Fitting Error Research Articles

Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM

In the prediction of single-well production in gas reservoirs, the traditional empirical formula of gas reservoirs generally shows poor accuracy. In the process of machine learning training and prediction, the problems of small data volume and dirty data are often encountered. In order to overcome the above problems, a single-well production prediction model of gas reservoirs based on CNN-BILSTM-AM is proposed. The model is built by long-term and short-term memory neural networks, convolutional neural networks and attention modules. The input of the model includes the production of the previous period and its influencing factors. At the same time, the fitting production and error value of the traditional gas reservoir empirical formula are introduced to predict the future production data. The loss function is used to evaluate the deviation between the predicted data and the real data, and the Bayesian hyperparameter optimization algorithm is used to optimize the model structure and comprehensively improve the generalization ability of the model. Three single wells in the Daniudi D28 well area were selected as the database, and the CNN-BILSTM-AM model was used to predict the single-well production. The results show that compared with the prediction results of the convolutional neural network (CNN) model, long short-term memory neural network (LSTM) model and bidirectional long short-term memory neural network (BILSTM) model, the error of the CNN-BILSTM-AM model on the test set of three experimental wells is reduced by 6.2425%, 4.9522% and 3.0750% on average. It shows that on the basis of coupling the empirical formula of traditional gas reservoirs, the CNN-BILSTM-AM model meets the high-precision requirements for the single-well production prediction of gas reservoirs, which is of great significance to guide the efficient development of oil fields and ensure the safety of China’s energy strategy.

Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

Lifetime mapping using femtosecond time-resolved photoemission electron microscopy.

Time-resolved photoemission electron microscopy (PEEM) has established itself as a versatile experimental technique to unravel the ultrafast electron dynamics of materials with nanometer-scale resolution. However, the approach of performing PEEM-based, pixel-by-pixel lifetime mapping has not been reported thus far. Herein, we describe in detail the data pre-processing procedure and an algorithm to perform time-trace fittings of each pixel. We impose an energy cutoff for each pixel prior to spectral integration to enhance the robustness of our approach. With the energy cutoff, the energy-integrated time traces show improved statistics and lower fitting errors, thus resulting in a more accurate determination of the fit parameters, e.g., decay time constants. Our work allows us to reliably construct PEEM-based lifetime maps, which potentially shed light on the effects of local microenvironment on the ultrafast processes of the material and allow spatial distributions of lifetimes to be correlated with observables obtained from complementary microscopic techniques, hence enabling a more comprehensive characterization of the material.

HF Trimer: A New Full-Dimensional Potential Energy Surface and Rigorous 12D Quantum Calculations of Vibrational States.

HF trimer, as the smallest and the lightest cyclic hydrogen-bonded (HB) cluster, has long been a favorite prototype system for spectroscopic and theoretical investigations of the structure, energetics, spectroscopy, and dynamics of hydrogen-bond networks. Recently, rigorous quantum 12D calculations of the coupled intra- and intermolecular vibrations of this fundamental HB trimer (J. Chem. Phys. 2023, 158, 234109) were performed, employing an older ab initio-based many-body potential energy surface (PES). While the theoretical results were found to be in reasonably good agreement with the available spectroscopic data, it was also evident that it is highly desirable to develop a more accurate 12D PES of HF trimer. Motivated by this, here we report a new, and the first fully ab initio 12D PES of this paradigmatic system. Approximately 42,540 geometries were sampled and calculated at the level of CCSD(T)-F12a/AVTZ. The permutationally invariant polynomial-neural network based Δ-machine learning approach (J. Phys. Chem. Lett. 2022, 13, 4729) was employed to perform cost-efficient calculations of the basis-set-superposition error (BSSE) correction. By strategically selecting data points, this approach facilitated the construction of a high-precision PES with BSSE correction, while requiring only a minimal number of BSSE value computations. The fitting error of the final PES is only 0.035 kcal/mol. To assess its performance, the 12D fully coupled quantum calculations of excited intra- and intermolecular vibrational states of HF trimer are carried out using the rigorous methodology developed by us earlier. The results are found to be in a significantly better agreement with the available spectroscopic data than those obtained with the previously existing semiempirical 12D PES.

