Geothermal resource magma heat source discrimination method, system, equipment and medium

Abstract

The invention provides a geothermal resource magma heat source discrimination method, system and device and a medium, and belongs to the technical field of geothermal resource development, and the method comprises the steps: obtaining the geological data of a target region geothermal resource; based on the geological data, judging whether a first preset condition for indicating the existence of the magma heat source is met; if the first preset condition is met, determining that the heat source of the geothermal resource in the target area is a magma heat source; if the first preset condition is not met, whether a second preset condition indicating a non-magma heat source is met or not is judged based on the geological data; if the second preset condition is met, determining that the heat source of the geothermal resource in the target area is a non-magma heat source; if neither the first preset condition nor the second preset condition is met, inputting the geological data into a pre-trained heat source discrimination model, and outputting a comprehensive discrimination value representing the type of the heat source; and judging the heat source type of the geothermal resources in the target area according to a comparison result of the comprehensive judgment value and a preset threshold value.

Description

Technical Field

[0001] This application relates to the field of geothermal resource development technology, and in particular to a method, system, equipment and medium for identifying magma heat sources in geothermal resources. Background Technology

[0002] Geothermal resources, as a clean and renewable energy source, have broad application prospects in heating, power generation, medical treatment and other fields; accurately identifying the heat source type of geothermal resources is a key prerequisite for the development and utilization of geothermal resources.

[0003] Current methods for identifying geothermal resource heat sources mainly rely on single geological features or simple discrimination rules, such as judging solely based on geological structure or rock type. This often results in significant errors and limitations in the identification results. Furthermore, for geothermal resources under complex geological conditions, existing methods struggle to accurately distinguish between magmatic and non-magmatic heat sources, thus failing to provide a reliable basis for the exploration and development of geothermal resources.

[0004] Therefore, there is an urgent need for a more accurate and comprehensive method for identifying geothermal heat sources to adapt to different geological conditions, improve the accuracy of identification, and provide strong support for the rational development and utilization of geothermal resources. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, system, equipment, and medium for identifying magma heat sources in geothermal resources, used to accurately determine the heat source type of geothermal resources.

[0006] A first aspect of this application provides a method for identifying magma heat sources in geothermal resources, including:

[0007] Obtain geological data for the target geothermal resource area;

[0008] Based on the geological data, determine whether the first preset condition indicating the existence of a magmatic heat source is met;

[0009] If the first preset condition is met, the heat source of the target geothermal resource is determined to be a magma heat source;

[0010] If the first preset condition is not met, then based on the geological data, it is determined whether the second preset condition indicating a non-magmatic heat source is met;

[0011] If the second preset condition is met, the heat source of the target geothermal resource is determined to be a non-magmatic heat source.

[0012] If neither the first preset condition nor the second preset condition is met, the geological data is input into the pre-trained heat source discrimination model, and a comprehensive discrimination value characterizing the heat source type is output.

[0013] Based on the comparison between the comprehensive discrimination value and the preset threshold, the heat source type of the target geothermal resource is determined.

[0014] A second aspect of this application provides a geothermal resource magma heat source identification system, comprising:

[0015] The data acquisition module is used to acquire geological data of the target geothermal resource area;

[0016] The first judgment module is used to determine, based on the geological data, whether the first preset condition indicating the existence of a magma heat source is met;

[0017] If the first preset condition is met, the heat source of the target geothermal resource is determined to be a magma heat source;

[0018] The second judgment module is used to determine whether the second preset condition indicating a non-magmatic heat source is met based on the geological data if the first preset condition is not met.

[0019] If the second preset condition is met, the heat source of the target geothermal resource is determined to be a non-magmatic heat source.

[0020] The third judgment module is used to input the geological data into the pre-trained heat source discrimination model and output a comprehensive discrimination value characterizing the heat source type if neither the first preset condition nor the second preset condition is met.

[0021] Based on the comparison between the comprehensive discrimination value and the preset threshold, the heat source type of the target geothermal resource is determined.

[0022] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for identifying geothermal resource magma heat sources.

[0023] In a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for identifying magma heat sources of geothermal resources.

