Surface heat flow determination method, device and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for determining surface heat flow, relating to the field of geothermal geology. The method includes: acquiring geological information, measured geothermal flow data, and seismic wave velocity data of a target area; determining first temperature profile data based on the measured geothermal flow data and a lithospheric layering model, wherein the lithospheric layering model is constructed based on geological information and regional survey results and includes thermal property parameters of each layer; determining second temperature profile data based on seismic wave velocity data and geological information; determining constraint points between the first and second temperature profile data within a preset depth range; and obtaining the surface heat flow of the target area through reverse heat conduction calculation based on the constraint points and the lithospheric layering model. This application reduces the dependence of the calculation process on deep drilling data through multi-source data fusion and joint constraint mechanisms, improving calculation accuracy and enhancing applicability to complex geological conditions.

Description

Technical Field

[0001] This application relates to the field of geothermal geology technology, and in particular to a method, apparatus and electronic equipment for determining surface heat flow. Background Technology

[0002] Surface heat flow, as a key observational parameter of the Earth's internal thermal state, occupies a pivotal position in geophysics, geology, and geodynamics research. It not only reveals the multi-stage evolutionary history and current thermal state of the lithosphere but also contains rich geophysical information, providing important evidence for understanding the Earth's internal structure, geothermal resource exploration, and geological hazard prediction. With the transformation of the global energy structure and the increasing urgency of the demand for clean energy, geothermal resources, with their green and sustainable characteristics, have become a focus of energy development. Accurate acquisition of surface heat flow data is the cornerstone for scientifically assessing the potential of geothermal resources, conducting in-depth geological structural analysis, and advancing geodynamics research.

[0003] Currently, obtaining surface heat flow mainly relies on traditional deep drilling temperature measurement technology. This method involves conducting steady-state temperature measurements during deep drilling and combining these measurements with actual values ​​of rock thermal properties to calculate surface heat flow. Specifically, during or after drilling, temperature sensors are deployed in the well for long-term or real-time temperature monitoring. Once the temperature field reaches a steady state, the temperature variation with depth is recorded. Simultaneously, samples of the surrounding rocks are collected, and their thermal properties, such as thermal conductivity and radioactive heat generation rate, are measured in a laboratory. Finally, using the heat conduction equation, combined with the measured temperature gradient and rock thermal conductivity, the geothermal heat flow value is calculated.

[0004] However, existing methods have limitations in applicability and computational accuracy when determining surface heat flow. Summary of the Invention

[0005] The surface heat flow determination method, apparatus, and electronic equipment provided in this application are intended to address the problems of insufficient applicability and calculation accuracy of existing methods in determining surface heat flow.

[0006] In a first aspect, embodiments of this application provide a method for determining surface heat flow, including:

[0007] Acquire geological information, measured geothermal flow data, and seismic wave velocity data for the target area;

[0008] Based on measured geothermal flow data and a lithospheric stratification model, the first temperature profile data was determined. The lithospheric stratification model was constructed based on geological information and regional exploration results, and includes the thermal property parameters of each layer.

[0009] Based on seismic wave velocity data and geological information, the second temperature profile data was determined;

[0010] Determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range;

[0011] Based on the constraint points and the lithosphere layering model, the surface heat flow of the target area is obtained through reverse heat conduction calculation.

[0012] In one possible implementation, the determination of first temperature profile data based on measured geothermal flow data and a lithospheric stratification model includes: vertically stratifying the lithosphere of the target area based on geological information and regional survey results to obtain a lithospheric stratification model; determining the thermal property parameters of each layer in the lithospheric stratification model, wherein the thermal property parameters of each layer include thermal conductivity parameters and heat generation rate parameters; and calculating the first temperature profile data based on measured geothermal flow data and the thermal property parameters of each layer using a one-dimensional thermal steady-state conduction equation.

[0013] In one possible implementation, determining the thermal property parameters of each layer in the lithospheric layered model includes: acquiring rock sample test data from the target area, the rock sample test data including measured values ​​of thermal conductivity and the content of target elements; determining the thermal conductivity parameters of each layer in the lithospheric layered model based on the measured values ​​of thermal conductivity; obtaining the heat generation rate parameters of each layer in the lithospheric layered model based on the content of target elements; or, obtaining the heat generation rate parameters of each layer in the lithospheric layered model based on seismic wave velocity data from the target area.

[0014] In one possible implementation, determining the second temperature profile data based on seismic wave velocity data and geological information includes: constructing a three-dimensional velocity structure model of the target area based on seismic wave velocity data; determining the mineral composition ratio of the upper mantle rocks in the target area based on geological information; and determining the second temperature profile data based on the three-dimensional velocity structure model, the mineral composition ratio of the upper mantle rocks, and preset mineral elastic parameters.

[0015] In one possible implementation, the second temperature profile data is determined based on a three-dimensional velocity structure model, the mineral composition ratio of upper mantle rocks, and preset mineral elastic parameters. This includes: performing hysteresis elastic correction on the seismic wave velocity in the three-dimensional velocity structure model to obtain the corrected seismic wave velocity; and calculating the second temperature profile data based on the corrected seismic wave velocity, the mineral composition ratio of upper mantle rocks, and preset mineral elastic parameters through the correlation between seismic wave velocity and temperature.

[0016] In one possible implementation, determining the constraint point between the first temperature profile data and the second temperature profile data within a preset depth range includes: comparing the first temperature profile data and the second temperature profile data; determining the target intersection point between the first temperature profile data and the second temperature profile data within the shallow crust, and using the target intersection point as the constraint point.

[0017] In one possible implementation, determining the target intersection point of the first temperature profile data and the second temperature profile data within the shallow crust includes: determining an initial intersection point of the first temperature profile data and the second temperature profile data within the shallow crust, wherein there are multiple initial intersection points; averaging the depth and temperature corresponding to the multiple initial intersection points to obtain an average depth and an average temperature; and using the average depth and average temperature as the depth and temperature of the target intersection point.

[0018] In one possible implementation, the surface heat flow of the target area is obtained by reverse heat conduction calculation based on the constraint point and the lithosphere layering model. This includes: using the temperature of the constraint point as the lower boundary condition of the one-dimensional thermal steady-state conduction equation; and using the one-dimensional thermal steady-state conduction equation to perform reverse calculation based on the thermal property parameters of each layer in the lithosphere layering model to obtain the surface heat flow of the target area.

[0019] Secondly, embodiments of this application provide a surface heat flow determination device, comprising:

[0020] The acquisition module is used to acquire geological information, measured geothermal flow data, and seismic wave velocity data of the target area.

[0021] The first determination module is used to determine the first temperature profile data based on measured geothermal flow data and a lithosphere layering model. The lithosphere layering model is constructed based on geological information and regional exploration results, and includes the thermal property parameters of each layer.

[0022] The second determination module is used to determine the second temperature profile data based on seismic wave velocity data and geological information;

[0023] The judgment module is used to determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range;

[0024] The module is used to obtain the surface heat flow of the target area through reverse heat conduction calculations based on constraint points and a lithospheric stratification model.

