A method for analyzing cold spring methane activity in combination with benthic foraminifera isotope analysis

Abstract

This invention discloses a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis. This invention relates to the field of Earth science and technology. It involves collecting and preparing sediment columns from cold seep activity areas, covering different activity periods; meticulously separating individual benthic foraminifera from the sediment columns and treating them to eliminate secondary influences; using mass spectrometry to determine the oxygen and carbon isotope values ​​of the benthic foraminifera, constructing continuous isotope time series data; and analyzing the degree of shift in the oxygen and carbon isotope values ​​of the benthic foraminifera based on the continuous isotope time series data. This invention achieves precise quantification of cold seep activity by measuring the oxygen and carbon isotope values ​​of the benthic foraminifera shells and combining sliding window technology and K-means clustering algorithm. It can identify short-term anomalous shift events and, combined with indicators such as isotope range and the number of zero crossover points, accurately classify cold seep activity types, providing a basis for understanding the dynamic changes of cold seep methane leakage.

Description

Technical Field

[0001] This invention relates to the field of Earth science technology, specifically to a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis. Background Technology

[0002] Natural gas hydrates are ice-like crystalline substances formed by natural gas (methane, ethane, and other hydrocarbon gases) and water under suitable high pressure and low temperature conditions. They are stored in large quantities in seabed sediments. Changes in temperature and pressure can cause the natural gas hydrates in the sediments to become unstable or decompose, releasing methane gas into the water and even the atmosphere. Methane is a gas with a strong greenhouse effect (its potential impact on air temperature is CO2). 2 Methane emissions are 25 times higher than atmospheric emissions, and the methane reservoir in sediments is three orders of magnitude higher than that in the atmosphere. Therefore, it has a potentially significant impact on Earth's climate and environmental changes. Cold seep systems are important windows for methane release, and benthic foraminifera, as sensitive organisms in cold seep environments, can record methane leakage information through their shell isotopes, making them an effective indicator for reconstructing methane release history. By analyzing benthic foraminifera isotopes, the timing, intensity, and triggering mechanisms of methane release can be revealed, providing key data for understanding the methane dynamics of the global ocean-atmosphere system.

[0003] In existing technologies, it is difficult to accurately quantify the intensity and duration of cold seep methane leakage, and it can only be described qualitatively or semi-quantitatively. Moreover, the spatial and temporal resolution of sediment samples is limited, making it difficult to fully reflect the dynamic changes of cold seep activity. Therefore, the problem to be solved by this invention is how to infer cold seep activity by the degree of isotopic shift of benthic foraminifera, capture short-term fluctuations of cold seep activity in the time series using the sliding window technique, and then use the K-means clustering algorithm to cluster cold seep activity based on the isotopic characteristics of benthic foraminifera in order to achieve accurate analysis of cold seep methane activity. To this end, a method for analyzing cold seep methane activity by combining benthic foraminifera isotopic analysis is proposed. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis, comprising the following steps:

[0005] S1. Collect and prepare sediment columns from the cold seep activity area to ensure that the samples cover different activity periods;

[0006] S2. Finely separate benthic foraminifera individuals from sediment column samples and perform benthic foraminifera treatment to eliminate secondary effects and ensure sample integrity and representativeness.

[0007] S3. Use mass spectrometry to determine the oxygen and carbon isotope values ​​of benthic foraminifera and construct continuous time series data of isotopes.

[0008] S4. Based on continuous time series data of isotopes, analyze the degree of shift in oxygen and carbon isotope values ​​of benthic foraminifera to infer the intensity of cold seep methane leakage in order to identify short-term abnormal shift events.

[0009] S5. Apply sliding window technique to time series data to capture short-term isotopic fluctuation characteristics of cold seep activity, including amplitude and frequency.

[0010] S6. Based on the characteristics of short-term isotope fluctuations, the K-means algorithm is used to cluster cold seep activities and classify the activity types.

[0011] S7. Combining clustering results with geological time, the historical evolution pattern of cold seep methane leakage is reconstructed to comprehensively assess the spatiotemporal dynamics of cold seep methane activity.

[0012] Preferably, S1 specifically includes:

[0013] In the known cold seep activity area, based on the geological background and previous survey data, determine the location of sampling points, covering different activity periods of active, weakening and quiescent periods, and plan the sampling route and depth, and prepare gravity piston samplers;

[0014] A gravity piston sampler was used to vertically collect columnar sediment samples to ensure sample continuity and integrity, and to record sampling depth, location, and time information.

[0015] Sediment columnar samples were cut into segments at preset depth intervals, numbered and their depth information was recorded, disturbed layers were removed, the original sedimentary structure was preserved, and the sediment samples were stored in layers. Some were used for the isolation of benthic foraminifera, and some were preserved at low temperature for geochemical retesting.

[0016] Preferably, S2 specifically includes:

[0017] Sediment column samples were divided into segments according to depth, each segment was weighed and recorded, the sediment was diluted with deionized water, stirred evenly, allowed to settle, the upper suspended impurities were removed, and the bottom sediment was retained.

[0018] Wet sieving method was used to separate benthic foraminifera. The pretreated sediment was filtered through a 125-micron sieve, the residue on the sieve was collected, and intact benthic foraminifera shells were picked out under a microscope to remove impurities and ensure that the separated foraminifera individuals were intact and undamaged.

[0019] The isolated benthic foraminifera shells were placed in an ultrasonic cleaner and ultrasonically cleaned with deionized water to remove surface deposits and secondary carbonates. After cleaning, the samples were rinsed multiple times with deionized water, dried, and stored to ensure the integrity and representativeness of the samples.

[0020] Preferably, S3 specifically includes:

[0021] The processed benthic foraminifera shells were ground into a uniform powder. A quantitative sample was weighed and placed in a reaction vessel. Dilute acid was added to carry out the reaction, releasing carbon dioxide gas. The sample was then converted and purified by connecting the mass spectrometer sample introduction system to the elemental analyzer.

