Method for evaluating correlation between geothermal development and land subsidence

Abstract

The invention discloses a correlation evaluation method for geothermal development and ground subsidence. The method comprises the following steps: S1, data collection: collecting ground subsidence monitoring data, geological background conditions, geothermal water exploitation amount, underground water exploitation amount, underground water level, long-term monitoring data of geothermal water level, rock-soil mechanical test data and the like of a research area;

Description

Technical Field

[0001] The invention belongs to the field of geological technology, and specifically relates to a method for evaluating the correlation between geothermal development and ground subsidence. Background Art

[0002] At present, there is a lack of research methods to systematically analyze the impact of geothermal development on ground subsidence. The general analysis often uses the method of comparing the trend of changes in the same period, which leads to a strong subjectivity in the analysis process and imprecise conclusions. The conclusions are only related or unrelated. It is impossible to compare the strength of the correlation between geothermal development and ground subsidence in different regions, and it is impossible to analyze the correlation between the two under the condition that there is a lag in the impact of geothermal development on ground subsidence. Summary of the invention

[0003] In order to solve the shortcomings of strong subjectivity and difficulty in quantification, the present invention provides a correlation evaluation method for geothermal development and ground subsidence.

[0004] This application proposes a correlation evaluation method between geothermal development and ground subsidence, including S1. Data collection: Collect the ground subsidence monitoring data (including remote sensing interpretation data), geothermal water extraction, groundwater extraction, groundwater level, long-term monitoring data of geothermal water level, geotechnical mechanics test data, etc. in the study area; S2. Data preprocessing: processing missing values ​​and outliers to ensure data accuracy, consistency of time and units, and correspondence of data; S3. Preliminary analysis: (1) Analysis of geological conditions of geothermal development layers: Analyze whether geothermal development will cause ground subsidence from the perspective of geological structure and rock and soil mechanics; (2) Time series analysis: 1) Draw a time series diagram of ground subsidence and geothermal extraction to observe the changing trends of the two; 2) Draw a time series diagram of ground subsidence and geothermal water level elevation or burial depth to observe the changing trends of the two; (3) Spatial analysis: Draw the spatial distribution map of ground subsidence and geothermal wells in the same period, and identify the spatial relationship between the subsidence center and the geothermal wells; draw the spatial distribution map of ground subsidence and geothermal water level in the same period, and identify the spatial relationship between the subsidence center and the center of the geothermal water funnel. For ground subsidence areas with a lack of spatial data, the Kriging interpolation method or remote sensing interpretation method can be used to obtain the subsidence distribution of unknown areas; S4. Correlation analysis: (1) Statistical correlation: calculate the Pearson correlation coefficient or Spearman correlation coefficient between the annual or multi-year land subsidence rate and variables such as geothermal extraction volume, geothermal water level, and number of geothermal wells.

[0005] The Pearson correlation coefficient r is a statistical indicator used to measure the linear correlation between the two variables of land subsidence and geothermal development. The Spearman correlation coefficient method is particularly suitable for processing data with nonlinear relationships between the two variables or with many outliers.

[0006] The calculation formula is as follows:

[0007] and Data values ​​for ground subsidence and geothermal extraction volume / geothermal water level / geothermal well number; and is the mean of the above two variables; The value range of r is between -1 and 1, and its specific meanings are as follows: 1: Indicates a completely positive correlation, with a perfect positive linear relationship between the two variables.

[0008] -1: indicates a perfect negative correlation, with a perfect negative linear relationship between the two variables.

[0009] 0: Indicates no linear correlation, there is no significant linear relationship between the two variables.

[0010] The Pearson correlation coefficient is used to measure linear relationships and is therefore suitable for situations where geothermal development and ground subsidence are linearly correlated and the correlation is strong, but may not work well when the data distribution deviates from normality or when nonlinear correlation is strong.

[0011] The Spearman correlation coefficient is a nonparametric statistical method used to measure the monotonic relationship between two variables: land subsidence and geothermal development. It is applicable to hierarchical data, that is, data that can be sorted but may not conform to a normal distribution. The Spearman correlation coefficient is denoted as 𝜌 and is calculated as:

[0012] in, is the ranking difference of each pair of data (i.e. ), n is the number of data in each group.