[0024] The beneficial effects of the geothermal resource magma heat source identification method, system, equipment, and medium provided in this application are as follows: By setting first and second preset conditions, this application first uses a simple and intuitive judgment method to quickly identify magma heat sources and non-magma heat sources that meet obvious characteristics, greatly improving the identification efficiency, reducing unnecessary complex calculations, and saving time and resource costs. Furthermore, for cases where direct identification through preset conditions is not possible, a pre-trained heat source identification model is introduced. Utilizing the model's powerful learning and analysis capabilities, a comprehensive identification value is output, and a final determination is made based on the comparison with a preset threshold; this improves the accuracy and reliability of the identification results and avoids missed and false identifications. This identification method combining multiple approaches can adapt to complex and varied geological conditions, providing accurate basis for the exploration and development of geothermal resources. Attached Figure Description

[0025] Figure 1 A flowchart illustrating a method for identifying magma heat sources in geothermal resources, provided in an embodiment of this application;

[0026] Figure 2 This application provides a structural block diagram of a geothermal resource magma heat source identification system according to an embodiment of the present application.

[0027] Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0029] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1-3 The following is an explanation using specific examples.

[0030] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for identifying magma heat sources in geothermal resources, provided in one embodiment of this application. The method includes:

[0031] S101: Obtain geological data on geothermal resources in the target area.

[0032] In this embodiment, geological data of geothermal resources in the target area are obtained through various means such as geological exploration, geophysical exploration, and geochemical analysis. The geological data includes at least stratigraphic structure data, rock type data, structural feature data, geophysical field data (such as gravity field data, magnetic field data, and geothermal field data), and geochemical data (such as hot spring water chemical composition data and gas composition data). Stratigraphic structure data is obtained through well core analysis and geological profile mapping; geophysical field data is collected using equipment such as gravimeters, magnetometers, and geothermal measuring devices; and geochemical data is obtained by collecting geothermal water samples and gas samples and performing component analysis using instruments such as spectrometers and chromatographs.

[0033] In this embodiment, various types of geological data are integrated to establish a unified data storage format and database. For missing or outlier values, preprocessing methods such as interpolation and statistical analysis are used to obtain complete geological data. Furthermore, this embodiment standardizes the data to ensure comparability of different types and units of data, preparing for subsequent heat source identification analysis.

[0034] S102: Based on geological data, determine whether the first preset condition indicating the existence of a magmatic heat source is met;

[0035] If the first preset condition is met, the heat source of the geothermal resources in the target area is determined to be a magma heat source.

[0036] In this embodiment, the acquired geological data is systematically analyzed according to pre-set geological characteristic standards for magmatic heat sources. The data is compared with a typical magmatic heat source geological characteristic database, which covers information such as common stratigraphic deformation characteristics, rock metamorphism degree, and distribution patterns of magmatic rocks in magmatic activity areas. Key indicators, such as the high-temperature metamorphic mineral assemblage of rocks and the morphology and scale of magmatic intrusions in the strata, are extracted from the geological data. Through quantitative analysis and comparison with the typical magmatic heat source geological characteristic database, it is determined whether the first preset condition is met. If the extracted indicators highly match the geological characteristics of magmatic heat sources, the condition is considered met; otherwise, it is not met.

[0037] In this embodiment, once the geological data meets the first preset condition, the geothermal resource source in the target area is clearly determined to be a magma source. Simultaneously, this determination result is recorded in the geological data archive, and a geological report related to the magma source is generated, detailing the basis for the determination and the geological characteristics of the target area related to the magma source.

[0038] S103: If the first preset condition is not met, then based on geological data, determine whether the second preset condition indicating a non-magmatic heat source is met;

[0039] If the second preset condition is met, the heat source of the geothermal resources in the target area is determined to be a non-magmatic heat source.

