Paleoclimate Inversion Method, System and Device Based on Markov Chain Monte Carlo

Abstract

The present invention belongs to the technical field of climate prediction, and specifically relates to a paleoclimate inversion method, system and device based on Markov chain Monte Carlo, aiming to solve the problems of accuracy and efficiency in tracking the details of climate change in global inversion. The present invention includes: determining the paleoclimate index to be inverted; inputting the paleoclimate index to be inverted into a paleoclimate inversion model to obtain a paleoclimate inversion result output by the paleoclimate inversion model; wherein, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted. The present invention realizes not only more accurate tracking of climate change details and more efficient calculation in global inversion, but also improves the response ability and adaptability to time series.

Description

Background Art

[0002] At present, many methods in the field of paleoclimate simulation are mainly based on forward models, that is, by presetting boundary conditions (such as temperature, carbon dioxide concentration, and solar radiation, etc.) in geological periods, and using numerical climate models to calculate the corresponding climate states. Forward models have efficiency advantages in determining climate trends and overall changes, but their results highly depend on initial conditions and parameter settings, and have certain limitations in dealing with complex and multi-variable paleoclimate data, making it difficult to fully explore the potential uncertainties and detailed changes of the climate system.

[0003] To overcome the limitations of the forward method, inversion techniques have gradually been introduced into paleoclimate reconstruction. The inversion method infers the possible evolutionary paths of climate change by integrating geological record data and statistical models, and combining observational and simulation data. Different from the forward method, the inversion technique allows starting from geological evidence and gradually reconstructing various possible states of climate change and their probability distributions, making the simulation results more diverse and scientifically explanatory.

[0004] In paleoclimate inversion techniques, the combination of the Markov chain Monte Carlo (MCMC) method and Bayesian inversion techniques has made remarkable progress in recent years. The MCMC algorithm is an algorithm for sampling probability distributions by constructing a Markov chain, which is particularly suitable for sampling problems of high-dimensional and complex probability distributions. In paleoclimate reconstruction, MCMC approximates the posterior distribution of climate variables by randomly sampling in the parameter space, and can effectively handle the complexity of multi-parameter and high-dimensional data. Bayesian inversion is based on Bayesian statistical theory, and combines the prior information of geological records and the likelihood function of observational data to estimate the posterior distribution of parameters. Through Bayesian inversion, researchers can gradually optimize the uncertainty range of parameters based on the matching degree of geological records, so as to obtain the climate change path that conforms to the observational data. This multi-parameter estimation method combining MCMC and Bayesian inversion is not only applicable to single variables or specific conditions, but also provides an effective way in multi-dimensional climate data reconstruction, providing guarantee for the accuracy and reliability of paleoclimate reconstruction.

[0005] However, based on global inversion, the overall climate change trend is identified by processing all time-step data at once. Although it performs well in identifying the overall change trend, there are the following defects in detail description and accuracy:

[0006] Insufficient detail resolution: Due to using global inversion, this method tends to preferentially find the overall trend in the data and ignores the subtle changes in the time series. This results in lower sensitivity of the inversion results to small-scale climate fluctuations and makes it difficult to accurately capture the details of climate change.

[0007] Reason: The global inversion method processes all time-step data simultaneously, making the method tend to find trend features rather than progressively depicting the changes at each time step, which is particularly obvious when dealing with long-time-series data.

[0008] Limited accuracy: In multi-parameter inversion, although the global model improves the calculation speed, it sacrifices a certain degree of parameter detail accuracy. In practical applications, this method is prone to over-smoothing of the inversion results, resulting in the smoothing of abnormal signals in complex climate events.

[0009] Reason: Global inversion introduces overall approximation, resulting in possible deviation from the actual parameter values at individual time steps and making it difficult to effectively reflect the true changes at each stage.

[0010] Lack of time progression: This technique lacks the ability to adjust and update in a time-sequence progressive manner and cannot effectively track parameters that change dynamically over time. In the case of rapidly changing geological and climatic conditions, the global inversion method has poor adaptability to non-linear changes in the time dimension.