[0013] The Spearman correlation coefficient ranges from -1 to 1, and its meaning is as follows: 1: There is a perfect positive correlation between the two variables of land subsidence and geothermal development, that is, as one variable increases, the other variable also increases strictly.

[0014] -1: There is a perfect negative correlation between the two variables of land subsidence and geothermal development, that is, as one variable increases, the other variable strictly decreases.

[0015] 0: There is no significant monotonic relationship between the two variables of land subsidence and geothermal development.

[0016] The Spearman correlation coefficient method is particularly suitable for processing data with nonlinear relationship between the two variables of land subsidence and geothermal development or with many outliers.

[0017] (2) Lag correlation: using the cross-correlation function to analyze the lag effect of geothermal mining on ground subsidence.

[0018] The calculation method is: 1) Prepare two sets of time series, one set of which is geothermal development activity data, such as geothermal extraction volume, geothermal reinjection volume, geothermal water depth, geothermal water elevation, etc., and the other set of which is ground subsidence data. The two sets of time series data are required to have the same sampling frequency (such as monthly or annual data) and the same length (missing values ​​are filled or interpolated). If the data has obvious trends or seasonal components, differential, detrending or smoothing processing must be used first to reduce interference with correlation analysis; 2) Calculate the standard deviation and mean of two time series.

[0019] 3) Calculate the cross-correlation function; For each lag period τ, the cross-correlation coefficient is calculated. The cross-correlation coefficient calculation formula is as follows:

[0020] Where N is the total length of the data; τ is the lag step. If τ is a positive number, it means that geothermal development has an impact on ground subsidence after τ time; if τ is a negative number, it means that geothermal development has no impact on ground subsidence.

[0021] and is the mean of the above two sequences.

[0022] 4) Calculate the cross-correlation coefficients of different lag times τ. The one with the largest absolute value of the cross-correlation coefficient is the lag between the two, which is the main lag time of the impact of geothermal development activities on ground subsidence; 5) Hysteresis test Use statistical significance tests (such as t-tests) to verify whether the cross-correlation coefficient is significant. A significant cross-correlation coefficient can more reliably reflect the lag relationship between geothermal development activities and ground subsidence.

[0023] The specific steps of the t-test are as follows: ① Calculate the t statistic For a certain lag period τ, the cross-correlation coefficient , the calculation formula of t statistic is:

[0024] in: is the cross-correlation coefficient at lag period τ, N is the total length of the sample data.

[0025] ②Determine the significance level Select a significance level 𝛼 (such as 0.05 or 0.01). Usually, a significance level of 0.05 is sufficient. A significance level of 0.05 means that there is a 5% probability of incorrectly judging a non-significant cross-correlation coefficient as significant.

[0026] ③Find critical value According to the significance level 𝛼 and the sample degrees of freedom df=N−2, find the corresponding critical value t from the t distribution table critical .

[0027] if , then the cross-correlation coefficient under the lag period τ is considered is significant.

[0028] ④Significance judgment if , it can be considered that the cross-correlation coefficient between geothermal development activities and ground subsidence is significant under this lag period τ, indicating that geothermal development activities have a significant impact on ground subsidence under this lag period.

[0029] In addition, the rationality of the lag time must be verified by combining geological knowledge, actual on-site conditions, and data obtained by other methods.

[0030] S5. Build the model: (1) Regression analysis A multivariate regression model was established to analyze the impact of geothermal extraction, geothermal water level and other factors on ground subsidence. The contribution of each factor to ground subsidence can be determined through the regression coefficient.

[0031] (2) Time Series Model Using methods such as vector autoregression model (VAR), vector error correction model (VECM), autoregression model with exogenous variables (ARIMAX), seasonal ARIMA model with exogenous variables (SARIMAX), Bayesian structured time series model (BSTS), long short-term memory network (LSTM), support vector regression (SVR), wavelet neural network, adaptive network fuzzy inference system, etc., a model of land subsidence changing with influencing factors is established to predict the impact and trend of geothermal mining on land subsidence; S6. Numerical simulation: Use one or more numerical simulation technology combinations such as finite element method, finite difference method, discrete element method, fluid-solid coupling method, elastoplastic model, geological process simulation, multi-physics field coupling model, nonlinear regression and machine learning methods, wavelet analysis and multi-scale simulation to simulate the impact of geothermal mining on underground geological structure and ground subsidence; S7. Experimental verification: Conduct field trials or laboratory simulations to verify analytical results.