[0025] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0026] The memory stores the instructions that the computer executes;

[0027] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0029] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0030] The surface heat flow determination method, apparatus, and electronic equipment provided in this application acquire geological information, measured geothermal flow data, and seismic wave velocity data of the target area; based on the measured geothermal flow data and a lithosphere layering model, determine first temperature profile data. The lithosphere layering model is constructed based on geological information and regional exploration results, and includes thermal property parameters of each layer; based on the seismic wave velocity data and geological information, determine second temperature profile data; determine constraint points between the first and second temperature profile data within a preset depth range; and based on the constraint points and the lithosphere layering model, perform reverse heat conduction calculations. The method for obtaining surface heat flow in the target area reduces reliance on deep drilling data by fusing multi-source data, expanding the data acquisition range. This not only improves applicability but also enhances the accuracy of surface heat flow data. Simultaneously, by using first and second temperature profile data to determine constraint points, the temperature distribution is reflected from different perspectives, allowing for a comprehensive consideration of the influence of multiple factors on temperature. Furthermore, the use of reverse heat conduction calculations combined with a layered model makes the calculation process more consistent with the actual thermal state of the lithosphere, improving calculation accuracy. This provides more reliable and comprehensive surface heat flow data support for geothermal resource exploration and geodynamics research in complex geological areas. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0032] Figure 1 Flowchart of the method for determining surface heat flow provided in this application Figure 1 ;

[0033] Figure 2 Flowchart of the method for determining surface heat flow provided in this application Figure 2 ;

[0034] Figure 3 A schematic diagram of the process for determining the first temperature profile data provided for this application;

[0035] Figure 4 A schematic diagram of the process for determining the second temperature profile data provided in this application;

[0036] Figure 5 A schematic diagram of the surface heat flow determination device provided in this application;

[0037] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0038] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0040] In existing technologies, the acquisition of surface heat flow mainly relies on steady-state temperature data from deep drilling and measured values ​​of rock thermophysical properties. However, deep drilling projects are costly, difficult to implement, and time-consuming, resulting in a very limited range of drilling data acquisition. In remote and complex geological areas and in marine locations where drilling is difficult, the lack of effective drilling data makes it impossible for traditional methods to accurately determine surface heat flow in these areas, greatly limiting their applicability. Consequently, existing heat flow data is of inconsistent quality and has many gaps outside of oil and gas exploration areas.

[0041] Besides applicability issues, the accuracy of existing technologies for determining surface heat flow is affected by various factors. In wellbore thermometry, the accuracy of temperature data is influenced by factors such as the measurement accuracy of temperature sensors, interference from the well environment, and operational errors during the measurement process. Furthermore, laboratory measurements of rock thermophysical parameters are also subject to errors due to limitations in the rock samples (which may not fully represent the actual formation characteristics) and the limitations of the measuring equipment and methods. These errors directly impact the calculation of surface heat flow, affecting the accuracy of the final results.

[0042] Due to limitations in applicability and computational accuracy, surface heat flow data suffers from significant spatiotemporal unevenness and unreliable data quality.

[0043] To address the aforementioned issues, this application provides a method, apparatus, and electronic device for determining surface heat flow. This method integrates three different types of data: geological information, measured geothermal flow data, and seismic wave velocity data. Geological information provides the fundamental geological background framework for the entire analysis; measured geothermal flow data is key data directly reflecting the surface thermal state; and seismic wave velocity data provides important clues for temperature analysis from a geophysical perspective. By fusing multi-source data, a more comprehensive and accurate determination of surface heat flow can be achieved. The combined application of multi-source data, along with constraint points and reverse heat conduction calculations, helps improve the accuracy of surface heat flow calculations. Specifically, the first and second temperature profiles reflect temperature distribution from different perspectives. By determining constraint points and linking them, the influence of multiple factors on temperature can be comprehensively considered, reducing errors caused by a single data source or calculation method. Simultaneously, the reverse heat conduction calculation, combined with a layered model, makes the calculation process more consistent with the actual thermal state of the lithosphere, thus making the calculation results closer to the true values. This method, through multi-parameter coupled modeling and joint constraint mechanism, has stronger adaptability and stability when facing complex and variable geological conditions, providing more reliable surface heat flow data support for research in geophysics, geology and other fields.

[0044] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0045] The execution entity of the surface heat flow determination method provided in this application embodiment can be a computing device such as a server or server cluster. The server can be a mobile phone, computer, tablet, or other device. This application embodiment does not impose any particular restrictions on the implementation method of the execution entity, as long as the execution entity can obtain geological information, measured geothermal flow data, and seismic wave velocity data of the target area; determine first temperature profile data based on the measured geothermal flow data and a lithosphere layering model (the lithosphere layering model is constructed based on geological information and regional exploration results, and includes thermal property parameters of each layer); determine second temperature profile data based on seismic wave velocity data and geological information; determine constraint points between the first and second temperature profile data within a preset depth range; and obtain the surface heat flow of the target area through reverse heat conduction calculation based on the constraint points and the lithosphere layering model.

[0046] Figure 1 Flowchart of the method for determining surface heat flow provided in this application Figure 1The execution entity of this method can be a system server or other server storing the surface heat flow determination method; this embodiment does not impose any special restrictions. Figure 1 As shown, the method may include:

[0047] S101. Obtain geological information, measured geothermal flow data, and seismic wave velocity data of the target area.

[0048] The target area can refer to the specific geographical range where the surface heat flow needs to be determined, such as a specific geological structure area, basin, or sea area.

[0049] Geological information can refer to data and knowledge related to crustal structure, rock types, and tectonic history. For example, existing drilling lithology columns, drilling data, stratigraphic information interpreted from seismic profiles, regional geological maps, and rock sample test reports (such as the content of radioactive heat-generating elements and density) in the target area. This information helps to understand the geological evolution history and thermal properties of rocks in the area.

[0050] Measured geothermal flow data can be obtained by conducting on-site measurements in the target area (such as well temperature measurement or seabed heat flow probes) of surface or near-surface heat flow values. These data may require environmental correction (such as topographic and climate change correction).

[0051] Seismic wave velocity data refers to the propagation speed of seismic waves in different rock layers underground. This data can be obtained through geophysical exploration methods such as seismic tomography, artificial seismic sounding, and receiver function analysis. The data can be in the form of two-dimensional profiles or three-dimensional velocity models.

[0052] In this step, relevant data on the target area can be collected through various means, including reviewing existing geological reports and scientific literature, and conducting field geological surveys and geophysical exploration.

[0053] In some embodiments, existing geological exploration reports can be collected to obtain geological information, such as detailed geological reports generated during oil and gas exploration in a certain area, which include information such as stratigraphic structure and rock types; field geological surveys can also be conducted, with geologists conducting on-site investigations of the geological structure and rock outcrops of the target area. For example, geological information may include publicly published regional geological records, drilling data from oil companies, and publicly available rock geochemical analysis data.

[0054] Measured geothermal flow data can come from global heat flow databases, geothermal resource survey reports, and steady-state temperature measurements from oil exploration wells. It represents high-quality measured data from at least one known point or area within the region. In some examples, this data can be obtained by drilling wells at suitable locations in the target area, installing high-precision temperature sensors at different depths within the wells, and then stabilizing the measurements over a period of time. For instance, this could be achieved by drilling wells in a geothermal field and installing temperature sensors to acquire heat flow data.

[0055] Seismic waves can be generated using artificial seismic sources, and the signals can be received by deploying multiple seismic detectors on the Earth's surface. Seismic wave velocity data can then be obtained through data processing and analysis. For example, the seismic wave velocity data can be high-resolution velocity structures obtained by deploying mobile seismic arrays for a specific region.

[0056] S102. Based on measured geothermal flow data and the lithosphere layering model, determine the first temperature profile data. The lithosphere layering model is constructed based on geological information and regional exploration results. The lithosphere layering model includes the thermal property parameters of each layer.

[0057] The lithospheric layering model can refer to a conceptual vertical structural model that divides the lithosphere of the target region into several layers with different physical properties (such as sedimentary layers, upper crust, middle crust, lower crust, upper mantle, etc.), and assigns corresponding thermal property parameters to each layer, such as thermal conductivity and heat generation rate. The first temperature profile data represents the temperature variation with depth, which can be determined through forward thermal simulation.

[0058] Furthermore, using measured surface heat flow as the upper boundary condition, and based on the constructed layered model and the thermal property parameters of each layer, a one-dimensional thermal steady-state conduction equation is used to calculate from the surface downwards, resulting in a continuous temperature curve from the surface to the depth (such as the Moho or deeper), which is the first temperature profile.

[0059] In this step, the construction of the lithospheric layering model may include: determining the approximate layered structure of the lithosphere, such as the crust and mantle, based on geological information; further refining the layered structure by combining regional survey results, such as geophysical exploration data, and determining appropriate thermal property parameters for each layer. For example, the thickness of the crust and the rock types of different layers are determined based on seismic wave velocity data, and then parameters such as thermal conductivity and heat generation rate are assigned to each layer based on measured data and by referring to existing rock thermal property databases.

