Data processing method and system for reconstructing paleoclimate

Abstract

The invention is suitable for the technical field of paleoclimate change research, and provides a data processing method and system for paleoclimate reconstruction, and the method comprises the following steps: obtaining data; according to the Pearson's correlation principle, correlation coefficients of the depth, the chromaticity and the magnetic susceptibility are calculated, and whether the chromaticity and the magnetic susceptibility are affected by the sedimentary metagenesis effect or not is analyzed; clustering and analyzing the chromaticity parameters and the magnetic susceptibility parameters, and dividing into large classes; calculating a mean value of each parameter of each large class as a characteristic value of each large class; determining a paleoclimate type represented by each large class in combination with paleoclimate indicating significance of the parameters; assigning each climate type, performing noise reduction and filtering on each parameter mean value, and calculating the mean value of each parameter after noise reduction; calculating a total average value of parameter assignment to obtain paleoclimate parameters; and analyzing a paleo-climate transition process. According to the method, whether the chromaticity and the magnetic susceptibility of the sediment are not interfered by the deposition metagenesis effect or not and whether the data have the same deposition attribute or not are effectively identified, and the credibility and the reliability of a deposition paleoclimate research result are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of paleoclimate change research, and in particular relates to a data processing method and system for reconstructing paleoclimate. Background Art

[0002] In paleoclimate research, exploring new paleoclimate proxy indicators and developing a scientific and feasible data verification and processing system to make the paleoclimate reconstruction results reflect the objective situation at the time to the greatest extent are issues of general concern in the paleoclimatology community. The use of sediment chromaticity and magnetic susceptibility to study paleoclimate began in the 1960s, developed rapidly in the 1970s, and has been rapidly and widely used in the past 20 years. However, in the application process, the phenomenon of blindly using data has affected the credibility and reliability of some analysis results. Objective verification and processing of sediment chromaticity and magnetic susceptibility data is a necessary prerequisite to avoid blind use, ensure that the data is representative, and make the analysis results scientific and reliable.

[0003] The study found that the sediment brightness L * A high value indicates a dry climate, and a low value indicates a humid climate; * High value and yellowness b * High values ​​indicate a warming climate; high values ​​of high-frequency magnetic susceptibility, high values ​​of low-frequency magnetic susceptibility, and high values ​​of frequency magnetic susceptibility correspond to a warm and humid climate environment, while lower low-frequency magnetic susceptibility and lower frequency magnetic susceptibility correspond to a drier and colder climate environment. Chromatism and magnetic susceptibility depend on the parent rock minerals of the sediments, the minerals or organic matter formed by synsedimentation, and the secondary minerals formed by epigenetic effects of sedimentation. Among them, the color and magnetic susceptibility of the parent rock minerals themselves are the background of the sedimentary system. The color and magnetic susceptibility formed during the synsedimentation process can truly reflect the paleoclimate background at the time of deposition. The color and magnetic susceptibility formed by epigenetic effects will interfere with the results of paleoclimate analysis and produce large errors, which need to be verified. Identifying whether the chromaticity and magnetic susceptibility of sediments are not affected by epigenetic effects of sedimentation is the basic prerequisite for the scientific use of the chromaticity and magnetic susceptibility of sediments to study paleoclimate, and is an important guarantee for making the results of paleoclimate reconstruction close to objectivity.

[0004] Therefore, the defect of the existing technology is that it does not consider whether the sediment chromaticity and magnetic susceptibility are affected by epigenetic effects, and blindly uses them in paleoclimate research, which affects the credibility of the results. Therefore, developing a scientific, clear, and feasible data verification and processing system to determine whether the obtained data source is not affected by epigenetic effects is an important issue that needs to be solved in the study of paleoclimate reconstruction based on chromaticity and magnetic susceptibility.

[0005] In addition, post-depositional diagenesis is not only related to its own mineral composition, but also affected by factors such as the temperature, pressure, structure, and foreign substances of the strata. The temperature and pressure of the strata are directly proportional to the depth. If post-depositional diagenesis exists, it will intensify with the increase of depth, and the chromaticity and magnetic susceptibility of the sediment will also change accordingly, thus generating a certain relationship with the depth; tectonic foreign substances will make the sediment discontinuous, resulting in sudden changes or anomalies in the color and magnetic susceptibility of the sediment. Therefore, when the chromaticity and magnetic susceptibility of the sediment are independent of the stratum depth on a continuous sedimentary section, it indicates that they have nothing to do with post-depositional diagenesis and are caused by the influence of the paleoclimate during sedimentation on the basis of the parent rock minerals. Such chromaticity and magnetic susceptibility are credible and reliable for paleoclimate reconstruction, and the chromaticity and magnetic susceptibility data that meet this property can be used in paleoclimate research. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a data processing method for reconstructing paleoclimate, aiming to solve the problems raised in the above background technology.