Structure Analysis for Plate Components Using an Advanced Boundary Element Method

Accurate and effective evaluation of physical variables is of great significance to the design safety and stability of plate structure. In this paper, an advanced boundary element method is developed to perform the structure analysis for plate components. The method is implemented as follows: Firstly, a novel interpolation method is developed to improve the simulation accuracy of the physical quantities on various structural models of plate components, which can improve the order of interpolation polynomials without changing the degrees of freedom of system equations, and eliminate the influence of the fitting error of physical variable in integral equation; And then an integral transformation frame is employed to remove the influence of the singular and nearly singular integrals in integral equation and ensure the accurate calculation of elastic parameters of plate components; Finally, several numerical examples (a porous plate, a deck framing and a propeller structure are included) are given to verify the feasibility of the proposed method. The results show that the proposed method can accurately evaluate the physical variables of the plate components.

Development and Experimental Study of an Experimental Setup for an Online Vibrating Tube Liquid Densitometer

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, the real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an online vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density; it also conducts experiments with an online vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant-temperature stirring system, a walk-in constant-temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device’s evaluation is U = 0.08 kg/m3, k = 2. We built a set of pressure-density static test systems, statically testing the online vibrating tube’s liquid-density meter vibration frequency at different pressures; the whole set of systems can be used to assess the specific density, temperature, and pressure range of online vibrating tube liquid density meters in the experimental research to derive the standard temperature. Through the experimental research, we can accurately derive the fitting coefficients under the standard temperature, specific temperature, and pressure of online vibrating tube liquid densitometers, and calculate the fitting error of online vibrating tube liquid densitometers under different temperatures and pressures within the experimental range through fitting equations and coefficients, so as to realize the practical application of online vibrating tube liquid densitometers in engineering by utilizing straight-tube-type and curved-type online vibrating tube densitometers. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system’s vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device’s conformity. The study verified the device’s scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research. Using this device in subsequent experiments can verify the effects of viscosity, inlet, installation, and other factors on the online vibrating tube liquid densitometer’s metrological performance. Further experimental research on the pressure–frequency–density test system and the establishment of a wide range of temperatures and pressures within the pressure standard density test system are needed to achieve a wide range of temperatures and pressures under the standard density test.

In-line spectroscopy for iodine quantification in dynamic contrast-enhanced dedicated breast CT.

In 4D dynamic contrast-enhanced dedicated breast computed tomography (4D DCE-bCT), the functional properties of the breast will be characterized by monitoring the uptake and washout of iodine-based contrast agents over time. This information could be valuable in breast cancer treatment. However, prior to clinical implementation, it is crucial to validate the quantitative estimates of iodine concentrations at each time point during acquisition. To develop an in-line spectroscopy system capable of measuring iodine concentrations in a dynamic x-ray breast phantom in real-time. Potassium iodide served as the contrast agent. The system was set-up at both the entrance and exit of the phantom. It comprises a fiber-coupled green LED and collimator, which together ensure that a parallel beam passes through the sample holder. Transmitted light is captured by a collimator on the opposite side and directed through a fiber optic cable to a photodetector for intensity measurement. The relationship between 13 iodine concentrations (0-6mg I/mL) and light transmission was tested, and the system's repeatability and accuracy were determined. The system exhibited a strong correlation between iodine concentration and transmission values, achieving a root-mean-square error of 0.007. The repeated measurements had relative standard deviations of 0.04% and 0.1% for repeated water measurements at the phantom's entrance and exit, respectively. Furthermore, the accuracy measurements gave a mean error of fitting of 0.008(±0.07) mg I/mL. The in-line spectroscopy system can effectively monitor iodine concentrations in a dynamic breast phantom, providing a reliable method for quantitative validation of 4D DCE-bCT.