[0022] Sample molecules are ionized using a mass spectrometer ion source, and oxygen is separated using a mass analyzer. 16 O / 18 O) and carbon ( 12 C / 13 C) Isotope peaks, ion current intensity recorded by detector, δ calculated. 18 O and δ 13 C ratio, to obtain raw isotope data;

[0023] The original isotope data were calibrated with standard materials and corrected for instrument drift. Combined with sediment chronology results, a continuous time series of oxygen and carbon isotopes of benthic foraminifera was constructed.

[0024] Preferably, S4 specifically includes:

[0025] The continuous time series of oxygen and carbon isotopes of benthic foraminifera was smoothed to eliminate high-frequency noise, and the offset of isotope values ​​at each time point relative to the baseline was calculated, and the offset amplitude and duration were quantified.

[0026] Based on the known isotopic characteristics of cold seep activity areas, a quantitative relationship model was established between methane leakage intensity and oxygen and carbon isotope shift amplitude. Regression analysis was used to determine the shift threshold and define the intensity range of short-term anomalous shift events, where the shift threshold is δ. 13 C≤-1.5‰ and δ 18 O ≥ 1.0‰;

[0027] Periods in continuous isotope time series where the offset exceeds the offset threshold are marked as short-term anomalous events. By combining sedimentary stratigraphic and geochronological data, the spatiotemporal distribution patterns of these anomalous events are analyzed to infer the intensity of cold seep methane leakage.

[0028] Preferably, the process of analyzing the spatiotemporal distribution patterns of abnormal events and inferring the intensity of cold spring methane leakage is as follows:

[0029] After scanning and smoothing the continuous time series of isotopes, the isotope values ​​(δ) at each time point were calculated. 18 O, δ 13 C) Offset relative to the baseline, filtering for values ​​exceeding a preset threshold (δ). 13 C≤-1.5‰ and δ 18For periods of O≥1.0‰, a sliding window algorithm is used to determine the start and end times of the offset. Combined with linear interpolation to mark critical points, an abnormal event list containing the start and end ages, offset amplitude, and duration is generated. The sequence resolution is recorded simultaneously to ensure that the event detection accuracy meets the research needs on a scale of thousands to tens of thousands of years.

[0030] By spatially correlating the marked short-term anomalous events with sedimentary stratigraphic correlation data and geochronological framework, the vertical distribution range (stratigraphic thickness) and lateral extension distance of the anomalous events in the core were determined. A two-dimensional spatial distribution map was constructed by stratigraphic correlation. The area affected by the events was calculated by combining the sedimentation rate. The frequency of events (times / ka) and the interval time were statistically analyzed to determine their coupling relationship with regional tectonic activity and sediment supply.

[0031] Spectral analysis was performed on the time series of anomalous events to identify the dominant period (10-100 ka scale) and its phase relationship with orbital parameters (eccentricity, inclination), thereby inferring the intensity of cold seep methane leakage.

[0032] Preferably, the formula for calculating the methane leakage intensity of the cold spring is as follows:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] In the formula: R is the cold seep methane leakage intensity, representing the amount of methane leakage per unit time; k is the correction coefficient, used to adjust the model output to match the actual observation data, determined through standard sample verification and Monte Carlo simulation; The isotopic shift magnitude represents the negative shift (in‰) of the carbon isotope value in the shell of benthic foraminifera relative to the baseline value during an anomalous event. These are the carbon isotope values ​​in the shells of benthic foraminifera. This serves as a baseline value for the carbon isotope values ​​in the shells of benthic foraminifera; The isotopic shift magnitude represents the positive shift (in‰) of the oxygen isotope value in the shell of benthic foraminifera relative to the baseline value during an anomalous event. The values ​​represent the oxygen isotope content in the shells of benthic foraminifera. The baseline value for oxygen isotopes in the shells of benthic foraminifera is given; D is the deposition rate, representing the thickness of sediment per unit time (in mm / ka, which can be calculated from geochronological data); T is the duration of the anomalous event, representing the duration of the isotopic shift event (in years, ka). The end time of the event. This is the start time of the event.

[0038] Preferably, S5 specifically includes:

[0039] The window width is set according to the time series resolution (3-5 times the resolution), and an overlap rate of 50% is selected to balance sensitivity and stability. This is applied to the original isotope data (δ... 13 C、δ 18 O) Perform linear interpolation to fill in missing values ​​and standardize time intervals to ensure data continuity when the window slides;

[0040] Within each sliding window, the isotopic range (maximum value - minimum value) is calculated as the fluctuation amplitude, and the number of fluctuations within the window (number of zero crossover points) is counted as the frequency index. An amplitude threshold (δ) is set. 13 C range ≥ 1.5‰) and frequency threshold (≥ 2 times / window), and select windows that meet the conditions as candidate segments for cold spring activity;

[0041] Overlap detection is performed on adjacent cold seep activity candidate segments. Continuous segments with an interval less than 50% of the window width are merged. The average amplitude and frequency of the merged segments are recalculated. False detection segments caused by noise are removed. The start and end times, average amplitude and dominant frequency of the final cold seep activity segment are output to form a feature parameter table.

[0042] Preferably, S6 specifically includes:

[0043] Short-term isotope fluctuation features are extracted from the sliding window. All short-term isotope fluctuation features are Z-score standardized to eliminate dimensional differences, ensure the consistency of clustering input data, and integrate the short-term isotope fluctuation features into a dataset, with each data point representing the feature information of a sliding window.

[0044] Based on the analysis of cold seep activity in the cold seep activity area, the types of cold seep activity based on short-term isotope fluctuation characteristics were determined, including high-frequency pulse type, continuous release type and complex fluctuation type.

[0045] Based on the standardized short-term isotope fluctuation characteristics, the K-means algorithm is used to perform unsupervised clustering of cold seep activity windows. The optimal number of clusters (K value) is determined by the elbow rule, and the cold seep activity is divided into the corresponding types.

[0046] Preferably, S7 specifically includes:

[0047] The K-means clustering results were sorted by time series, and the absolute age of each window was determined by combining sediment chronology data to construct a temporal evolution sequence of cold seep activity types, ensuring spatiotemporal continuity.