[0040] In this embodiment, if the first preset condition is not met, the analysis then shifts to whether the geological data meets the second preset condition indicating a non-magmatic heat source. Based on the typical geological characteristics of non-magmatic heat sources, key parameters in the geological data, such as geothermal gradient, rock radioactive heat generation rate, and the presence of melt or magma chamber characteristic signals, are analyzed in depth. A professional data analysis model is used to comprehensively evaluate these parameters to determine whether they meet the requirements of the second preset condition. Once it is determined that the geological data meets the second preset condition, the heat source of the geothermal resources in the target area is identified as a non-magmatic heat source. Similarly, this determination result is recorded in detail, and a non-magmatic heat source geological report is generated, analyzing the possible types of non-magmatic heat sources (such as radioactive decay heat generation, tectonic friction heat generation, etc.) and their impact on geothermal resources.

[0041] S104: If neither the first preset condition nor the second preset condition is met, the geological data will be input into the pre-trained heat source discrimination model, and a comprehensive discrimination value representing the type of heat source will be output.

[0042] Based on the comparison between the comprehensive discrimination value and the preset threshold, the heat source type of geothermal resources in the target area is determined.

[0043] In this embodiment, when the geological data does not meet either the first preset condition or the second preset condition, the complete geological data is input into a pre-trained heat source discrimination model. This model uses a machine learning algorithm and is trained based on a large amount of historical geothermal resource sample data. It can perform in-depth feature extraction and analysis on the input geological data and output a comprehensive discrimination value that characterizes the type of heat source through complex calculations and judgments. This value reflects the probability of different types of heat sources.

[0044] This embodiment compares the comprehensive discrimination value output by the heat source discrimination model with a pre-set threshold; based on different comparison results, the heat source type of the geothermal resources in the target area is clearly determined. If the comprehensive discrimination value is higher than a certain threshold, it is determined to be a specific heat source type; if it is lower than another threshold, it is determined to be another heat source type; if it is within a specific range, further supplementary judgment is made by combining other geological information or using more refined analysis methods to improve the accuracy and reliability of the final judgment result.

[0045] When the comprehensive discrimination value is within a specific range, supplementary judgment can be made by expert consultation and multi-source data fusion. Experts in geology, geophysics, geochemistry and other fields can be invited to analyze and discuss the geological data and model output results. At the same time, other relevant data, such as satellite remote sensing data and hydrogeological data, can be integrated to conduct comprehensive analysis from multiple perspectives and finally determine the heat source type of geothermal resources in the target area.

[0046] As can be seen from the above, this application, by setting first and second preset conditions, quickly identifies magmatic and non-magmatic heat sources that meet obvious characteristics using a simple and intuitive judgment method, greatly improving the discrimination efficiency, reducing unnecessary complex calculations, and saving time and resource costs. Furthermore, for cases where direct judgment cannot be made using preset conditions, a pre-trained heat source discrimination model is introduced. Utilizing the model's powerful learning and analysis capabilities, a comprehensive discrimination value is output, and the final judgment is made based on the comparison with preset thresholds; this improves the accuracy and reliability of the discrimination results and avoids missed and false judgments. This multi-method discrimination approach of this application can adapt to complex and varied geological conditions, providing accurate basis for the exploration and development of geothermal resources.

[0047] In one embodiment of this application, determining whether geological data meets a first condition indicating the presence of a magmatic heat source includes:

[0048] Calculate the similarity between the pre-defined geological sample dataset of magma heat sources and the geological data;

[0049] If the similarity is greater than the first threshold, then the first condition is satisfied.

[0050] If the similarity is less than or equal to the first threshold, then the first condition is not met.

[0051] In this embodiment, calculating the similarity between the preset magma heat source geological sample dataset and geological data includes:

[0052] Based on a pre-set geological sample dataset of magma heat sources, feature vectors of the sample magma heat sources are extracted;

[0053] Based on geological data, the target feature vector is extracted;

[0054] Calculate the similarity between the feature vector of the sample magma heat source and the feature vector of the target.

[0055] In this embodiment, the preset magma heat source geological sample dataset is derived from the long-term geological exploration and research results accumulated in typical magma heat source areas around the world. The method for processing the preset magma heat source geological sample dataset includes: cleaning the dataset to remove noisy data and outliers; then, using professional geological data analysis tools, such as geostatistics software and petrology analysis software, to extract key geological feature information from the sample data. The geological feature information includes multiple dimensions such as the mineral composition, structure, chemical composition, stratigraphic contact relationship, and magmatic activity chronology data.

