Analysis methods, systems, media and terminals for reconstructing paleoclimate using loss on ignition data

Abstract

The present invention belongs to the technical field of paleoclimate global change analysis, and discloses an analysis method, system, medium and terminal for reconstructing paleoclimate using loss on ignition data. The analysis method for reconstructing paleoclimate using loss on ignition data includes: based on the original data of paleoclimate indicators in sediments, firstly performing signal analysis, using time-frequency analysis and time series analysis to divide the original data into different cycles that are inherently self-consistent, using Pearson correlation analysis within each cycle to reveal the relevant characteristics of the original data, and using noise reduction filtering and statistical methods on the basis of correlation analysis to analyze the characteristics and change laws of the content of climate indicators within each cycle; at the same time, obtaining multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-change processes, summarizing the climate types on the sedimentary profile, and establishing a climate model. The present invention provides basic technical support for promoting global change research and predicting climate change.

Description

Technical Field

[0001] The present invention belongs to the technical field of paleoclimate global change analysis, and in particular relates to an analysis method, system, medium and terminal for reconstructing paleoclimate using loss on ignition data. Background Art

[0002] Understanding and predicting future global climate change trends is a basic task for the sustainable development of human society. Using paleoclimate indicators in geological records to restore paleoclimate change characteristics and frequencies can provide a basic model and basis for understanding the process and mechanism of climate change and predicting future climate change trends.

[0003] Minerals, fossils, geochemistry and geophysics in the geological record play an important role in paleoclimate reconstruction, but the identification time is long, the testing cost is high, and the integrity of paleoclimate information interpretation needs to be improved. The loss on ignition (LOI) of the same sediment sample after heating at different temperatures (550℃, 1000℃) is currently the most important method for estimating sediment organic matter (LOI). 550 ) and carbonate content (LOI 1000 ) is the most economical and simplest method, and is also a good indicator for studying paleoclimate change. Since the 1970s, many scholars have used the loss on ignition (LOI) of loess, lakes, oceans and other sediments to study paleoclimate change. 550 and LOI 1000 Obtain changes in organic matter and carbonate content to analyze the paleoclimate evolution process.

[0004] Theoretically, the climate system itself has two characteristics: temperature and humidity. However, the current LOI 550 and LOI 1000 In paleoclimate research, it is mostly used to analyze paleotemperature changes, and the analysis method remains at the direct interpretation of raw data. In fact, humid, high temperature and cold climates can all cause higher LOI. 550 Value, reflecting the LOI of carbonate content 1000 The change of LOI is also affected by many factors such as temperature, dryness and wetness, and water salinity. It can be seen that the multiple interpretations of LOI and the incompleteness of information interpretation have caused great troubles in paleoclimate research, causing the self-consistency of the interpretation results and the consistency with other evidence to deviate.

[0005] Therefore, the problems and defects of the existing technology are: the self-consistency of the analysis results of the existing technology and the consistency with other evidence are poor; and it is only for the loss on ignition LOI 550 and LOI 1000There is no clear and feasible method or approach to directly analyze the raw data and apply it to climate analysis and climate change prediction; clear and feasible methods for the complete interpretation of paleoclimate information from mineral, fossil, geochemical and geophysical indicators also need to be clarified. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides an analysis method, system, medium and terminal for reconstructing paleoclimate using loss on ignition data, which can be extended to the analysis and interpretation of other paleoclimate indicators.

[0007] The present invention is achieved in this way. A method for analyzing paleoclimate reconstruction using loss on ignition data is provided. The method comprises: starting from the original data of paleoclimate indicators in sediments, firstly, from the perspective of signal analysis, using time-frequency analysis and time series analysis to divide the original data into different cycles that are inherently self-consistent, using Pearson correlation analysis within each cycle to reveal the relevant characteristics of the original data, and using noise reduction filtering and statistical methods on the basis of correlation analysis to analyze the characteristics and variation laws of the content of climate indicators within each cycle, thereby effectively solving the multi-solution problem of climate indicators, and obtaining multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-variation processes, thereby summarizing the climate types on the sedimentary profile, and further establishing a climate model to serve the prediction of climate change trends, which has the advantages of being economical, convenient, efficient and scientific.

[0008] Furthermore, the analysis method of reconstructing paleoclimate using loss on ignition data also includes:

[0009] Based on the original data of LOI at 550℃ and 1000℃, the original data were divided into different self-consistent cycles using time-frequency analysis and time series analysis. The LOI was determined within each cycle using Pearson correlation analysis. 550 and LOI 1000 Based on the correlation analysis, noise reduction filtering and statistical methods are used to analyze the content characteristics and change patterns of organic matter and carbonate in each cycle. At the same time, the multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-change process are obtained, the climate type on the sedimentary profile is obtained, and a climate model is constructed.

[0010] Furthermore, the analysis method of reconstructing paleoclimate using loss on ignition data comprises the following steps:

[0011] Step 1: Obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at sampling points at different depths (or positions) on the sedimentary profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sedimentary profile; average the original data of organic matter content and carbonate content on the sedimentary profile to obtain their respective total average values; use wavelet transform to perform spectral analysis on the two sets of data to obtain spectrum diagrams at different scales; the total average value provides the basic background for the changes in organic matter and carbonate content on the profile, and the spectra at different scales objectively show the synchronization and differences in the changes of the two parameters.

[0012] Step 2: Based on the mirror symmetry analysis of the spectrum diagram of organic matter content and carbonate content, the high, medium and low frequency change characteristics are identified, and scale point A and scale point B are determined; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at scale point B of the spectrum diagram of time-frequency analysis is used to divide the stages of organic matter content and carbonate content, and a spectrum stage division scheme M is obtained; objective short-term stages of the same changes in organic matter and carbonate content in the frequency domain are obtained.

[0013] Step three, use the CONISS function to perform time series clustering analysis on the original data of organic matter content and carbonate content; use the Savitzky-Golay method to perform denoising and filtering on the LOI original data with several adjacent points as windows, remove the high-frequency part, and obtain the medium- and low-frequency change trend lines of organic matter content and carbonate content; use the change characteristics of the change trend line in each segment in the division scheme N to determine the monotonicity of the organic matter content and carbonate content data in each segment; compare with the minimum period of the spectrum to clarify the minimum period of change in organic matter and carbonate content, which is the basic premise of Savitzky-Golay filtering and also the basic unit for the division of short-term climate cycles.

[0014] Step 4: According to the principle that dry and cold to warm and humid is an ascending semi-cycle, and warm and humid to dry and cold is a descending semi-cycle, identify the short-term climate half-cycle in the division scheme N; average the original data of organic matter content and carbonate content in each short-term climate half-cycle in sections to obtain the average value of each short-term climate half-cycle, and draw a depth bar graph; perform Pearson correlation analysis on the original data of organic matter content and carbonate content in each short-term climate half-cycle; based on the relationship between the average value of organic matter and carbonate content in the short-term climate cycle and the total mean of the profile and the Pearson correlation coefficient between the two, qualitatively identify the relationship between changes in organic matter and carbonate content and climate change.

[0015] Step five: determine the relationship between organic matter content and carbonate content based on the value range of the Pearson correlation coefficient; draw the change curve of the Pearson correlation coefficient on the sedimentary profile; evaluate the relative changes of organic matter content and carbonate content based on the correspondence between the Pearson correlation coefficient of organic matter content and carbonate content, the average value of each stage and the total average value; in the overall context, clarify the corresponding climate type through the relationship between the correlation coefficient of organic matter content and carbonate content and climate change.

[0016] Step six, determining the climate type of the short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establishing a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtaining a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; performing a Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes at the same scale; confirming the coupling relationship between parameter changes and climate types at the same scale through trend analysis and correlation tests of the semi-quantitative change curves.

[0017] Step 7: Combine the characteristics of the time-frequency analysis spectrum at scale point A to analyze the medium-term cycle characteristics of climate change; combine the overall change characteristics of temperature and humidity to analyze the long-term cycle characteristics of climate change; establish the climate type on the sedimentary profile to analyze the law of climate change; analyze the coupling relationship between paleotemperature and paleohumidity to determine the climate change model for the corresponding period. Obtain the climate change model corresponding to the paleotemperature and paleohumidity at different scales on the profile.

[0018] Further, the determining of scale point A and scale point B includes:

[0019] The transition point from symmetry to weakened symmetry in the mid-frequency part is taken as scale point A;

[0020] The transition point where the symmetry in the mid-high frequency part is weakened to the point where the symmetry is complicated is taken as the scale point B.