[0048] The frequency distribution of various cold seep activities in different geological eras was statistically analyzed, the relative proportion of high-frequency pulsed and continuous release types was calculated, and the methane leakage intensity was quantified by combining the mean of isotopic ranges to reveal the stage-specific characteristics of cold seep activities.

[0049] By associating clustering results with regional tectonic activity and global climate change, and identifying dominant driving factors through multivariate regression analysis, a geological model of the spatiotemporal evolution of cold seep methane leakage is constructed to comprehensively assess the spatiotemporal dynamic changes of cold seep methane activity.

[0050] This invention provides a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis. It has the following beneficial effects:

[0051] (i) This method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis measures the oxygen and carbon isotope values ​​of the shells of benthic foraminifera and combines sliding window technology and K-means clustering algorithm to achieve accurate quantification of cold seep activity. It can identify short-term abnormal shift events and accurately classify cold seep activity types by combining isotope range and zero crossover point number, providing a basis for understanding the dynamic changes of cold seep methane leakage.

[0052] (ii) This method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis fills in the missing windows in the chronological data through sliding window technology and linear interpolation, ensuring the continuity of the time series and avoiding analytical biases caused by missing data or inconsistent resolution. It improves the integrity and reliability of the cold seep activity time series and can more accurately identify the start and end times, duration and transition nodes of cold seep activity, thereby providing high-resolution time series data for analyzing the long-term evolutionary patterns of cold seep activity.

[0053] (III) This method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis uses the K-means clustering algorithm to classify cold seep activity. It can effectively distinguish between high-frequency pulsed, continuous release, and complex wave-like cold seep activities. Furthermore, by standardizing the data and using the elbow rule to determine the optimal number of clusters, the scientific validity and accuracy of the clustering results are ensured. Combined with predefined type features, the clustering results can be mapped to the actual cold seep activity type, thereby achieving automatic classification of cold seep activities. This not only improves the accuracy of classification but also enables the identification of complex fluid release mechanisms. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the workflow of a cold seep methane activity analysis method combining benthic foraminifera isotope analysis according to the present invention.

[0055] Figure 2 This is a schematic flowchart of a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis according to the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1, please refer to Figure 1 , Figure 2 This invention provides a technical solution: a method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis, comprising the following steps:

[0058] S1. Collect and prepare sediment columns from cold seep activity areas to ensure that the samples cover different activity periods. In known cold seep activity areas, determine the sampling point locations based on geological background and previous survey data, covering different activity periods such as active, weakening, and quiescent periods. Plan the sampling route and depth, prepare a gravity piston sampler, and use the gravity piston sampler to vertically collect sediment columns to ensure sample continuity and integrity. Record the sampling depth, location, and time information. Cut the sediment columns into segments at preset depth intervals, number and record the depth information, remove disturbed layers, retain the original sedimentary structure, and store the sediment samples in layers. Some are used for the isolation of benthic foraminifera, and some are preserved at low temperature for geochemical retesting.

[0059] The specific work involves: In known cold seep activity areas, combining geological background data (seafloor topography, structural features) and previous survey data (distribution of cold seep carbonate rocks, evidence of methane leakage), determining sampling point locations through GIS spatial analysis. Sampling points cover different periods of cold seep activity: active, weakening, and quiescent phases. A sampling route perpendicular to the cold seep diffusion direction is planned, and sampling depths are set for different water depths and sedimentary layers. Simultaneously, a gravity piston sampler is prepared according to sampling requirements, and its sampling tube sealing, piston sliding smoothness, and counterweight system stability are checked to ensure penetration of loose sediments and acquisition of continuous sediment column samples. The gravity piston sampler is vertically inserted into the seafloor sediments, and continuous sediment column samples are obtained through free-fall impact and piston negative pressure. During collection, the gravity piston sampler is kept vertically stable to avoid tilting and disturbing sample stratification. The descent speed is controlled to ≤0.5 m / s to ensure the integrity of the sediment structure. After sampling, the sampling measurements are measured and recorded. The depth (vertical distance from the seabed to the top of the sampling tube), precise location (latitude and longitude coordinates), and sampling time (UTC standard time) are determined on-site. Preliminary characteristics of sediment color, odor, and stratification interfaces are observed within the sampling tube to determine the presence of cold seep activity markers, namely black sulfide layers and shell debris layers. If sample breakage or contamination is found, re-sampling or adjustment of the sampling point location is immediately performed to ensure data reliability. Sediment columnar samples are cut into segments at preset depth intervals (5 cm). Each segment is numbered and its upper and lower depth information is recorded. During cutting, a stainless steel blade is used to slowly cut along the axial direction to avoid mechanical compression that could lead to particle recombination. Simultaneously, the surface layer disturbed by drill bit disturbance or biological trawls is removed, preserving the original sedimentary structure. The stratified samples are divided into two parts: one part is immediately used for benthic foraminifera isolation; the other part is placed in sterile polyethylene bags, sealed after removing air, and stored in a 4°C freezer for subsequent geochemical re-testing. All operations are performed in a clean laboratory to prevent cross-contamination and ensure sample originality.

[0060] S2. Benthic foraminifera individuals were meticulously isolated from the sediment column sample and subjected to benthic foraminifera treatment to eliminate secondary effects and ensure sample integrity and representativeness. The sediment column sample was divided into segments according to depth, and the weight of each segment was weighed and recorded. The sediment was diluted with deionized water, stirred evenly, and allowed to settle. The upper suspended impurities were removed, and the bottom sediment was retained. Benthic foraminifera were separated using the wet sieving method. The pretreated sediment was filtered through a 125-micron sieve, and the residue on the sieve was collected. Intact benthic foraminifera shells were picked out under a microscope to remove impurities and ensure that the isolated foraminifera individuals were intact and undamaged. The isolated benthic foraminifera shells were placed in an ultrasonic cleaner and ultrasonically cleaned with deionized water to remove surface deposits and secondary carbonates. After cleaning, the shells were rinsed multiple times with deionized water, dried, and stored to ensure sample integrity and representativeness.