[0056] The extracted feature information is quantified and encoded to construct a sample magma heat source feature vector, which can comprehensively and accurately reflect the essential characteristics of the geological sample of the magma heat source.

[0057] In this embodiment, the acquired geological data of the target area are also subjected to data preprocessing operations, including data standardization and normalization; this operation makes data of different types and magnitudes comparable.

[0058] Based on the preset dimension of the magma heat source feature vector, the corresponding feature information is extracted from the geological data of the target area to generate the target feature vector. In the extraction process, this embodiment fully considers the multi-source and complexity of the geological data, so that the extracted feature information can truly reflect the geological conditions of the target area and the potential correlation between the magma heat source and the magma heat source.

[0059] In this embodiment, to accurately calculate the similarity between the sample magma heat source feature vector and the target feature vector, a suitable similarity calculation algorithm is selected based on the characteristics of the geological data and the computational requirements. Examples include Euclidean distance, cosine similarity, and Manhattan distance algorithms. This embodiment can calculate the similarity using Euclidean distance and / or cosine similarity algorithms.

[0060] In this embodiment, the constructed sample magma heat source feature vector and target feature vector are input into a selected similarity calculation algorithm for specific similarity calculation. During the calculation process, different types of geological feature data are assigned corresponding weights based on their importance and sensitivity to magma heat source indication. For example, features such as high-temperature metamorphic mineral assemblages in rocks, and the morphology and scale of magmatic intrusions are more critical to indicating magma heat sources and can be assigned higher weights; while some minor stratigraphic structural features are assigned relatively lower weights. Through weighted calculation, the final similarity value between the sample magma heat source feature vector and the target feature vector is obtained.

[0061] In this embodiment, the first threshold is the key basis for determining whether the first condition is met; the first threshold can be determined based on historical sample data statistics. Alternatively, this embodiment can also determine a reasonable similarity threshold as the first threshold by performing similarity calculations and analyses on geological data from a large number of known magma heat source regions and non-magma heat source regions, combined with the experience and research results of geological experts.

[0062] In this embodiment, the calculated similarity value is compared with a first threshold. If the similarity value is greater than the first threshold, it indicates that the geological data of the target area is highly similar to the preset magma heat source geological sample dataset, and the geological features of the target area are highly consistent with the geological features of the magma heat source. Thus, it is determined that the first condition is met, that is, the target area may have a magma heat source. If the similarity value is less than or equal to the first threshold, it indicates that the geological data of the target area is less similar to the preset magma heat source geological sample dataset, and does not have typical magma heat source geological features. It is determined that the first condition is not met, and it is necessary to further determine whether the second preset condition indicating a non-magma heat source is met or to analyze it with the help of a pre-trained heat source discrimination model.

[0063] This embodiment identifies magmatic heat sources by calculating the similarity between a preset magmatic heat source geological sample dataset and geological data of the target area. This method transforms geological feature comparison into quantitative similarity calculation, making the identification of magmatic heat sources more objective. With the help of the preset magmatic heat source geological sample dataset, this embodiment can quickly match similar geological features, which greatly improves the identification efficiency compared with traditional manual identification. At the same time, the judgment rule based on similarity threshold reduces the interference of human factors and improves the consistency and accuracy of magmatic heat source identification.

[0064] In one embodiment of this application, calculating the similarity between a preset magma heat source geological sample dataset and geological data further includes:

[0065] Seasonal characteristics are extracted based on environmental parameters from geological data.

[0066] Based on seasonal characteristics, the similarity is obtained by weighting the feature vectors of the sample magma heat source and the target feature vector.

[0067] In this embodiment, environmental parameters related to seasonal changes are comprehensively collected from geological data, including but not limited to atmospheric temperature, precipitation data, vegetation cover, and soil moisture. This embodiment can monitor environmental parameters of the target area using ground-based meteorological stations, satellite remote sensing platforms, and underground monitoring sensors. The ground-based meteorological stations are equipped with thermometers, hygrometers, barometers, anemometers, and other equipment to collect parameters such as air temperature, precipitation, and relative humidity at hourly intervals. The satellite remote sensing platform acquires information such as surface temperature and vegetation index. Underground monitoring sensors are deployed at key locations in the thermal reservoir to monitor groundwater temperature and level changes in real time.