[0021] Further, the time series clustering of the original data of organic matter content and carbonate content using the CONISS function includes:

[0022] The clustering point is G, and the G value is used as the division standard to group the time series clusters to obtain the division scheme N;

[0023] When the division criterion is G or less than the G value, compare the spectrum stage division scheme M and the cluster grouping scheme N. N is included in M ​​and N is a refinement of M. Then G is determined as the division point required for the time series clustering analysis this time, and the division scheme N is the division result required this time.

[0024] Furthermore, in step 5, the types of changes in organic matter content and carbonate content include:

[0025] (1) Organic matter content and carbonate content are positively correlated, and both organic matter content and carbonate content are less than 1%;

[0026] (2) Organic matter content and carbonate content are positively correlated, with organic matter content being lower than the average and carbonate content being higher than the average;

[0027] (3) Organic matter content and carbonate content are positively correlated, and both contents are higher than the average;

[0028] (4) Organic matter content and carbonate content are positively correlated and tend to decrease, with organic matter content close to the average and carbonate content below the average;

[0029] (5) The organic matter content showed an obvious upward trend and was positively correlated with the carbonate content, and both contents were higher than the average value;

[0030] (6) Organic matter content and carbonate content are negatively correlated, with carbonate content decreasing and organic matter content far above the average;

[0031] (7) The organic matter content and carbonate content of the whole section are positively correlated, with a decreasing trend. When the organic matter content is higher than 10%, the carbonate content is higher than the average value;

[0032] (8) Organic matter content and carbonate content are positively correlated, both showing a decreasing trend, and the total content is lower than the average;

[0033] (9) Organic matter content and carbonate content are positively correlated and tend to increase, and both are higher than the average value;

[0034] (10) Organic matter content and carbonate content are negatively correlated, with a decreasing trend, where organic matter content is lower than the average and carbonate content is higher than the average;

[0035] (11) Organic matter content and carbonate content are positively correlated, and both are close to the average value;

[0036] (12) Organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are higher than the average;

[0037] (13) Organic matter content and carbonate content are positively correlated, and the total carbonate content is lower than the average;

[0038] (14) Organic matter content and carbonate content are positively correlated, but the organic matter content is lower than the average, and the total carbonate content is lower than the average;

[0039] (15) Organic matter content and carbonate content are positively correlated, with organic matter content slightly higher than the average and total carbonate content lower than the average;

[0040] (16) Organic matter content and carbonate content are negatively correlated, with organic matter content being lower than the average and carbonate content being higher than the average;

[0041] (17) Organic matter content and carbonate content are positively correlated. When the organic matter content is less than 1%, the carbonate content is lower than the average value;

[0042] (18) Organic matter content and carbonate content are positively correlated, and carbonate content is lower than the average;

[0043] (19) The organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are slightly lower than the average value.

[0044] Further, the climate type of the short-term semi-cycle determined based on the relative changes in organic matter content and carbonate content includes:

[0045] (1) When the organic matter content is negatively correlated with the carbonate content, the paleotemperature and paleohumidity are in equilibrium:

[0046] 1) When the organic matter content is higher than the average and the carbonate content is lower than the average, the climate is hot and humid;

[0047] 2) When the organic matter content is lower than the average and the carbonate content is higher than the average, the climate is dry and cold;

[0048] (2) When organic matter and carbonate content are positively correlated, the balance between paleotemperature and paleohumidity is broken:

[0049] 1) Organic matter content higher than the average indicates a hotter paleotemperature, while content lower than the average indicates a colder paleotemperature;

[0050] 2) carbonate content above the average indicates semi-dry paleohumidity, and carbonate content below the average indicates semi-wet paleohumidity;

[0051] 3) When the organic matter content is greater than 10% and the carbonate content is higher than the average, it means that the climate is hot and humid, and the primary productivity and plankton of the lake increase;

[0052] 4) When the organic matter content is less than 1% and the carbonate content is lower than the average, it indicates a dry and cold climate.

[0053] Another object of the present invention is to provide an analysis system for reconstructing paleoclimate using loss on ignition data, which implements the analysis method for reconstructing paleoclimate using loss on ignition data. The analysis system for reconstructing paleoclimate using loss on ignition data comprises:

[0054] The spectrum acquisition module is used to obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at different depth sampling points on the sediment profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sediment profile; average the original data of organic matter content and carbonate content on the sediment profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum graphs at different scales;

[0055] The spectrum stage division scheme module is used to identify the high, medium and low frequency change characteristics based on the mirror symmetry analysis of the spectrum graph of organic matter content and carbonate content, and determine the scale point A and scale point B; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum graph of time-frequency analysis is used to divide the organic matter content and carbonate content into stages, and obtain the spectrum stage division scheme M;

[0056] The data monotonicity determination module is used to perform time series clustering analysis on the original data of organic matter content and carbonate content using the CONISS function; the Savitzky-Golay method is used to perform noise filtration on the LOI original data with several adjacent points as windows to remove the high-frequency part and obtain the medium- and low-frequency change trend lines of organic matter content and carbonate content; the monotonicity of the organic matter content and carbonate content data in each segment is determined by using the change characteristics of the change trend line in each segment in the division scheme N;

[0057] The Pearson correlation analysis module is used to identify the short-term climate semi-cycle in the division scheme N according to the principle that dry and cold changes to warm and humid as an ascending semi-cycle, and warm and humid changes to dry and cold as a descending semi-cycle; the original data of organic matter content and carbonate content in each short-term climate semi-cycle are averaged in sections to obtain the average value of each short-term climate semi-cycle, and a depth bar graph is drawn; the original data of organic matter content and carbonate content in each short-term climate semi-cycle are subjected to Pearson correlation analysis;

[0058] The module of relative content change is used to determine the relationship between organic matter content and carbonate content based on the value range of Pearson correlation coefficient; draw the change curve of Pearson correlation coefficient on sedimentary profile; evaluate the relative change of organic matter content and carbonate content based on the corresponding relationship between Pearson correlation coefficient of organic matter content and carbonate content, average value of each stage and total average value;

[0059] A curve processing module is used to determine the climate type of a short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establish a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtain a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; perform a Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes on the same scale;

[0060] The climate change model determination module is used to analyze the medium-term cycle characteristics of climate change by combining the characteristics of the time-frequency analysis spectrum at scale point A; analyze the long-term cycle characteristics of climate change by integrating the overall change characteristics of temperature and humidity; establish climate types on sedimentary profiles and analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity and determine the climate change model for the corresponding period.

[0061] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps of the analysis method of reconstructing paleoclimate using loss on ignition data:

[0062] Step 1: Obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at sampling points at different depths on the sedimentary profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sedimentary profile; average the original data of organic matter content and carbonate content on the sedimentary profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum diagrams at different scales;

[0063] Step 2: According to the mirror symmetry analysis of the spectrum of organic matter content and carbonate content, the high, medium and low frequency change characteristics are identified, and the scale point A and scale point B are determined; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum of time-frequency analysis is used to divide the stage of organic matter content and carbonate content, and the spectrum stage division scheme M is obtained;

[0064] Step 3: Use the CONISS function to perform time series cluster analysis on the original data of organic matter content and carbonate content; use the Savitzky-Golay method to perform noise filtration on the LOI original data with several adjacent points as windows, remove the high-frequency part, and obtain the medium and low-frequency change trend lines of organic matter content and carbonate content; use the change characteristics of the change trend line in each segment in the division scheme N to determine the monotonicity of the organic matter content and carbonate content data in each segment;

[0065] Step 4: According to the principle that dry and cold to warm and humid is an ascending semi-cycle, and warm and humid to dry and cold is a descending semi-cycle, identify the short-term climate semi-cycle in the division scheme N; average the original data of organic matter content and carbonate content in each short-term climate semi-cycle in sections to obtain the average value of each short-term climate semi-cycle, and draw a depth bar chart; perform Pearson correlation analysis on the original data of organic matter content and carbonate content in each short-term climate semi-cycle;

[0066] Step 5: Determine the relationship between organic matter content and carbonate content based on the value range of the Pearson correlation coefficient; draw a curve of the change of the Pearson correlation coefficient on the sedimentary profile; evaluate the relative changes of organic matter content and carbonate content based on the corresponding relationship between the Pearson correlation coefficient of organic matter content and carbonate content, the average value of each stage and the total average value;

[0067] Step 6: Determine the climate type of the short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establish a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtain a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; perform Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes on the same scale;

[0068] Step seven, combine the characteristics of the time-frequency analysis spectrum at scale point A to analyze the medium-term cycle characteristics of climate change; integrate the overall change characteristics of temperature and humidity to analyze the long-term cycle characteristics of climate change; establish the climate type on the sedimentary profile to analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity to determine the climate change model for the corresponding period.

