Monte Carlo Probability Research Articles

Bayesian-Based Standard Values of Effective Friction Angle for Clayey Strata

In this study, a methodology using probabilistic distribution techniques to determine the parameters of the soil’s effective internal friction angle (φ’) was proposed. The method was grounded in quantitative survey information extracted from geotechnical reports. Extensive equivalent samples were estimated using Markov chain Monte Carlo (MCMC) simulations and probability density functions (PDFs). The effective internal friction angle (φ’) of silty clay layers was probabilistically characterized using the plasticity index (PI), in situ static cone penetration test (qc), and standard penetration test (NSPT). A systematic quantitative analysis integrated prior information from different sources was systematically integrated with sampling data. By establishing a Bayesian framework that incorporated the regression relationship and uncertainties associated with the effective internal friction angle (φ’), the model ensured balance and symmetry in the treatment of prior information and observed data. The model was then transformed into equivalent sample values based on three models, reflecting the symmetrical consideration of different data sources. Further considerations involved correcting the three different analysis methods. A comparison of equivalent sample values with the mean values of the sampling data, along with the parameter optimization updates, was performed by combining the three models. Using three sets of sampling data, a linear relationship model for the new soil parameters was derived. The analysis results demonstrated that the proposed method could obtain equivalent samples for the effective internal friction angle.

An intelligent self-optimization SMC-PHD filter for multitarget tracking

The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is an effective multitarget tracking algorithm in nonlinear and non-Gaussian conditions, but its states-estimation accuracy depends on the state extraction method. In order to extract multitarget states, an intelligent self-optimization SMC-PHD filter is proposed in this paper. The main framework of the intelligent SMC-PHD filter, which transforms the clustering problem into an optimization problem, is constructed. To solve this optimization problem, a self-optimization bat algorithm (SBA) is proposed to extract multitarget states of the SMC-PHD filter, which has the ability to search for a better solution by designing global search and local search strategies. The simulation results show that the performance of the SBA is better than that of the k-means algorithm for extracting multitarget states when the targets are close to each other or even overlapping.

An attention-based deep learning method for safety of uncertain vehicle-bridge system with random near fault earthquakes

In this paper, a novel approach, based on the principle of the deep learning method, is proposed to study the stochastic responses of vehicle-bridge system (VBS) subjected to near fault earthquakes (NFEs), which also considers the effects of uncertain parameters. To generate the training data as the input of the proposed deep learning model, the dynamic formulas of the VBS are deduced by Newmark-β method. The proposed analysis model comprises three modules: the CNN module for seismic data feature extraction, the Attention Mechanism module for enhancing the selection for information between time series to improve the accuracy and efficiency of the final prediction, and the Bidirectional Gated Recurrent Unit (BiGRU) for predicting VBS responses. The mapping connection between earthquake action and the system response is established. The BiGRU model is capable of conveying both the excitation's randomness and the system's uncertain parameters. An actual railway cable-stayed bridge subjected to the running railway vehicle and NFEs is utilized to verify the proposed model. The uncertain train weight, bridge damping ratio and the randomness of NFEs are incorporated into the dynamic responses analysis of VBS. As a result, the time-varying responses obtained by the proposed model show significant agreement with results from a validated dynamic VBS framework. The mean value and standard deviation (STD) of the responses obtained by the proposed method are also compared with those by the Monte Carlo method and probability density evolution method. Therefore, both the individual sample of the dynamic response and the statistical data from diverse stochastic responses are chosen to validate the model's accuracy and efficiency in the VBS analysis under NFEs. In addition, the effects of the stochastic characteristics on the system's random vibrations are also explored through the time-histories of statistical data and the probability density function of the absolute maximum of responses.

Evaluation of multiple machine learning models for ASR expansion of concrete

Machine learning algorithms (MLA) show promise in predicting alkali-silica reaction (ASR) expansion. Nevertheless, the predictive performance of different machine learning algorithms (MLAs) varies across various engineering scenarios and datasets. Given that, this study was devoted to constructing an appropriate predictive model for ASR expansion of concrete. Multiple MLAs including evolutionary-based, support vector-based, assembly-based, and neural network-based algorithms, were employed to establish predictive models. An optimization technique, the particle swarm optimization algorithm, was also utilized and integrated with the aforementioned MLA models to effectively search for optimal hyperparameters and topological structures. The predictive performance was evaluated using statistical methods, which showed that the extreme gradient boosting (XGBoost) model had the best fitting goodness among the standard algorithms, with an R2 value of 0.9695. Moreover, the Particle Swarm Optimization (PSO) model significantly improved the predictive performance of all Machine Learning Algorithm (MLA) models. The error distribution analysis was then performed. There are fewer large errors in the predicted values of the PSO-XGBoost model. Monte Carlo simulation and probability distribution functions were used to thoroughly analyze the stability of each model after multiple independent runs. The results show that all models have excellent stability. The PSO-XGBoost model performed the best and was subsequently employed to conduct feature analysis to determine the significance of each input parameter and capture the underlying evidence of ASR expansion.