[0032] (1) Field test The goal of the field test is to monitor ground subsidence and groundwater changes at a real geothermal mining site, so as to analyze the relationship between geothermal development and ground subsidence.

[0033] 1) Main steps: ①Selection of test site: Select a representative geothermal field and set up multiple monitoring points to ensure that different geological conditions and geothermal well locations are covered. The selected geothermal field should not have other underground resource exploitation, or the ground deformation caused by other resource exploitation can be monitored.

[0034] ② Establish a ground subsidence monitoring network: deploy high-precision GPS, laser rangefinders or inclinometers, automatic ground subsidence monitoring equipment, or conduct regular leveling to monitor ground subsidence and record the subsidence amplitude and rate. Alternatively, build a layered standard group that can indicate the ground layered subsidence to obtain the deformation of different layers.

[0035] ③ Groundwater monitoring: Install observation wells to monitor groundwater level, geothermal water level, water temperature and other data in order to study the relationship between groundwater flow, compression changes and ground subsidence.

[0036] ④Geothermal well parameter recording: record the temperature, pressure, flow and other parameters of geothermal wells in order to study the impact of geothermal fluid development on geological structure.

[0037] ⑤Data collection and analysis: Long-term data collection and analysis, and analysis of the relationship between geothermal development and ground subsidence through numerical simulation or statistical methods (such as inversion algorithms).

[0038] 2) Key points: ① The monitoring period should be long enough to obtain long-term subsidence trends; ② The frequency of data collection is appropriate to capture the impact of geothermal development on ground subsidence in the early, middle and late stages; ③ Pay attention to selecting a control group with a smaller background sedimentation rate to eliminate the interference of environmental factors. Try to select areas with similar geological and other environments and no underground resource mining as the control group. (2) Laboratory simulation Laboratory simulations can explore under controlled conditions the changes in the physical properties of rock and soil during geothermal development, as well as the subsidence caused by groundwater extraction.

[0039] 1) Simulation method: ① Soil column experiment: Use a simulated soil column device to fill soil or rock similar to the on-site strata, and set up geothermal simulated water sources at different depths. By applying changes in hydraulics, temperature and pressure to the soil column, the effect of groundwater fluid extraction on ground subsidence is simulated.

[0040] ② Rock and soil sample compression test: Collect stratum rock and soil samples (such as sandstone, mudstone, etc.), and study the compressibility and deformation characteristics of the rock and soil samples by applying different temperatures, pressures and fluid flow rates in a triaxial compression instrument or permeability compression test device.

[0041] ③ Water-heat-force coupling test: Use a hot water circulation device to simulate the water circulation of a geothermal well, and analyze the deformation response of rock or soil layers under different conditions by adjusting the water temperature and water pressure.

[0042] 2) Key points: ① The experimental materials and parameters should be as close as possible to the actual conditions on site; ② Pay attention to controlling variables such as temperature, pressure, flow rate, etc. to ensure that the experiment has high repeatability; ③ Compare the experimental results with the on-site monitoring data to verify the reliability and applicability of the experimental results.

[0043] S8. Result analysis: Combining the above qualitative, quantitative, model, simulation and experimental results, comprehensive analysis can more comprehensively reveal the impact mechanism of geothermal development on ground subsidence, draw conclusions on whether there is a correlation between geothermal development and ground subsidence, and provide a scientific basis for the rational use of geothermal resources.

[0044] Specifically, the geological conditions of the geothermal development layer are analyzed in (1) of S3, including the depth, distribution, lithology, geotechnical mechanics, development and utilization intensity of the geothermal development layer, the lithology, geotechnical mechanics, development and utilization intensity of the strata above the geothermal development layer, and the impact of geothermal development on the stratum stress field of the geothermal development layer is qualitatively analyzed under different geothermal development intensities in different periods; the impact of geothermal development on the stratum stress above the geothermal development layer is qualitatively analyzed in combination with the lithology, thickness, geotechnical mechanics parameters, development and utilization strata and intensity of the overlying strata of the geothermal development layer.

[0045] Specifically, the geothermal exploitation volume in S3 (2) 1) shows a synchronous or delayed increase / decrease with the ground subsidence development rate, indicating that geothermal development and ground subsidence have a positive / negative correlation. If the area where the geothermal water exploitation volume is collected also has other underground resource exploitation, the conclusion needs to be determined in combination with the exploitation of other resources.