[0007] The embodiments of the present invention are implemented as follows. A data processing method for reconstructing paleoclimate includes the following steps:

[0008] Obtain data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths;

[0009] Analyze the data. According to the Pearson correlation principle, calculate the correlation coefficients between depth and chromaticity as well as magnetic susceptibility. Based on the obtained coefficients, analyze whether the chromaticity and magnetic susceptibility are affected by post-depositional diagenesis;

[0010] Conduct cluster analysis on the chromaticity parameters and magnetic susceptibility parameters to divide them into major categories;

[0011] Calculate the mean values of each parameter for each major category as the characteristic values of each major category;

[0012] Combined with the paleoclimate indication significance of the parameters, determine the paleoclimate types represented by each major category;

[0013] Assign each climate type, perform noise reduction filtering on the mean values of each parameter, and calculate the average value of each parameter after noise reduction;

[0014] Calculate the total average value of the parameter assignment to obtain paleoclimate parameters;

[0015] Analyze the process of paleoclimate change.

[0016] Preferably, in the step of obtaining data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths, the chromaticity parameters include L * value, a * value, b * value, and the magnetic susceptibility parameters include low-frequency magnetic susceptibility χ1f 、High-frequency magnetic susceptibility χ hf 、Frequency magnetic susceptibility χ fd 。

[0017] Preferably, in the step of obtaining data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths, it further includes plotting curves of each chromaticity parameter and each magnetic susceptibility parameter varying with depth.

[0018] Preferably, in the step of analyzing data, calculating the correlation coefficients between depth and chromaticity as well as magnetic susceptibility according to the Pearson correlation principle, and based on the obtained coefficients, analyzing whether chromaticity and magnetic susceptibility are affected by post-depositional processes, when the correlation coefficient |r| ≤ 0.2, chromaticity and magnetic susceptibility are not affected by post-depositional processes; when the correlation coefficient |r| ≥ 0.2, chromaticity and magnetic susceptibility are affected by post-depositional processes.

[0019] Preferably, in the step of clustering and analyzing chromaticity parameters and magnetic susceptibility parameters and dividing them into major categories, the chromaticity parameters and magnetic susceptibility parameters are parameters that are not affected by post-depositional processes.

[0020] Preferably, the step of determining the paleoclimate types represented by each major category by combining the paleoclimate indicating significance of the parameters is specifically: comparing and analyzing the clustering analysis results of each parameter, and determining the paleoclimate types reflected by chromaticity and magnetic susceptibility from the classification perspectives of cold, cool, warm, hot, dry, and wet in climatology according to the paleoclimate indicating significance of chromaticity and magnetic susceptibility.

[0021] Preferably, between the step of determining the paleoclimate types represented by each major category by combining the paleoclimate indicating significance of the parameters and the step of assigning values to each climate type, performing noise reduction filtering on the mean values of each parameter, and calculating the average value of each parameter after noise reduction, it further includes plotting a curve of the eigenvalue varying with depth.

[0022] Preferably, the step of calculating the total average value of the parameter assignment to obtain paleoclimate parameters further includes plotting a curve of the paleoclimate parameters varying with depth.

[0023] Another object of the embodiments of the present invention is to provide a data processing system for reconstructing paleoclimate, which is used to implement the above-mentioned data processing method for reconstructing paleoclimate, including:

[0024] A data acquisition module, which is used to acquire data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths;

[0025] A data analysis module, which is used to analyze data, calculate the correlation coefficients between depth and chromaticity as well as magnetic susceptibility according to the Pearson correlation principle, and based on the obtained coefficients, analyze whether chromaticity and magnetic susceptibility are affected by post-depositional processes;

[0026] A clustering module, which is used for clustering and analyzing chromaticity parameters and magnetic susceptibility parameters to divide into major categories;

[0027] An average value calculation module, which is used for calculating the average value of each parameter of each major category as the eigenvalue of each major category;

[0028] An ancient climate type determination module, which is used for determining the ancient climate type represented by each major category by combining the ancient climate indicating significance of the parameters;

[0029] An average value processing module, which is used for assigning each climate type, performing noise reduction filtering on the average value of each parameter, and calculating the average value of each parameter after noise reduction;

[0030] An ancient climate parameter determination module, which is used for calculating the total average value of parameter assignment to obtain ancient climate parameters;

[0031] An analysis module, which is used for analyzing the process of ancient climate change.