[0011] Reason: The global processing mode cannot be dynamically adjusted according to the results of each time step, resulting in a weak time-series logic and being unfavorable for the dynamic climate inversion requirements. Summary of the Invention

[0012] To solve the above problems in the prior art, that is, the accuracy and efficiency of tracking climate change details in global inversion, the present invention provides a paleoclimate inversion method, system and device based on Markov chain Monte Carlo, which not only makes the tracking of climate change details more accurate and the calculation more efficient in global inversion, but also improves the response ability and adaptability to time series.

[0013] In one aspect of the present invention, a paleoclimate inversion method based on Markov chain Monte Carlo is proposed, including:

[0014] Determine the paleoclimate index to be inverted;

[0015] Input the paleoclimate index to be inverted into the paleoclimate inversion model to obtain the paleoclimate inversion result output by the paleoclimate inversion model;

[0016] Wherein, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted.

[0017] Preferably, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted, including:

[0018] Randomly perturb the initial parameter set using a time - sequential progressive perturbation method to generate multiple parameter sets to be inverted as perturbation parameter sets;

[0019] Based on the initial parameter set and the perturbation parameter sets, obtain the simulated values output by the model;

[0020] Based on the climate index, the simulated values, and the target values of the target curve, obtain the log - likelihood or error value;

[0021] Based on the log - likelihood or error value, iteratively update the parameter sets to be inverted to obtain a paleoclimate inversion model including the final inverted parameter set.

[0022] Preferably, the step of randomly perturbing the initial parameter set using a time - sequential progressive perturbation method to generate multiple parameter sets to be inverted as perturbation parameter sets includes:

[0023] Preset the initial time and the time windows of multiple time periods;

[0024] Based on the preset initial time and pushing forward segment by segment according to the preset time windows, randomly perturb the parameter values of the initial parameter set within a preset range in each time period to obtain the multiple parameter sets to be inverted as the perturbation parameter sets.

[0025] Preferably, the step of obtaining the simulated values output by the model based on the initial parameter set and the perturbation parameter sets includes:

[0026] Input the initial parameter set and the perturbation parameter sets into the paleoclimate inversion model respectively to obtain the corresponding simulation results in the initial state and the perturbed state.

[0027] Preferably, the climate index includes one or more of organic carbon isotope, atmospheric carbon dioxide concentration, and global temperature;

[0028] The step of obtaining the log - likelihood or error value based on the climate index, the simulated values, and the target values of the target curve includes:

[0029] When the climate index is organic carbon isotope, calculate the log - likelihood according to the simulated values and the target values of the target curve;

[0030] Otherwise, calculate the error value according to the simulated values and the target values of the target curve.

[0031] Preferably, the step of calculating the error value according to the simulated values and the target values of the target curve includes:

[0032] If the simulated value is within the confidence interval of the observed data of the target curve, set the error value to zero;

[0033] If the analog value exceeds the confidence interval of the target curve observation data, the error value is calculated according to the mean deviation.

[0034] Preferably, iteratively updating the parameter set to be inverted based on the log-likelihood or error value includes:

[0035] Calculating the total error ll0 of the initial parameter set and the errors ll1 to llN of the perturbation parameter sets based on the error value, and comparing them: if lli > ll0, then replace φ0 with the new parameter set φi and enter the next iteration; otherwise, accept the new candidate value with the log-likelihood; where 1 <= i <= N.

[0036] On the other hand, the present invention proposes a paleoclimate inversion system based on Markov chain Monte Carlo, including:

[0037] An index determination module for determining paleoclimate indices to be inverted;

[0038] A climate inversion module for inputting the paleoclimate indices to be inverted into a paleoclimate inversion model to obtain a paleoclimate inversion result output by the paleoclimate inversion model;

[0039] Wherein, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted.

[0040] On the third aspect of the present invention, an electronic device is proposed, including:

[0041] At least one processor;

[0042] And a memory communicatively connected to at least one of the processors;

[0043] Wherein, the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned paleoclimate inversion method based on Markov chain Monte Carlo.

[0044] On the fourth aspect of the present invention, a computer-readable storage medium is proposed, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned paleoclimate inversion method based on Markov chain Monte Carlo.