The relationship of voltage variations in the third quadrant and the Schottky area ratios in a 4H-SiC SBD-embedded MOSFET

Based on the measured and the TCAD simulation results, this paper provides a detailed description of the third quadrant characteristics of the Schottky barrier diode (SBD) embedded MOSFETs with different Schottky area ratios of 2.6 %, 5.3 %, and 14.7 %. As the Schottky area ratio increases, the shift of the third quadrant turn-on voltages of the MOSFETs influenced by the gate voltage decreases. The shift rates in the conventional MOSFET and SBD-embedded MOSFETs are from 57.2 % to 0 % when Vgs changes from 0 V to −4 V. The shift rate becomes 0 % when the Schottky area ratio is larger than 5.3 %. Simultaneously, the TCAD simulation results and the measured results indicate that the SBD-embedded MOSFETs can effectively suppress the conduction of the low barrier diode and the body diode.Furthermore, the SPICE models were built to explain the physics mechanisms. The fitting error between the measured and fitting results is as low as 1.3 % among the models with the different Schottky area ratios.

Practical Probabilistic Model-Based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling.

This article addresses the prediction stability, prediction accuracy, and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach to dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed, where the system uncertainty is stably predicted by combining the Monte Carlo dropout (MC Dropout) and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open-source code of DPETS is available at https://github.com/mrjun123/DPETS.

Optimal Curve Fitting for Serial Chain in Six-Crease Origami Unit

Abstract Origami-inspired structures have been widely used in aerospace and robotics for three-dimensional (3D) symmetrical configurations using crease-symmetrical origami basic patterns. These patterns offer advantages in repeatable and systematic modeling and mass production. However, few studies have focused on 3D non-symmetrical structures using symmetrical origami basic patterns due to their structure complexity, limiting their application. Therefore, we aim to analyze the folding behavior in 3D non-symmetrical structures using a 6-crease symmetry origami base pattern. To achieve this goal, we first focus on behavior in a two-dimensional (2D) plane. This paper presents a scheme for the behavior of origami units with an optimal curve-fitting algorithm. The curve can be any 2D space curve. The fitting curve, constructed by numerical analysis and an optimal approaching scheme, can satisfy error requirements and retain foldable origami unit features. The paper verifies the feasibility of the curve-fitting scheme by presenting two curve examples, including a quadratic curve and a sin wave function. The results show that the fitting error is reduced by 99% when no boundary conditions are applied. This research provides valuable insights into understanding origami unit kinematic optimization through forward and inverse kinematics. It offers potential applications in the engineering design of foldable structures and precision origami-inspired mechanism, thereby opening avenues for further exploration of complex origami structures and their applications in emerging technologies.

Estimation of the biaxial tensile behavior of ovine esophageal tissue using artificial neural networks

Diseases of the esophagus affect its function and often lead to replacement of long sections of the organ. Current healing methods involve the use of bioscaffolds processed from other animal models. Although the properties of these animal models are not exactly the same as those of the human esophagus, they nevertheless present a reasonable means of assessing the biomechanical properties of the esophageal tissue. Besides, sheep bear many similarities physiologically to humans and they also suffer from same diseases as humans. The morphology of their esophagus is also comparable to that of humans. Thus, in the study, an ovine esophagus was studied. Studies on the planar biaxial tests of the gross esophageal anatomy are limited. The composite nature of the gross anatomy of the esophagus makes the application of structure-based models such as Holzapfel-type models very difficult. In current studies the tissue is therefore often separated into specific layers with substantial collagen content. The effects of adipose tissue and other non-collagenous tissue often make the mechanical behavior of the esophagus widely diverse and unpredictable using deterministic structure-based models. Thus, it may be very difficult to predict its mechanical behavior. In the study, an NARX neural network was used to predict the stress–strain response of the gross anatomy of the ovine esophagus. The results show that the NARX model was able to achieve a correlation above 99.9% within a fitting error margin of 16%. Therefore, the use of artificial neural networks may provide a more accurate way of predicting the biaxial stress–strain response of the esophageal tissue, and lead to further improvements in the design and development of synthetic replacement materials for esophageal tissue.