[0061] The specific work involves: cutting sediment columnar samples for benthic foraminifera isolation into segments at predetermined depth intervals; accurately weighing each segment and recording its weight and corresponding depth information; diluting each sediment segment with deionized water; thoroughly mixing to disperse the particles; allowing the mixture to settle naturally; removing suspended impurities from the upper layer, retaining only the relatively pure sediment at the bottom; and using a wet sieving method to separate benthic foraminifera from the pretreated sediment. Specifically, the sediment is slowly passed through a 125-micron sieve, utilizing the water flow and sieve vibration to allow smaller particles and impurities to pass through, while larger benthic foraminifera shells are trapped on the sieve. The residue on the sieve is then collected and examined under a microscope. The process involves sorting to select intact, undamaged benthic foraminifera shells. During sorting, impurities and other non-target substances attached to the shells are removed to ensure the high purity and integrity of the isolated foraminifera individuals. The isolated benthic foraminifera shells are then placed in an ultrasonic cleaner and ultrasonically cleaned with deionized water. The cavitation effect generated by ultrasound removes deposits and secondary carbonates attached to the shell surface, improving the cleanliness of the shells. The ultrasonic power and time are controlled during the cleaning process to avoid damaging the shells. After cleaning, the shells are rinsed multiple times with deionized water to ensure the complete removal of residual cleaning solution and impurities. Finally, the cleaned benthic foraminifera shells are dried and properly stored in a dry, clean environment to maintain the integrity and representativeness of the sample.

[0062] S3. The oxygen and carbon isotope values ​​of benthic foraminifera were determined using a mass spectrometer to construct a continuous time series of isotope data. The treated benthic foraminifera shells were ground into a uniform powder. A quantitative sample was weighed and placed in a reaction vessel, and dilute acid was added to initiate the reaction, releasing carbon dioxide gas. The sample was then connected to the mass spectrometer's sample introduction system via an elemental analyzer for sample conversion and gas purification. The sample molecules were ionized using the mass spectrometer's ion source, and oxygen was separated using a mass analyzer. 16 O / 18 O) and carbon ( 12 C / 13 C) Isotope peaks, ion current intensity recorded by detector, δ calculated. 18 O and δ 13 C ratio was used to obtain raw isotope data. The raw isotope data were then calibrated with standard materials and corrected for instrument drift. Combined with sediment chronology results, a continuous time series of oxygen and carbon isotopes of benthic foraminifera was constructed.

[0063] The specific work involves: placing the cleaned and dried benthic foraminifera shells into a grinding device (ball mill) and grinding them into a uniform powder (particle size ≤ 50 μm) to ensure complete crushing of the shell material. Weigh 50-100 mg of the ground sample using a precision balance of 0.01 g / mL and transfer it to a reaction vessel (glass reaction flask). Slowly add 1-2 mL of dilute phosphoric acid (concentration 1-3 mol / L) to the reaction vessel, controlling the adding rate (≤ 0.5 mL / min) to avoid vigorous reaction. Maintain the reaction temperature at 25-30℃ using a constant temperature water bath or heating plate to ensure complete decomposition of the carbonate components into CO2. Continue the reaction until no more bubbles are generated (30 minutes). -60 minutes, during which the container is gently shaken to promote uniform reaction and avoid violent reaction that could lead to gas escape or sample loss. The generated carbon dioxide gas is introduced into the sample introduction system of the elemental analyzer connected to the mass spectrometer through a conduit. At the same time, the gas is purified using a purification system containing a desiccant (anhydrous calcium chloride) and a cold trap (liquid nitrogen or dry ice-ethanol mixture) to remove impurities and water vapor. The purified gas is further filtered through a molecular sieve (type 5A) to ensure a purity ≥99.9%, meeting the sample introduction requirements of the mass spectrometer. The purified carbon dioxide gas is then ionized by the ion source of the mass spectrometer, converting the sample molecules into charged ions. The mass analyzer is used to separate the ions, focusing on capturing oxygen isotopes ( 16 O / 18 O) and carbon isotopes ( 12 C / 13 C) Characteristic peaks; the detector simultaneously records the flux intensity signals of each isotope ion; by comparing the signal intensity with that of the standard substance, δ is calculated. 18 O and δ 13 The raw isotope data, including sample number, reaction conditions, and isotope ratio information, is obtained by using raw C ratio data. The raw isotope data is then calibrated with standard materials. By comparing it with standard samples with known isotope compositions, the effects of instrument measurement errors and baseline drift are eliminated. At the same time, the data sequence is dynamically adjusted by combining periodically measured instrument drift correction values ​​to ensure the accuracy and consistency of isotope ratios. The calibrated isotope data is then matched with sediment chronology results to construct a continuous time series of oxygen and carbon isotopes of benthic foraminifera.

[0064] S4. Based on continuous isotopic time series data, analyze the degree of shift in oxygen and carbon isotope values ​​of benthic foraminifera to infer the intensity of cold seep methane leakage, thereby identifying short-term anomalous shift events. Smooth the continuous oxygen and carbon isotope time series of benthic foraminifera to eliminate high-frequency noise, calculate the shift of isotope values ​​relative to the baseline at each time point, and quantify the shift amplitude and duration. Based on the known isotopic characteristics of cold seep activity areas, establish a quantitative relationship model between methane leakage intensity and oxygen and carbon isotope shift amplitude. Determine the shift threshold through regression analysis to define the intensity range of short-term anomalous shift events, where the shift threshold is δ. 13 C≤-1.5‰ and δ 18 O≥1.0‰, the period in which the offset in the continuous time series of scanned isotopes exceeds the offset threshold is marked as a short-term anomalous event, and the spatiotemporal distribution pattern of the anomalous event is analyzed in combination with sedimentary strata and geochronological data to infer the intensity of cold seep methane leakage.