BUILDING YOUR TOOLKIT FOR QUANTIFYING CYBER RISK

This article delves into the significance of quantifying cybersecurity risk, emphasizing its role as a decision-support mechanism for organizations. It discusses the process of Cyber Risk Quantification (CRQ) and its benefits in prioritizing risks, allocating resources effectively, and mitigating cyber threats. The article explores various questions related to cyber risk assessment, methods for quantifying risk, and the importance of selecting the right approach based on organizational needs. It highlights key models such as Factor Analysis for Information Risk (FAIR), Monte Carlo simulations, probability distributions, vulnerability analysis, and Bayesian models. The article concludes by recommending open-source solutions like those offered by Boise State University’s Cybersecurity Risk Quantification course to streamline risk assessment and management processes.

Two-stage stochastic programming for multi-objective optimization of sustainable utility systems integrating with combined heat and power units

Utility systems provide thermal energy and power for industrial processes while emitting significant amounts of carbon dioxide. This study investigates the integration of Combined Heat and Power (CHP) units and renewable energy sources into utility systems in the context of diverse energy demands and low-carbon operations. A two-stage stochastic programming model (TSSP) is proposed for sustainable utility systems. The main objective of the model is to minimize the annual total cost and the other two objectives are to maximize renewable energy penetration rate and minimize grid net interaction level. The model tackles renewable energy sources’ uncertainty and evaluates environmental metrics by computing carbon emission costs. In TSSP, the first stage involves new equipment capacity planning. The second stage involves considering uncertainties related to wind speed and solar radiation. Monte Carlo simulations and probability density functions were used to generate a variety of scenarios for the optimization of operational scheduling decisions. Finally, a case study was conducted to validate the effectiveness of the proposed model. Compared to traditional deterministic optimization in utility systems, the two-stage stochastic programming of sustainable utility systems can reduce carbon emissions by 5.6 percent annually and overall costs by 8,907,733 CNY. It provides decision-makers with a range of reference options by balancing multiple objectives, including economic factors, renewable energy penetration rate, and grid interaction level.

Assessing uncertainty in the optimal placement of distributed generators in radial distribution feeders

The surge in renewable energy sources (RESs) and decentralized generation has reshaped power distribution, with distributed generation systems (DGS) like solar photovoltaic (PV) and wind turbines offering advantages including reduced losses and improved grid resilience. However, optimal placement of DGSs within radial distribution feeders faces challenges due to uncertainties in minimal power loss and voltage profile improvements tied to load variations, RES generation, and system parameters. Despite previous investigations, a research gap stays concerning variability in minimal power loss and voltage profile alterations beyond specified limits, hampering well-informed decision-making and potentially leading to sub-optimal solutions and reduced grid reliability. This research paper introduces an uncertainty analysis framework to address this gap, integrating deterministic and probabilistic simulation techniques. Validated initially in the IEEE 33 bus system, the framework is then applied to the Katunje Distribution Feeder in Bhaktapur, Nepal. Utilizing Monte Carlo simulations and probability density functions, the framework quantifies uncertainty levels in power loss and voltage profiles, offering valuable insights for decision-makers. The study underscores the importance of incorporating uncertainty into DGS placement strategies to enhance grid resilience and ensure consistent voltage profiles, even under fluctuating conditions.

Cost-Effectiveness of Diffusion-Weighted Magnetic Resonance Imaging Versus Second-Look Surgery in Treating Cholesteatoma: A Modeling Study.