[0045] Advantages of the present invention:

[0046] (1) The Bayesian inversion method based on the progressive time series model processes time series data step by step, so as to more accurately track the details of climate change;

[0047] (2) By introducing a time-sequential progressive model, the inversion process can be dynamically updated at each time step, improving the response ability and adaptability to time series;

[0048] (3) By combining the Markov chain Monte Carlo (MCMC) method with the time-sequential progressive model, it can not only retain high-precision detailed inversion but also maintain computational efficiency, providing better performance in long-time-series climate data inversion. DESCRIPTION OF THE DRAWINGS

[0049] Other features, objectives, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0050] Figure 1 is a flowchart of a paleoclimate inversion method based on Markov chain Monte Carlo;

[0051] Figure 2 is a flowchart of iterative matching of inversion parameters in a paleoclimate inversion model;

[0052] Figure 3 is a schematic diagram of the iterative process of Markov chain Monte Carlo time-sequential progressive Bayesian inversion for δ13C data;

[0053] Figure 4 is a high-precision fitting effect diagram of Bayesian inversion for the target curve;

[0054] Figure 5 is a structural block diagram of a paleoclimate inversion system based on Markov chain Monte Carlo;

[0055] Figure 6 is a schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only for explaining the related invention and not for limiting the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0057] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0058] The present invention provides a paleoclimate inversion method based on Markov Chain Monte Carlo. By introducing a time-sequential progressive model, the inversion process can be dynamically updated at each time step, improving the response ability and adaptability to time series. At the same time, by combining the Markov Chain Monte Carlo (MCMC) method with the time-sequential progressive model, both high-precision detailed inversion can be retained and computational efficiency can be maintained.

[0059] A paleoclimate inversion method based on Markov Chain Monte Carlo of the present invention includes:

[0060] Determine the paleoclimate index to be inverted;

[0061] Input the paleoclimate index to be inverted into the paleoclimate inversion model to obtain the paleoclimate inversion result output by the paleoclimate inversion model;

[0062] Among them, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted.

[0063] For a clearer description of the paleoclimate inversion method based on Markov Chain Monte Carlo of the present invention, the following combines Figure 1 Elaborate on each step in the embodiments of the present invention.

[0064] The paleoclimate inversion method based on Markov Chain Monte Carlo in the first embodiment of the present invention, as Figure 1 shown, includes step S101-step S102, and each step is described in detail as follows:

[0065] Step S101, determine the paleoclimate index to be inverted;

[0066] Specifically, first, for the needs of paleoclimate inversion, organic carbon isotope (δ13C), atmospheric carbon dioxide concentration (pCO2), temperature, etc. are selected as the main proxy indicators. These indicators can more directly reflect the climate change characteristics in the geological period and have high accessibility and reliability in the existing geological records. As an important record of the carbon cycle, the organic carbon isotope can indicate the processes of carbon burial and changes in atmospheric carbon dioxide, providing the correlation information between the carbon cycle and climate change. Carbon dioxide is one of the key driving factors of paleoclimate change, and the change of its concentration directly affects the global temperature fluctuation. Therefore, as an important climate indicator, it can provide a reliable target curve for inversion. It should be noted that due to the latitude effect of temperature, the temperature change in different geographical regions will be affected by the latitude factor. Therefore, during the simulation process, the local temperature needs to be converted into the global average temperature to eliminate the latitude deviation and obtain a more accurate global climate change trend.

[0067] Step S102: Input the paleoclimate index to be inverted into the paleoclimate inversion model to obtain the paleoclimate inversion result output by the paleoclimate inversion model;

[0068] Among them, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted.

[0069] Specifically, to ensure the temporal consistency of each proxy index, for the resolution difference, this method uses the interpolation method to unify the resolution of the target curve to ensure that δ13C, pCO2, and temperature data can be obtained at any time point. In the subsequent calculations of the pre-processed target curve, the consistency of the difference calculation between the simulation result and the target value at any moment can be guaranteed, thereby improving the accuracy and reliability of the inversion result.

[0070] After determining and completing the selection and pre-processing of the target curve, next, determine the parameter range required for inversion and incorporate these parameters into the parameter set to be inverted (denoted as φN). The setting of the initial parameter set φ0 takes into account the constraint conditions of the target curve to ensure that the inversion result conforms to the actual climate change trend.

[0071] The present invention introduces a time-sequential progressive model, enabling the inversion process to be dynamically updated at each time step, improving the response ability and adaptability to the time series. At the same time, by combining the Markov chain Monte Carlo (MCMC) method with the time-sequential progressive model, it can not only retain the high-precision detailed inversion but also maintain the calculation efficiency.