[0068] The collected environmental parameters are filtered to remove data with a large number of outliers and missing values.

[0069] Time series analysis was used to process the selected environmental parameters. The processing methods included dividing the year into four seasons: spring, summer, autumn, and winter, and calculating the average temperature and standard deviation of temperature for each season to characterize its temperature features. For precipitation data, the total precipitation and number of rainy days for each season were statistically analyzed. Simultaneously, principal component analysis was used to reduce the dimensionality of multiple environmental parameters, extracting the main components that comprehensively reflect seasonal variations to form a seasonal feature vector.

[0070] The impact of environmental factors on geothermal resources varies across seasons, thus affecting the heat source characteristics reflected in geological data. For example, increased rainfall in summer may lead to rising groundwater levels, altering the heat conduction conditions of geothermal reservoirs and causing changes in geothermal data; in winter, low temperatures may affect the accuracy of surface heat flow measurements. Therefore, when calculating the similarity between the sample magma heat source feature vector and the target feature vector, it is necessary to weight features of different dimensions according to seasonal characteristics, highlighting features more relevant to the current season and reducing the weight of features more susceptible to seasonal interference and less relevant to heat source identification. This will more accurately reflect the true similarity between the target area and the magma heat source sample.

[0071] This embodiment employs the analytic hierarchy process (AHP) to determine the weight coefficients of each feature dimension. The method includes: constructing a hierarchical model, using seasonal features as the target layer and various features in the geological data (such as rock chemical composition and geothermal gradient) as the criterion layers; inviting geological experts to conduct pairwise comparisons of the importance of each criterion layer feature for heat source identification in different seasons to construct a judgment matrix; and calculating the maximum eigenvalue and eigenvector of the judgment matrix to obtain the weight coefficients of each feature dimension in the current season. For example, in summer, because precipitation's dilution effect on geothermal fluids may affect geochemical features, the weight of geochemical features can be reduced and the weight of rock structure features, which are less affected by precipitation, increased when calculating similarity.

[0072] This embodiment combines the extracted seasonal feature vector with determined weighting coefficients to weight each dimension of the sample magma heat source feature vector and the target feature vector. Specifically, the calculation method is as follows: for each dimension of the feature vector, the feature value of that dimension is multiplied by the corresponding weighting coefficient to obtain the weighted feature value. Then, according to the selected similarity calculation algorithm (such as cosine similarity algorithm, Euclidean distance algorithm, etc.), the similarity between the weighted sample magma heat source feature vector and the target feature vector is calculated.

[0073] Since environmental changes in different seasons can cause fluctuations in geothermal data, this embodiment uses weighted processing based on seasonal characteristics to dynamically adjust the weight of geological features, highlighting features that are more indicative of heat source identification in specific seasons. This effectively reduces the interference caused by seasonal factors, makes the similarity calculation results more consistent with the actual geothermal heat source situation, and improves the accuracy and stability of heat source identification.

[0074] In this embodiment, the seasonal characteristics are assumed to be: (For example, using 0 to 1 to represent the time of year, 0 for the beginning of spring, 0.25 for summer, 0.5 for autumn, and 0.75 for winter, or 0 for the beginning of the dry season, 0.5 for the peak of the rainy season, and 1 for the end of the dry season), then for each feature k, define a seasonal weighting function:

[0075]

[0076] in, Based on the weights, For the range of seasonal variation, This is the phase shift (determined based on the sensitivity of different features to the seasons).

[0077] In this embodiment, the similarity calculation formula is:

[0078]

[0079] in, This represents the total number of feature dimensions. Indexed by feature dimension; For sample feature values; These are the feature values ​​of the target region.

[0080] In one embodiment of this application, determining whether geological data meets the first condition indicating the existence of a magmatic heat source further includes:

[0081] The determination of whether the first preset condition is met is based on the geothermal water lithium concentration of the target area and the preset first concentration.

[0082] If the concentration of lithium in geothermal water in the target area is greater than or equal to the first concentration (5 mg / L), then the first preset condition is met.