[0032] A data processing method for reconstructing ancient climate provided by an embodiment of the present invention can perform verification processing on continuous sediment chromaticity and magnetic susceptibility data, effectively identify whether the chromaticity and magnetic susceptibility of the sediment are not interfered by post-depositional processes, and whether the data has synsedimentary attributes, ensuring the credibility and reliability of the research results of sedimentary ancient climate. Description of the Drawings

[0033] Figure 1 It is a flow block diagram of a data processing method for reconstructing ancient climate provided by an embodiment of the present invention;

[0034] Figure 2 It is a curve of magnetic susceptibility and chromaticity parameters changing with depth provided by an embodiment of the present invention;

[0035] Figure 3 It is a scatter diagram of Pearson correlation relationship between depth and chromaticity and magnetic susceptibility parameters provided by an embodiment of the present invention;

[0036] Figure 4 It is a clustering analysis diagram of continuous sediment magnetic susceptibility and chromaticity data provided by an embodiment of the present invention ((a) is the clustering result of low-frequency magnetic susceptibility, (b) is the clustering result of high-frequency magnetic susceptibility, (c) is the clustering result of frequency magnetic susceptibility, (d) is the clustering result of brightness L, (e) is the clustering result of redness a, (f) is the clustering result of yellowness b);

[0037] Figure 5 It is a curve diagram of the average value of each major category to which each sample belongs in the depth domain provided by an embodiment of the present invention;

[0038] Figure 6 It is a comprehensive curve diagram of ancient climate parameters in the depth domain provided by an embodiment of the present invention;

[0039] Figure 7This is a comprehensive analysis and comparison chart of paleoclimate processes provided by an embodiment of the present invention. Detailed implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0041] In the embodiments of the present invention, starting from the original data of sediment chromaticity and magnetic susceptibility, Pearson correlation analyses are respectively performed on depth and brightness L * value, depth and redness a * value, depth and yellowness b * value, depth and low-frequency magnetic susceptibility value χ 1f 、depth and high-frequency magnetic susceptibility value χ hf 、depth and frequency magnetic susceptibility χ fd ; According to the Pearson correlation intensity classification, |r|≥0.8 is extremely strong correlation, 0.6 - 0.8 is strong correlation, 0.4 - 0.6 is medium, 0.2 - 0.4 is weak correlation, and ≤0.2 is meaningless. The relationships between chromaticity, magnetic susceptibility and depth are determined, that is: when |r|≤0.2, it is considered that chromaticity and magnetic susceptibility are not affected by post-depositional processes, and the data is reliable for indicating the paleoenvironment; on this basis, self-clustering filtering of each parameter is performed to divide into large categories, and the average values of each large category are calculated to represent the characteristic values of the corresponding large category parameters; then, the analysis of the dispersion degree and curve change trend of each parameter is carried out. Using the sliding window method, combined with the paleoclimate indicating significance of magnetic susceptibility and chromaticity, from the aspects of cold, warm, dry and wet in climatology, the paleoclimate characteristics represented by each large category are clarified; the mean values of the large categories to which each sample belongs are assigned in the depth domain, and the change curves of the mean values of each parameter belonging to the large category with depth are drawn; at the same time, each large category is assigned values, noise reduction processing is carried out in the depth domain, the average values of each parameter after noise reduction are obtained and their change curves in the depth domain are drawn, and the paleoclimate change curve is drawn comprehensively after noise reduction.