[0069] Another object of the present invention is to provide an information data processing terminal, which is used to execute the analysis method of reconstructing paleoclimate using loss on ignition data.

[0070] In combination with the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solutions to be protected by the present invention from the following aspects:

[0071] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, the technical solutions to be protected by the present invention and the results and data during the research and development process are closely combined to analyze in detail and deeply how the technical solutions of the present invention solve the technical problems, and some creative technical effects brought about after solving the problems. The specific description is as follows:

[0072] The present invention provides a method for analyzing the loss on ignition (LOI) in sediments. 550 and LOI 1000 The method contains the temperature and humidity change characteristics of the ancient environment, effectively extracts the ancient climate characterization factors, analyzes the climate types and change patterns they represent, and provides basic technical support for promoting global change research and predicting climate change.

[0073] Second, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are described in detail as follows:

[0074] The present invention can clearly analyze the amplitude and degree of relative changes in paleoclimate proxy indicators on the profile; it can scientifically, relatively simply and clearly divide the climate change stages, which plays an important role in analyzing the regularity of paleoclimate changes, helps to quickly interpret the results of paleoclimate reconstruction, and promotes the effective implementation of global comparison work.

[0075] Third, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0076] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: Today, human society is in a climate evolution stage with global warming and increasing extreme climates. Sudden cooling, cold waves, freezing rain, blizzards, heat waves, droughts and off-season phenomena have caused huge interference and threats to agricultural and animal husbandry production, transportation, industrial production and the personal safety of residents. Understanding and predicting future global climate change trends is a basic task for the sustainable development of human society, among which predicting medium- and short-term climate change is a top priority. The analysis method, system, medium and terminal for reconstructing paleoclimate from loss on ignition data provided by the present invention are a research method that realizes the study of the past and the present, clarifies the laws of climate change in geological history, and can provide a model and basis for predicting global climate change today. The promotion of the present invention can accelerate and supplement the establishment of a global climate change model. With the continuous improvement and supplementation of the global climate change model, mankind will be able to continuously improve the accuracy and efficiency of medium- and short-term climate forecasts, reduce social costs and reduce the loss of life and property of the people, thereby generating great social benefits and commercial value.

[0077] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad: the climate system itself has two characteristics of temperature and humidity, but the current loss on ignition LOI 550 and LOI 1000 In paleoclimate research, it is mostly used to analyze paleotemperature changes, and the analysis method remains at the direct interpretation of raw data. In fact, humid, hot and cold climates can all cause higher LOI. 550 Value, reflecting the LOI of carbonate content 1000 The change is also affected by many factors such as temperature, dryness and wetness, and water salinity. It can be seen that the complexity of LOI information interpretation has caused great trouble for paleoclimate research. The first is the self-consistency of the data itself, that is, whether the same data indicates the same meaning, and whether it is consistent with other evidence. The self-consistency of the data itself has not been well solved in the process of paleoclimate research at home and abroad.

[0078] From the perspective of sedimentology, the continuous sedimentation process will include different levels of sedimentation cycles. The controlling factors of sedimentation cycles can be divided into internal factors and external factors. For medium- and short-term sedimentation cycles, their external factors are often climate factors. Therefore, LOI550 Short- to medium-term cycles and LOIs formed by changes 1000 The internal causes of the short- and medium-term cycles of changes may vary, but the external causes are changes in temperature and humidity. 550 and LOI 1000 The two sets of quantities are combined for cyclic analysis. In different cycles, the same value may have different interpretations, but in the same cycle, the interpretation results of temperature and humidity must be self-consistent in theory. 550 and LOI 1000 No one has attempted to measure the temperature and humidity changes referred to in the data.

[0079] The division of traditional sedimentary cycles is somewhat subjective, which is not conducive to objective analysis of the climate factors behind them. The present invention divides the original data of loss on ignition into cycles through frequency domain analysis and time domain analysis. On the one hand, it changes the subjective and arbitrary division of cycles, and on the other hand, it can filter out the noise information in the climate parameters, which greatly improves the in-depth analysis of climate control factors; in particular, it solves the self-consistency problem of multiple parameters of the same sample in the cycle, filling many gaps in the analysis of loss on ignition at home and abroad.

[0080] (3) The technical solution of the present invention solves a technical problem that people have been eager to solve but have never succeeded in solving: objectively interpreting the paleoclimate indicator information of LOI in sediments and reducing its ambiguity have always been the focus and difficulty in the field of paleoclimate research, but progress has been slow and the results are not optimistic. The present invention starts from the sedimentary cycle and converts LOI into 550 and LOI 1000 The two sets of quantities are placed in the same objective sedimentary cycle to ensure that they have common temperature and humidity control factors. Combining the research results independently carried out by predecessors, using Pearson phase analysis and overall average and average values ​​of each cycle to unify them, a complete discrimination standard is established to solve the multi-solution problem of data interpretation.

[0081] The criteria are as follows: LOI 550 With LOI 1000The Pearson correlation coefficient, the correspondence between the average value of each stage and the total average value are used as the benchmark to evaluate the relative changes of organic matter and carbonate. There are two situations of negative correlation and positive correlation between the changes of organic matter and carbonate in the lake basin: 1) When the organic matter and carbonate content are negatively correlated: ① When the organic matter content is higher than the average value, it indicates a hot and humid climate. ② When the organic matter content is lower than the average value, it indicates a dry and cold climate. 2) When the organic matter and carbonate content are positively correlated: ① When the organic matter content is higher than the average value, it indicates a hot ancient temperature, and when it is lower than the average value, it indicates a cold ancient temperature. ② When the carbonate content is higher than the average value, it indicates semi-dry ancient humidity, and the low average value indicates semi-humid ancient humidity. ③ When the organic matter content is greater than 10% and the carbonate content is higher than the average value, it indicates a hot and humid climate; ④ When the organic matter content is less than 1% and the carbonate content is lower than the average value, it indicates a dry and cold climate.

[0082] The temperature and humidity parameters are separated through discrimination criteria to reveal the continuous temperature and humidity change process.

[0083] (4) The technical solution of the present invention overcomes technical prejudice: the method of loss on ignition is a paleoclimate index method proposed in the 1940s and 1950s. 550 Benchmarking organic matter in sediment, LOI 1000 By comparing carbonates in sediments to reveal paleoclimate changes, we can 550 With LOI 1000 Direct interpretation of continuous data to analyze paleoclimate changes. With the progress of the times, TOC testing and continuous progress in major elements, trace elements, XRD and other methods, after the 1990s, most scholars gradually gave up relying on loss on ignition data and turned to using TOC that directly shows the organic matter content and XRD or elemental analysis of carbonate rock mineral content to reflect climate change. The loss on ignition method is classified as a method that is cheap and economical and has a fast test speed, but has poor data accuracy and strong multi-solution.

[0084] Thanks to the progress of the times, the popularity of high-precision balances and high-precision constant-temperature medium-high temperature muffle furnaces has made the measurement accuracy of loss on ignition higher and higher. The real reason for the application of this method of loss on ignition lies in the self-consistency between data and the multiple solutions of data interpretation in paleoclimate research.

[0085] At present, the present invention divides the loss on ignition data of continuous sediments into sedimentary cycles of different levels from the frequency domain and the time domain. The self-consistency of the data itself is solved according to the same external control factors in the same sedimentary cycle. Through Pearson analysis and the relationship between the total mean and the mean of each segment, the isolated research results of predecessors are summarized, the discrimination criteria under different circumstances are established, the external factors of paleoclimate are separated into two parameters, temperature and humidity, and then the continuous temperature and humidity changes in the historical period are restored. It can provide method tools for medium- and short-term climate research. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a schematic diagram of the analysis method for reconstructing paleoclimate using loss on ignition data provided in an embodiment of the present invention.

[0087] Figure 2 It is a flow chart of the analysis method for reconstructing paleoclimate using loss on ignition data provided by an embodiment of the present invention.