[0072] Although the above steps are described in the above sequential order in the above embodiments, those skilled in the art can understand that for the purpose of achieving the effects of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of the present invention.

[0073] Based on the above embodiments, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted. As Figure 2 shown, it includes the following steps S201 - step S204:

[0074] Step S201: Randomly perturb and generate multiple parameter sets to be inverted as perturbation parameter sets by using a time-sequential progressive perturbation method based on the initial parameter set;

[0075] Step S202: Based on the initial parameter set and the perturbation parameter sets, obtain the simulated values output by the model;

[0076] Step S203: Obtain the log-likelihood or error value based on the climate index, the simulation value, and the target value of the target curve.

[0077] Step S204: Iteratively update the parameter set to be inverted based on the log-likelihood or error value to obtain a paleoclimate inversion model including the final inverted parameter set.

[0078] Specifically, when the error in the iteration process converges within a preset tolerance range or reaches the specified maximum number of iterations, the simulation result of the model is considered to have effectively approximated the target curve. At this time, the final inversion result is output. The output parameter set includes the best estimate value of the climate index and its uncertainty range, providing an accurate numerical description of the characteristics of the target curve.

[0079] It should be noted that when the parameters are output, the inversion method of the present invention will record the optimal value and the confidence interval of each parameter, and generate the finally fitted climate curve to visually display the fitting situation between the simulation result and the target curve. The output includes the capture results of the global trend and local high-frequency fluctuation characteristics, providing a panoramic restoration of the climate change process. At the same time, the output result includes the complete parameter path and perturbation record, providing support for subsequent model verification and parameter sensitivity analysis, making the inversion model have good reproducibility and interpretability.

[0080] Based on the above embodiments, the method of randomly perturbing the initial parameter set in a time-sequential progressive perturbation manner to generate multiple parameter sets to be inverted as perturbation parameter sets includes:

[0081] Preset the initial time and the time windows of multiple time periods;

[0082] Based on the preset initial time and pushing forward segment by segment according to the preset time window, randomly perturb the parameter values of the initial parameter set within a preset range in each time period to obtain the multiple parameter sets to be inverted as the perturbation parameter sets.

[0083] Specifically, starting from the initial time, this method progresses segment by segment according to the set time window, and perturbs the parameters within a small range in each time period. This sequential perturbation method is implemented through the Monte Carlo random generation method, ensuring that the parameter values are randomly generated within each time window, enhancing the global exploration ability of the algorithm. Within each time window, the generated parameter perturbations are independently and randomly generated, avoiding the mutual influence between parameters, thus ensuring the full coverage of the parameter space. Finally, this process will generate N sets of parameters to be inverted (denoted as φN), each parameter set corresponding to a different climate state, and being able to effectively capture the uncertainties in the climate change process. At the same time, the perturbation process of this method can dynamically adjust the perturbation amplitude, gradually optimize the parameter range according to the fitting degree of the target curve, making the generated parameters more in line with the characteristics of the target curve, and improving the accuracy and stability of the inversion results.

[0084] It should be noted that there can be different choices for the sequential progression mode. The most direct way is to progress step by step according to the minimum resolution of the target curve. Although this way can achieve the highest resolution fitting effect, it has a large amount of calculation and a slow running speed. To improve the calculation efficiency, another way is to set multiple combination schemes, allowing the time window to gradually shorten. For example, in the initial stage, a larger time window is used for rough global trend fitting, and then the window is gradually narrowed to capture finer high-frequency changes. This progressive progression mode can achieve a high-resolution fitting effect while maintaining the calculation efficiency, and can meet different research accuracy requirements.

[0085] Based on the above embodiments, obtaining the simulated values of the model output based on the initial parameter set and the perturbed parameter set includes:

[0086] Input the initial parameter set and the perturbed parameter set into the paleoclimate inversion model respectively, and obtain the corresponding simulation results under the initial state and the perturbed state respectively.

[0087] Specifically, after setting the initial parameter set φ0 and the perturbed parameter set φN, input the two sets of parameters into the model for operation respectively, and obtain the simulation results under the initial state and the perturbed state.