[0046] Specifically, the geothermal water level elevation value in S3 (2) 2) and the ground subsidence development rate value show synchronous or lagging increase / decrease, indicating that geothermal development and ground subsidence have a negative / positive correlation. If the area where the geothermal water level is collected also has other underground resource exploitation, this conclusion needs to be determined in combination with the exploitation of other resources.

[0047] Specifically, the ground subsidence in the area with dense geothermal wells in S3 (2) (3) is large, indicating that geothermal development and ground subsidence are positively correlated, and the ground subsidence in the area with dense geothermal wells is small, indicating that geothermal development and ground subsidence are negatively correlated. If other underground resources are mined in the area with dense geothermal wells, this conclusion needs to be determined in combination with the mining of other resources.

[0048] Specifically, in S3 (3), if the area where the geothermal development and utilization data is obtained also has other underground resource exploitation, the conclusion needs to be determined comprehensively in combination with the specific exploitation conditions of other resources. Compared with the prior art, the present invention has the following beneficial effects: 1. Previous research and analysis often used the method of comparing trends of changes over the same period, which resulted in a highly subjective analysis process and imprecise conclusions. The conclusions were either correlated or uncorrelated, and it was impossible to compare the strength of the correlation between geothermal development and ground subsidence in different regions, nor was it possible to analyze the correlation between the two under the condition that there was a lag in the impact of geothermal development on ground subsidence. This method can qualitatively and quantitatively evaluate the impact of geothermal development on ground subsidence, and determine the discrimination thresholds for qualitative and quantitative evaluations. This method introduces the discrimination criteria for qualitative evaluation, clarifies the thresholds for "uncorrelated" or "correlated", and constructs a model that can provide a direct basis for quantitatively analyzing the impact of geothermal on ground subsidence and comparing the degree of impact of geothermal development on ground subsidence in different regions; 2. This method standardizes and proceduralizes the evaluation method. By comparing and analyzing the qualitative and quantitative results, the impact of geothermal development on ground subsidence is comprehensively obtained. This method expands and enriches the evaluation method of the impact analysis of geothermal development on ground subsidence, greatly reduces the influence of human factors on the results during the qualitative analysis process, quantifies the impact of geothermal development on ground subsidence, and obtains the degree of impact of geothermal development on ground subsidence through comprehensive analysis of multiple methods. 3. The research results obtained in different regions using the same method can be compared and analyzed to obtain the degree and ranking of the impact of geothermal development on ground subsidence in different regions, which enhances the comparability of the analysis of the impact of geothermal development on ground subsidence between different regions and has a reference significance for guiding the region to carry out ground subsidence prevention and control work. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0050] Figure 1 It is the technical roadmap of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] A method for evaluating the correlation between geothermal development and ground subsidence. 1. Data Collection Long-term monitoring data were collected, including geological background conditions of a certain area in Shandong Province (including geological structure, groundwater, geothermal water development layer), ground subsidence monitoring data, groundwater level elevation and water level depth data, groundwater extraction volume, geothermal extraction volume, geothermal water level elevation and water level depth data.

[0053] 2. Data Preprocessing Preprocessing of data mainly includes: processing missing values ​​and outliers, ensuring data accuracy, consistency of time and units, and correspondence of data. This includes annual sorting of geothermal production in the same area, annual ground subsidence volume / rate of ground subsidence centers, and pre-season recovery water levels in geothermal water funnel centers as annual water levels.

[0054] 3. Preliminary analysis (qualitative analysis) (1) Analysis of geological conditions of geothermal development layers From the perspective of geological conditions and geotechnical mechanics, it is analyzed whether there is a correlation between geothermal development and ground subsidence. The vertical distribution characteristics of regional lithology are sorted out, including the depth, lithology characteristics, geotechnical mechanics characteristics, development and utilization intensity of the geothermal development layer, the lithology, geotechnical mechanics characteristics, development and utilization intensity of the strata above the geothermal development layer, and the lithology, thickness, geotechnical mechanics parameters, development and utilization layer and strength of the overlying strata above the geothermal development layer are combined to qualitatively analyze the impact of geothermal development on the strata above the geothermal development layer. Considering that the geothermal development layer is generally deep, the support of the strata above the geothermal development layer needs to be considered. It is generally believed that the overlying strata with good consolidation and large thickness (more than 100m), or mudstone, shale and other well-consolidated strata without groundwater exploitation, have good support capacity. Under such stratum conditions, the lower geothermal development has little impact on the upper strata, and the ground subsidence of the geothermal layer that may be caused by geothermal development is less likely to cause ground subsidence on the surface.