To evaluate whether canal wall-up (CWU) tympanomastoidectomy with diffusion-weighted magnetic resonance imaging (DW-MRI) is a cost-effective method of treating cholesteatoma compared with CWU with second-look surgery. Cost-effectiveness analysis was conducted using a Markov state transition model. The simulation model adhered to the Panel Recommendations on Cost-Effectiveness in Health and Medicine established by the US Public Health Service. One-way and Monte Carlo probability sensitivity analyses were conducted for validation. Tympanomastoidectomy with DW-MRI versus tympanomastoidectomy with second-look surgery. Effectiveness and health utility were measured using quality-adjusted life years (QALYs). Costs were derived from Medicare reimbursement using the perspective of the payer. Probabilities for outcomes and complications were taken from existing literature. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio. With base case analysis, the total cost was $15,069 when treated with CWU and second-look surgery versus $13,126 when treated with CWU and DW-MRI. The second-look treatment pathway yielded 17.05 QALYs, whereas the DW-MRI pathway yielded 16.91 QALYs in terms of health benefit accrued across the lifetime of the patient. The cost-effectiveness incremental cost-effectiveness ratio was $21,800/QALY. Using the conventional $50,000 willingness-to-pay threshold, second-look surgery was the more cost-effective approach 63.7% of the time by simulation. Both treatment pathways were found to be cost-effective, with second-look surgery incrementally cost-effective 63.7% of the time. Assumptions were validated by one-way and Monte Carlo probability sensitivity analysis. There is ample variation in treatment pathways regarding usage of DW-MRI and second-look surgery for cholesteatoma. To evaluate the cost-effectiveness of DW-MRI and second-look surgery approaches, accounting for health-related quality-of-life outcomes and costs for the duration of the patient lifetimes. To inform the discussion on the treatment for cholesteatoma given emergent noninvasive technologies.Level of Evidence: Level III.Indicate IRB or IACUC: Exempt.

A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China’s total energy consumption, China’s total electricity generation and China’s total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China.

Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses

This study introduces an innovative framework aimed at enhancing the reliability, lifespan, and cost-effectiveness of floating wind turbines. By seamlessly integrating transient and steady-state response surface methods, this approach reduces the dimensionality of high-cost, high-fidelity simulation data for floating wind turbines. It effectively utilizes time-domain stochastic response history samples, addressing two critical aspects in reliability analysis, including statistical properties related to maximum loads relevant for static strength design and the distribution characteristics of vibration amplitudes pertinent to fatigue damage design. The processed data is used to construct a forward 1st metamodel, which is integrated into a multi-objective optimization algorithm. This algorithm determines optimal hyperparameters based on stochastic environmental conditions. Subsequently, an inverse 2nd metamodel is established, working in conjunction with the 1st metamodel, forming a dual structure for reliability analysis. Extensive Monte Carlo simulations and probability density analyses demonstrate the effectiveness of the adaptive flexible controller frequency strategy, obtained using the proposed framework, in reducing vibration-induced blade-tip-tower collision risks and long-term vibration damage, particularly at low to medium wind speeds. This significantly improves turbine stability and safety, enhancing overall performance. The proposed methodology provides valuable insights into the design, manufacturing, and operation of floating wind turbines.

Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving the stochastic optimal operation problem (OOP) in the IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, and pricing. The proposed I-WaOA utilizes three strategies: the fitness-distance balance method, quasi-opposite-based learning, and Cauchy mutation. The I-WaOA optimally locates and sizes photovoltaic (PV) ratings and wind turbine (WT) capacities and determines the optimal power factor of WT with DSR. Using Monte Carlo simulations (MCS) and probability density functions (PDF), the uncertainties in renewable energy generation, load demand, and energy costs are represented. The results show that the proposed I-WaOA approach can significantly reduce costs, improve voltage stability, and mitigate voltage deviations. The total annual costs are reduced by 91%, from 3.8377 × 107 USD to 3.4737 × 106 USD. Voltage deviations are decreased by 63%, from 98.6633 per unit (p.u.) to 36.0990 p.u., and the system stability index is increased by 11%, from 2.444 × 103 p.u. to 2.7245 × 103 p.u., when contrasted with traditional methods.

Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

A new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping considering uncertainties

This study proposes a new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping (MVMO-SH) optimisation​ for planning Photovoltaic Distributed Generation (PVDG) in the urban Radial Distribution Network (RDN). The Active Power Loss (APL) index was calculated considering the risk of uncertain photovoltaic generation and urban load distributions. The Monte Carlo Probability Density Function method was initially used to manage uncertainties. The Monte Carlo-embedded MVMO-SH was then used to optimise PVDG in the urban RDN. Simulations were run for several scenarios in three load cases based on 288 segments: residential, commercial, and industrial urban loads. The MVMO-SH had the lowest APL index compared to genetic algorithm and particle swarm optimisation when the probabilistic power flow with PVDG was optimised under uncertainty. The APL indexes with three PVDG installations in the 33-bus RDN for residential, commercial, and industrial urban load models were 0.4094, 0.4811, and 0.4655, respectively. In the 69-bus RDN, the APL indexes with three PVDG installations for residential, commercial, and industrial urban load models were 0.3403, 0.3570, and 0.3504, respectively. For all load models examined, there was a significant reduction in the APL index for the case of three PVDGs compared to the system without PVDG. The findings showed that uncertainty significantly impacted the optimal location and size of PVDG in the RDN.