[0042] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0043] As Figure 1 shown, it is a flowchart of a data processing method for reconstructing paleoclimate provided by an embodiment of the present invention, including the following steps:

[0044] The first step: Continuously sample the sediment samples of the studied section, and measure the L * value, a * value, b * value of chromaticity, and the low-frequency magnetic susceptibility χ 1f , high-frequency magnetic susceptibility χ hf , frequency magnetic susceptibility χ of magnetic susceptibility according to the chromaticity and magnetic susceptibility measurement standardsfd Plot the obtained results as curves of chromaticity and magnetic susceptibility varying with depth;

[0045] Step 2: Use SPSS software to perform Pearson correlation analysis on depth and L * value, a * value, b * value, χ 1f , χ hf , χ fd in sequence;

[0046] Step 3: According to the Pearson correlation principle, starting from the obtained correlation coefficients, analyze whether the chromaticity and magnetic susceptibility of sediments are affected by post-depositional processes. When |r| ≤ 0.2, it is considered that the chromaticity and magnetic susceptibility are not affected by post-depositional processes, and the data is reliable for indicating the paleoenvironment; if |r| ≥ 0.2, the chromaticity and magnetic susceptibility are affected by post-depositional processes, and the data is not directly used for paleoclimate research, which will not be delved into here;

[0047] Step 4: For the profiles not affected by post-depositional processes, perform cluster analysis on the chromaticity parameter L * value, a * value, b * value and the magnetic susceptibility parameters low-frequency magnetic susceptibility χ 1f , high-frequency magnetic susceptibility χ hf , frequency magnetic susceptibility χ fd respectively to divide into major categories;

[0048] Step 5: Calculate the mean values for each major category corresponding to each parameter as the characteristic values of each major category;

[0049] Step 6: Compare and analyze the cluster analysis results of the six parameters, and determine the paleoclimate types reflected by chromaticity and magnetic susceptibility from the grading perspectives of cold, cool, warm, hot, dry, and wet in climatology according to the paleoclimate indicating significance of chromaticity and magnetic susceptibility;

[0050] Step 7: Plot the mean value curve of the major category to which each sample belongs in the depth domain;

[0051] Step 8: Assign values to the paleoclimate types, perform noise reduction filtering in combination with the mean values of the major categories to which each parameter belongs, and calculate the average value of each parameter after noise reduction;

[0052] Step 9: Calculate the mean values of each parameter after noise reduction on the time series scale to obtain paleoclimate parameters, and further plot the paleoclimate change curve on the time series scale;

[0053] Step 10: Analyze the paleoclimate change curve to obtain the change process of paleoclimate on the profile.

[0054] For Well ZKQA1-1 in the nuclear industry, at depths of 140 - 380.5 m, the lower part is gray and purplish-red mudstone, intercalated with gray and gray-green fine sandstone; the upper part is mainly gray-green fine to medium sandstone, intercalated with purplish-red mudstone, gray fine to medium sandstone, and sandy conglomerate, with continuous deposition. Data analysis and processing for reconstructing paleoclimate are carried out as follows:

[0055] S1. Obtain the chromaticity, magnetic susceptibility parameters L*, a*, b* values, and χ 1f (×10 -8 m 3 / kg), χ hf (×10 -8 m 3 / kg), χ fd (%) values of sediments at different depths (unit: m). In the embodiment of the present invention, samples are taken at 25 cm intervals, and a total of 963 samples are obtained;

[0056] S2. On the basis of S1, obtain the variation curves of chromaticity parameters L*, a*, b* values and magnetic susceptibility parameter χ 1f (×10 - 8 m 3 / kg), χ hf (×10 -8 m 3 / kg), χ fd (%) values with depth (as shown in Figure 2 );

[0057] S3. On the basis of S1, perform correlation analysis on depth and chromaticity data (L* value, a* value, b* value) and magnetic susceptibility (frequency magnetic susceptibility, low-frequency magnetic susceptibility, and high-frequency magnetic susceptibility) respectively to verify whether the data is interfered by post-depositional diagenesis:

[0058] As shown in Figure 3 , in the embodiment of the present invention, the Pearson correlation coefficients between depth and lightness L*, redness a*, and yellowness b* are -0.1023, 0.0913, and 0.0984 respectively, and the Pearson correlation coefficients between depth and frequency magnetic susceptibility, low-frequency magnetic susceptibility, and high-frequency magnetic susceptibility are 0.04987, 0.0982, and 0.0891 respectively, all approaching 0 and far less than 0.2, indicating that the chromaticity and magnetic susceptibility data are not affected by post-depositional diagenesis, and the data can reflect the evolutionary characteristics of paleoclimate on the time scale;

[0059] S4. On the basis of S1, use SPSS software to analyze the lightness L* value, redness a* value, yellowness b* value, and low-frequency magnetic susceptibility χ lf , high-frequency magnetic susceptibility χ hf , frequency magnetic susceptibility χ fdPerform cluster analysis on the values respectively, and conduct a first filtering process on each index;