[0088] It should be noted that in the model execution part, different global biogeochemical models can be selected according to specific research needs to achieve higher applicability. For example, models related to the carbon cycle or climate simulation models can be selected to adapt to different geological events and target curve requirements. This flexible model selection method can ensure the expansion of the applicable range without changing the core algorithm framework, enabling this method to be widely applied to paleoclimate inversion research under different climate events and chemical environments.

[0089] Based on the above embodiments, the paleoclimate indicators include one or more of organic carbon isotopes, atmospheric carbon dioxide concentration, and global temperature;

[0090] It should be noted that the paleoclimate proxy indicators used can be adjusted and extended according to research needs. In addition to the organic carbon isotopes, atmospheric carbon dioxide concentration, and temperature selected in the present invention, multiple isotope indicators (such as carbon, mercury, and zinc isotopes) can also be used in combination to achieve the inversion of more parameters. In principle, based on the principle of "solving several unknowns with several equations", increasing the proxy indicators can more comprehensively characterize the changes in the carbon cycle and climate system. For different geological events, such as climate change caused by volcanic eruptions, large-scale carbon emission events, etc., using different combinations of proxy indicators can flexibly adapt to different parameter solving requirements, thereby improving the inversion accuracy and applicability.

[0091] Obtaining the log-likelihood or error value based on the paleoclimate indicator, the simulated value, and the target value of the target curve includes:

[0092] When the paleoclimate indicator is organic carbon isotope, the log-likelihood is calculated according to the simulated value and the target value of the target curve;

[0093] Otherwise, the error value is calculated according to the simulated value and the target value of the target curve.

[0094] Specifically, to evaluate the matching degree between the model result and the target curve, this method uses a combination of log-likelihood calculation and error calculation. Specifically, the log-likelihood is calculated for δ13C data to capture its subtle changes; for pCO2 concentration and temperature data, the error is directly calculated. The calculation formulas are as follows:

[0095] (1)

[0096] (2)

[0097] (3)

[0098] (4)

[0099] Among them, is the total error, is the calculation error of the is the calculation error of the concentration, is the calculation error of the temperature data, represents the log-likelihood, is the observed value, is The standard deviation represents the uncertainty or degree of variation of the statistical distribution of the observed data. For the simulation results obtained by the model, let be the carbon dioxide concentration obtained by the model, let be the observed surface seawater temperature value, let represent the selection of the maximum value, represent the selection of the minimum value.

[0100] In this process, the error calculation methods for different target indicators vary due to different data characteristics and reliabilities. For δ13C data, as a key indicator of the carbon cycle, it has obtained high precision and reliability through geological records and is suitable for calculating the log-likelihood based on the normal distribution. Using the normal distribution can make full use of the high-quality records of δ13C, thereby reflecting the true fluctuation characteristics of climate change. The log-likelihood provides an accurate fitting evaluation of the simulation results by calculating the probability density function values of the model output and the δ13C observed values.

[0101] It should be noted that in the error calculation, the weights of different target curves can be flexibly allocated according to the effect requirements of the inversion target curve. Different climate indicators may have different importance in the inversion. Therefore, by adjusting the weights of each indicator in the log-likelihood calculation, an accurate fitting of specific indicators can be achieved. For example, in some events, temperature may be the main concerned indicator, and its weight can be increased accordingly to more precisely control the temperature fitting effect. This adjustment scheme of weight allocation enhances the flexibility and pertinence of this method, enabling the fitting effect to be optimized according to specific research needs.

[0102] Based on the above embodiments, calculating the error value according to the simulation value and the target value of the target curve includes:

[0103] If the simulation value is within the confidence interval of the observed data of the target curve, the error value is set to zero;

[0104] If the simulation value exceeds the confidence interval of the observed data of the target curve, the error value is calculated according to the mean deviation.

[0105] In contrast, the paleoclimate reconstruction of pCO2 concentration and temperature data is limited by the uncertainty of paleo-data, and the accuracy of the observed values is restricted by various natural and technical factors. Therefore, for pCO2 concentration and temperature data, an error calculation method is adopted to evaluate their matching degree. Specifically, if the model output value is within the confidence interval of the observed data, the error is set to zero, indicating that the model simulation value is consistent with the observation; if it exceeds the confidence interval, the error value is calculated according to the mean deviation to reflect the deviation degree between the simulation value and the observed data. This method can reasonably evaluate the matching degree of pCO2 and temperature data within a general range, avoiding the comparison deviation caused by data uncertainty.