A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction

The importance of energy in modern life is self-evident. Forecasting future energy consumption can help governments and businesses formulate reasonable energy supply and demand policies to ensure energy security and economic development. To this end, a novel adaptive fractional grey model with fractional derivative was established. Firstly, a novel fractional cumulative operator is proposed that operates in a fractional-order domain and has the potential to alternate between giving priority to new or old information. This method facilitates the effective utilization of data when working with a limited number of samples. Secondly, the model's adaptability and flexibility were improved through the introduction of a nonlinear term in the whitening equation; and the fractional derivative was introduced into the whitening equation to solve the problem of poor adaptability of existing integer-order derivative to nonlinearity and volatility. To enhance the model’s performance, the study utilized the Grey Wolf Optimization (GWO) algorithm to optimize the model parameters. Furthermore, the robustness of the proposed model was verified using Monte Carlo simulations and probability density analysis; and the experimental results indicated that the proposed model exhibits better robustness. Finally, three actual cases of China’s total energy consumption, total crude oil consumption and domestic heat consumption are predicted.

Study on Dynamic Interval Power Flow Calculation of Microgrid Based on Monte Carlo Algorithm

In order to effectively monitor the stability of the microgrid, based on the advantages of the Monte Carlo algorithm, a dynamic interval power flow calculation method for microgrid is designed. First, based on the multilayer complex structure of the microgrid, the hierarchical topology of its interval structure is analyzed. Then, the micropower flow model is designed, the affine algorithm is used to accurately describe the relationship between the variables in the microgrid structure, and the dynamic interval affine calculation is completed. Therefore, in the dynamic interval of the microgrid, the Monte Carlo probability method is applied to obtain more random data. Based on this, a model is established for the probability of wind power generation and photovoltaic power generation to simulate the output characteristics of the microgrid. Finally, the voltage variable, active power variable, and reactive power variable are calculated and embedded in the iterative algorithm to realize the stochastic power flow calculation of the microgrid. After experimental verification, the voltage amplitude calculated using the method proposed in this article has a small error value compared to the actual voltage, with a minimum error value of 0.2, close to 0. Under the condition of convergence accuracy of 10−6, the minimum convergence frequency is 3 times, and the power flow calculation process time does not exceed 38 seconds. This proves that the algorithm has high calculation accuracy, good convergence performance and timeliness, and can provide certain technical support for the stable operation of microgrids.

Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model

The generation forecast helps to develop the scheduling plan of power sources, build a power balance system and ensure the adequacy of power supply to the power system at the macro level. Therefore, a novel structurally adaptive discrete grey Bernoulli model, called DHGBM(1,1), is proposed in this paper for the prediction of power generation. Firstly, the Hausdorff fractional order cumulative generation operation makes it possible to achieve new information prioritisation. As for the structure of the model, we have extended the nonlinear grey Bernoulli model with the introduction of a nonlinear dynamic structure term, which allows the model to simulate variable generation data and improves the adaptability of the model. In a comparative test of the algorithms, the well-performing differential evolution algorithm was used for hyperparameter optimisation of the model to obtain better predictive performance. Monte Carlo simulations and probability density analysis verified the excellent robustness of the model. In addition, through the simulation tests, we obtained a reasonable range of parameters for the structural terms, thus limiting the complexity of the model, and the phenomenon of model overfitting was effectively prevented. Finally, five actual cases of China's hydro, China's thermal, China's nuclear, China's wind power generation and China's electricity exports are predicted.

Experimental investigation of an image-based method for determining the combustion temperature of aluminum particles

Aluminum is characterised by its high calorific value, stable performance, and abundant reserves. It is a commonly used additive in solid propellants that are widely used in aerospace and underwater propulsion applications. As aluminum is a key component of these solid propellants, combustion temperature measurement of aluminum particles is crucial in investigating their combustion characteristics. This paper presents a method to determine the combustion temperature of aluminum particles in solid propellants. A combination of radiation imaging and Monte Carlo probability simulation is applied in the proposed method. Further, a numerical model is established to determine the surface temperature of aluminum particles. The method is validated through numerical simulation considering a target with a pre-set temperature distribution, as well as a practical implementation in solid-propellant combustion experiments. Results of the experimental analysis establish the reliability of the proposed method and also demonstrate the method's ease of operation, good practicability, and potential for future application.