[0083] If the concentration of lithium in the geothermal water of the target area is less than the first concentration (5 mg / L), it is determined that the first preset condition is not met.

[0084] In one embodiment of this application, determining whether geological data meets the first condition indicating the existence of a magmatic heat source further includes:

[0085] The geothermal reservoir temperature of the target area is calculated using a quartz temperature scale.

[0086] If the concentration of lithium in the geothermal water of the target area is greater than or equal to the first concentration (5 mg / L), and the thermal storage temperature of the geothermal resources in the target area is greater than or equal to the first thermal storage temperature (150°C), then the first preset condition is met.

[0087] If the concentration of lithium in the geothermal water of the target area is less than the first concentration (5 mg / L) and the thermal storage temperature of the geothermal resources in the target area is less than the first thermal storage temperature (150°C), then the first preset condition is not met.

[0088] In one embodiment of this application, determining whether a second preset condition indicating a non-magmatic heat source is met based on geological data includes:

[0089] If the geological data meets at least one of the combined characteristics, then the second preset condition is not met.

[0090] When geological data simultaneously satisfy all the criteria in the combined characteristics, it is determined that the second preset condition is met.

[0091] The second preset condition includes: combined features

[0092] Combined features, including:

[0093] The geothermal gradient value in the geological data is less than the preset gradient threshold;

[0094] In geological data, the radioactive heat generation rate of the thermal reservoir rocks is greater than the preset heat generation rate threshold.

[0095] No signals indicating melt or magma chamber characteristics were detected in the geological data.

[0096] In this embodiment, the geothermal gradient refers to the rate of change of the Earth's internal temperature with increasing depth, expressed in °C / km. The geothermal gradient in non-magmatic geothermal source regions is mainly controlled by factors such as rock thermal conductivity and radioactive heat generation rate. Through statistical analysis of over 500 known non-magmatic geothermal fields globally, combined with numerical simulations of the heat conduction equation, it was determined that the geothermal gradient in non-magmatic geothermal source regions is less than 30 °C / km. Therefore, a preset gradient threshold of 30 °C / km is set. When the geothermal gradient value in the target area is less than this threshold, the first criterion of the combined feature is met.

[0097] In this embodiment, the preset heat generation rate threshold of 2.5 μW / m³ was determined by testing the radioactivity heat generation rate of typical non-magmatic heat source reservoir rocks such as granite and gneiss. Field measurements were performed using a gamma-ray spectrometer, combined with thin-section microscopy and electron probe microanalysis to obtain data on the rock mineral composition and radioactive element content.

[0098] This embodiment clarifies the judgment logic for meeting and not meeting the second preset condition, making the identification process of non-magmatic heat sources more standardized and regulated, reducing ambiguity and uncertainty in the identification process. At the same time, it improves the accuracy and efficiency of non-magmatic heat source identification.

[0099] In one embodiment of this application, the method for constructing a pre-trained heat source discrimination model includes:

[0100] Obtain a historical geothermal resource sample dataset and label the heat source type of each sample.

[0101] Feature engineering is performed on the sample dataset to generate multidimensional feature vectors, which include geological structural features, geochemical features, and geothermal field features.

[0102] The multidimensional feature vectors are input into the machine learning algorithm model for training, resulting in a heat source discrimination model; where:

[0103] When the sample dataset meets the first preset condition, it is forcibly labeled as a magma heat source.

[0104] When the sample dataset meets the second preset condition, it is forcibly labeled as a non-magmatic heat source.

[0105] The parameters of the machine learning algorithm model are optimized through cross-validation until the overall discriminant value output by the machine learning algorithm model matches the heat source type label to a preset accuracy threshold.

[0106] In this embodiment, a global geothermal resource database is established. When the similarity between sample data and known magmatic heat source samples is greater than a first threshold, and the sample data meets the geological characteristic combination of magmatic heat sources, it is forcibly labeled as a magmatic heat source. When sample data simultaneously meets the three criteria for non-magmatic heat sources, and the similarity with known non-magmatic heat source samples is greater than a second threshold, it is forcibly labeled as a non-magmatic heat source. For samples that cannot meet the forced labeling conditions, the original manual labeling results are retained. The labeled data is input into the heat source discrimination model obtained by the random forest model. The heat source discrimination model in this embodiment can output a comprehensive discrimination value between 0 and 1. This embodiment can determine whether the heat source type of the geothermal resources in the target area is a magmatic heat source or a non-magmatic heat source by comparing it with a preset threshold.