[0106] It should be noted that during this process, only the parameter set within the current time window is perturbed each time to ensure that the parameter adjustment is gradually optimized within a local range. However, when running the model and calculating the error, the evaluation process is global, that is, the global matching degree of the entire simulation result is calculated within each time period. As the time window gradually slides, the new parameter perturbations gradually cover the entire time period after the model runs, ultimately achieving optimization and evaluation within the global range. By combining the local perturbation within the time window with the global matching calculation, the present invention ensures the accuracy of the global fitting of the model while realizing the sequential progressive perturbation.

[0107] Based on the above embodiments, iteratively updating the parameter set to be inverted based on the log-likelihood or error value includes:

[0108] Calculating the total error ll0 of the initial parameter set and the errors ll1 to llN of the perturbed parameter sets based on the error value, and comparing them: if lli > ll0, then replace φ0 with the new parameter set φi and enter the next iteration; otherwise, accept the new candidate value with the log-likelihood; where 1 <= i <= N.

[0109] That is to say, in each iteration, calculate the total error ll0 of the initial parameter set and the errors ll1 to llN of the perturbed parameter sets, and compare them. If lli > ll0, then replace φ0 with the new parameter set φi and enter the next iteration; otherwise, according to the Metropolis algorithm, accept the new candidate value with the probability of the likelihood ratio. This update mechanism ensures that the parameters gradually converge to the posterior distribution and obtain a better solution.

[0110] To improve adaptability, the present invention sets up a dynamic adjustment strategy: if a new parameter combination is rejected for 5 consecutive times, the average width and amplitude of the perturbation will be reduced by 10%, and readjusted in the next time window to increase the acceptance probability of the newly proposed parameters. The initial time window is usually set to 20% of the target curve time length, and the iteration times are increased by gradually shortening the window, so as to improve the model's ability to capture and fit high-frequency information. This sequential adjustment method in time series ensures that the model gradually approaches the target curve based on the global trend, and improves the accuracy of the inversion result.

[0111] In the initial stage of inversion, the model adopts a low-frequency search strategy to capture the global trend of overall climate change with a relatively long time window. This process quickly identifies the macroscopic characteristics of the target curve by establishing a preliminary fitting curve within a large time period, providing a reliable reference point for subsequent piecemeal fitting. As the inversion progresses, the time window is gradually shortened to capture the subtle changes in the climate curve with more refined high-frequency perturbations. This strategy of progressively shortening the window enables the model to gradually transition from the macroscopic trend to the detailed fitting, effectively capturing the high-frequency fluctuation characteristics in the climate record. Through the piecemeal optimization of high-frequency perturbations, the model gradually reduces the degrees of freedom of the inversion parameters, enhances the sensitivity to short-term climate fluctuations, and improves the stability of the simulation output, ultimately achieving the convergence of the steady-state distribution.

[0112] The method of the present invention has significant application effects in the analysis of extreme carbon emission events in the geological history, especially suitable for the simulation and parameter inversion of the rapid response of the carbon cycle and climate system. By accurately fitting the key climate indicators and capturing high frequencies in a sequential manner over time, this method can effectively solve different carbon emission scenarios, deduce possible emission mechanisms and environmental responses, and provide an in-depth analysis tool for paleoclimate and paleoecology research.

[0113] For example, during the intense carbon emission event in the Toarcian stage of the Early Jurassic, the event recorded a large negative shift in the carbon isotope (δ13C) of about 7‰, accompanied by a sharp rise in atmospheric carbon dioxide concentration and global temperature, becoming one of the possible driving forces for the contemporary biological extinction. Due to the large amplitude and fast speed of the carbon emission event in the Toarcian stage, a high-precision time series fitting method is required to identify the carbon emission amount and the climate change process during the event. This method selects δ13C, pCO2, and temperature as the target curves for inversion, and captures the high-frequency changes by gradually shortening the time window, ensuring accurate simulation in the long-time series carbon emission event.