[0060] In one example, thermal conductivity can be determined by: directly measuring core samples; assigning a value based on a standard value table consulted according to lithology; or estimating it using the empirical relationship between seismic wave velocity and thermal conductivity. The heat generation rate can be determined by: calculating it by measuring the abundance of U, Th, and K in the rock; estimating it using the empirical relationship between seismic wave velocity and heat generation rate (such as the Rybach relationship); or assigning a typical value according to crustal type (such as ancient cratons or active orogenic belts).

[0061] Alternatively, numerical methods, such as the finite difference method or the finite element method, can be used to calculate the first temperature profile data. This involves substituting measured geothermal flow data and the thermal properties of the lithospheric layering model into the heat conduction equation for solution. For example, using the finite difference method, the depth direction is divided into multiple grids, and the temperature value at each grid point is calculated according to the heat conduction equation, thus obtaining the first temperature profile data.

[0062] S103. Based on seismic wave velocity data and geological information, determine the second temperature profile data.

[0063] The second temperature profile data is another type of data on temperature variation with depth, calculated through different methods.

[0064] In this step, the relationship between seismic wave velocity and temperature can be analyzed, and this relationship can be corrected and adjusted by combining geological information. Then, the second temperature profile data can be calculated based on the seismic wave velocity data.

[0065] Furthermore, by measuring the seismic wave velocity of rocks at different temperatures in the laboratory, an empirical formula relating seismic wave velocity to temperature can be established. For example, by measuring the P-wave velocity of granite at different temperatures, a functional relationship between P-wave velocity and temperature can be fitted.

[0066] Furthermore, considering the influence of factors such as the geological structure and rock type of the target area on the seismic wave velocity-temperature relationship, the empirical formula is modified. Then, based on the actual acquired seismic wave velocity data, the modified formula is used to calculate the second temperature profile data. For example, in a sedimentary basin, due to the presence of sedimentary rocks, the seismic wave velocity-temperature relationship is adjusted accordingly before calculating the temperature profile.

[0067] In some embodiments, based on the geological information of the target area, the mantle composition (such as peridotite, orthopyroxene peridotite) in the area is determined, and the proportion of each mineral (olivine, pyroxene, garnet, etc.) is determined. Then, the velocity-temperature relationship established using mineral physical experimental data (such as the measurement results of mineral elastic modulus under high temperature and pressure) is inverted; based on the mantle composition and observed wave velocity, the equilibrium mineral assemblage and the corresponding temperature are calculated (e.g., using thermodynamic calculation software).

[0068] In addition, before inversion, the original seismic wave velocity can be corrected for hysteresis elasticity to eliminate the influence of the rock’s incomplete elasticity (attenuation) on the wave velocity, thereby obtaining a wave velocity value that is closer to the ideal elastic state for temperature inversion.

[0069] S104. Determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range.

[0070] The preset depth range can be a pre-defined depth interval based on the research objectives and geological characteristics of the target area. Within this interval, constraint points for the first and second temperature profile data are identified. Furthermore, the preset depth range can be determined based on the geological structural characteristics of the target area and research needs. For example, if the focus is on the thermal state of the shallow crust, the preset depth range can be set to 0-20 km; if the study focuses on the thermal effects of the mantle, the preset depth range may be larger, such as extending from the lower crust to the top of the lithospheric mantle (e.g., 20-100 km).

[0071] The constraint points are points within a preset depth range that have the same or similar depth in the first temperature profile data and the second temperature profile data and whose temperature values ​​meet certain matching conditions.

[0072] In this step, a manual comparison method can be used to compare the temperature values ​​of two temperature profiles point by point within a preset depth range, and select the points where the temperature values ​​differ within a certain error range as constraint points; alternatively, mathematical algorithms, such as graphical methods, statistical methods, and least squares methods, can be used to automatically find constraint points that meet the optimal matching conditions.

[0073] For example, two temperature-depth curves can be plotted on the same coordinate system, and their intersection point can be read directly. Alternatively, the depth point that minimizes the temperature difference between the two curves can be determined. Or, when multiple measured points yield multiple possible intersection points, the average or median of the depth and temperature at all intersection points can be used as a representative constraint point for the region.

[0074] S105. Based on the constraint points and the lithosphere layering model, the surface heat flow of the target area is obtained through reverse heat conduction calculation.

[0075] Among them, the reverse heat conduction calculation is the opposite of the forward heat conduction calculation. It is based on known deep temperature information (i.e., the temperature at the constraint point) and the lithosphere layering model to infer the process of surface heat flow.

[0076] In this step, the temperature value at the constraint point is taken as a known condition, and combined with the thermal property parameters of the lithosphere layering model, reverse heat conduction calculation is performed to obtain the surface heat flow of the target area.

[0077] Furthermore, in some examples, the temperature value at the constraint point can be used as a known lower boundary condition. Combined with the thermal properties of each layer of the lithosphere layering model, the one-dimensional heat conduction equation can be solved upwards (towards the surface) from the depth of the constraint point to obtain the surface heat flow of the target area.

[0078] The surface heat flow of the target area includes geothermal flow data from blank areas within the target area. It should be noted that blank areas can refer to areas where actual geothermal flow data cannot be obtained through deep drilling temperature measurement.

[0079] In some embodiments, after obtaining the constraint points of the target area, if some blank areas in other areas lack measured geothermal flow data, but their lithosphere stratification and thermal properties are similar to those of the target area, then seismic wave velocity data of the blank areas can be obtained, and second temperature profile data of the blank areas can be calculated. Based on the target depth corresponding to the constraint points of the target area, the target temperature corresponding to the target depth in the second temperature profile data is determined. Then, the target depth and target temperature are used as new constraint points (i.e., lower boundary conditions) to perform reverse heat conduction calculations, ultimately reversing the unknown (or unmeasurable) surface heat flow of the blank areas.

[0080] It is evident that this method is particularly suitable for blank areas lacking measured geothermal flow data. By simply acquiring seismic wave velocity data in the blank area and determining the constraint points of the area through the aforementioned steps, the surface heat flow of the area can be inferred, thereby enabling the prediction and mapping of regional heat flow. This improves the reliability of temperature estimation and allows for more accurate and physically well-founded predictions and estimates of surface heat flow in areas where measured data is missing or sparse. Consequently, it expands the data foundation and accuracy in fields such as geothermal resource assessment and lithospheric thermal structure research.

[0081] The surface heat flow determination method provided in this application integrates multi-source information such as geological information, measured geothermal flow data, and seismic wave velocity data to fully explore the geological and physical significance behind different data and construct a multi-parameter coupled heat flow calculation framework. Within this framework, a lithospheric layering model is used to refine the thermal properties of each layer. Temperature profiles are calculated and constraint points are determined using measured geothermal flow data and seismic wave velocity data. Then, through reverse heat conduction calculations, surface heat flow is inferred from deep-seated information, thereby achieving accurate calculation of surface heat flow under complex geological conditions. This enhances its applicability and overcomes the problem of regional dependence. Even in remote areas with limited data or data gaps, surface heat flow can be accurately determined by multi-source data fusion and geophysical calculation methods, filling the data gaps in these areas compared to traditional methods. Furthermore, this method fully considers the influence of various geological and physical factors on heat flow, improving calculation accuracy while maintaining prediction efficiency, providing more reliable data support for geothermal resource exploration and other research.

[0082] Based on the above embodiments, the method described in S102 for determining the first temperature profile data based on measured geothermal flow data and a lithosphere stratification model may include: vertically stratifying the lithosphere of the target area based on geological information and regional survey results to obtain a lithosphere stratification model; determining the thermal property parameters of each layer in the lithosphere stratification model, wherein the thermal property parameters of each layer include thermal conductivity parameters and heat generation rate parameters; and calculating the first temperature profile data based on measured geothermal flow data and the thermal property parameters of each layer through a one-dimensional thermal steady-state conduction equation.