[0060] S5. On the basis of S3, according to the calculation results of the cluster analysis, divide each index into different major categories, take the average value of each major category, and use the average value to represent the corresponding characteristic value of the parameters of this major category:

[0061] As Figure 4 shown, in the embodiment of the present invention, the cluster analysis results show that the brightness L* value, redness a* value, yellowness b* value, and high- and low-frequency magnetic susceptibilities, and frequency magnetic susceptibility can all be divided into four major categories. The four major category characteristic values of the brightness L* are 49.92, 60.62, 64.23, and 72.5 respectively; the four characteristic values of the redness a* value are -0.87, 1.86, 7.67, and 11.1; the four characteristic values of the yellowness b* value are 5.9, 9.23, 15.83, and 22.19 respectively; the four major category characteristic values of the low-frequency magnetic susceptibility are 72.14×10 -8 m 3 / kg, 65.92×10 -8 m 3 / kg, 42.2×10 -8 m 3 / kg and 32.25×10 -8 m 3 / kg; the four major category characteristic values of the high-frequency magnetic susceptibility are 64.4×10 -8 m 3 / kg, 46.41×10 -8 m 3 / kg, 33.08×10 -8 m 3 / kg and 18.12×10 -8 m 3 / kg; the four major category characteristic values of the frequency magnetic susceptibility are 14.38%, 24.6%, 35.24%, and 41.73%;

[0062] S6. According to the degree of dispersion and the curve change trend of the six index parameters, select the sliding window method in the variance change point detection statistical analysis method, and combine the indicative meanings of each parameter of the magnetic susceptibility and chromaticity. According to the cluster analysis results, correspond the climate types to dry and cold, relatively dry and cold, relatively warm and humid, and warm and humid respectively:

[0063] As Figure 4 shown, in the embodiment of the present invention, the characteristic values of the brightness L* category corresponding to the dry and cold climate are 72.5, the characteristic value of the redness a* is -0.87, the characteristic value of the yellowness b* is 5.9, the characteristic value of the low-frequency magnetic susceptibility is 72.14×10 -8 m 3 / kg, the characteristic value of the high-frequency magnetic susceptibility is 64.4×10 -8m 3 / kg, the characteristic value of frequency magnetic susceptibility is 14.38%; the characteristic values of brightness L* class corresponding to drier and colder climate are 64.23, the characteristic value of redness a* is 1.86, the characteristic value of yellowness b* is 9.23, and the characteristic value of low-frequency magnetic susceptibility is 65.92×10 -8 m 3 / kg, the characteristic value of high-frequency magnetic susceptibility is 46.41×10 -8 m 3 / kg; the characteristic value of frequency magnetic susceptibility is 24.66%; the characteristic values of brightness L* class corresponding to warmer and wetter climate are 60.62, the characteristic value of redness a* is 7.67, the characteristic value of yellowness b* is 15.83, and the characteristic value of low-frequency magnetic susceptibility is 42.2×10 -8 m 3 / kg, the characteristic value of high-frequency magnetic susceptibility is 33.08×10 -8 m 3 / kg; the characteristic value of frequency magnetic susceptibility is 35.24%; the characteristic values of brightness L* class corresponding to warm and wet climate are 49.92, the characteristic value of redness a* is 11.1, the characteristic value of yellowness b* is 22.19, and the characteristic value of low-frequency magnetic susceptibility is 32.25×10 -8 m 3 / kg, the characteristic value of high-frequency magnetic susceptibility is 18.12×10 -8 m 3 / kg, the characteristic value of frequency magnetic susceptibility is 41.73%;

[0064] S7. Plot the mean value curve of each sample belonging to the major category in the depth domain, as Figure 5 shown;

[0065] S8. The units of brightness L* value, redness a* value, yellowness b* value, and high-frequency, low-frequency, and frequency magnetic susceptibilities are different. In the embodiments of the present invention, based on the clustering analysis in S6 and the climate type division in S7, four climate major categories are assigned values. The dry and cold climate is assigned a value of 0.5, the relatively dry and cold climate is assigned a value of 1.5, the relatively warm and wet climate is assigned a value of 2.5, and the warm and wet climate is assigned a value of 3.5;

[0066] S9. Based on S5, S6, S7, and S8, perform data noise reduction and filtering processing, take the average value of the 6 parameter assignments after noise reduction, and obtain the change curve in the depth domain after comprehensive noise reduction, as Figure 6 shown:

[0067] In the embodiments of the present invention, according to the results obtained from the assignment calculation, the paleoclimate processes experienced by the studied sedimentary profile can be divided into 10 stages: relatively dry and cold from 380.5 to 353.5 m, relatively warm and humid from 353.5 to 352.0 m, warm and humid from 352.0 to 338.5 m, relatively warm and humid from 338.5 to 251.5 m, relatively dry and cold from 251.5 to 241.5 m, dry and cold from 241.5 to 217.5 m, relatively dry and cold from 217.5 to 196.0 m, dry and cold from 196.0 to 172.0 m, relatively dry and cold from 172.0 to 164.5 m, and relatively warm and humid from 164.6 to 138.0 m;

[0068] S10. Analyze the paleoclimate changes on the profile by combining the paleoclimate change curves obtained from S7 and S9, and convert them into the paleoclimate evolution process in the time domain:

[0069] In the embodiments of the present invention, the studied profile is located in the Qian'an area of the Songliao Basin. The lithological characteristics of the depth section from 380.0 to 140.0 m shown in the borehole geological background data belong to the Sifangtai Formation. The dating results of boreholes such as Well Songke-1 show that the Sifangtai Formation covers the time period from 79.1 to 72.2 Ma. Combining this dating result, the depth domain is converted into the time domain, and the formation ages corresponding to the 10 stages are obtained by interpolation calculation: 380.5 - 353.5 m corresponds to 76.08 - 75.65 Ma, 353.5 - 352.0 m corresponds to 75.65 - 74.62 Ma, 352.0 - 338.5 m corresponds to 74.62 - 75.45 Ma, 338.5 - 251.5 m corresponds to 75.45 - 74.32 Ma, 251.5 - 241.5 m corresponds to 74.32 - 74.19 Ma, 241.5 - 217.5 m corresponds to 74.19 - 73.88 Ma, 217.5 - 196.0 m corresponds to 73.88 - 73.6 Ma, 196.0 - 172.0 m corresponds to 73.6 - 73.31 Ma, 172.0 - 164.5 m corresponds to 73.31 - 73.19 Ma, 164.6 - 138.0 m corresponds to 73.19 - 72.86 Ma. Therefore, the paleoclimate change process in the time domain can be divided into 10 corresponding small stages of relatively dry and cold - relatively warm and humid - warm and humid - relatively warm and humid - relatively dry and cold - dry and cold - relatively dry and cold - dry and cold - relatively dry and cold - relatively warm and humid changes;

[0070] Combining the characteristic value curves of 6 parameters of chromaticity and magnetic susceptibility, the paleoclimate process on the profile can be divided into 4 categories: dry and cold from 76.08 to 75.65 Ma, relatively dry and cold from 75.65 to 74.32 Ma, relatively warm and humid from 74.32 to 73.19, and warm and humid from 73.19 to 72.86 Ma. The overall trend is cold - warm - cold - warm, with relatively warm and arid climate characteristics. This result corresponds to the fluctuation process of the spontaneous potential and resistivity curves of the sediments on the profile, indicating the feasibility and accuracy of the method (such asFigure 7 as shown);

[0071] Combined with the global Cretaceous climate characteristics, 76.08 - 75.65 Ma was in the middle Campanian. At this time, the global atmospheric carbon dioxide concentration was relatively high, and the surface and bottom seawater temperatures and the global sea level relatively rose; 75.65 - 74.32 Ma was between the middle and late Campanian. The CMBE climate event that occurred during this stage led to a decrease in the atmospheric carbon dioxide concentration and global cooling; during the period of 74.32 - 73.19 Ma, the paleoclimate turned warm and humid again; 73.19 - 72.86 Ma was between the late Campanian - early Maastrichtian, and the climate was relatively warm and humid. The MME climate event that occurred at this time led to an increase in the atmospheric carbon dioxide concentration and thus an increase in the global temperature. The dry and cold period from 76.08 - 75.65 Ma, the relatively dry and cold period from 75.65 - 74.32 Ma, the relatively warm and humid period from 74.32 - 73.19 Ma, and the warm and humid period from 73.19 - 72.86 Ma discovered in the embodiments of the present invention reveal the regional characteristics of the global paleoclimate process to a certain extent.