[0088] Figure 3 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Schematic diagram of depth variation curve;

[0089] Figure 4 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Wavelet transform spectrum of raw data;

[0090] Figure 5 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Schematic diagram of the phase division of parameters based on the spectrum diagram;

[0091] Figure 6 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Schematic diagram of the trend line of medium and low frequency changes;

[0092] Figure 7 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Schematic diagram of the stage division of raw data time series clustering;

[0093] Figure 8 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Mean depth histogram of the original data time series clustering stage;

[0094] Fig. 9 LOI of the Dalianhe Formation in the Yilan Basin provided by the embodiment of the present invention 550 and LOI 1000 Schematic diagram of the change curve of the average value Pearson coefficient of the original data time series clustering stage;

[0095] Fig.10 A relative change diagram of organic matter and carbonate in the Dalianhe Formation of the Yilan Basin provided by an embodiment of the present invention;

[0096] Fig.11A schematic diagram of the short-term climate change cycle and its characteristics in the Dalianhe Formation of the Yilan Basin provided by an embodiment of the present invention;

[0097] Fig.12 A schematic diagram of a temperature and humidity variation curve and a trend line reflected by a short-term climate semi-cycle of the Dalianhe Formation in the Yilan Basin provided by an embodiment of the present invention;

[0098] Fig.13 A schematic diagram of the medium- and long-term climate change cycle of the Dalianhe Formation in the Yilan Basin provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the 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.

[0100] 1. Explanatory Examples In order to enable those skilled in the art to fully understand how to implement the present invention, this section provides an illustrative example that expands and describes the technical solution of the claims.

[0101] The analysis method for reconstructing paleoclimate using loss on ignition data provided in an embodiment of the present invention includes: based on the original data of paleoclimate indicators in sediments, first performing signal analysis, using time-frequency analysis and time series analysis to divide the original data into different cycles that are inherently self-consistent, using Pearson correlation analysis within each cycle to reveal the relevant characteristics of the original data, and using noise reduction filtering and statistical methods on the basis of correlation analysis to analyze the characteristics and change patterns of the content of climate indicators within each cycle; at the same time, obtaining multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-change processes, summarizing the climate types on the sedimentary profile, and establishing a climate model.

[0102] The analysis method for reconstructing paleoclimate using loss on ignition data provided by the embodiment of the present invention also includes:

[0103] Based on the original data of LOI at 550℃ and 1000℃, the original data were divided into different self-consistent cycles using time-frequency analysis and time series analysis. The LOI was determined within each cycle using Pearson correlation analysis. 550 and LOI 1000 Based on the correlation analysis, noise reduction filtering and statistical methods are used to analyze the content characteristics and change patterns of organic matter and carbonate in each cycle. At the same time, the multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-change process are obtained, the climate type on the sedimentary profile is obtained, and a climate model is constructed.

[0104] like Figure 1 As shown, the analysis method for reconstructing paleoclimate using loss on ignition data provided by the embodiment of the present invention comprises the following steps:

[0105] S101, obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at sampling points at different depths on the sedimentary profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sedimentary profile; average the original data of organic matter content and carbonate content on the sedimentary profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum diagrams at different scales;

[0106] S102, according to the mirror symmetry analysis of the spectrum of organic matter content and carbonate content, the high, medium and low frequency change characteristics are identified, and the scale point A and the scale point B are determined; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum of time-frequency analysis is used to divide the organic matter content and the carbonate content into stages, and a spectrum stage division scheme M is obtained;

[0107] S103, using CONISS function to perform time series cluster analysis on the original data of organic matter content and carbonate content; using Savitzky-Golay method to filter the LOI original data with several adjacent points as windows, remove the high-frequency part, and obtain the medium and low-frequency change trend lines of organic matter content and carbonate content; using the change characteristics of the change trend line in each segment in the division scheme N, determine the monotonicity of the organic matter content and carbonate content data in each segment;

[0108] S104, according to the principle that dry and cold to warm and humid is an ascending semi-cycle, and warm and humid to dry and cold is a descending semi-cycle, identify the short-term climate semi-cycle in the division scheme N; average the original data of organic matter content and carbonate content in each short-term climate semi-cycle in sections to obtain the average value of each short-term climate semi-cycle, and draw a depth bar graph; perform Pearson correlation analysis on the original data of organic matter content and carbonate content in each short-term climate semi-cycle;

[0109] S105, determining the relationship between organic matter content and carbonate content based on the value range of the Pearson correlation coefficient; drawing a curve of the change of the Pearson correlation coefficient on the sedimentary profile; evaluating the relative change of organic matter content and carbonate content based on the corresponding relationship between the Pearson correlation coefficient of organic matter content and carbonate content, the average value of each stage and the total average value;

[0110] S106, determining the climate type of a short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establishing a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtaining a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; performing a Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of changes in temperature and humidity on the same scale;

[0111] S107, combine the characteristics of the time-frequency analysis spectrum at scale point A to analyze the medium-term cycle characteristics of climate change; integrate the overall change characteristics of temperature and humidity to analyze the long-term cycle characteristics of climate change; establish climate types on the sedimentary profile to analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity to determine the climate change model for the corresponding period.

[0112] The method of determining scale point A and scale point B provided in the embodiment of the present invention includes:

[0113] The transition point from symmetry to weakened symmetry in the mid-frequency part is taken as scale point A;

[0114] The transition point where the symmetry in the mid-high frequency part is weakened to the point where the symmetry is complicated is taken as the scale point B.

[0115] The time series clustering of the original data of organic matter content and carbonate content using the CONISS function provided in the embodiment of the present invention includes:

[0116] The clustering point is G, and the G value is used as the division standard to group the time series clusters to obtain the division scheme N;

[0117] When the division criterion is G or less than the G value, compare the spectrum stage division scheme M and the cluster grouping scheme N. N is included in M ​​and N is a refinement of M. Then G is determined as the division point required for the time series clustering analysis this time, and the division scheme N is the division result required this time.

[0118] In step S105, the types of changes in organic matter content and carbonate content provided by the embodiment of the present invention include:

[0119] (1) Organic matter content and carbonate content are positively correlated, and both organic matter content and carbonate content are less than 1%;

[0120] (2) Organic matter content and carbonate content are positively correlated, with organic matter content being lower than the average and carbonate content being higher than the average;

[0121] (3) Organic matter content and carbonate content are positively correlated, and both contents are higher than the average;

[0122] (4) Organic matter content and carbonate content are positively correlated and tend to decrease, with organic matter content close to the average and carbonate content below the average;

[0123] (5) The organic matter content showed an obvious upward trend and was positively correlated with the carbonate content, and both contents were higher than the average value;

[0124] (6) Organic matter content and carbonate content are negatively correlated, with carbonate content decreasing and organic matter content far above the average;

[0125] (7) The organic matter content and carbonate content of the whole section are positively correlated, with a decreasing trend. When the organic matter content is higher than 10%, the carbonate content is higher than the average value;

[0126] (8) Organic matter content and carbonate content are positively correlated, both showing a decreasing trend, and the total content is lower than the average;

[0127] (9) Organic matter content and carbonate content are positively correlated and tend to increase, and both are higher than the average value;

[0128] (10) Organic matter content and carbonate content are negatively correlated, with a decreasing trend, where organic matter content is lower than the average and carbonate content is higher than the average;

[0129] (11) Organic matter content and carbonate content are positively correlated, and both are close to the average value;

[0130] (12) Organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are higher than the average;

[0131] (13) Organic matter content and carbonate content are positively correlated, and the total carbonate content is lower than the average;

[0132] (14) Organic matter content and carbonate content are positively correlated, but the organic matter content is lower than the average, and the total carbonate content is lower than the average;

[0133] (15) Organic matter content and carbonate content are positively correlated, with organic matter content slightly higher than the average and total carbonate content lower than the average;

[0134] (16) Organic matter content and carbonate content are negatively correlated, with organic matter content being lower than the average and carbonate content being higher than the average;

[0135] (17) Organic matter content and carbonate content are positively correlated. When the organic matter content is less than 1%, the carbonate content is lower than the average value;

[0136] (18) Organic matter content and carbonate content are positively correlated, and carbonate content is lower than the average;

[0137] (19) The organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are slightly lower than the average value.

[0138] The climate type for determining a short-term semi-cycle based on the relative changes in organic matter content and carbonate content provided by the embodiment of the present invention includes:

[0139] (1) When the organic matter content is negatively correlated with the carbonate content, the paleotemperature and paleohumidity are in equilibrium:

[0140] 1) When the organic matter content is higher than the average and the carbonate content is lower than the average, the climate is hot and humid;

[0141] 2) When the organic matter content is lower than the average and the carbonate content is higher than the average, the climate is dry and cold;

[0142] (2) When organic matter and carbonate content are positively correlated, the balance between paleotemperature and paleohumidity is broken:

[0143] 1) Organic matter content higher than the average indicates a hotter paleotemperature, while content lower than the average indicates a colder paleotemperature;

[0144] 2) carbonate content above the average indicates semi-dry paleohumidity, and carbonate content below the average indicates semi-wet paleohumidity;

[0145] 3) When the organic matter content is greater than 10% and the carbonate content is higher than the average, it means that the climate is hot and humid, and the primary productivity and plankton of the lake increase;

[0146] 4) When the organic matter content is less than 1% and the carbonate content is lower than the average, it indicates a dry and cold climate.