[0083] In this embodiment, regional exploration results mainly refer to geophysical exploration results, particularly seismic sounding profiles, deep reflection seismic profiles, and magnetotelluric sounding results. These data can reveal discontinuities such as wave velocity interfaces and electrical interfaces within the Earth's crust, and are key evidence for delineating the vertical structure of the lithosphere.

[0084] In some embodiments, vertical stratification can be achieved by: determining the thickness and lithology of the sedimentary cover using regional geological maps; identifying the boundary between the upper and middle crust by combining strong reflection zones (which may represent ductile shear zones or lithological boundaries) identified by deep reflection seismic profiles; identifying the boundary between the middle and lower crust using significant jumps in P-wave velocities on wide-angle seismic profiles; and determining the boundary between the lower crust and upper mantle using PmP phases or depths where velocities reach preset values. Alternatively, stratification can be performed directly based on velocity models obtained from high-resolution three-dimensional seismic tomography. A series of velocity thresholds are set to transform the continuous velocity field into a discrete stratification model. Furthermore, for areas with extremely low exploration levels, standard stratification models from neighboring areas with similar geological backgrounds and detailed studies within the same tectonic zone can be referenced, with appropriate adjustments made based on the limited number of seismic monitoring points or gravity anomalies in the area.

[0085] In one example, the lithospheric layering model can be specifically divided into: ① sedimentary cover; ② upper, middle, and lower crustal layers of the crystalline basement; ③ upper mantle layer.

[0086] Furthermore, thermal conductivity reflects the ability of a rock to conduct heat, and different rock types have different thermal conductivity; heat generation rate indicates the rate at which radioactive elements (such as U, Th, K) decay and generate heat inside the rock, and is related to the content of radioactive elements in the rock.

[0087] The one-dimensional steady-state thermal conduction equation is a mathematical equation that describes the stable conduction of heat in one-dimensional space. After determining the lithospheric layering model and the thermal property parameters of each layer, and combining measured geothermal flow data, this equation can be used to calculate the heat conduction at different layers of the lithosphere, thereby obtaining the first temperature profile data.

[0088] In some examples, after constructing a lithospheric layered model, the thermal conductivity parameters of rock samples from each layer are precisely measured in the laboratory using specialized thermal conductivity measuring instruments, such as thermal probe methods and laser scintillation methods. For the heat generation rate parameter, the content of radioactive elements (such as uranium, thorium, and potassium) in the rock is determined through chemical analysis, and then the heat generation rate is calculated using relevant formulas. The measured geothermal flow data and these precisely measured thermophysical parameters are then substituted into the one-dimensional steady-state thermal conduction equation to calculate the first temperature profile data.

[0089] In other examples, when it is difficult to obtain sufficient rock samples for laboratory testing, the thermal properties of each layer can be estimated using empirical formulas based on the rock type and geological characteristics. For instance, for common sedimentary rocks, thermal conductivity can be estimated using existing empirical formulas based on parameters such as porosity and mineral composition; the content of radioactive elements can be estimated based on the formation environment and geological history of the sedimentary rocks, and then the heat generation rate can be calculated. Substituting the estimated thermal properties and measured geothermal flow data into the one-dimensional steady-state thermal conduction equation yields the first temperature profile data.

[0090] Furthermore, the one-dimensional thermal steady-state conduction equation can satisfy:

[0091] ;

[0092] in, , The temperatures at depth Z and the top surface of the calculation section are respectively, in °C; To calculate the heat flow at the top surface of the section, mW / m 2 ; The thickness of the calculated segment is in km; The thermal conductivity of the rock in the calculation section is expressed in W / m·K. The rock heat generation rate for the calculation section is expressed in μW / m. 3 .

[0093] It should be noted that in actual calculations, the calculation segment can be each layer in the lithosphere layering model. If the parameters of each layer are different, the calculation is performed in segments. The temperature and heat flow of the bottom surface of the upper layer are used as the top surface conditions of the next layer, and so on. The final first temperature profile data is a temperature-depth curve for the entire depth range (e.g., 0-100km), which is calculated by forward heat conduction from heat flow and layered thermal properties.

[0094] By clearly defining the specific steps for obtaining the first temperature profile data, it is ensured that the first temperature profile data can be obtained more accurately and reliably under the influence of different geological conditions, rock types, and the characteristics of measured geothermal flow data. This lays a solid data foundation for subsequent comparison with seismic wave velocity and temperature models, accurate determination of constraint points, and accurate inversion of the final surface heat flow.

[0095] Based on the above embodiments, the thermal property parameters of each layer in the lithosphere layered model are determined, including: obtaining rock sample test data of the target area, the rock sample test data including measured values ​​of thermal conductivity and target element content; determining the thermal conductivity parameters of each layer in the lithosphere layered model based on the measured values ​​of thermal conductivity; obtaining the heat generation rate parameters of each layer in the lithosphere layered model based on the target element content; or, obtaining the heat generation rate parameters of each layer in the lithosphere layered model based on seismic wave velocity data of the target area.

[0096] In this embodiment, rock sample test data refers to data obtained by analyzing and testing rock samples (such as cores, drilling cuttings, and surface outcrops) collected from the target area. The measured thermal conductivity value is a numerical value obtained by measuring the rock's ability to conduct heat using specific experimental methods, such as directly measuring the thermal conductivity of the rock sample using instruments like the transient thermal wire method or optical scanning method. The target element content mainly refers to the content of radioactive elements (such as U, Th, K, etc.) in the rock; the decay of these elements generates heat and is closely related to the heat generation rate parameter.

[0097] In some embodiments, rock samples are collected at different locations in the target area, and the thermal conductivity of the samples is tested in a laboratory using specialized equipment to obtain measured values. Simultaneously, the content of target elements in the samples is determined using chemical analysis methods. Then, based on the obtained measured thermal conductivity values ​​and the test results of rock samples corresponding to each layer in the lithosphere layering model, the thermal conductivity parameter of each layer is determined. When determining the heat generation rate parameter, it can be based on the target element content: according to the quantitative relationship between the target element content in the rock and the heat generation rate, the measured target element content is substituted into relevant formulas to calculate the heat generation rate parameter of each layer in the lithosphere layering model; or it can be based on seismic wave velocity data: the relationship between seismic wave velocity data and rock thermal property parameters is studied, and corresponding mathematical models or empirical formulas are established. The heat generation rate parameter of each layer in the lithosphere layering model is obtained using seismic wave velocity data through these models or formulas.

[0098] In other words, thermal conductivity parameters are preferentially based on laboratory measurements, while heat generation rate parameters are calculated based on U-Th-K element content test results or through an empirical formula relating P-wave velocity and heat generation rate. For strata lacking measured data, a typical value range can be selected using regional analogy.

[0099] As a further example, rock samples are collected from multiple representative locations within the target area, such as outcrops in different strata and well core samples. In the laboratory, the thermal conductivity of the rock samples is measured using the thermal probe method. When multiple measurements are included, statistical analysis can be performed on the measurements within the same stratum, such as taking the arithmetic mean, median, or weighted average by sample depth, to eliminate anomalies in individual samples and obtain representative thermal conductivity parameters for that stratum. For strata with limited data, analogy can be used to refer to typical values ​​of similar rocks. Mass spectrometry and other methods are then used to determine the content of target elements (U, Th, K). Based on the lithospheric stratification model, statistical analysis is performed on the test data of rock samples from the same layer to determine the thermal conductivity and heat generation rate parameters of that layer.

[0100] In another example, the average P-wave or S-wave velocity of each layer in the layered model is extracted from the acquired seismic wave velocity data (such as a three-dimensional velocity model). The average wave velocity is then substituted into a pre-established empirical formula for wave velocity-heat generation rate for calculation. This empirical formula can be a linear / exponential empirical formula obtained by regression analysis of wave velocity-heat generation rate data of known areas, or it can be a set of different empirical formulas established according to different geotectonic backgrounds. Alternatively, the main lithology of the stratum can be inferred based on the wave velocity range, and then the typical value range of heat generation rate of that lithology in a pre-set database can be queried. Finally, the specific wave velocity is interpolated or selected within this range to determine the value.