[0072] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data processing method for reconstructing paleoclimate, characterized in that, It includes the following steps: Obtain data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths; Analyze the data, calculate the correlation coefficients between depth and chromaticity as well as magnetic susceptibility according to the Pearson correlation principle, and based on the obtained coefficients, analyze whether chromaticity and magnetic susceptibility are affected by post-depositional processes; Conduct cluster analysis on the chromaticity parameters and magnetic susceptibility parameters to divide them into major categories; Calculate the mean values of each parameter for each major category as the characteristic values of each major category; Combined with the paleoclimate indication significance of the parameters, determine the paleoclimate types represented by each major category; Assign values to each climate type, perform noise reduction filtering on the mean values of each parameter, and calculate the average values of each parameter after noise reduction; Calculate the total average value of the parameter assignment to obtain paleoclimate parameters; Analyze the process of paleoclimate change.

2. The data processing method for reconstructing paleoclimate according to claim 1, wherein In the step of obtaining data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths, the chromaticity parameters include L * value, a * value, b * value, and the magnetic susceptibility parameters include low-frequency magnetic susceptibility χ 1f , high-frequency magnetic susceptibility χ hf , and frequency magnetic susceptibility χ fd .

3. The data processing method for reconstructing paleoclimate according to claim 1, wherein In the step of obtaining data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths, it also includes plotting curves of each chromaticity parameter and each magnetic susceptibility parameter changing with depth.

4. The data processing method for reconstructing paleoclimate according to claim 1, characterized in that In the step of analyzing the data, calculating the correlation coefficients between depth and chromaticity as well as magnetic susceptibility according to the Pearson correlation principle, and based on the obtained coefficients, analyzing whether chromaticity and magnetic susceptibility are affected by post-depositional processes, when the correlation coefficient |r| ≤ 0.2, then chromaticity and magnetic susceptibility are not affected by post-depositional processes; When the correlation coefficient |r| ≥ 0.2, chromaticity and magnetic susceptibility are affected by post-depositional processes.

5. The data processing method for reconstructing paleoclimate according to claim 4, characterized in that, In the step of conducting cluster analysis on the chromaticity parameters and magnetic susceptibility parameters to divide them into major categories, the chromaticity parameters and magnetic susceptibility parameters are parameters not affected by post-depositional processes.

6. The data processing method for reconstructing paleoclimate according to claim 1, wherein In the step of combining the paleoclimate indication significance of the parameters to determine the paleoclimate types represented by each major category, specifically: conduct a comparative analysis of the cluster analysis results of each parameter, and from the classification perspectives of cold, cool, warm, hot, dry, and wet in climatology, determine the paleoclimate types reflected by chromaticity and magnetic susceptibility according to the paleoclimate indication significance of chromaticity and magnetic susceptibility.

7. The data processing method for reconstructing paleoclimate according to claim 1, wherein Between the step of combining the paleoclimate indication significance of the parameters to determine the paleoclimate types represented by each major category and the step of assigning values to each climate type, performing noise reduction filtering on the mean values of each parameter, and calculating the average values of each parameter after noise reduction, it also includes plotting a curve of the characteristic values changing with depth.

8. The data processing method for reconstructing paleoclimate according to claim 1, characterized in that, In the step of calculating the total average value of the parameter assignment to obtain paleoclimate parameters, it also includes plotting a curve of the paleoclimate parameters changing with depth.

9. A data processing system for reconstructing paleoclimate, which is used to implement the data processing method for reconstructing paleoclimate as described in any one of claims 1-8, characterized in that, It includes: A data acquisition module for obtaining data, where the data are chromaticity parameters and magnetic susceptibility parameters of sediments at different depths; A data analysis module for analyzing the data, calculating the correlation coefficients between depth and chromaticity as well as magnetic susceptibility according to the Pearson correlation principle, and based on the obtained coefficients, analyzing whether chromaticity and magnetic susceptibility are affected by post-depositional processes; A clustering module for conducting cluster analysis on the chromaticity parameters and magnetic susceptibility parameters to divide them into major categories; A mean value calculation module for calculating the mean values of each parameter for each major category as the characteristic values of each major category; A paleoclimate type determination module for combining the paleoclimate indication significance of the parameters to determine the paleoclimate types represented by each major category; A mean value processing module for assigning values to each climate type, performing noise reduction filtering on the mean values of each parameter, and calculating the average values of each parameter after noise reduction; A paleoclimate parameter determination module for calculating the total average value of the parameter assignment to obtain paleoclimate parameters; An analysis module for analyzing the process of paleoclimate change.

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