[0147] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0148] Embodiment 1:

[0149] The present invention is a 768.45m continuous high-precision sampling core of the Dalianhe Formation in the Yilan Basin in Northeast China. The bottom 899.2m-708.2m is sandstone, coal and oil shale section (E 1-2 d 1 ); the middle part 708.2m-590.3m is the oil shale section (E 1-2 d 2 ); the upper part 590.3m-132.0m is the sandstone shale section, which is composed of interbedded sandstone, siltstone and mudstone (E 1-2 d 3 ).

[0150] The specific implementation steps are as follows:

[0151] 1. Obtain the LOI of sediments at different depth sampling points on the sediment profile 550 (%) and LOI 1000 (%) and its corresponding depth value d i (cm).

[0152] In the embodiment of the present invention, profile samples were collected at 5 cm intervals, and loss on ignition experiments were performed to obtain the LOI of the deposited samples at continuous depths. 550 and LOI 1000 There are 649 data each.

[0153] 2. Based on 1, further obtain LOI on the sedimentary profile 550 and LOI1000 Variation curve with depth. As Figure 3 .

[0154] 3. Further on the basis of 1, for the LOI on the sedimentary profile 550 and LOI 1000 The original data are averaged to obtain their respective total average values LOI 550A and LOI 1000A .

[0155] 4. Further on the basis of 1, the wavelet transform of Matlab software is used to perform spectral analysis on the two groups of data to obtain spectrograms at different scales. According to the principle in wavelet transform that a large scale a shows the characteristics of the low-frequency part and a small scale a shows the characteristics of the high-frequency part, based on the mirror symmetry characteristics of the spectrograms of LOI 550 and LOI 1000 , the variation characteristics of high, medium, and low frequencies are divided. As Figure 4 .

[0156] In the embodiments of the present invention, the different scales are two scales of a = 1000 and a = 450, where a = 1000 belongs to the low frequency; a = 450 belongs to the medium frequency; a = 150 belongs to the high frequency.

[0157] In the embodiments of the present invention, when the wavelet transform scale a of LOI 550 and LOI 1000 satisfies 450 < a < 1000, the spectrograms of LOI 550 and LOI 1000 are basically symmetric, that is, the spectral variations in the low and medium frequency parts (a = 450 - 1000) are basically the same, indicating that under large scales, the control factors affecting the changes in organic matter and carbonate content are the same. However, although the middle and high frequency parts with a < 450 show similar patterns, the correspondence begins to show differences. Especially at a = 150, there is an obvious mutation. When a < 150, the spectral correspondence relationship becomes more complex, which indicates that there are obvious differences in the control factors of LOI 550 and LOI 1000 in the middle and high frequency parts. According to previous literature, it is inferred that the organic matter represented by LOI 550 is significantly affected by temperature, while the carbonate represented by LOI 1000 is more significantly affected by humidity.

[0158] 5. Further on the basis of 4, the spectrograms of time-frequency analysis are used to perform stage division of the LOI 550 and LOI 1000 parameters to obtain the stage characteristics of the parameters in the high, medium, and low frequency bands. As Figure 5 .

[0159] The embodiment of the present invention adopts the color change boundary. When a=450, three parts can be identified, of which the first part corresponds to the Early-Middle Eocene, and the second and third parts correspond to the Late Eocene. When a=150, LOI 550 Six clear stages can be identified: 1, 5, 6, 7, 8, and 9. 1000 Eight clear stages can be identified: 2, 3, 4, 5, 6, 7, 8, and 11.

[0160] 6. Based on 1, the Savitzky-Golay method is further used to perform noise denoising and filtering on the LOI raw data with several adjacent points as windows to remove the high-frequency part and obtain the LOI 550 and LOI 1000 The trend line of medium and low frequency changes. Figure 6 .

[0161] In the embodiment of the present invention, the Savitzky-Golay method is used to remove the high-frequency part of the LOI raw data by using 50 adjacent points as a window to obtain the LOI 550 and LOI 1000 The medium and low frequency change trend line.

[0162] 7. Based on 1 and 4, we further use the CONISS function to calculate the LOI 550 and LOI 1000 The original data is subjected to time series cluster analysis, and the parameters on the profile are subdivided into different change stages according to the stage division and clustering level in the high-frequency band, so that the boundaries of the change stages are in the best harmony state, thereby obtaining the best short-term stage boundaries. Figure 7 .

[0163] In the embodiment of the present invention, the sampling well section is subdivided into 19 short-term stages at a clustering level of 34. When the clustering level is less than 34, the boundaries of the time series analysis division are included in the boundaries of the division when the time-frequency analysis scale a=150, so the 19 short-term stages are further subdivided from the 11 time-frequency analysis stages and are also the optimal short-term stages in the embodiment of the present invention.

[0164] 8. Based on 7, further, for each stage of LOI 550 and LOI 1000 The original data is averaged in each short-term stage to obtain the average LOI of each short-term stage of the two 550Ai and LOI 1000Ai , and draw the corresponding depth histogram. Figure 8 .

[0165] In the embodiment of the present invention, based on 7, 19 short-term LOIs are respectively 550 and LOI1000 The original data was averaged in stages to obtain the average LOI of each cycle of the two 550Ai and LOI 1000Ai , and the corresponding depth histogram.

[0166] 9. Based on 8, further, the average LOI of each stage 550Ai and LOI 1000Ai Pearson correlation analysis was performed. The Pearson correlation coefficient was between 0 and 1, indicating that LOI 550 (Organic Matter Content) and LOI 1000 (Carbonate content) is positively correlated, with the correlation coefficient between -1 and 0, and the two are negatively correlated.

[0167] 10. Based on 9, further, the variation curve of the Pearson correlation coefficient on the sedimentary profile is plotted. Fig. 9 .

[0168] 11. Based on 3, 8, 9, and 10, further, LOI 550 With LOI 1000 The Pearson correlation coefficient and the correspondence between the average values ​​of each stage and the total average value were used as the basis to evaluate the relative changes of organic matter and carbonate. Fig.10 .

[0169] In the embodiment of the present invention, based on 3, 8, 9, and 10, further, LOI 550 With LOI 1000 The Pearson correlation coefficient and the correspondence between the average values ​​of each stage and the total average value were used as the benchmark to evaluate the relative changes of organic matter and carbonate, and the significant characteristics of 19 short-term stages were identified:

[0170] (1) Organic matter and carbonate are positively correlated, and the contents of organic matter and carbonate are both less than 1%.

[0171] (2) Organic matter and carbonate are positively correlated, with organic matter content lower than the average and carbonate content higher than the average.

[0172] (3) Organic matter and carbonate are positively correlated, and the contents of both are higher than the average.

[0173] (4) Organic matter and carbonate are positively correlated and tend to decrease. The organic matter content is close to the average value, while the carbonate content is lower than the average value.

[0174] (5) The content of organic matter showed an obvious upward trend and was positively correlated with carbonate. The contents of both were higher than the average.

[0175] (6) Organic matter and carbonate are negatively correlated, with carbonate content decreasing and organic matter content much higher than the average.

[0176] (7) The organic matter and carbonate are positively correlated throughout the section, with a decreasing trend. When the organic matter content is higher than 10%, the carbonate is higher than the average value.

[0177] (8) Organic matter and carbonate are positively correlated, both showing a decreasing trend, and their total contents are lower than the average value.

[0178] (9) Organic matter is positively correlated with carbonate, with an increasing trend, and both are above the average value.

[0179] (10) Organic matter and carbonate are negatively correlated, with a decreasing trend. The organic matter content is lower than the average, and the carbonate content is higher than the average.

[0180] (11) Organic matter and carbonate are positively correlated, and both are close to the average value.

[0181] (12) Organic matter is positively correlated with carbonate, and the organic matter content and carbonate content are higher than the average.

[0182] (13) Organic matter is positively correlated with carbonate, and the total carbonate content is lower than the average.