[0101] By employing various specific methods for determining the thermal property parameters of each layer in the lithospheric layering model, a complete, reliable, and flexible technical solution is provided for the determination of thermal property parameters, from direct measurement to indirect estimation. This enhances the objectivity, scientific rigor, and adaptability to data scarcity in the parameter assignment process, ensuring universal applicability under different geological conditions, and thereby improving the accuracy and reliability of determining the first temperature profile data.

[0102] Based on the above embodiments, the method for determining the second temperature profile data based on seismic wave velocity data and geological information described in S103 may include: constructing a three-dimensional velocity structure model of the target area based on seismic wave velocity data; determining the mineral composition ratio of the upper mantle rocks in the target area based on geological information; and determining the second temperature profile data based on the three-dimensional velocity structure model, the mineral composition ratio of the upper mantle rocks, and preset mineral elastic parameters.

[0103] In this embodiment, the three-dimensional velocity structure model describes the distribution of seismic wave propagation velocity in the underground medium of the target area in three-dimensional space. It can intuitively present the differences in seismic wave propagation velocity at different underground locations and reflect the influence of underground geological structures (such as different rock layers, tectonic zones, etc.) on seismic wave propagation.

[0104] In some examples, based on the acquired seismic velocity data, appropriate interpolation methods (such as Kriging interpolation, inverse distance weighted interpolation, etc.) or geophysical inversion methods (such as seismic travel time inversion, waveform inversion, etc.) are used to transform discrete seismic velocity data into a continuous three-dimensional spatial distribution, thereby constructing a three-dimensional velocity structure model of the target area.

[0105] For example, P-wave travel time data of multiple seismic events recorded by a regional seismic network are collected, and a preset inversion algorithm (such as tomography) is used to invert and obtain a three-dimensional perturbation model or absolute velocity model of P-wave velocity for the target area (e.g., with a horizontal resolution of about 0.5°×0.5° and a vertical resolution of about 10-20km).

[0106] Another example is the extraction of phase velocity or group velocity dispersion curves of Rayleigh waves and Love waves from cross-correlation records of seismic events or background noise. This allows for the inversion of surface wave velocity distribution maps for different periods (corresponding to different depths), and then the acquisition of a three-dimensional S-wave velocity model through joint inversion or direct inversion.

[0107] The mineral composition ratio of upper mantle rocks refers to the proportional relationship of various mineral components in upper mantle rocks. The upper mantle is mainly composed of minerals such as olivine and pyroxene. The relative contents of these minerals will vary in different regions and under different geological conditions. This proportional relationship is of great significance for studying the physical properties and thermal state of the upper mantle.

[0108] In some examples, by combining geological information, such as rock sample analysis results and regional geological tectonic evolution history, and referring to existing petrological research results and empirical formulas, the proportional relationships of various mineral components in the upper mantle rocks of the target area are determined.

[0109] For example, upper mantle rock samples are collected from the target area, and X-ray diffraction analysis, electron probe microanalysis, and other techniques are used in the laboratory to accurately determine the content of various minerals (such as olivine and pyroxene) in the rocks, thereby determining the mineral composition ratio of the upper mantle rocks. For instance, based on available data, the mineral composition ratio of the upper mantle rocks in the target area's lithosphere can be determined as follows: olivine approximately 68%, orthopyroxene approximately 18%, clinopyroxene approximately 11%, and garnet approximately 3%.

[0110] Among them, mineral elastic parameters refer to the fundamental physical parameters such as elastic constants, density, and coefficient of thermal expansion of the main minerals constituting the upper mantle, which are measured under high temperature and pressure conditions in the laboratory or calculated through first-principles calculations. These parameters are usually obtained from authoritative mineral physics databases or known data. These parameters are related to the chemical composition and crystal structure of minerals, and are an important basis for calculations when using seismic wave velocity data to study underground temperature distribution.

[0111] In some examples, a mathematical relationship model between seismic wave velocity and temperature can be established based on a pre-constructed three-dimensional velocity structure model, a determined proportion of upper mantle rock mineral components, and preset mineral elastic parameters, using theories and methods combining geophysics and thermodynamics. By calculating the temperature values ​​at different depths in the target area, a second temperature profile data can be formed.

[0112] For example, firstly, based on the viscoelastic model of the mantle and preset parameters such as activation energy H and activation volume V, the measured wave velocity at a specific depth extracted from the three-dimensional velocity model is corrected, extrapolating it from the laboratory measurement frequency to the seismic wave frequency to obtain the corrected elastic wave velocity. Secondly, using the multiphase rock physics mixing principle, combined with the determined mineral component ratio and preset mineral elastic parameters, the theoretical elastic wave velocity and density of the rock under given pressure (depth) and assumed temperature (T) conditions are calculated. Finally, within a set temperature range, an iterative search is performed to find the temperature value that best matches the theoretical elastic wave velocity with the corrected elastic wave velocity after viscoelastic correction. Repeating this process for different depths yields a temperature-depth profile (i.e., second temperature profile data).

[0113] As another example, mineral physics equations and thermodynamic equations can be directly coupled into the inversion framework of seismic wave waveform simulation or travel-time tomography. During the inversion process, state parameters (such as temperature and composition) are solved directly as unknowns, thereby obtaining the three-dimensional temperature field in one step, making the calculation more concise.

[0114] In another example, a synthetic dataset containing a large number of known temperature-depth-composition-velocity correspondences can be built to train a deep neural network model. After training, the model can directly take observed seismic wave data (or features extracted from the data) as input and directly output temperature profile predictions. This method is more efficient when dealing with massive amounts of data and complex nonlinear relationships.

[0115] By clarifying the specific steps for determining the second temperature profile data based on seismic wave velocity data and geological information, the reliability of inferring lithospheric temperature using seismic wave data is improved, providing higher quality and comparable input for subsequent comparison and joint constraints with the first temperature profile data.

[0116] Based on the above embodiments, the second temperature profile data is determined based on the three-dimensional velocity structure model, the mineral composition ratio of the upper mantle rocks, and preset mineral elastic parameters. This includes: performing hysteresis elastic correction on the seismic wave velocity in the three-dimensional velocity structure model to obtain the corrected seismic wave velocity; and calculating the second temperature profile data based on the corrected seismic wave velocity, the mineral composition ratio of the upper mantle rocks, and preset mineral elastic parameters through the correlation between seismic wave velocity and temperature.

[0117] In this embodiment, when seismic waves propagate through the Earth's medium, the seismic wave velocity changes due to the medium's viscoelastic properties (i.e., the characteristic that strain lags behind stress during deformation). Viscoelastic correction corrects the seismic wave velocity in the three-dimensional velocity structure model, eliminating or reducing the wave velocity deviation caused by the medium's viscoelasticity. This ensures that the corrected seismic wave velocity more accurately reflects the true characteristics of the subsurface medium, laying the foundation for subsequent accurate calculation of temperature profile data.

[0118] Furthermore, it is essential to first clarify the relationship model between the viscoelasticity of the medium and seismic wave velocity, which can be derived from experimental research and theoretical derivation in rock physics. Then, based on the geological conditions of the target area (such as temperature and pressure range), the relevant parameters in the formula are determined. Finally, the seismic wave velocity from the three-dimensional velocity structure model is substituted into the formula to calculate the correction, thus obtaining the corrected seismic wave velocity. After obtaining the corrected seismic wave velocity, combined with the mineral composition ratio of the upper mantle rocks and the preset mineral elastic parameters, and based on the correlation between seismic wave velocity and temperature (such as empirical formulas or physical models established through extensive experimental and theoretical analysis), the corrected seismic wave velocity, mineral composition ratio, and elastic parameters are substituted into numerical calculation methods to solve for the temperature values ​​corresponding to different depths, thereby forming the second temperature profile data.