[0065] The specific work involved: preprocessing the continuous time series of oxygen and carbon isotopes of benthic foraminifera; using a low-pass filtering algorithm (Savitzky-Golay filter) to eliminate high-frequency noise while preserving long-term variation trends on millennia- to tens of thousands of years scale; balancing signal fidelity and smoothing effect by adjusting the window width (500-1000 years) to ensure the time series resolution met research requirements; and calculating the isotope values ​​(δ¹⁸O) at each time point based on the smoothed baseline. 18 O, δ 13 C) The offset relative to the benchmark is quantified using the standard deviation method, i.e., ≥2σ is considered a significant offset, and the duration of the offset is also statistically analyzed; based on the known isotopic characteristics of cold seep activity areas, i.e., δ 13 negative C and δ 18 By studying the synergistic changes in positive and negative oxygen (O) bias, a quantitative model was established to demonstrate the relationship between methane leakage intensity and oxygen and carbon isotope shift amplitudes. Multiple regression analysis was used to integrate shift amplitude, duration, and deposition rate to determine the linear or nonlinear relationship between methane leakage rate and isotope response. The reliability of the model was validated using standard samples, and parameter uncertainties were assessed through Monte Carlo simulations. Based on the model output, a shift threshold, δ, was set. 13 C≤-1.5‰ and δ 18 For an intensity of O ≥ 1.0‰, short-term anomalous migration events are classified into three levels: low-intensity migration, medium-intensity migration, and high-intensity migration. For the smoothed isotope continuous time series, periods where the migration exceeds a preset migration threshold are designated as short-term anomalous events. The start and end times, migration amplitude, and duration are recorded. This is then combined with sedimentary stratigraphic correlation (core scan images, grain size analysis) and geochronological data (…). 14C-dating was used to determine the spatial distribution range (stratum thickness, lateral extension) and frequency of anomalous events. Wavelet transform was used to identify the periodicity of anomalous events (10-100 ka scale) in order to infer the intensity of cold seep methane leakage.

[0066] Furthermore, the process of analyzing the spatiotemporal distribution patterns of abnormal events and inferring the intensity of cold seep methane leakage is as follows: Scanning the smoothed continuous time series of isotopes, and calculating the isotope values ​​(δ¹⁸) at each time point. 18 O, δ 13 C) Offset relative to the baseline, filtering for values ​​exceeding a preset threshold (δ). 13 C≤-1.5‰ and δ 18 For periods of O≥1.0‰, a sliding window algorithm is used to determine the start and end times of the shift. Combined with linear interpolation to mark critical points, a list of anomalous events including start and end ages, shift amplitude, and duration is generated. The sequence resolution is recorded simultaneously to ensure that the event detection accuracy meets the needs of research on millennial to 10,000-year scales. The marked short-term anomalous events are spatially correlated with sedimentary stratigraphic correlation data and geochronological framework to determine the vertical distribution range (stratigraphic thickness) and lateral extension distance of the anomalous events in the core. A two-dimensional spatial distribution map is constructed through stratigraphic correlation. The event impact area is calculated by combining the sedimentation rate. The frequency of event occurrence (times / ka) and interval time are statistically analyzed to analyze its coupling relationship with regional tectonic activity and sediment supply. Spectral analysis is performed on the time series of anomalous events to identify the dominant cycle (10-100 ka scale) and its phase relationship with orbital parameters (eccentricity, dip angle), thereby inferring the intensity of cold seep methane leakage.

[0067] The formula for calculating the intensity of cold spring methane leakage is as follows:

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula: R is the cold seep methane leakage intensity, representing the amount of methane leakage per unit time; k is the correction coefficient, used to adjust the model output to match the actual observation data, determined through standard sample verification and Monte Carlo simulation; The isotopic shift magnitude represents the negative shift (in‰) of the carbon isotope value in the shell of benthic foraminifera relative to the baseline value during an anomalous event. These are the carbon isotope values ​​in the shells of benthic foraminifera. This serves as a baseline value for the carbon isotope values ​​in the shells of benthic foraminifera; The isotopic shift magnitude represents the positive shift (in‰) of the oxygen isotope value in the shell of benthic foraminifera relative to the baseline value during an anomalous event. The values ​​represent the oxygen isotope content in the shells of benthic foraminifera. The baseline value for oxygen isotopes in the shells of benthic foraminifera is given; D is the deposition rate, representing the thickness of sediment per unit time (in mm / ka, which can be calculated from geochronological data); T is the duration of the anomalous event, representing the duration of the isotopic shift event (in years, ka). The end time of the event. The product of the two isotopic shifts, representing the start time of the event, reflects the intensity of the cold seep methane leak event. The negative offset indicates methane leakage. The positive offset indicates changes in the depositional environment, and both reflect the intensity of methane leakage. The product of the deposition rate and the duration of the anomalous event represents the total thickness or mass of the sediment during the anomalous event. By dividing the isotopic offset amplitude by the product of the deposition rate and the duration, the amount of methane leakage per unit time can be obtained, thus quantifying the intensity of methane leakage.

[0073] S5. Apply sliding window technique to time series data to capture short-term isotopic fluctuation characteristics of cold seep activity, including amplitude and frequency.

[0074] S6. Based on the characteristics of short-term isotope fluctuations, the K-means algorithm is used to cluster cold seep activities and classify the activity types.

[0075] S7. Combining clustering results with geological time, the historical evolution pattern of cold seep methane leakage is reconstructed to comprehensively assess the spatiotemporal dynamics of cold seep methane activity.