[0183] (14) Organic matter is positively correlated with carbonate, but the organic matter content is lower than the average and the total carbonate content is lower than the average.

[0184] (15) Organic matter and carbonate are positively correlated, with the organic matter content slightly higher than the average and the total carbonate content lower than the average.

[0185] (16) Organic matter is negatively correlated with carbonate, with organic matter content being lower than the average and carbonate content being higher than the average.

[0186] (17) Organic matter is positively correlated with carbonate. When the organic matter content is less than 1%, the carbonate carbon content is lower than the average.

[0187] (18) Organic matter is positively correlated with carbonate, and the carbonate content is lower than the average.

[0188] (19) Organic matter is positively correlated with carbonate, and the contents of organic matter and carbonate are slightly lower than the average values.

[0189] 12. Based on 11, the time series clustering stages are further divided into climate change cycles by combining the medium and low frequency change trends. Fig.11 .

[0190] There are two types of changes in organic matter and carbonate in lake basins: negative correlation and positive correlation.

[0191] 1) When the organic matter and carbonate contents are negatively correlated, the paleotemperature and paleohumidity are in equilibrium. ① When the organic matter is higher than the average and the carbonate is lower than the average, it indicates a hot and humid climate. ② When the organic matter is lower than the average and the carbonate is higher than the average, it indicates a dry and cold climate.

[0192] 2) When the organic matter and carbonate contents are positively correlated and the balance between paleotemperature and paleohumidity is broken, the organic matter content is greatly affected by temperature, while the carbonate content is more greatly affected by humidity. ① When the organic matter content is higher than the average value, it indicates that the paleotemperature was hotter, and when it is lower than the average value, it indicates that the paleotemperature was colder. ② When the carbonate content is higher than the average value, it indicates that the paleohumidity is semi-dry, and when it is lower than the average value, it indicates that the paleohumidity is semi-wet. ③ When the organic matter content is greater than 10% and the carbonate content is higher than the average value, it indicates that the climate is hot and humid, and the primary productivity and plankton of the lake increase; ④ When the organic matter content is less than 1% and the carbonate content is lower than the average value, it indicates that the climate is dry and cold.

[0193] According to the above principle, in the embodiment of the present invention, the change from dry and cold to warm and humid is defined as an ascending semi-cycle, and the change from warm and humid to dry and cold is defined as a descending semi-cycle. The 19 short-term stages are divided into different short-term climate semi-cycles. The characteristics of each short-term climate semi-cycle are:

[0194] (1) Mainly dry and cold; (2) Semi-dry and relatively cold; (3) Semi-dry and relatively hot; (4) Semi-humid and relatively hot; (5) Mainly semi-dry and relatively hot; (6) Most humid and hot; (7) Mainly humid and hot; (8) Mainly semi-humid and relatively cold; (9) Semi-dry and relatively cold; (10) Mainly dry and cold; (11) Semi-dry and relatively cold; (12) Transitioning to semi-humid and relatively cold; (13) Mainly semi-humid and relatively cold; (14) Mainly semi-humid and relatively cold; (15) More semi-humid and relatively hot; (16) Semi-dry and relatively cold; (17) Dry and cold; (18) Mainly semi-humid and relatively cold; (19) Mainly semi-dry and relatively cold.

[0195] 13. Based on 12, further, a semi-quantitative change curve of temperature and humidity is established according to the characteristics of each short-term climate semi-cycle, that is, Fig.12 Green line for medium temperature and solid light blue line for humidity.

[0196] 14. Based on 13, the temperature and humidity curves are further smoothed and filtered to obtain the temperature and humidity trend line. Fig.12 Black dashed lines for temperature and humidity.

[0197] 15. Based on 14, Pearson correlation analysis was performed on the temperature and humidity curves to verify the consistency between the large-scale changes in temperature and humidity and the low-frequency features of the original data in the time-frequency analysis.

[0198] 16. Further, based on 5, 11, 12, 13, and 14, the mid-term cycle characteristics of climate change are analyzed in combination with the time-frequency spectrum characteristics. Fig.13The mid-cycle part of the mesoclimatic cycle.

[0199] In the embodiment of the present invention, the medium-term climate cycle is consistent with the stage division result when the time-frequency analysis scale a=450, and is generally divided into three parts and six medium-term semi-cycles: in the first part, the initial climate is dry and cold, and the temperature and humidity rise to hot and humid twice in succession, followed by an overall decline of semi-dry and cold, which can be divided into an ascending medium-term semi-cycle {including short-term cycles (1), (2), (3), and (4)} + an ascending medium-term semi-cycle {including short-term cycles (5) and (6)} + a descending medium-term semi-cycle {including short-term cycles (7), (8)} 8) and (9)}; the second part has a dry and cold initial climate, which changes to semi-humid and cold after a continuous increase in temperature and humidity, and has only one rising mid-term semi-cycle {including short-term cycles (10), (11), (12), (13), and (14)}; the third part has a semi-humid and hot initial climate, which changes to semi-dry and cold after a decrease in temperature and humidity, and can be divided into a falling mid-term semi-cycle {including short-term cycles (15) and (16)} and an rising mid-term semi-cycle {including short-term cycles (17), (18), and (19)}.

[0200] 17. Based on 16, the overall changes in temperature and humidity and the characteristics of video spectrum are further integrated to analyze the long-term cyclic characteristics of climate change. Fig.13 The long-term portion of a climate cycle.

[0201] In the embodiment of the present invention, the long-term climate change feature on the profile is a gradual rise, reaching a peak in the early and middle stages, and then gradually falling, which can be interpreted as a long-term rising semi-cycle {including short-term cycles (1), (2), (3), (4), (5), (6)} + a long-term falling semi-cycle {including short-term cycles (7), (8), (9), (10), (11), (12), (13), (14), (15), (16), (17), (18), (19)}.

[0202] 18. Based on 17, establish the climate types on the sedimentary profile and analyze the laws of climate change.

[0203] In the embodiment of the present invention, there are six climate types in the Yilan Basin in the Eocene, namely, hot and humid, semi-hot and semi-dry and hot, dry and cold, semi-dry and cold, and semi-hot and semi-humid.

[0204] 19. Based on 13, 14, 16, 17, and 18, we further analyzed the coupling relationship between paleotemperature and paleohumidity, and proposed a climate change model for this period, providing a basis for predicting local climate change trends.

[0205] In the embodiment of the present invention, the temperature and humidity are low in the lower part of the profile, and then the temperature rises, and the humidity first decreases and then increases; the temperature is high and the humidity is suitable in the middle part, and then the temperature decreases and the humidity decreases; the temperature in the upper part of the profile is lower than that in the middle and lower parts, and the humidity increases in the middle period and continues to decrease in the later period. This paleoclimate change model provides an important paleoclimate basis for the paleoclimate change in the Eocene in the Yilan Basin, and provides an important geological basis for predicting the current climate change trend.

[0206] 2. Application Examples: In order to prove the creativity and technical value of the technical solution of the present invention, this section provides application examples of the technical solution of the claims on specific products or related technologies.

[0207] The application embodiment of the present invention applies the analysis method of reconstructing paleoclimate from loss on ignition data to the climate change prediction system. In the Eocene (56-33.9Ma), the global climate first warmed up and then cooled down, transitioning from a greenhouse to an icehouse, during which there were a series of extreme climate events and typical climate types such as the Extreme Thermal Event (PETM) and the Climate Optimum Period (EECO). Today, humans are in a climate evolution stage with global warming and increasing extreme climates, which is highly comparable to the Eocene. Therefore, the study of the climate evolution of the Eocene will provide a research type for the current global climate change with increased greenhouse gases. The Eocene is highly comparable to the present, and studying its climate evolution can provide a basis for studying global change.

[0208] A continuous loss on ignition experiment was conducted on the core data of the Eocene Dalianhe Formation in the Yilan Basin in Northeast China. The loss on ignition data of the Eocene continuous sediments in the Yilan Basin were divided into two long-term semi-cycles, six medium-term semi-cycles, and 19 short-term semi-cycles using time-frequency time series analysis. The six medium-term semi-cycles are included in three stages.