[0055] The upper part of the geothermal development layer in the study area is covered by a stratum mainly composed of mud shale with a thickness of about 400m, and this layer lacks development and utilization. The mud shale is well consolidated, and the stratum has good supporting force, forming a supporting layer. The upper part of the supporting layer is the deep groundwater development layer, which separates the upper deep groundwater development layer from the lower geothermal development layer. The upper groundwater exploitation activities are difficult to receive the recharge of the geothermal water aquifer, and the ground subsidence of the geothermal layer that may be caused by geothermal development is also difficult to cause ground subsidence on the surface.

[0056] (2) Time series analysis 1) Draw a time series diagram of land subsidence, geothermal extraction and groundwater extraction by year, and analyze the changing trends of the two. The study found that the groundwater extraction and land subsidence development rates increase synchronously (or lagging), while the land subsidence rate and geothermal water extraction do not increase or decrease synchronously (or lagging), indicating that groundwater development and land subsidence are positively correlated, while geothermal development and land subsidence are not significantly correlated.

[0057] 2) Draw a time series diagram of land subsidence rate, geothermal water depth, and groundwater depth by year to observe the changing trends of land subsidence and the two. The study found that the land subsidence rate and groundwater depth showed a synchronous (or lagging) trend of "water depth increased significantly - land subsidence rate developed significantly" or "water depth increased trend slowed down - land subsidence rate developed slowly", while land subsidence and geothermal water depth did not show the above law synchronously (or lagging), indicating that geothermal development and land subsidence were not significantly correlated, but geothermal development and groundwater were correlated.

[0058] (3) Spatial analysis Draw the spatial distribution map of ground subsidence and the spatial distribution map of geothermal wells, and identify the spatial relationship between the subsidence center and the geothermal extraction wells.

[0059] This case study found that areas with dense geothermal wells are concentrated in the centers of urban areas, with a small and concentrated distribution range. Relative to the distribution range of geothermal wells, the distribution range of ground subsidence is wide, and the center of ground subsidence does not correspond to the distribution center of geothermal wells. There are also many cases where there is no geothermal development but the ground subsidence rate is still relatively high, indicating that there may be no correlation between geothermal development and ground subsidence.

[0060] The above qualitative analysis conclusions still cannot directly give a definite conclusion on whether geothermal development is related or not to ground subsidence. The final conclusion still needs to be obtained in combination with quantitative analysis and comprehensive evaluation.

[0061] 4. Correlation analysis (quantitative analysis) Since there is groundwater exploitation in a geothermal development area in Shandong, we collected and organized data such as the ground subsidence rate of a certain city's ground subsidence center from 1997 to the present, geothermal exploitation in the urban area where the subsidence center is located, shallow groundwater exploitation, deep groundwater exploitation, geothermal water level, and deep groundwater level.

[0062] The Pearson correlation coefficient r was calculated from the above data, and the Pearson correlation coefficient between the ground subsidence rate and the amount of geothermal water extraction was -0.40, the Pearson correlation coefficient between ground subsidence and shallow groundwater was 0.25, and the Pearson correlation coefficient between ground subsidence and deep groundwater was 0.55. From the above results, it can be seen that geothermal extraction is negatively correlated with ground subsidence, ground subsidence is significantly positively correlated with deep groundwater extraction, and has no obvious correlation with shallow groundwater extraction.

[0063] The Pearson correlation coefficient of the ground subsidence rate and the annual variation of geothermal water level and the annual variation of deep groundwater level (water level drop is recorded as a negative value, and water level rise is recorded as a positive value) was calculated, and the Pearson correlation coefficient of the ground subsidence rate and the annual variation of geothermal water level was 0.35, indicating that the greater the annual variation of geothermal water level (the greater the rise in water level), the greater the ground subsidence rate. In fact, geothermal exploitation will cause the geothermal water level to drop. Therefore, this study shows that geothermal exploitation will not cause ground subsidence; the Pearson correlation coefficient of the ground subsidence rate and the deep groundwater level variation is -0.40, indicating that the smaller the deep groundwater level variation (the smaller the water level variation, or even a drop in water level), the greater the subsidence rate, and groundwater exploitation will cause the water level in the corresponding layer to drop. This study shows that deep groundwater exploitation will cause ground subsidence.