[0107] Corresponding to the geothermal resource magma heat source identification method in the above embodiment, Figure 2 This is a structural block diagram of a geothermal resource magma heat source identification system provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The geothermal resource magma heat source identification system 20 includes: a data acquisition module 21, a first judgment module 22, a second judgment module 23, and a third judgment module 24.

[0108] Among them, the data acquisition module 21 is used to acquire geological data of the target geothermal resource area;

[0109] The first judgment module 22 is used to determine, based on geological data, whether the first preset condition indicating the existence of a magma heat source is met;

[0110] If the first preset condition is met, the heat source of the target geothermal resource is determined to be a magma heat source;

[0111] The second judgment module 23 is used to determine whether the second preset condition indicating a non-magmatic heat source is met based on geological data if the first preset condition is not met.

[0112] If the second preset condition is met, the heat source of the target geothermal resource is determined to be a non-magmatic heat source.

[0113] The third judgment module 24 is used to input geological data into the pre-trained heat source discrimination model and output a comprehensive discrimination value that characterizes the type of heat source if neither the first preset condition nor the second preset condition is met.

[0114] Based on the comparison between the comprehensive discrimination value and the preset threshold, the heat source type of the target geothermal resource is determined.

[0115] In one embodiment of this application, the first determination module 22 is specifically used for:

[0116] Calculate the similarity between the pre-defined geological sample dataset of magma heat sources and the geological data;

[0117] If the similarity is greater than the first threshold, then the first condition is satisfied.

[0118] If the similarity is less than or equal to the first threshold, then the first condition is not met.

[0119] In one embodiment of this application, the first determination module 22 is specifically used for:

[0120] Based on a pre-set geological sample dataset of magma heat sources, feature vectors of the sample magma heat sources are extracted;

[0121] Based on geological data, the target feature vector is extracted;

[0122] Calculate the similarity between the feature vector of the sample magma heat source and the feature vector of the target.

[0123] In one embodiment of this application, the first determination module 22 is further specifically used for:

[0124] Seasonal characteristics are extracted based on environmental parameters from geological data.

[0125] Based on seasonal characteristics, the similarity is obtained by weighting the feature vectors of the sample magma heat source and the target feature vector.

[0126] In one embodiment of this application, the second determination module 23 is specifically used for:

[0127] If the geological data meets at least one of the combined characteristics, then the second preset condition is not met.

[0128] When geological data simultaneously satisfy all the criteria in the combined features, it is determined that the second preset condition is met.

[0129] Combined features, including:

[0130] The geothermal gradient value in the geological data is less than the preset gradient threshold;

[0131] In geological data, the radioactive heat generation rate of the thermal reservoir rocks is greater than the preset heat generation rate threshold.

[0132] No signals indicating melt or magma chamber characteristics were detected in the geological data.

[0133] In one embodiment of this application, the third determination module 24 is specifically used for:

[0134] Obtain a historical geothermal resource sample dataset and label the heat source type of each sample.

[0135] Feature engineering is performed on the sample dataset to generate multidimensional feature vectors, which include geological structural features, geochemical features, and geothermal field features.

[0136] The multidimensional feature vectors are input into the machine learning algorithm model for training to obtain the heat source discrimination model, where:

[0137] When the sample dataset meets the first preset condition, it is forcibly labeled as a magma heat source.

[0138] When the sample dataset meets the second preset condition, it is forcibly labeled as a non-magmatic heat source.

[0139] The parameters of the machine learning algorithm model are optimized through cross-validation until the overall discriminant value output by the machine learning algorithm model matches the heat source type label to a preset accuracy threshold.

[0140] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the data acquisition module 21, the first judgment module 22, the second judgment module 23, and the third judgment module 24 are shown.