[0209] Then, Pearson correlation analysis, noise reduction filtering and statistical methods were used to analyze the content characteristics and change patterns of organic matter and carbonate. 550 With LOI 1000 The Pearson correlation coefficient, the correspondence between the average value of each stage and the total average value are used as the benchmark to evaluate the relative changes of organic matter and carbonate. There are two situations of negative correlation and positive correlation between the changes of organic matter and carbonate in the lake basin: 1) When the organic matter and carbonate content are negatively correlated: ① When the organic matter content is higher than the average value, it indicates a hot and humid climate. ② When the organic matter content is lower than the average value, it indicates a dry and cold climate. 2) When the organic matter and carbonate content are positively correlated: ① When the organic matter content is higher than the average value, it indicates a hot ancient temperature, and when it is lower than the average value, it indicates a cold ancient temperature. ② When the carbonate content is higher than the average value, it indicates semi-dry ancient humidity, and the low average value indicates semi-humid ancient humidity. ③ When the organic matter content is greater than 10% and the carbonate content is higher than the average value, it indicates a hot and humid climate; ④ When the organic matter content is less than 1% and the carbonate content is lower than the average value, it indicates a dry and cold climate.

[0210] Six climate types were identified in the Yilan Basin during the Eocene: hot and humid, hot and semi-humid, hot and semi-dry, cold and dry, cold and semi-dry, and cold and semi-humid. The continuous change process of temperature and humidity was separated according to the climate type. It is believed that the medium- and long-term change trend of temperature and humidity in the Northeast during the Eocene was to increase first, reach the highest temperature and humidity in the entire Eocene at the end of the Early Eocene, then gradually decrease, reach the lowest temperature in the early Late Eocene, then rise again, and then decrease slightly.

[0211] In particular, an important sign of cooling was found, that is, in the process of short-term climate change, when the temperature and humidity transition from a synchronous equilibrium state to the next synchronous equilibrium state, the temperature remains unchanged and the humidity continues to decrease, which is an important sign of the upcoming cooling. This study will provide an important supplement to the paleoclimate change in Yilan and Northeast China in the Eocene, and provide an important geological basis for predicting the current climate change trend.

[0212] 3. Evidence of the effects of the embodiments. The embodiments of the present invention have achieved some positive effects during the development or use process, and indeed have great advantages over the prior art. The following content is described in conjunction with the data, charts, etc. of the test process.

[0213] The long-term climate cycle analysis revealed by the present invention in the Yilan area of ​​Northeast China shows that the temperature rises from the early Eocene to the middle Eocene, and the temperature decreases from the middle Eocene to the late Eocene, which is consistent with the general trend of temperature change in Northeast China and the world. The ancient temperature revealed by deep-sea isotopes has been increasing since the Eocene, reaching the highest temperature at about 51Ma, and then continuously cooling down, with a warming fluctuation at about 41.8Ma, and the temperature reaches the lowest at the end of the Eocene. The temperature changes in the Eocene in Northeast China reported by Qun C et al. began to warm up in the Eocene, reaching the highest temperature at about 50Ma, then cooling down, reaching the lowest temperature at about 44Ma, and then slightly warming up. The Eocene temperature in the Yilan area disclosed by the present invention has been warming up since the Eocene, reaching the highest temperature at about 50-44Ma, and then continuously cooling down to reach the lowest temperature at about 40Ma, and then slightly warming up, and there is a cooling fluctuation near 38Ma.

[0214] Taking into account the dating error, the long-term climate change in this paper is basically consistent with the results reported by Qun C et al., which are all warming up, then cooling down, and then slightly warming up again. The comparison results with deep-sea isotopes show that the paleotemperature of the Yilan Basin and the Northeast region as a whole lags behind the paleotemperature changes revealed by deep-sea isotopes.

[0215] The temperature predictions of the present invention are basically consistent with those of previous studies, verifying the consistency compared with other methods. At the same time, the present invention also reveals the continuous change of humidity, which is a problem that is currently difficult to solve by other methods.

[0216] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0217] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. An analytical method for reconstructing paleoclimate using loss on ignition data, characterized in that: The analysis method for reconstructing paleoclimate using loss on ignition data includes: based on the original data of paleoclimate indicators in sediments, firstly performing signal analysis, using time-frequency analysis and time series analysis to divide the original data into different self-consistent cycles, using Pearson correlation analysis within each cycle to reveal the relevant characteristics of the original data, and using noise reduction filtering and statistical methods on the basis of correlation analysis to analyze the characteristics and change laws of the content of climate indicators within each cycle; at the same time, obtaining multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics - temperature and humidity - change processes, summarizing the climate types on the sedimentary profile, and establishing a climate model; The analytical method for reconstructing paleoclimate using loss on ignition data comprises the following steps: Step 1: Obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at sampling points at different depths on the sedimentary profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sedimentary profile; average the original data of organic matter content and carbonate content on the sedimentary profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum diagrams at different scales; Step 2: According to the mirror symmetry analysis of the spectrum of organic matter content and carbonate content, the high, medium and low frequency change characteristics are identified, and the scale point A and scale point B are determined; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum of time-frequency analysis is used to divide the stage of organic matter content and carbonate content, and the spectrum stage division scheme M is obtained; Step 3: Use the CONISS function to perform time series cluster analysis on the original data of organic matter content and carbonate content; use the Savitzky-Golay method to perform noise filtration on the LOI original data with several adjacent points as windows, remove the high-frequency part, and obtain the medium and low-frequency change trend lines of organic matter content and carbonate content; use the change characteristics of the change trend line in each segment in the division scheme N to determine the monotonicity of the organic matter content and carbonate content data in each segment; Step 4: According to the principle that dry and cold to warm and humid is an ascending semi-cycle, and warm and humid to dry and cold is a descending semi-cycle, identify the short-term climate semi-cycle in the division scheme N; average the original data of organic matter content and carbonate content in each short-term climate semi-cycle in sections to obtain the average value of each short-term climate semi-cycle, and draw a depth bar chart; perform Pearson correlation analysis on the original data of organic matter content and carbonate content in each short-term climate semi-cycle; Step 5: Determine the relationship between organic matter content and carbonate content based on the value range of the Pearson correlation coefficient; draw a curve of the change of the Pearson correlation coefficient on the sedimentary profile; evaluate the relative changes of organic matter content and carbonate content based on the corresponding relationship between the Pearson correlation coefficient of organic matter content and carbonate content, the average value of each stage and the total average value; Step 6: Determine the climate type of the short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establish a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtain a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; perform Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes on the same scale; Step seven, combine the characteristics of the time-frequency analysis spectrum at scale point A to analyze the medium-term cycle characteristics of climate change; integrate the overall change characteristics of temperature and humidity to analyze the long-term cycle characteristics of climate change; establish the climate type on the sedimentary profile to analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity to determine the climate change model for the corresponding period.

2. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: The analysis method for reconstructing paleoclimate using loss on ignition data also includes: based on the original data of loss on ignition at 550°C and 1000°C of sediments, using time-frequency analysis and time series analysis to divide the original data into different cycles that are inherently self-consistent, and using Pearson correlation analysis within each cycle to determine the LOI 550 and LOI 1000 Based on the correlation analysis, noise reduction filtering and statistical methods are used to analyze the content characteristics and change patterns of organic matter and carbonate in each cycle. At the same time, the multi-dimensional continuous qualitative to semi-quantitative paleoenvironmental characteristics-temperature and humidity-change process are obtained, the climate type on the sedimentary profile is obtained, and a climate model is constructed.

3. The analytical method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: Determining the scale point A and the scale point B includes: The transition point from symmetry to weakened symmetry in the mid-frequency part is taken as scale point A; The transition point where the symmetry in the mid-high frequency part is weakened to the point where the symmetry is complicated is taken as the scale point B.

4. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: The division boundary of the spectrum stage division scheme M is the color change position or the maximum value or minimum value of the color.

5. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: The method of using the CONISS function to perform time series clustering on the original data of organic matter content and carbonate content includes: The clustering point is G, and the G value is used as the division standard to group the time series clusters to obtain the division scheme N; When the division criterion is G or less than the G value, compare the spectrum stage division scheme M and the cluster grouping scheme N. N is included in M ​​and N is a refinement of M. Then G is determined as the division point required for the time series clustering analysis this time, and the division scheme N is the division result required this time.

6. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: In step 5, the types of changes in organic matter content and carbonate content include: (1) Organic matter content and carbonate content are positively correlated, and both organic matter content and carbonate content are less than 1%; (2) Organic matter content and carbonate content are positively correlated, with organic matter content being lower than the average and carbonate content being higher than the average; (3) Organic matter content and carbonate content are positively correlated, and both contents are higher than the average; (4) Organic matter content and carbonate content are positively correlated and tend to decrease, with organic matter content close to the average and carbonate content below the average; (5) The organic matter content showed an obvious upward trend and was positively correlated with the carbonate content, and both contents were higher than the average value; (6) Organic matter content and carbonate content are negatively correlated, with carbonate content decreasing and organic matter content far above the average; (7) The organic matter content and carbonate content of the whole section are positively correlated, with a decreasing trend. When the organic matter content is higher than 10%, the carbonate content is higher than the average value; (8) Organic matter content and carbonate content are positively correlated, both showing a decreasing trend, and the total content is lower than the average; (9) Organic matter content and carbonate content are positively correlated and tend to increase, and both are higher than the average value; (10) Organic matter content and carbonate content are negatively correlated, with a decreasing trend, where organic matter content is lower than the average and carbonate content is higher than the average; (11) Organic matter content and carbonate content are positively correlated and both are close to the average value; (12) Organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are higher than the average; (13) Organic matter content and carbonate content are positively correlated, and the total carbonate content is lower than the average; (14) Organic matter content and carbonate content are positively correlated, but the organic matter content is lower than the average, and the total carbonate content is lower than the average; (15) Organic matter content and carbonate content are positively correlated, with organic matter content slightly higher than the average and total carbonate content lower than the average; (16) Organic matter content and carbonate content are negatively correlated, with organic matter content being lower than the average and carbonate content being higher than the average; (17) Organic matter content and carbonate content are positively correlated. When the organic matter content is less than 1%, the carbonate content is lower than the average value; (18) Organic matter content and carbonate content are positively correlated, and carbonate content is lower than the average; (19) The organic matter content and carbonate content are positively correlated, and the organic matter content and carbonate content are slightly lower than the average value.

7. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: The climate types determined based on the relative changes in organic matter content and carbonate content for short-term semi-cycles include: (1) When the organic matter content is negatively correlated with the carbonate content, the paleotemperature and paleohumidity are in equilibrium: 1) When the organic matter content is higher than the average and the carbonate content is lower than the average, the climate is hot and humid; 2) When the organic matter content is lower than the average and the carbonate content is higher than the average, the climate is dry and cold; (2) When organic matter and carbonate content are positively correlated, the balance between paleotemperature and paleohumidity is broken: 1) Organic matter content higher than the average indicates a hotter paleotemperature, while content lower than the average indicates a colder paleotemperature; 2) carbonate content above the average indicates semi-dry paleohumidity, and carbonate content below the average indicates semi-wet paleohumidity; 3) When the organic matter content is greater than 10% and the carbonate content is higher than the average, it means that the climate is hot and humid, and the primary productivity and plankton of the lake increase; 4) When the organic matter content is less than 1% and the carbonate content is lower than the average, it indicates a dry and cold climate.

8. The method for reconstructing paleoclimate using loss on ignition data as claimed in claim 1, characterized in that: The principle for identifying short-term climate half-cycles in the division scheme N is that the change from dry and cold to warm and humid is an ascending half-cycle, and the change from warm and humid to dry and cold is a descending half-cycle.

9. An analysis system for reconstructing paleoclimate using loss on ignition data, which implements the analysis method for reconstructing paleoclimate using loss on ignition data as claimed in any one of claims 1 to 8, characterized in that: The analysis system for reconstructing paleoclimate using loss on ignition data includes: The spectrum acquisition module is used to obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at different depth sampling points on the sediment profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sediment profile; average the original data of organic matter content and carbonate content on the sediment profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum graphs at different scales; The spectrum stage division scheme module is used to identify the high, medium and low frequency change characteristics and determine the scale point A and scale point B according to the mirror symmetry analysis of the spectrum graph of organic matter content and carbonate content; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum graph of time-frequency analysis is used to divide the organic matter content and carbonate content into stages, and obtain the spectrum stage division scheme M; The data monotonicity determination module is used to perform time series clustering analysis on the original data of organic matter content and carbonate content using the CONISS function; the Savitzky-Golay method is used to perform noise filtration on the LOI original data with several adjacent points as windows to remove the high-frequency part and obtain the medium- and low-frequency change trend lines of organic matter content and carbonate content; the monotonicity of the organic matter content and carbonate content data in each segment is determined by using the change characteristics of the change trend line in each segment in the division scheme N; The Pearson correlation analysis module is used to identify the short-term climate semi-cycle in the division scheme N according to the principle that dry and cold changes to warm and humid as an ascending semi-cycle, and warm and humid changes to dry and cold as a descending semi-cycle; the original data of organic matter content and carbonate content in each short-term climate semi-cycle are averaged in sections to obtain the average value of each short-term climate semi-cycle, and a depth bar graph is drawn; the original data of organic matter content and carbonate content in each short-term climate semi-cycle are subjected to Pearson correlation analysis; The module of relative content change is used to determine the relationship between organic matter content and carbonate content based on the value range of Pearson correlation coefficient; draw the change curve of Pearson correlation coefficient on sedimentary profile; evaluate the relative change of organic matter content and carbonate content based on the corresponding relationship between Pearson correlation coefficient of organic matter content and carbonate content, average value of each stage and total average value; A curve processing module is used to determine the climate type of a short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establish a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtain a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; perform a Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes on the same scale; The climate change model determination module is used to analyze the medium-term cycle characteristics of climate change by combining the characteristics of the time-frequency analysis spectrum at scale point A; analyze the long-term cycle characteristics of climate change by integrating the overall change characteristics of temperature and humidity; establish climate types on sedimentary profiles and analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity and determine the climate change model for the corresponding period.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps of the method for reconstructing paleoclimate using loss on ignition data according to any one of claims 1 to 8: Step 1: Obtain the organic matter content and carbonate content of the sediment loss on ignition and the corresponding depth values ​​at sampling points at different depths on the sedimentary profile; obtain the curve of the change of organic matter content and carbonate content with depth on the sedimentary profile; average the original data of organic matter content and carbonate content on the sedimentary profile to obtain their respective total average values; use wavelet transform to perform spectrum analysis on the two sets of data to obtain spectrum diagrams at different scales; Step 2: According to the mirror symmetry analysis of the spectrum of organic matter content and carbonate content, the high, medium and low frequency change characteristics are identified, and the scale point A and scale point B are determined; the color change position is used as the boundary point or the maximum / minimum value of the color is used as the boundary point, and the change of the spectrum value at the scale point B of the spectrum of time-frequency analysis is used to divide the stage of organic matter content and carbonate content, and the spectrum stage division scheme M is obtained; Step 3: Use the CONISS function to perform time series cluster analysis on the original data of organic matter content and carbonate content; use the Savitzky-Golay method to perform noise filtration on the LOI original data with several adjacent points as windows, remove the high-frequency part, and obtain the medium and low-frequency change trend lines of organic matter content and carbonate content; use the change characteristics of the change trend line in each segment in the division scheme N to determine the monotonicity of the organic matter content and carbonate content data in each segment; Step 4: According to the principle that dry and cold to warm and humid is an ascending semi-cycle, and warm and humid to dry and cold is a descending semi-cycle, identify the short-term climate semi-cycle in the division scheme N; average the original data of organic matter content and carbonate content in each short-term climate semi-cycle in sections to obtain the average value of each short-term climate semi-cycle, and draw a depth bar chart; perform Pearson correlation analysis on the original data of organic matter content and carbonate content in each short-term climate semi-cycle; Step 5: Determine the relationship between organic matter content and carbonate content based on the value range of the Pearson correlation coefficient; draw a curve of the change of the Pearson correlation coefficient on the sedimentary profile; evaluate the relative changes of organic matter content and carbonate content based on the corresponding relationship between the Pearson correlation coefficient of organic matter content and carbonate content, the average value of each stage and the total average value; Step 6: Determine the climate type of the short-term semi-cycle based on the relative changes in the organic matter content and the carbonate content; establish a semi-quantitative change curve of temperature and humidity according to each short-term semi-cycle climate type; obtain a temperature and humidity trend line after smoothing and filtering the temperature and humidity curves; perform Pearson correlation analysis on the temperature and humidity trend line to verify the consistency of temperature and humidity changes on the same scale; Step seven, combine the characteristics of the time-frequency analysis spectrum at scale point A to analyze the medium-term cycle characteristics of climate change; integrate the overall change characteristics of temperature and humidity to analyze the long-term cycle characteristics of climate change; establish the climate type on the sedimentary profile to analyze the laws of climate change; analyze the coupling relationship between paleotemperature and paleohumidity to determine the climate change model for the corresponding period.

11. An information data processing terminal, characterized in that: The information data processing terminal is used to execute the steps of the analysis method for reconstructing paleoclimate using loss on ignition data as described in any one of claims 1 to 8.

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