[0064] The Spearman correlation coefficient of the ground subsidence rate and the geothermal water level fluctuation (water level drop is recorded as a negative value, and water level rise is recorded as a positive value) was calculated. The Spearman correlation coefficient of the subsidence rate and the geothermal water level fluctuation was calculated to be about 0.489, indicating that there is a moderate positive correlation between the two sets of data on the subsidence rate and the geothermal water level fluctuation. That is, the greater the fluctuation of the geothermal water level (i.e. the increase of the geothermal water level), the greater the ground subsidence rate. In fact, geothermal development will cause the geothermal water level to drop. Therefore, this study shows that geothermal exploitation does not cause ground subsidence.

[0065] The Spearman correlation coefficient of the ground subsidence rate and the deep groundwater level fluctuation (water level drop is recorded as a negative value, and water level rise is recorded as a positive value) was calculated. The Spearman correlation coefficient of the subsidence rate and the deep groundwater level fluctuation was calculated to be about -0.146, indicating that there is a weak negative correlation between the two sets of data on the subsidence rate and the deep groundwater level fluctuation. That is, the smaller the groundwater level fluctuation value (i.e. the groundwater level drops), the greater the ground subsidence rate. In fact, deep groundwater development will cause the deep groundwater level to drop. Therefore, this study shows that deep groundwater exploitation will cause ground subsidence.

[0066] The results obtained from the combined Pearson correlation coefficient and Spearman correlation coefficient show that geothermal development will not lead to ground subsidence, and the main influencing factor of ground subsidence in the area is groundwater exploitation.

[0067] Since the measured data did not find any correlation between geothermal development and ground subsidence, the model was no longer established. In order to further verify the analysis results, experimental verification is planned.

[0068] 5. Experimental verification Conduct field tests or laboratory simulations to validate model results.

[0069] In 2020, a layered marker group was built near the geothermal development project with the longest history of geothermal development in the area. The deepest layered marker is 1200m. The bottom of the 1200m layered marker is located on the mudstone above the geothermal development layer. Through the multi-year monitoring data of the elevation of the layered marker head, the impact of the geothermal development layer on the ground subsidence can be obtained. After leveling the height of the 1200m layered marker head in 2021 and 2022, it was found that the elevation of the marker head has not dropped. Therefore, the measured data of the layered marker shows that the geothermal development in the area did not induce ground subsidence in 2021-2022. In order to ensure the accuracy of the field test data, the observation period of the measured results should be extended.