[0114] Such as Figure 3As shown, the iterative process of δ13C data through Markov Chain Monte Carlo (MCMC) sequential Bayesian inversion is presented. In each iteration, the red curve gradually fits the black observed data points, indicating that the model parameters are continuously adjusted to gradually improve the goodness of fit to the data. The initial model is deliberately set as a straight line, demonstrating the strong search and convergence ability of the algorithm, independent of the initial value. Early iteration process: Although the initial model has a large difference from the target, the algorithm quickly adjusts and searches for the general trend of the data. In the first few iterations, the red curve gradually begins to approach the variation of the black data points, indicating that the algorithm can quickly identify the main trend direction. Mid-iteration process: As the number of iterations increases, the red curve gradually captures the general trend of δ13C, especially the V-shaped variation characteristics, showing the deep-level fitting ability to the data structure. At this time, the model already shows a high fitting accuracy. Late iteration process: After reaching 250 iterations, the model is further refined and gradually fits the subtle fluctuations of the data. By 350 iterations, the red curve almost perfectly fits the observed data, indicating the strong convergence performance of the algorithm.

[0115] Overall, the algorithm quickly converges from the initial straight line, demonstrating its advantage of not relying on the initial value and its excellent performance in parameter search and fitting accuracy. This step-by-step optimization process shows the application effect of Bayesian inversion in complex time-series data.

[0116] As Figure 4 shown, the high-precision fitting effect of MCMC Bayesian inversion on the target curve in the convergence state. The results show that the fitting quality of the target curve is good, and the key variables such as the inversion solution of CO 2 and methane release flux and isotope composition also show high resolution. These inversion results fully reflect the accuracy of the algorithm and its good ability to capture complex gas release processes.

[0117] In the simulation of the Toarcian carbon emission event, the parameter perturbations adjusted section by section enable the model to closely track the rapid negative offset of carbon isotopes and accurately capture the dynamic changes of pCO2 and temperature associated with it, ensuring a high consistency between the model output and the actual observed characteristics. The present invention can not only reproduce the significant negative offset process of δ13C in the Toarcian period, but also reveal the high-frequency oscillation characteristics of rapid warming during this event through the fine simulation of temperature and pCO2. Through this method, the carbon release rate, emission sources, and the impact of different emission scenarios on the climate system can be inferred, providing a reliable basis for understanding the climate driving mechanism in the Toarcian period.

[0118] This method is also applicable to extreme climate events and carbon emission events in other geological history periods, such as the mass extinction event at the end of the Permian, the greenhouse climate in the Cretaceous, and the glacial-interglacial cycle in the Quaternary. The drastic fluctuations of the carbon cycle and the rapid response of the climate in these events are important research topics in geology and climatology. Through the flexible adjustment of parameters and the high-resolution inversion method of piecewise fitting, the present invention can refine the complex climate change process in these events, so as to achieve a comprehensive exploration of the climate-carbon cycle system under different driving mechanisms, and provide valuable scientific references for future climate change prediction and carbon management.

[0119] The paleoclimate inversion system based on Markov chain Monte Carlo according to the second embodiment of the present invention, as Figure 5 shown, includes:

[0120] An index determination module 501, configured to determine the paleoclimate index to be inverted;

[0121] A climate inversion module 502, configured to input the paleoclimate index to be inverted into a paleoclimate inversion model, and obtain a paleoclimate inversion result output by the paleoclimate inversion model;

[0122] Wherein, the paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted.

[0123] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related explanations of the above-described system can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated herein.

[0124] It should be noted that the paleoclimate inversion system based on Markov chain Monte Carlo provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing each module or step, and are not regarded as an improper limitation of the present invention.

[0125] An electronic device according to the third embodiment of the present invention includes:

[0126] At least one processor;

[0127] And a memory communicatively connected to at least one of the processors;

[0128] Among them, the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned paleoclimate inversion method based on Markov chain Monte Carlo.

[0129] A computer-readable storage medium according to a fourth embodiment of the present invention, the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned paleoclimate inversion method based on Markov chain Monte Carlo.

[0130] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes and related descriptions of the above-mentioned storage device and processing device can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well-known in the art. For the sake of clearly illustrating the interchangeability of electronic hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0132] Next, refer to Figure 6 , which shows a schematic structural diagram of a computer system of a server for implementing the method, system, and device embodiments of the present application. Figure 6 The server shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0133] As Figure 6As shown, the computer system includes a central processing unit (CPU), 601, which can perform various appropriate actions and processes according to a program stored in a read only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for system operations are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0134] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (local area network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as required so that a computer program read therefrom can be installed into the storage section 608 as required.