[0119] In some embodiments, the relationship between seismic wave velocity and temperature can refer to a physical calculation process rather than a single formula. The core principle is that, under the constraints of corrected wave velocity (elastic wave velocity), known mineral composition and elastic parameters, and pressure estimated from depth, temperature becomes the key variable determining the wave velocity magnitude. Temperature is retrieved by establishing a forward model of wave velocity as a function of temperature and composition and matching it with observed values.

[0120] Furthermore, for a given depth point (corresponding to a pressure P), based on the determined proportions of upper mantle rock mineral components and preset elastic constants, densities, and thermal expansion coefficients of each mineral, the theoretical elastic tensor of the multi-mineral aggregate is calculated using the rock physics mixing principle. Then, from this theoretical elastic tensor, a given assumed temperature is calculated (…). Theoretical P-wave velocity under the condition of () ) and / or S-wave velocity ( The effect of temperature is introduced through the variation of the mineral's elastic constants with temperature (usually a linear or quadratic function). Finally, the calculated theoretical wave velocity ( , The assumed temperature was compared with the observed wave velocity after hysteresis correction. The assumed temperature was iteratively adjusted within a reasonable temperature range (e.g., 500-1600°C). The temperature value that best matches the theoretical value with the observed value (minimizes the residual) is found using the least squares method or grid search method. This temperature is the inversion temperature at that depth point. Then, the above steps are repeated for different depths to finally obtain a curve of temperature variation with depth, which is the second temperature profile data.

[0121] In some examples, the formula for viscoelasticity correction can satisfy:

[0122] ;

[0123] in, ; The velocity of the hysteretic elastic shear wave obtained from earthquake observation (i.e., the seismic wave velocity in the three-dimensional velocity structure model). This is the shear wave velocity under the pure elastic assumption (i.e., the corrected seismic wave velocity). It is the viscoelasticity factor; , The inelastic constant of the rock; For pressure; For temperature; The frequency of the seismic waves; It is the activation energy; For activation volume; This is a universal gas constant.

[0124] In general, , The values ​​can be 0.148 and 0.15 respectively; Take 500 kJ / mol; Take 20 cm3 / mol.

[0125] Furthermore, the relationship between seismic wave velocity and temperature satisfies:

[0126] (1) Formula for the change of elastic modulus with temperature, pressure and composition:

[0127] ;

[0128] in, It is the elastic modulus of rock under pressure P, temperature T, and iron content X (it can be shear modulus or bulk modulus). In reference state ( The elastic modulus under the following conditions; Let be the difference between the temperature and the initial reference temperature, where , This is the initial reference temperature; This represents the iron content in the upper mantle.

[0129] (2) Formulas for density changes with temperature, pressure, and composition:

[0130] ;

[0131] in, Let X be the density of the rock under pressure P, temperature T, and iron content X. Density under reference conditions; is the coefficient of thermal expansion of the rock.

[0132] (3) The relationship between shear wave velocity (i.e., theoretical wave velocity) and elastic modulus and density:

[0133] ;

[0134] ;

[0135] ;

[0136] in, , These represent the average shear modulus and average density of the rock. denoted as the volume proportion of the i-th mineral in the rock; , Let be the density and elastic modulus of the i-th mineral.

[0137] In this example, the method for determining the second temperature profile data includes:

[0138] (1) Viscoelastic effect correction: Input measured data, including the velocities of viscoelastic shear waves obtained from seismic observations. Seismic wave frequency Rock inelastic constants and ,activation energy Activation volume Initial reference temperature ; Calculate the hyperelasticity factor The corrected seismic wave velocity (i.e., the observed wave velocity) is calculated by back-calculating using the viscoelastic correction formula. .

[0139] (2) Temperature inversion based on mineral physics theory: Input mineral composition and basic physical properties (i.e., the proportion of mineral components in upper mantle rocks and preset mineral elastic parameters), including the mineral volume proportion of regional rocks. Reference modulus of each mineral Reference density Coefficient of thermal expansion Parameters such as bulk modulus, elastic modulus, and partial derivatives of elastic modulus with respect to temperature, pressure, and composition; calculation of the elastic modulus of each mineral. ; Calculate the density of each mineral based on the formulas for density changes with temperature, pressure, and composition. ; Calculate the average shear modulus of the rock and average density ; Calculate the theoretical pure elastic wave velocity (i.e., the theoretical wave velocity). .

[0140] (3) Iterative solution for temperature T: comparison and If the two do not match, update the temperature in step (2). Repeat the above steps to calculate and If the conditions are met or the preset error criteria are satisfied (i.e., convergence), the result is obtained with respect to depth (i.e., pressure). The temperature corresponding to each depth point is calculated to obtain the second temperature profile data.

[0141] By performing viscoelastic correction on the seismic wave velocity in the three-dimensional velocity structure model and considering the dynamic changes of relevant parameters, the errors caused by the viscoelastic properties of the medium and inaccurate parameters are reduced, improving the accuracy and reliability of the second temperature profile data and further enhancing the calculation accuracy of surface heat flow.

[0142] Based on the above embodiments, the method for determining the constraint point of the first temperature profile data and the second temperature profile data within a preset depth range as described in S104 may include: comparing the first temperature profile data and the second temperature profile data; determining the target intersection point of the first temperature profile data and the second temperature profile data within the shallow crust, and using the target intersection point as the constraint point.

[0143] By identifying constraint points, temperature profile data obtained from two different methods can be matched and coordinated at that point, enabling more accurate integration of the two datasets and improving the accuracy of geothermal heat flow data determination. The target intersection point can further refer to a point where the first and second temperature profile data are numerically close to or equal within the shallow crust (referring to the interior of the crust but not exceeding the depth of the middle crust). Since shallow crustal temperature data is relatively easier to verify and reference using other geological or geophysical methods, selecting intersection points within this range as constraint points makes the constraints more reliable and practically significant. Furthermore, within this range, the one-dimensional thermal steady-state conduction model is less affected by assumptions in the shallow region and is relatively reliable, while seismic wave velocity-temperature inversion is less affected by compositional uncertainties and pressure effects in the shallow region compared to the deeper region. Therefore, the intersection point of the two methods, based on different physical principles, within this depth range is more significant for mutual verification and constraint.

[0144] Furthermore, the intersection point can be determined using graphical overlay and visual interpretation, or based on the numerical intersection point search method, or based on the global search method of minimum distance. In this case, there may be one or more intersection points. The shallowest unique intersection point can be selected as the target intersection point, or a preset optimization strategy can be used, such as selecting the average intersection point or the midpoint of the closest segment of the curve as the target intersection point.

[0145] In this embodiment, the first and second temperature profile data can be arranged and organized according to depth, and two curves can be plotted with depth as the abscissa and temperature as the ordinate to show the temperature variation with depth. The two temperature profile data can be compared and analyzed by visually observing the trend and numerical changes of the curves, or by using numerical calculation methods (such as calculating the difference in temperature values ​​between the two curves at different depths). Within the shallow crustal depth range, the point where the values ​​of the first and second temperature profile data curves are closest or equal is found. This point is designated as the target intersection point and used as a constraint point for further processing and analysis of the two temperature profile data.

[0146] Furthermore, in rare cases, the two curves may not intersect within the shallow crust. In such cases, the search range can be expanded to the entire crust, or a third type of information (such as the geothermal flow value itself) can be introduced to impose a constraint, specifying a reasonable depth as the constraint point (such as the average crustal depth), with the temperature being the weighted average of the temperatures of the two curves at that depth.

[0147] By comparing the first temperature profile data with the second temperature profile data, the target intersection point within the shallow crust is determined as the constraint point, providing a reliable correlation point for the integration of the two temperature profile data. This solves the problem of data mismatch, improves the accuracy and reliability of data integration, provides reliable conditions for subsequent reverse calculations, and thus improves the accuracy of determining surface heat flow.