[0141] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0142] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0143] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0144] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the geothermal resource magma heat source discrimination method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0145] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0146] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0147] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0148] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0151] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying a geothermal resource magma heat source, characterized by, The method comprises the following steps: obtaining geological data of a target area geothermal resource; determining whether a first preset condition indicating the existence of a magmatic heat source is met based on the geological data; if the first preset condition is met, determining that the heat source of the target area geothermal resource is a magmatic heat source; if the first preset condition is not met, determining whether a second preset condition indicating a non-magmatic heat source is met based on the geological data; if the second preset condition is met, determining that the heat source of the target area geothermal resource is a non-magmatic heat source; if neither the first preset condition nor the second preset condition is met, inputting the geological data into a pre-trained heat source discrimination model to output a comprehensive discrimination value representing the type of heat source; determining the type of heat source of the target area geothermal resource according to the comparison result of the comprehensive discrimination value and a preset threshold value.

2. The method according to claim 1, characterized in that, The determination of whether the geological data meets the first condition indicating the existence of a magmatic heat source comprises the following steps: calculating the similarity between a preset magmatic heat source geological sample data set and the geological data; if the similarity is greater than a first threshold value, determining that the first condition is met; if the similarity is less than or equal to the first threshold value, determining that the first condition is not met.

3. The method according to claim 2, characterized in that, The calculation of the similarity between the preset magmatic heat source geological sample data set and the geological data comprises the following steps: extracting a sample magmatic heat source feature vector based on the preset magmatic heat source geological sample data set; extracting a target feature vector based on the geological data; calculating the similarity between the sample magmatic heat source feature vector and the target feature vector.

4. The method according to claim 3, characterized in that, Further comprising: extracting a seasonal feature based on an environmental parameter in the geological data; performing weighted calculation on the sample magmatic heat source feature vector and the target feature vector based on the seasonal feature to obtain the similarity.

5. The method according to claim 1, characterized in that, The second preset condition comprises a combination feature. The combination feature comprises: a geothermal gradient value in the geological data is less than a preset gradient threshold value; a heat reservoir rock radioactivity heat generation rate in the geological data is greater than a preset heat generation rate threshold value; no feature signal of a melt or magma chamber is detected in the geological data.

6. The method according to claim 5, characterized in that, The determination of whether the geological data meets the second preset condition indicating a non-magmatic heat source comprises the following steps: when the geological data meets at least one of the combination features, it is determined that the second preset condition is not met; when the geological data meets all the determination items in the combination features, it is determined that the second preset condition is met.

7. The method according to claim 1, characterized in that, The construction method of the pre-trained heat source discrimination model comprises the following steps: obtaining a historical geothermal resource sample data set and labeling the heat source type label of each sample; performing feature engineering processing on the sample data set to generate a multi-dimensional feature vector, wherein the multi-dimensional feature vector comprises geological structure features, geochemical features and geothermal field features; inputting the multi-dimensional feature vector into a machine learning algorithm model for training to obtain a heat source discrimination model; wherein: when the sample data set meets a first preset condition, it is forcibly labeled as a magmatic heat source class; when the sample data set meets a second preset condition, it is forcibly labeled as a non-magmatic heat source class; The parameters of the machine learning algorithm model are optimized through cross-validation until the matching degree of the comprehensive discriminant value output by the machine learning algorithm model and the heat source type label reaches a preset accuracy threshold.

8. A geothermal resource magma heat source discrimination system characterized by, The method comprises the steps of: a data acquisition module is configured to acquire geological data of a target geothermal resource area; a first judgment module is configured to determine whether a first preset condition indicating the existence of a magmatic heat source is met based on the geological data; if the first preset condition is met, it is determined that the heat source of the target geothermal resource is a magmatic heat source; a second judgment module is configured to determine whether a second preset condition indicating a non-magmatic heat source is met based on the geological data if the first preset condition is not met; if the second preset condition is met, it is determined that the heat source of the target geothermal resource is a non-magmatic heat source; a third judgment module is configured to input the geological data into a pre-trained heat source discrimination model and output a comprehensive discriminant value representing the type of heat source if neither the first preset condition nor the second preset condition is met; the type of heat source of the target geothermal resource is determined according to the comparison result of the comprehensive discriminant value and a preset threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the method according to any one of claims 1 to 7.

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