[0076] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: S5 specifically includes: setting the window width (3-5 times the resolution) according to the time series resolution, selecting an overlap rate of 50% to balance sensitivity and stability, and processing the original isotope data (δ... 13 C、δ 18 O) Perform linear interpolation to fill in missing values ​​and standardize time intervals to ensure data continuity during window sliding. Within each sliding window, calculate the isotopic range (maximum - minimum) as the fluctuation amplitude, and count the number of fluctuations within the window (zero crossover points) as the frequency index. Set an amplitude threshold (δ). 13C range ≥ 1.5‰) and frequency threshold (≥ 2 times / window), screen windows that meet the conditions as candidate segments of cold seep activity, perform overlap detection on adjacent candidate segments of cold seep activity, merge continuous segments with an interval less than 50% of the window width, recalculate the average amplitude and frequency of the merged segment, remove false detection segments caused by noise, and output the start and end time, average amplitude and dominant frequency of the final cold seep activity segment to form a feature parameter table;

[0077] The specific work involves: setting the sliding window width based on the resolution of the original isotopic data, with the window width being 3-5 times the resolution to balance sensitivity and stability. Simultaneously, a 50% overlap rate is used, ensuring that adjacent windows share 50% of the data points, avoiding information loss and improving detection continuity. Linear interpolation is performed on the original isotopic data to fill in missing values ​​and standardize the time interval to a fixed step size, ensuring continuous and evenly spaced data during window sliding and eliminating analytical biases caused by missing data or inconsistent resolution. Within each sliding window, the isotopic range (maximum value - minimum value) is calculated as a fluctuation amplitude indicator, reflecting the degree of drastic change in isotopic values ​​within the window. Simultaneously, the number of zero-crossing points (the number of times the isotopic curve crosses the baseline) is counted as a frequency indicator, characterizing the number of fluctuations within a short timescale. A dual threshold is set to screen cold seep activity candidates. Segment: The δ13C range must be ≥1.5‰, indicating a significant negative bias in carbon isotopes; the number of zero crossovers must be ≥2 times / window, reflecting high-frequency fluctuation characteristics. Window segments that meet either criterion are marked as candidate segments. Through the dual constraints of amplitude and frequency, the abnormal fluctuations caused by cold seep activity are effectively distinguished from background noise, improving the accuracy of candidate segment selection. Overlap detection is performed on adjacent cold seep activity candidate segments. If the interval between two segments is less than 50% of the window width, they are merged into a continuous segment to avoid segment breakage caused by window sliding. After merging, the average amplitude of the segment (mean of the range of all windows in the merged segment) and the dominant frequency (mode of the number of zero crossovers in the merged segment) are recalculated. False detection segments caused by single-point noise or transient disturbances are eliminated. Finally, the start and end times (based on chronological framework interpolation), average amplitude, and dominant frequency of the cold seep activity segment are output, forming a structured feature parameter table.

[0078] S6 specifically includes: extracting short-term isotope fluctuation features from the sliding window, standardizing all short-term isotope fluctuation features with Z-Score to eliminate dimensional differences and ensure the consistency of clustering input data, and integrating the short-term isotope fluctuation features into a dataset, with each data point representing the feature information of a sliding window; based on the analysis of cold seep activity in the cold seep activity area, determining the type of cold seep activity based on short-term isotope fluctuation features, including high-frequency pulse type, continuous release type, and composite fluctuation type; based on the standardized short-term isotope fluctuation features, using the K-means algorithm to perform unsupervised clustering of the cold seep activity window, determining the optimal number of clusters (K value) through the elbow rule, and classifying the cold seep activity into the corresponding type.

[0079] The specific work involves: extracting multi-dimensional indicators, including amplitude and frequency, from a sliding window to quantify the dynamic changes in isotope values ​​within the window, reflecting the intensity and periodicity of cold seep activity; subsequently, performing Z-score standardization on all features, converting the data into a distribution with a mean of 0 and a standard deviation of 1 by calculating the mean and standard deviation, eliminating dimensional differences between different features, ensuring balanced weights for each feature in cluster analysis, and integrating the features of each sliding window into a dataset, where each data point corresponds to a feature vector of a window, forming a structured input matrix; based on isotope records and geological background analysis of cold seep activity areas, defining three typical types of cold seep activity: high-frequency pulse type, continuous release type, and composite wave type. The classification of these three types combines the comprehensive performance of amplitude and frequency. Among them, the short-term isotope wave characteristic of the high-frequency pulse type is δ 13 The C range is significant (≥2‰), with a high number of zero crossovers (≥4 times / window), exhibiting dramatic fluctuations over a short period with short cycles (decades to centuries). This reflects intermittent pulsed methane releases, associated with staged triggering of tectonic activity (fault slip) or gas hydrate decomposition, and is commonly found in active marginal basins. Continuous release-type short-term isotopic fluctuations are characterized by δ¹⁸ Ω⁻¹. 13 The C range is small (1.0‰~1.5‰), but the fluctuation frequency is low (zero crossover ≤ 1 time / window), exhibiting a long-term stable negative bias with a long fluctuation period (millennial scale), reflecting continuous methane leakage. This is associated with deep hydrothermal activity or the slow decomposition of stable gas hydrate layers and is commonly found in passive continental margins. The short-term isotopic fluctuations of the composite fluctuation type combine high-frequency pulses with low-frequency persistence, δ... 13 The C-range and the number of zero crossover points are both in the middle range (1.5‰~2.0‰, 2~3 times / window), and the fluctuations show multi-scale superposition, reflecting a complex fluid release mechanism driven by tectonic-sedimentary coupling (fault + sediment compaction), which is common in transform fault zones. The K-means algorithm is used to perform unsupervised clustering on the standardized short-term isotope fluctuation characteristics. The optimal number of clusters K is determined by the elbow method: the within-group sum of squares (WSS) under different K values ​​is calculated, and the inflection point where the rate of decrease of WSS slows down is selected as the K value. The sliding window is divided into K classes according to feature similarity, and each class corresponds to a cold seep activity type. Finally, combined with predefined type features, the clustering results are mapped to the actual type to realize the automatic classification of cold seep activities.