Interior Profile Accuracy Assessment Method of Deep-Hole Parts Based on Servo Drive System.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

Design and Stress Analysis of Bevel Line Gears with Vertical Flank Suitable for Micro Machining

The line gear (LG) exhibits promising applications in micromachinery owing to its simple structure and minimal teeth count. This paper aims to advance the meshing theory of LG and introduce a conical LG tooth configuration specifically tailored for micro-machining that is capable of driving with high performance. Based on the meshing principle of LG, a conical line gear pair with vertical flank (VFLG) of the driving gear was designed. Subsequently, adhering to the curvature non-interference principle that no local interference occurs in the meshing process and the specified range of fitting error in one direction, the design parameters for LG were determined. A comparison of the contact stress between two sets of different contact line types revealed the distinct advantage of VFLG with parameters identical to those of traditional LG. The experimental results conclusively demonstrate a transmission ratio error of 0.004, affirming the feasibility of the design. The curvature non-interference design formula proposed in this paper refines the LG design theory, and the novel LG design method presented holds significant implications for subsequent micromachining.

A novel dynamic structural adaptive multivariable grey model and its application in China's solar energy generation forecasting

Accurately predicting China's solar energy generation is crucial for energy planning and environmental protection. However, the nonlinearity and complexity of the data pose challenges to prediction. This article proposes a dynamic structural adaptive multivariate grey model to overcome these challenges. The model constructs a dynamic nonlinear correction function with an uncertain structure that can adaptively adjust to the data characteristics. Additionally, the Gaussian function is utilized to derive the time response function, overcoming the mechanism defects present in traditional multivariate grey models. The whale optimization algorithm is applied to optimize the background value of the model, improving accuracy. The model is compatible with 14 classical models via parameter changes and has strong compatibility. The results show that the fitting error and test error of the model are 0.23 % and 1.95 % respectively, which is significantly better than the other 11 methods, indicative of strong robustness and accuracy. The forecast indicates that China's solar energy generation will experience a consistent upward trajectory from 2023 to 2030, with an average annual growth rate of 12.11 %. This study significantly contributes to the advancement of the theory of multivariate grey dynamic prediction and provides essential data support for strategic planning within China's solar power industry.

Estimation of parameters for 3D geomechanical modeling from triaxial test results

Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks, crucial for developing accurate 3D geomechanical models. This paper presents a novel method for determining strength parameters by incorporating the dependence of uniaxial compressive strength (UCS) on P-wave velocity into the Hoek-Brown criterion. Additionally, a new approach is introduced to process triaxial test data efficiently using Python libraries such as SciPy, NumPy, Matplotlib, and Pandas. Furthermore, the paper addresses challenges in determining elastic parameters through triaxial testing. A Python script is developed to automate the calculation of elastic modulus and Poisson's ratio, overcoming subjectivity in selecting the linear portion of stress-strain curves. The script optimally identifies the linear region by minimizing the fit error with appropriate constraints, ensuring a more objective and standardized approach. The proposed methodologies are demonstrated using limestone specimens from Central Asian gas fields. These innovations offer faster, more reliable results, reducing error and enhancing the comparability of analyses in geomechanics, with potential applications across various geological settings.

A multiscale Gaussian expansion method for low-frequency multimodal damping analysis of variable stiffness acoustic black hole beams

To address the issues of slow convergence and poor accuracy in existing dynamics methods caused by non-uniform variable cross-sections and layered material stiffness variations in variable stiffness acoustic black hole structure (VSABH). A multiscale Gaussian expansion method (MSGEM) is proposed in this paper. The structural dimensions are taken into account, and the structural parameters of multiple shape functions are adaptively selected based on stiffness and cross-sectional variation parameters. This results in the formation of shape function groups of various scales, which in turn creates the matrix of multi-scale Gaussian function groups. The feature information of the displacement field is realized to be extracted from multiple scales, and the fitting error of MSGEM to the displacement field of VSABH beam is within 2.02%. The missing solution of eigenfrequency of Gaussian expansion method is avoided, and the modal vibration patterns are basically matched, which verifies the validity of MSGEM. Meanwhile, the advantages of low-frequency multimodal vibration reduction of VSABH beams are highlighted from several aspects, and the trend of the adjustment of the vibration reduction characteristics of VSABH beams by different parameters is clarified, which provides valuable design guidance for ABH metamaterials oriented to low-frequency vibration reduction.

Examination of methods to separate overlapping metabolites at 7T.

