLayer Perceptron and bi‐directional Long Short‐Term Memory for ensemble learning model (MBiLE). The MBiLE model was trained by using only the thermospheric mass density from Swarm C satellite at ∼450 km altitude. To assess the performance of the MBiLE model, the model predictions were compared with observations from several satellites, namely, the Swarm C, the Challenging Minisatellite Payload (CHAMP) and the Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE) satellites. The determination coefficients (R2) for the three satellites are 0.98, 0.99, and 0.98, respectively. The MBiLE model predicts the thermospheric mass density well not only at Swarm C altitude but also at lower altitudes. Earlier empirical models based on multivariate least‐square‐fitting approach failed to achieve this good altitude generalization (e.g., Liu et al., 2013, https://doi.org/10.1002/jgra.50144; Xiong et al., 2018a, https://doi.org/10.5194/angeo‐2018‐25). Further tests have been made by checking the MBiLE model prediction deviations in relation to magnetic local time, day of year, solar flux level, and magnetic activities. No obvious dependences are found for these parameters. Comparing with the NRLMSIS‐2.0 model, the MBiLE model improves prediction accuracy by 91%, 66%, and 56% at the three satellites altitudes. The results indicate that the MBiLE model has the ability to predict well the thermospheric mass density over a wide altitude range, for example, from 224 to 528 km, offering potential for atmospheric research applications.

Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM.

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as protein folding, interconversion between folded states, ligand binding, and allosteric regulation. Accurate quantification of these ensembles remains a challenge for conventional molecular simulations of all but the simplest molecules due to insufficient sampling. Enhanced sampling approaches, such as metadynamics, were designed to overcome this challenge; however, the nonuniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of nonuniform frame weights into a structural clustering method entitled shapeGMM. The result of frame-weighted shapeGMM is a high dimensional probability density and generative model for the unbiased system from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a shapeGMM probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the underlying free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantifying biomolecular ensembles from biased simulations.

All thresholds barred: direct estimation of call density in bioacoustic data

Passive acoustic monitoring (PAM) studies generate thousands of hours of audio, which may be used to monitor specific animal populations, conduct broad biodiversity surveys, detect threats such as poachers, and more. Machine learning classifiers for species identification are increasingly being used to process the vast amount of audio generated by bioacoustic surveys, expediting analysis and increasing the utility of PAM as a management tool. In common practice, a threshold is applied to classifier output scores, and scores above the threshold are aggregated into a detection count. The choice of threshold produces biased counts of vocalizations, which are subject to false positive/negative rates that may vary across subsets of the dataset. In this work, we advocate for directly estimating call density: The proportion of detection windows containing the target vocalization, regardless of classifier score. We propose a validation scheme for estimating call density in a body of data and obtain, through Bayesian reasoning, probability distributions of confidence scores for both the positive and negative classes. We use these distributions to predict site-level call densities, which may be subject to distribution shifts (when the defining characteristics of the data distribution change). These methods may be applied to the outputs of any binary detection classifier operating on fixed-size audio input windows. We test our proposed methods on a real-world study of Hawaiian birds and provide simulation results leveraging existing fully annotated datasets, demonstrating robustness to variations in call density and classifier model quality.

Modelling of sap flux density of oak in a humid region in China

AbstractTranspiration plays a vital role in determining the watershed water cycle. However, we still have little knowledge of the characteristics of tree transpiration in the Hanjiang River Basin, which is the water source for the middle route of South‐to‐North water diversion project. Here, we measured sap flux density of oak trees (Quercus, the dominant species here) at the 10‐min resolution for 2 years and explored its response to the environmental conditions. The incoming short‐wave radiation and vapour pressure deficit well explained the variation of daytime sap flux density, and a statistical model was then proposed to calculate the daytime sap flux density correspondingly; then a nighttime sap flux density module was proposed based on the daytime sap flux density calculation. Sap flux density showed clear counter‐clockwise hysteresis response to incoming short‐wave radiation, and clockwise hysteresis response to vapour pressure deficit. Our sap flux density model can well reproduce the corresponding hysteresis response to incoming short‐wave radiation and vapour pressure deficit. This study unravelled the environmental controls of sap flux density of the oak trees in the Hanjiang River Basin, proposed an efficient model for the sap flux density simulation, provided important knowledge for understanding the corresponding forests' water use, which is of critical significance in determining the water availability for the middle route of the South‐to‐North water diversion project.

Groundwater Flow Model Calibration Using Variable Density Modeling for Coastal Aquifer Management

The paper investigates the mechanism of seawater intrusion and the performance of free and open-source codes for the simulation of variable density flow problems in coastal aquifers. For this purpose, the research focused on the Marathon Watershed, located in the northeastern tip of Attica, Greece. For the simulation of the groundwater system, MODFLOW, MT3DMS and SEAWAT codes were implemented, while sensitivity analysis and calibration processes were carried out with UCODE. Hydraulic head calibration was performed on the MODFLOW model, and TDS concentration was validated in the SEAWAT model. The calibrated parameters of the MODFLOW model were obtained for the variable density flow simulation with SEAWAT. The MODFLOW and SEAWAT hydraulic head outputs were analyzed and compared to one another. The outcome of this analysis is that SEAWAT produced slightly better results in terms of the hydraulic heads, concluding that parameter transferability can take place between the two models. For the purpose of the seawater intrusion assessment, the use of the SEAWAT code revealed that the aquifer is subjected to passive and passive–active seawater intrusion during wet and dry seasons, respectively. Finally, an irregular shape of a saltwater wedge is developed at a specific area associated with the hydraulic parameters of the aquifer.

METHODS FOR 3D INVERSION OF GRAVITY DATA IN INDENTIFYING TECTONIC FACTORS CONTROLLING HYDROCARBON ACCUMULATIONS IN THE EL ZEIT BASIN AREA, SOUTHWESTERN GULF OF SUEZ, EGYPT

The Gebel EL-Zeit area in the southwestern Gulf of Suez, Egypt, is an area with a significant hydrocarbon potential in sedimentary basins, so that the three-stage inversion method was proposed for the Bouguer anomalies observed therein. Salt diapirs obscured the deep structure of the main central El-Zeit basin; hence, this method was implemented to overcome challenges in 3D seismic modeling. Our study included direct and inverse parameterization sequences that involved analyzing the inputs and outputs within trial-and-error initiations and inverse estimations to assess whether and how much the constraining parameters used in the calculations could achieve the intended aim. Data reduction, filtering, optimization, and constraint assumptions were used to determine the minimal set of density model parameters needed to set limits on the acceptable range of density contrasts that are required to study the basement depths, swells, troughs, faulting/folding and intra-sedimentary structures, and for direct modeling aimed at creating a simple model to save time. The thirteen constrained wells with a total depth ranging from shallow to deep were not involved in direct modeling but provided quality control over the graphical display of the inverse results for the entire study area. Moreover, many parameter constraints were inverted to regulate the way the calculated data are related to the model’s solution that allowed us to determine which inversion trial provided the best parameterization sequence and, therefore, yielded the most appropriate solution for the depth-density model which is approximating reality with a minimal computation error in the study area.