[0080] Furthermore, the expression for calculating the sum of squares within groups is as follows:

[0081] ;

[0082] In the formula: WSS is the sum of squares within groups, which measures the dispersion of data points within each cluster. The smaller the WSS, the more closely the data points within the cluster are packed. K is the number of clusters, i.e., the number of clusters in the K-means algorithm. Let x be the set of all data points in the i-th cluster; x is the cluster size. A data point in the vector represents a feature vector. Let be the centroid (mean vector) of the i-th cluster, representing the average value of all data points in that cluster; Let data point x and centroid be... The square of the Euclidean distance between data points measures the degree of deviation of a data point from its centroid. The WSS reaches its minimum value of 0 when all data points completely overlap their respective centroids, which is almost impossible in practice but can be used as a theoretical reference. The WSS reaches its maximum value when the data points are completely randomly distributed and have no clustering structure. As K increases, the WSS usually decreases. When K increases, the number of data points in each cluster decreases, and the distance between the data points and the centroid also decreases, thus reducing the WSS. When K approaches the total number of data points, each cluster contains almost only one data point, and the WSS approaches 0. The inflection point where the rate of WSS decrease slows significantly is selected as the optimal number of clusters. Before the inflection point, the WSS decreases rapidly with increasing K; after the inflection point, the rate of WSS decrease slows significantly. The inflection point indicates that the number of clusters K is sufficient to capture the main structure in the data, and further increasing K will lead to overfitting.

[0083] S7 specifically includes: sorting the K-means clustering results by time series, calibrating the absolute age of each window by combining sedimentary geochronological data, constructing a time evolution sequence of cold seep activity types to ensure spatiotemporal continuity, statistically analyzing the frequency distribution of various cold seep activities in different geological periods, calculating the relative proportion of high-frequency pulsed and continuous release types, quantifying methane leakage intensity by combining isotopic range mean, revealing the stage-specific change characteristics of cold seep activities, correlating clustering results with regional tectonic activities and global climate change, identifying the dominant driving factors through multivariate regression analysis, constructing a geological model of the spatiotemporal evolution pattern of cold seep methane leakage, and comprehensively assessing the spatiotemporal dynamic changes of cold seep methane activities;

[0084] The specific work involves: arranging the K-means clustering results in chronological order according to sliding windows; assigning an absolute age to each window based on sediment dating data; constructing a temporal evolution sequence of cold seep activity types; filling in missing windows in the chronological data using linear interpolation or spline fitting methods to ensure the continuity of the time series; smoothing the types of adjacent windows to avoid abrupt changes in type due to single-point noise; preserving the true stage-by-stage changes of cold seep activity; generating a high-resolution (resolution ≤ millennium) time series; clarifying the start and end times, durations, and transition points of high-frequency pulse-type, continuous-release, and complex-wave cold seep activities; based on the time evolution sequence, statistically analyzing the frequency distribution of various types of cold seep activities within different geological ages; calculating the relative proportion of high-frequency pulse-type and continuous-release-type cold seeps to reveal the spatiotemporal preference of cold seep activity types; further, weighted statistical analysis of the mean isotopic range within each geological age to quantify the stage-by-stage changes in methane leakage intensity: high-frequency pulse-type... The dominant phase corresponds to a high mean range (high methane flux), while the dominant phase of continuous release corresponds to a low mean range (stable methane flux). By calculating the dynamic changes in the type proportion and mean range through a sliding time window, cold seep activity types are identified. Combined with sedimentation rate and sea-level change data, the correlation between methane leakage intensity and tectonic-climate coupling is analyzed. The clustering results are correlated with regional tectonic activity (fault activity rate, earthquake frequency) and global climate change (orbital cycles, carbon isotope shifts). Multivariate regression analysis is used to quantify the contribution of each driving factor to cold seep activity types. Specifically, the proportion of high-frequency pulsed cold seep, the proportion of continuous release cold seep, and the mean isotope range are used as dependent variables, while the intensity of tectonic activity, sea-level change rate, and bottom water temperature are used as independent variables. A geological model is constructed to reveal the dominant driving mechanism. By integrating the spatiotemporal evolution sequence and driving factor analysis, a tectonic-climate dual-control model of cold seep methane leakage is constructed to assess the potential impact of methane release in different geological periods on the global carbon cycle.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0086] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing cold spring methane activity in combination with benthic foraminifera isotope analysis, characterized by, Includes the following steps: S1. Collect and prepare sediment columns from cold seep activity areas, covering different activity periods; S2. Finely separate benthic foraminifera individuals from sediment column samples and treat them with benthic foraminifera to eliminate secondary effects; S3. Use mass spectrometry to determine the oxygen and carbon isotope values ​​of benthic foraminifera and construct continuous time series data of isotopes. S4. Based on continuous time series data of isotopes, analyze the degree of shift in oxygen and carbon isotope values ​​of benthic foraminifera to infer the intensity of cold seep methane leakage in order to identify short-term abnormal shift events. S4 specifically includes: The continuous time series of oxygen and carbon isotopes of benthic foraminifera was smoothed to eliminate high-frequency noise, and the offset of isotope values ​​at each time point relative to the baseline was calculated, and the offset amplitude and duration were quantified. Based on the isotopic characteristics of known cold spring activity areas, a quantitative relationship model between methane seepage intensity and oxygen and carbon isotope excursion amplitude is established, and the excursion threshold is determined through regression analysis to define the intensity range of short-term abnormal excursion events, wherein the excursion threshold is δ 13 C≤-1.5‰ and δ 18 O≥1.0‰. Periods in the continuous time series of scanned isotopes where the offset exceeds the offset threshold are marked as short-term anomalous events. By combining sedimentary stratigraphic and geochronological data, the spatiotemporal distribution patterns of these anomalous events are analyzed to infer the intensity of cold seep methane leakage. The process of analyzing the spatiotemporal distribution patterns of abnormal events and inferring the intensity of cold spring methane leakage is as follows: After scanning the smoothed isotope continuous time series, the offset of the isotope value at each time point relative to the baseline is calculated, the time period exceeding the preset threshold is filtered, the start and end time of the offset is determined by the sliding window algorithm, and the critical point is marked by linear interpolation to generate a list of abnormal events containing the start and end age, offset amplitude and duration. By spatially correlating the marked short-term anomalous events with sedimentary stratigraphic correlation data and geochronological framework, the vertical distribution range and lateral extension distance of the anomalous events in the core were determined. A two-dimensional spatial distribution map was constructed by stratigraphic correlation. The area affected by the events was calculated by combining the sedimentation rate. The frequency and interval of the events were statistically analyzed, and their coupling relationship with regional tectonic activity and sediment supply was analyzed. Spectral analysis of the time series of anomalous events is performed to identify the dominant period and its phase relationship with orbital parameters, thereby inferring the intensity of cold seep methane leakage. The formula for calculating the intensity of methane leakage from the cold spring is as follows: ； ； ； ； In the formula: The intensity of methane leakage from the cold spring; For correction factors; The isotopic shift magnitude represents the negative shift of the carbon isotope value in the shell of benthic foraminifera relative to a baseline value during an anomalous event. These are the carbon isotope values ​​in the shells of benthic foraminifera. This serves as a baseline value for the carbon isotope values ​​in the shells of benthic foraminifera; The isotopic shift magnitude represents the positive shift of the oxygen isotope value in the shell of benthic foraminifera relative to a baseline value during an anomalous event. The values ​​represent the oxygen isotope content in the shells of benthic foraminifera. This serves as a baseline value for the oxygen isotope content in the shells of benthic foraminifera. The deposition rate represents the thickness of the sediment per unit time. The duration of the anomalous event indicates the duration of the isotope shift event. The end time of the event. The start time of the event; S5. Apply sliding window technique to time series data to capture short-term isotopic fluctuation characteristics of cold seep activity, including amplitude and frequency. S6. Based on the characteristics of short-term isotope fluctuations, the K-means algorithm is used to cluster cold seep activities and classify the activity types. S6 specifically includes: Short-term isotope fluctuation features are extracted from the sliding window, Z-score is applied to all short-term isotope fluctuation features, and the short-term isotope fluctuation features are integrated into a dataset, with each data point representing the feature information of a sliding window. Based on the analysis of cold seep activity in the cold seep activity area, the types of cold seep activity based on short-term isotope fluctuation characteristics were determined, including high-frequency pulse type, continuous release type and complex fluctuation type. Based on the standardized short-term isotope fluctuation characteristics, the K-means algorithm is used to perform unsupervised clustering of cold seep activity windows. The elbow rule is used to determine the optimal number of clusters and classify cold seep activities into corresponding types. S7. Combining clustering results with geological time, reconstruct the historical evolution pattern of cold seep methane leakage and assess the spatiotemporal dynamics of cold seep methane activity. Specifically, S7 includes: The K-means clustering results were sorted by time series, and the absolute age of each window was determined by combining sediment chronology data to construct a time evolution sequence of cold seep activity types. The frequency distribution of various cold seep activities in different geological eras was statistically analyzed, the relative proportion of high-frequency pulsed and continuous release types was calculated, and the methane leakage intensity was quantified by combining the mean isotopic range, revealing the stage-specific characteristics of cold seep activity. By associating clustering results with regional tectonic activity and global climate change, and identifying dominant driving factors through multivariate regression analysis, a geological model of the spatiotemporal evolution of cold seep methane leakage is constructed to comprehensively assess the spatiotemporal dynamic changes of cold seep methane activity.