[0135] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-described functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0136] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0138] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or represent a specific order or sequence.

[0139] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, so that a process, method, article, or device / equipment that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in these processes, methods, articles, or devices / equipment.

[0140] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A paleoclimate inversion method based on Markov chain Monte Carlo, characterized in that: include: Determine the paleoclimate indicators to be inverted; Inputting the paleoclimate index to be inverted into the paleoclimate inversion model to obtain the paleoclimate inversion result output by the paleoclimate inversion model; The paleoclimate inversion model is obtained by iteratively matching the target curve of the paleoclimate index and the initial parameter set determined based on the target curve of the paleoclimate index to continuously update the parameter set to be inverted, and includes: Based on the initial parameter set, a plurality of parameter sets to be inverted are randomly perturbed in a time-series progressive perturbation manner to generate a plurality of perturbation parameter sets as perturbation parameter sets; Based on the initial parameter set and the disturbance parameter set, obtaining a simulated value of a model output; Obtaining a logarithmic likelihood or an error value based on the climate index, the simulation value, and the target value of the target curve; iteratively updating the parameter set to be inverted by Markov chain Monte Carlo time-series progressive Bayesian inversion based on the logarithmic likelihood or the error value, so as to obtain a paleoclimate inversion model including a final inversion parameter set; The paleoclimate indicators include one or more of organic carbon isotopes, atmospheric carbon dioxide concentration, and global temperature; The logarithmic likelihood or error value is obtained based on the paleoclimate index, the simulation value and the target value of the target curve, including: When the paleoclimate indicator is organic carbon isotope, the logarithmic likelihood is calculated based on the simulation value and the target value of the target curve; Otherwise, the error value is calculated according to the simulation value and the target value of the target curve.

2. The paleoclimate inversion method based on Markov Chain Monte Carlo according to claim 1, characterized in that: The method of randomly perturbing the initial parameter set in a time-series progressive perturbation manner to generate a plurality of parameter sets to be inverted as perturbation parameter sets includes: Preset initial time and time window for multiple time periods; Based on a preset initial time and gradually expanding according to a preset time window, the parameter values ​​of the initial parameter set are randomly perturbed within a preset range in each time period to obtain the multiple parameter sets to be inverted as the perturbed parameter sets.

3. The paleoclimate inversion method based on Markov Chain Monte Carlo according to claim 1, characterized in that: The obtaining of a simulated value of a model output based on the initial parameter set and the disturbance parameter set comprises: The initial parameter set and the disturbance parameter set are respectively input into the paleoclimate inversion model to obtain corresponding simulation results under the initial state and the disturbance state.

4. The paleoclimate inversion method based on Markov Chain Monte Carlo according to claim 1, characterized in that: The calculating the error value according to the simulation value and the target value of the target curve includes: If the simulated value is within the confidence interval of the target curve observation data, the error value is set to zero; If the simulated value exceeds the confidence interval of the target curve observation data, the error value is calculated based on the mean deviation.

5. The paleoclimate inversion method based on Markov Chain Monte Carlo according to claim 1, characterized in that: The iterative updating of the parameter set to be inverted based on the log-likelihood or the error value comprises: Based on the error value, the total error ll0 of the initial parameter set and the errors ll1 to llN of the perturbation parameter set are calculated and compared: if lli > ll0, φ0 is replaced by the new parameter set φi to enter the next iteration; otherwise, the new candidate value is accepted with the log-likelihood; wherein 1<=i<=N.

6. A paleoclimate inversion system based on Markov chain Monte Carlo, based on a paleoclimate inversion method based on Markov chain Monte Carlo according to any one of claims 1 to 5, characterized in that: include: An index determination module is used to determine the paleoclimate index to be inverted; A climate inversion module, used for inputting the paleoclimate index to be inverted into the paleoclimate inversion model to obtain the paleoclimate inversion result output by the paleoclimate inversion model; The paleoclimate inversion model is obtained by iteratively matching a target curve of paleoclimate indicators and an initial parameter set determined based on the target curve of paleoclimate indicators to continuously update the parameter set to be inverted.

7. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to at least one of the processors; The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the Markov Chain Monte Carlo-based paleoclimate inversion method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the Markov Chain Monte Carlo-based paleoclimate inversion method described in any one of claims 1 to 5.

Images