Neurochemicals of interest quantified by MRS are often composites of overlapping signals. At higher field strengths (i.e., 7T), there is better separation of these signals. As the availability of higher field strengths is increasing, it is important to re-evaluate the separability of overlapping metabolite signals. This study compares the ability of stimulated echo acquisition mode (STEAM-8; TE = 8 ms), short-TE semi-LASER (sLASER-34; TE = 34 ms), and long-TE semi-LASER (sLASER-105; TE = 105 ms) acquisitions to separate the commonly acquired neurochemicals at 7T (Glx, consisting of glutamate and glutamine; total N-acetyl aspartate, consisting of N-acetyl aspartate and N-acetylaspartylglutamate; total creatine, consisting of creatine and phosphocreatine; and total choline, consisting of choline, phosphocholine, and glycerophosphocholine). sLASER-34 produced the lowest fit errors for most neurochemicals; however, STEAM-8 had better within-subject reproducibility and required fewer subjects to detect a change between groups. However, this is dependent on the neurochemical of interest. We recommend short-TE STEAM for separation of most standard neurochemicals at 7T over short-TE or long-TE sLASER.

EXPERIMENTAL AND OPTIMIZATION-BASED ANALYSIS TO MAXIMIZE MECHANICAL-RELATED ATTRIBUTES OF FRICTION STIR WELDED CDA101 CU ALLOY JOINTS

This paper deals with the identification of optimized process parameters for FSW of 6[Formula: see text]mm thick CDA 101 Cu alloy plates. Three distinctive parameters, the tool’s traverse speed, rotational speed, axial load, have been considered as self-reliant variables for formulating a numerical model based on nonlinear regression-type approach, with the objective of anticipating mechanical attributes of joints. From the surface- and contour-type plots, it was revealed that the axial load in combination with rotational speed has played a dominant role in influencing the tensile and yield strength of the CDA101 Cu alloy joints. At the same time, the traverse speed of the tool has played a dominant role in enhancing the ductility of the CDA101 joints through the generation of ample volumes of frictional heat, which in turn have facilitated both coarsening of grain structures and softening of materials in an ideal manner. Employing tool rotational speed, traverse speed and axial load in the ranges of 1750–1900[Formula: see text]rpm, 23–27[Formula: see text]mm/min and 5.75–6.5[Formula: see text]kN, respectively, was found to enhance the tensile strength of the CDA101 Cu alloy joints. FSW process parameters attained through the numerical meta-model of RSM have led to the attainment of the highest value of tensile strength of 209.5[Formula: see text]MPa together with a 141.4[Formula: see text]MPa yield strength with very minimal fitting errors of 0.28% and 0.52%, respectively. A collection of additional experimental data has been employed to verify the formulated empirical-based formula and additionally, an entirely different optimization technique namely PSO (i.e. particle swarm optimization) was implemented for comparison and validation purposes. The formulated meta-model of RSM generated the largest value of % of elongation of 14.23%, though the relative percent error was around 1.988%, which was slightly larger than that of the value (i.e. 14.11%) generated by PSO, with a relative error of 0.58%.

Analytical Method for Determining the Viscosity Index of Engine Lubricating Oils

This paper proposes a simple analytical method for determining the viscosity index (VI) that effectively aligns with results obtained from the applicable standards. This method simply needs the kinematic viscosity of the tested oil at 40 and 100 °C as input parameters and does not need to use tables that are an integral part of the mentioned standards. This work presents a method and evaluates the accuracy of determining auxiliary parameters in the form of kinematic viscosity values at 40 °C for two hypothetical oils. These oils have a kinematic viscosity at 100 °C equal to that of the oil under testing and have VI = 0 and 100. The relative fitting error percentage and the coefficient of determination are found for the specified auxiliary indicators. The method is validated using data obtained from previous studies in the form of the kinematic viscosity of lubricating oil–diesel mixtures. The mixtures of viscosity grades SAE 30 and SAE 40 lubricating oil with diesel oil at concentrations of 0, 1, 2, 5, 10, 20, and 50% w/w are tested. The viscosity index for each mixture is determined using a standard-based manual calculation using the Anton Paar viscosity index calculator and the proposed method. The results obtained from the proposed analytical method are compared with those from two other methods. The maximum percentage relative fitting error (δmax ≈ 1%) and the coefficient of determination (R2 > 0.999) are determined. The obtained results demonstrate a very good fit and, thus, confirm the usefulness of the proposed approach.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.