2. The method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis according to claim 1, characterized in that: S1 specifically includes: In the known cold seep activity area, based on the geological background and previous survey data, different activity periods, including active, weakening and quiescent periods, are covered, and sampling routes and depths are planned, and gravity piston samplers are prepared. A columnar sediment sample was collected vertically using a gravity piston sampler, and the sampling depth, location, and time information were recorded. Sediment columnar samples were cut into segments at preset depth intervals, numbered and their depth information was recorded, disturbed layers were removed, the original sedimentary structure was preserved, and the sediment samples were stored in layers. Some were used for the isolation of benthic foraminifera, and some were preserved at low temperature for geochemical retesting.

3. The method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis according to claim 1, characterized in that: S2 specifically includes: Sediment column samples were divided into segments according to depth, each segment was weighed and recorded, the sediment was diluted with deionized water, stirred evenly, allowed to settle, the upper suspended impurities were removed, and the bottom sediment was retained. Benthic foraminifera were separated using a wet sieving method. The pretreated sediment was filtered through a 125-micron sieve, the residue on the sieve was collected, and intact benthic foraminifera shells were picked out under a microscope to remove impurities. The separated benthic foraminifera shells were placed in an ultrasonic cleaner and ultrasonically cleaned with deionized water to remove deposits and secondary carbonates attached to the surface. After cleaning, they were rinsed multiple times with deionized water, dried, and stored.

4. The method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis according to claim 1, characterized in that: S3 specifically includes: The processed benthic foraminifera shells were ground into a uniform powder. A quantitative sample was weighed and placed in a reaction vessel. Dilute acid was added to carry out the reaction, releasing carbon dioxide gas. The sample was then converted and purified by connecting the mass spectrometer sample introduction system to the elemental analyzer. The sample molecules are ionized by the ion source of the mass spectrometer, and the oxygen and carbon isotope peaks are separated by the mass analyzer. The detector records the ion current intensity, and calculates δ 18 O and δ 13 C ratio, and obtains the original isotope data; The original isotope data were calibrated with standard materials and corrected for instrument drift. Combined with sediment chronology results, a continuous time series of oxygen and carbon isotopes of benthic foraminifera was constructed.

5. The method for analyzing cold seep methane activity by combining benthic foraminifera isotope analysis according to claim 1, characterized in that: S5 specifically includes: Set the window width according to the time series resolution, select an overlap rate of 50%, perform linear interpolation on the original isotope data, fill in missing values ​​and unify time intervals; Within each sliding window, the isotopic range is calculated as the fluctuation amplitude, and the number of fluctuations within the window is counted as the frequency index. An amplitude threshold and a frequency threshold are set, and windows that meet the conditions are selected as candidate segments for cold seep activity. Overlap detection is performed on adjacent cold seep activity candidate segments. Continuous segments with an interval less than 50% of the window width are merged. The average amplitude and frequency of the merged segments are recalculated. False detection segments caused by noise are removed. The start and end times, average amplitude and dominant frequency of the final cold seep activity segment are output to form a feature parameter table.

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