[0070] 6. Results Analysis A comprehensive summary and analysis of the above results showed that geothermal development had no impact on ground subsidence.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the correlation between geothermal development and ground subsidence, characterized in that: include S1. Data collection: Collect the ground subsidence monitoring data, groundwater level elevation, geological background conditions, geothermal water extraction, groundwater extraction, groundwater level, long-term monitoring data of geothermal water level, geotechnical mechanics test data, etc. in the study area; S2. Data preprocessing: processing missing values ​​and outliers to ensure data accuracy, consistency of time and units, and correspondence of data; S3. Preliminary analysis: (1) Analysis of geological conditions of geothermal development layers: Analyze whether there is a correlation between geothermal development and ground subsidence from the perspectives of geological structure, rock and soil mechanics, etc.; (2) Time series analysis: 1) Draw a time series diagram of ground subsidence and geothermal extraction, and analyze their changing trends; 2) Draw a time series diagram of ground subsidence and geothermal water level elevation or burial depth, and analyze the changing trends of the two; (3) Spatial analysis: Draw the spatial distribution map of ground subsidence and geothermal wells in the same period, and identify the spatial relationship between the subsidence center and the geothermal wells; draw the spatial distribution map of ground subsidence and geothermal water level in the same period, and identify the spatial relationship between the subsidence center and the center of the geothermal water funnel. For ground subsidence areas with lack of spatial data, use Kriging interpolation or remote sensing interpretation to obtain the subsidence distribution of unknown areas; S4. Correlation analysis: (1) Statistical correlation: calculate the Pearson correlation coefficient or Spearman correlation coefficient between the annual or multi-year land subsidence rate and geothermal production, geothermal water level, number of geothermal wells and other variables. The Pearson correlation coefficient is calculated as follows: and Data values ​​for ground subsidence and geothermal extraction volume / geothermal water level / geothermal well number; and is the mean of the above two variables, and the value range of r is between -1 and 1; (2) lagged correlation, using the cross-correlation function to analyze the lagged effect of geothermal mining on ground subsidence; The calculation method is: 1) Prepare two sets of time series, one set of series represents geothermal development, and the other set of series represents ground subsidence development. The two sets of time series are required to be simultaneous and of the same length; 2) Calculate the standard deviation and mean of two time series; 3) Calculate the cross-correlation function, For each lag period τ, the cross-correlation coefficient is calculated. The cross-correlation coefficient is calculated as follows: 4) Plot the cross-correlation coefficients of different lags τ. The lag period corresponding to the maximum absolute value of the cross-correlation coefficient is the lag of the two; S5. Build the model: (1) Regression analysis A multivariate regression model was established to analyze the impact of geothermal extraction, geothermal water level and other factors on ground subsidence. The contribution of each factor to ground subsidence can be determined through the regression coefficient. (2) Time Series Model Using vector autoregression model, vector error correction model, autoregression model with exogenous variables, seasonal ARIMA model with exogenous variables, Bayesian structural time series model, long short-term memory network, support vector regression, wavelet neural network, adaptive network fuzzy inference system, etc., a model of land subsidence changing with influencing factors is established to predict the impact and trend of geothermal mining on land subsidence; S6. Numerical simulation: Use one or more numerical simulation technology combinations such as finite element method, finite difference method, discrete element method, fluid-solid coupling method, elastoplastic model, geological process simulation, multi-physics field coupling model, nonlinear regression and machine learning methods, wavelet analysis and multi-scale simulation to simulate the impact of geothermal mining on underground geological structure and ground subsidence; S7. Experimental verification: Conduct field tests or laboratory simulations to verify analytical results; S8. Result analysis: Based on the above qualitative, quantitative, model, simulation and experimental results, a comprehensive analysis is conducted to reach a conclusion.

2. The method for evaluating the correlation between geothermal development and ground subsidence according to claim 1, characterized in that: The geological conditions of the geothermal development layer are analyzed in (1) of S3, including the depth, lithology, geotechnical mechanics, development and utilization intensity of the geothermal development layer, the lithology, geotechnical mechanics, development and utilization intensity of the strata above the geothermal development layer, and the impact of geothermal development on ground subsidence of the geothermal development layer under different periods of geothermal development intensity. The impact of geothermal development on the geothermal development layer and the strata above the geothermal development layer is qualitatively analyzed in combination with the lithology, thickness, geotechnical mechanics parameters, development and utilization layer and intensity of the overlying strata above the geothermal development layer.

3. The method for evaluating the correlation between geothermal development and ground subsidence according to claim 1, characterized in that: The amount of geothermal extraction in S3 (2) 1) and the rate of ground subsidence increase / decrease synchronously or with a lag, indicating that geothermal development and ground subsidence have a positive / negative correlation.

4. The method for evaluating the correlation between geothermal development and ground subsidence according to claim 1, characterized in that: The geothermal water level elevation values ​​in S3 (2) 2) and the ground subsidence development rate values ​​show synchronous or delayed increase / decrease, indicating that geothermal development and ground subsidence have a negative / positive correlation.

5. The method for evaluating the correlation between geothermal development and ground subsidence according to claim 1, characterized in that: In S3 (2) (3), the ground subsidence is large in the area with dense geothermal wells, indicating that geothermal development is positively correlated with ground subsidence, while the ground subsidence is small in the area with dense geothermal wells, indicating that geothermal development is negatively correlated with ground subsidence.

6. The method for evaluating the correlation between geothermal development and ground subsidence according to claim 1, characterized in that: In S3(2) and S3(3) above, if the area where geothermal development and utilization data is obtained also has other underground resource exploitation, the conclusion shall be determined comprehensively in combination with the specific exploitation conditions of other resources.

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