[0148] As can be seen from the above embodiments, in actual operation, due to factors such as data errors, limitations of different calculation methods, and the complexity of geological conditions, there may be multiple seemingly similar intersection points between the first temperature profile data and the second temperature profile data within the shallow crust. Randomly selecting one of these points as the target intersection point may lead to inaccurate constraints due to the randomness of that point, thereby affecting the subsequent integration of the two temperature profile data and the accuracy of determining surface heat flow. Therefore, based on the above embodiments, the method for determining the target intersection point between the first temperature profile data and the second temperature profile data within the shallow crust may include: determining initial intersection points between the first temperature profile data and the second temperature profile data within the shallow crust, including multiple initial intersection points; averaging the depth and temperature corresponding to the multiple initial intersection points to obtain average depth and average temperature; and using the average depth and average temperature as the depth and temperature of the target intersection point.

[0149] In this embodiment, the initial convergence points are multiple depth points within the shallow crust that are numerically similar and potentially serve as target convergence points during the comparison of the first and second temperature profile data. These points are initially selected based on a certain error range or judgment criteria, but have not undergone further processing and screening, and may contain some uncertainty and randomness.

[0150] Furthermore, in some embodiments, considering that the reliability and importance of data at different depth ranges may vary, different weights are assigned to initial junctions at different depths; the depths and temperatures of multiple initial junctions are weighted and averaged according to the set weights to obtain more reasonable average depths and average temperatures, thereby determining the target junction.

[0151] Optionally, the initial intersection points can be screened before averaging. For example, outliers that are too shallow or too deep can be removed before averaging the remaining points. This can eliminate spurious intersections caused by the model's low reliability at extreme depths. Alternatively, the mean and standard deviation of depth and temperature for all initial intersection points can be calculated first; outliers whose depth or temperature differs from the mean by more than twice the standard deviation can be removed, and then the mean can be recalculated using the remaining points. This process can be iterated to improve the robustness of the mean. If the initial intersection points are clearly concentrated in two or more depth clusters, cluster analysis (such as K-means) can be performed first, and then the points in the cluster with the most points can be averaged, or the mean can be averaged again using the center point of each cluster.

[0152] By determining multiple initial junctions and averaging their depth and temperature, the target junction is determined, avoiding the inaccuracy of the target junction due to the randomness of a single initial junction. This makes the determined target junction more representative and stable, further improving the accuracy of integrating the first and second temperature profile data, thereby enhancing the accuracy of determining surface heat flow and its anti-interference capability.

[0153] Based on the above embodiments, the method described in S105 for obtaining the surface heat flow of the target area through reverse heat conduction calculation based on constraint points and a lithospheric layering model may include: using the temperature of the constraint points as the lower boundary condition of the one-dimensional thermal steady-state conduction equation; and using the one-dimensional thermal steady-state conduction equation to perform reverse calculation based on the thermal property parameters of each layer in the lithospheric layering model to obtain the surface heat flow of the target area.

[0154] In this embodiment, the lower boundary condition refers to the temperature value at a certain depth boundary set when calculating using the one-dimensional thermal steady-state conduction equation. This boundary condition is crucial for solving the equation, providing a known starting point or constraint point. In this embodiment, the temperature of the constraint point is used as the lower boundary condition for subsequent reverse heat conduction calculations.

[0155] Reverse heat conduction calculations are the opposite of conventional forward heat conduction calculations. Forward heat conduction calculations calculate temperature distribution from known heat sources and boundary conditions; while reverse heat conduction calculations derive the heat flow at the Earth's surface by using a one-dimensional steady-state heat conduction equation, given the temperature at certain depths (lower boundary conditions) and the thermophysical parameters of each layer of the lithosphere.

[0156] The temperature values ​​of the previously determined constraint points are substituted into the one-dimensional thermal steady-state conduction equation as the temperature condition of the equation at the boundary of the constraint point depth. Based on the thermal properties (such as thermal conductivity and specific heat capacity) of each rock layer in the lithospheric layering model, these parameters are substituted into the one-dimensional thermal steady-state conduction equation. Starting from the constraint point depth, the heat flow value at the Earth's surface is gradually derived in reverse according to the mathematical relationship of the equation.

[0157] For example, the entire column from the constraint point depth to the Earth's surface is divided into N layers according to the lithosphere layering model, each with a known thickness, thermal conductivity, and heat generation rate. The constraint point is located at the bottom or somewhere inside the Nth layer (the lowest layer). Taking the Nth layer as an example, the temperature at its bottom boundary is known. For this layer, the temperature and heat flow at the top of the Nth layer are obtained using another form of the heat conduction equation (i.e., calculated upwards from a point within the layer), which is the temperature and heat flow at the bottom of the (N-1)th layer. Utilizing this continuity, combined with the parameters and equations of the (N-1)th layer, this can be recursively extrapolated upwards until the Earth's surface, as shown in the one-dimensional steady-state thermal conduction equation in the above embodiment, where heat flow... These are parameters for an unknown solution to the requirements.

[0158] By using the temperature of the constraint point as the lower boundary condition of the one-dimensional thermal steady-state conduction equation, and performing reverse heat conduction calculations based on the thermal property parameters of each layer in the lithosphere layering model, a scientific and effective technical solution is provided for accurately calculating the surface heat flow of the target area, thereby improving the accuracy and reliability of surface heat flow calculation.

[0159] Based on the above embodiments, the surface heat flow determination method provided in this application can be applied to scenarios such as geothermal resource exploration, lithospheric thermal state research, oil and gas field development planning, and geological hazard risk assessment. In oil and gas field development, this method can be used to predict formation temperature gradients and assist in the analysis of oil and gas generation and migration patterns; in geothermal resource exploration, it can provide heat flow data support for the assessment of geothermal field development potential; in geological structural research, it can reveal the thermal evolution history of the lithosphere and assist in the analysis of plate tectonics mechanisms. Furthermore, in complex geological areas (such as high mountains, deserts, or deep seas) where drilling is difficult, this method, by integrating seismic wave velocity and geophysical inversion techniques, can achieve efficient acquisition of heat flow data, filling the gap in spatial coverage of traditional methods. This method utilizes a nonlinear relationship model between seismic wave velocity and temperature, combined with the inversion capability of the heat conduction equation, and introduces heat flow-wave velocity coupling constraint points as anchoring conditions for shallow temperature profiles, thus solving the shortcomings of traditional methods in terms of data coverage, model accuracy, and applicability.

[0160] Figure 2 Flowchart of the method for determining surface heat flow provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, the method for determining surface heat flow is described in detail, and the method includes:

[0161] S201. Based on geological information and literature research, construct a lithospheric layering model and assign values ​​to thermal property parameters.

[0162] In this step, existing measured geothermal flow data and a three-dimensional velocity structure model within a depth range of 0-200 km are integrated in the study area. High-quality, calibrated observational data is prioritized for geothermal flow data, while seismic velocity data is spatially interpolated based on high-resolution seismic tomography results. Based on regional geological characteristics and deep exploration results, lithospheric stratification is performed, constructing a multi-scale vertical lithospheric stratification system (i.e., a lithospheric stratification model), which can be divided into: sedimentary cover, upper crust, middle crust, lower crust, and upper mantle.

[0163] The specific methods for determining the thermal conductivity and heat generation rate parameters of each stratum include: ① giving priority to using the thermal conductivity values ​​of rock samples measured in the laboratory; ② calculating the heat generation rate parameters based on the U-Th-K element content test results or through the empirical formula between P-wave velocity and heat generation rate; ③ selecting typical value ranges for strata lacking measured data through regional analogy.

[0164] S202. Calculate the lithospheric temperature using the one-dimensional thermal steady-state conduction equation and seismic wave velocity.

[0165] In other words, the lithospheric temperature was calculated using two methods: the first temperature profile data was calculated using a one-dimensional thermal steady-state equation, and the second temperature profile data was obtained by inverting seismic wave velocity.