SA-HPMHT for Maritime Dim Targets Tracking With Sensor Location Uncertainty

To address the problem of tracking low signal-to-clutter ratio (SCR) maritime targets with sensor location uncertainty, an effective track-before-detect (TBD) method, called the state augmentation histogram probabilistic multihypothesis tracking (SA-HPMHT) algorithm, is proposed in this article. In this algorithm, the initialization of new tracks is realized based on the constant false alarm rate (CFAR) detector with a low threshold and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering algorithm. The target and sensor states are then simultaneously estimated in an iterative manner via a synthetic histogram and state augmentation (SA) method. In each iteration, we update the estimated sensor state and yield more accurate measured images, allowing for improved performance of detection and tracking targets. Finally, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}/{N}$ </tex-math></inline-formula> logic technology is invoked to confirm or terminate the tracks. The results of simulation and real dataset verification show that the SA-HPMHT algorithm has a better performance compared with the histogram probabilistic multihypothesis tracking (H-PMHT) algorithm and the sequential Monte Carlo probability hypothesis density (SMC-PHD) algorithm.

Probabilistic assessment of induced seismicity at the Alberta No. 1 geothermal project site

Alberta No. 1 (ABNo1) is a geothermal project targeting deep carbonate, conglomerates, and sandstone formations in a potential production and injection zone for geothermal energy exploitation within the Municipal District of Greenview south of Grande Prairie, Alberta, Canada. In geothermal systems without a steam fraction (typically systems under 170 °C), rapid widespread pore pressure changes and slow temperature changes have led to increased deviatoric stresses, resulting in induced seismicity. A concern for the ABNo1 Geothermal Project is that anthropogenic seismicity from oil, gas, and well field fluid injection has created felt events in Alberta. Thus, at the beginning of this type of project, it is prudent to review the potential for induced seismicity. In this study, a geomechanical study of the Leduc and Granite Wash Formations, two potential geothermal fluid exploitation zones, has been undertaken based on borehole geophysics and regional injection-induced earthquake data. Determining subsurface properties such as state of stress, pore pressure, and fault properties, however, poses uncertainties in the absence of actual data from the target formations. Geomechanical analysis results (with associated uncertainties) are used to assess the potential for injection-induced earthquakes. A Monte Carlo probability analysis is employed to estimate the likelihood of slippage of the known faults close to the ABNo1 Geothermal Project. A cumulative distribution function of the critical pore pressure on each fault is derived from the local tectonic stress state and Mohr–Coulomb shear parameter analyses. The resultant probabilistic fault stability maps can serve as a baseline for future fluid injection projects in the region including wastewater disposal, hydraulic fracture stimulation, CO2 sequestration, as well as geothermal energy extraction.

Potential and Distribution of Natural Gas Hydrate Resources in the South China Sea

The amount of natural gas contained in the world’s gas hydrate accumulations is enormous, but these estimates remain highly speculative. So far, it is still challenging to locate spatial distribution of marine gas hydrate and quantitatively characterize the evaluation parameters and systematic improvement of evaluation systems. Considering the systematic review of the key accumulation factors, such as heat flow, deposition rate and total organic carbon in the typical passive continental margin, the evaluation results of global marine gas hydrate resources were analyzed based on the characteristics of gas hydrate geology, geophysics and geochemistry anomalies in the South China Sea. We analyze the problems on the evaluation of marine gas hydrate resources, probing into the geological characteristics and distribution laws of marine gas hydrate resources in the South China Sea, and estimate the parameters for in-place resource evaluation, in which the volume method based on Monte Carlo probability was used to evaluate the gas hydrate potential resources in the South China Sea. The probability distribution ranges from 37.6 billion (with 90% probability) to 117.7 billion (with 10% probability) tons of oil equivalent, with an expected value of 74.4 billion tons of oil equivalent. The study results show that the gas hydrate resource density in the South China Sea is similar to that in the typical sea areas, and the estimated global resources are basically consistent with the assessment results at this stag; this shows that the South China Sea has great potential for gas hydrate resources. The research results can provide guidance for the evaluation of global climate change and the exploration and development of hydrate resources.