[0166] S203. Compare the calculation results of the one-dimensional thermal steady-state equation with the calculation results of seismic wave velocity to determine the intersection point of the temperature profile in the shallowest part, which is the "constraint point".

[0167] Furthermore, a joint constraint mechanism between the heat conduction model and the seismic wave velocity model is established. The calculation results of the one-dimensional thermal steady-state equation are compared with the calculation results of the seismic wave velocity. The temperature profiles obtained by the two methods have an intersection point in the shallow part. This intersection point is defined as the heat flow-wave velocity coupling constraint point (Z,T). z ).

[0168] The constraint points determined at different measured heat flow points may not be consistent; they may be a range. Therefore, the final value can be the average of these values. For example, if the intersection of the one-dimensional thermal steady-state conduction equation and the temperature calculated by the seismic wave velocity model is at 50 km, then the constraint point is 50 km and the temperature T at that point. 50km .

[0169] S204. Construct a reverse heat conduction model based on the determined constraint points, and use the determined constraint point temperature as the lower boundary condition of the one-dimensional thermal steady-state conduction equation to realize the inversion of surface heat flow using the heat conduction equation.

[0170] In this step, for example, when the constraint point of the study area is 50km, the temperature of the lithosphere in the blank area is calculated using the seismic wave model to obtain the temperature at 50km. Then, based on the determined lithosphere layering model, the surface heat flow is calculated using the one-dimensional thermal steady-state conduction equation.

[0171] Furthermore, in some embodiments, Figure 3 A schematic diagram of the process for determining the first temperature profile data provided in this application is shown below. Figure 3 As shown, the method may include:

[0172] S301. Collect and integrate existing measured geothermal flow data in the study area.

[0173] Among them, high-quality observation data that has been corrected is preferred for geothermal flow data.

[0174] S302. Based on regional geological characteristics and deep exploration results, a multi-scale vertical stratification system of the lithosphere is constructed.

[0175] Specifically, it is divided into: ① sedimentary cover layer; ② upper, middle, and lower crustal units of the crystalline basement; ③ upper mantle layer.

[0176] S303. Assign values ​​to the thermal conductivity and heat generation rate parameters of each layer.

[0177] Based on both experimental measurements and previous results, thermal conductivity and heat generation rate parameters were determined for each layer. Laboratory-measured values ​​were preferentially used for rock thermal conductivity, while the heat generation rate parameter was calculated based on U-Th-K elemental content test results or using an empirical formula relating P-wave velocity and heat generation rate. For layers lacking experimental data, typical value ranges were selected using regional analogy.

[0178] S304. Calculate the lithospheric temperature using the one-dimensional thermal steady-state conduction formula and measured heat flow.

[0179] Furthermore, the lithosphere temperature of the region represented by each measured heat flow point (i.e., the first temperature profile data) is calculated using the one-dimensional steady-state thermal conduction formula, as shown in the following equation:

[0180] .

[0181] Furthermore, in some embodiments, Figure 4 A schematic diagram of the process for determining the second temperature profile data provided in this application is shown below. Figure 4 As shown, the method may include:

[0182] S401, Integrating the three-dimensional velocity structure model within the 0-200km depth range of the research area.

[0183] Spatial interpolation can be performed based on high-resolution seismic tomography results.

[0184] S402. Determine the elastic parameters of minerals and the proportion of mineral components in upper mantle rocks.

[0185] Based on available data, the mineral composition of upper mantle rocks can be determined. For example, the mineral composition of upper mantle rocks in the South China lithosphere can be divided as follows: olivine approximately 68%, orthopyroxene approximately 18%, clinopyroxene approximately 11%, and garnet approximately 3%. Simultaneously, the elastic parameters of each mineral, such as density and shear modulus, can be determined based on available data.

[0186] S403. Introduce a viscoelasticity factor to eliminate the influence of viscoelasticity effect.

[0187] To eliminate the influence of the viscoelastic effect, a viscoelasticity factor Q is introduced for viscoelasticity correction. The correction formula is as follows:

[0188] .

[0189] S404. Based on mineral physics theory, calculate the lithospheric temperature using the relationship between seismic wave velocity of upper mantle minerals and their elastic modulus, density, and temperature.

[0190] In this context, based on the formulas for the variation of elastic modulus and density with temperature, pressure, and composition in the above embodiments, and the expression relationship between shear wave velocity (i.e., theoretical wave velocity) and elastic modulus and density, the lithospheric temperature (i.e., the second temperature profile data) is calculated.

[0191] The surface heat flow determination method provided in this application constructs a lithospheric layering model using geological information and geophysical data, and determines the thermal property parameters of each layer. It calculates the lithospheric temperature in areas with known heat flow using a one-dimensional thermal steady-state conduction equation and a seismic wave temperature calculation model, compares the temperature results, and determines the temperature constraint point in the study area. It then calculates the deep lithospheric temperature in areas with unknown heat flow using the seismic wave temperature model, determines the constraint point temperature in the unknown area, and uses this temperature as the lower boundary condition for the one-dimensional thermal steady-state conduction equation, ultimately calculating the surface heat flow in the unknown area. This method balances prediction accuracy with high applicability even with limited data. By introducing geophysical calculation methods, it constructs a multi-parameter coupled heat flow calculation framework to overcome the technical bottlenecks of existing technologies, such as strong regional dependence and difficulty in clearly defining heat flow in blank areas. It aims to provide a more efficient and reliable solution for geothermal resource exploration and lithospheric thermal state research.

[0192] Figure 5 A schematic diagram of the surface heat flow determination device provided in this application is shown below. Figure 5 As shown, the surface heat flow determination device 50 provided in this embodiment includes:

[0193] The acquisition module 501 is used to acquire geological information, measured geothermal flow data, and seismic wave velocity data of the target area.

[0194] The first determining module 502 is used to determine the first temperature profile data based on measured geothermal flow data and a lithosphere layering model. The lithosphere layering model is constructed based on geological information and regional exploration results, and includes thermal property parameters of each layer.

[0195] The second determining module 503 is used to determine the second temperature profile data based on seismic wave velocity data and geological information;

[0196] The judgment module 504 is used to determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range;

[0197] Module 505 is obtained, which is used to calculate the surface heat flow of the target area based on the constraint points and the lithosphere layering model through reverse heat conduction.

[0198] In one possible implementation, the first determining module 502 can also be used to: vertically stratify the lithosphere of the target area based on geological information and regional exploration results to obtain a lithosphere stratification model; determine the thermal property parameters of each layer in the lithosphere stratification model, wherein the thermal property parameters of each layer include thermal conductivity parameters and heat generation rate parameters; and calculate the first temperature profile data based on measured geothermal flow data and the thermal property parameters of each layer through a one-dimensional thermal steady-state conduction equation.

[0199] In one possible implementation, the first determining module 502 can also be used to: acquire rock sample test data of the target area, the rock sample test data including measured values ​​of thermal conductivity and target element content; determine the thermal conductivity parameters of each layer in the lithosphere layering model based on the measured values ​​of thermal conductivity; obtain the heat generation rate parameters of each layer in the lithosphere layering model based on the target element content; or, obtain the heat generation rate parameters of each layer in the lithosphere layering model based on seismic wave velocity data of the target area.

[0200] In one possible implementation, the second determining module 503 can also be used to: construct a three-dimensional velocity structure model of the target area based on seismic wave velocity data; determine the proportion of upper mantle rock mineral components in the target area based on geological information; and determine second temperature profile data based on the three-dimensional velocity structure model, the proportion of upper mantle rock mineral components, and preset mineral elastic parameters.

[0201] In one possible implementation, the second determining module 503 can also be used to: perform hysteretic elastic correction on the seismic wave velocity in the three-dimensional velocity structure model to obtain the corrected seismic wave velocity; and calculate the second temperature profile data based on the corrected seismic wave velocity, the proportion of mineral components in the upper mantle rock, and preset mineral elastic parameters through the correlation between seismic wave velocity and temperature.

[0202] In one possible implementation, the judgment module 504 can also be used to: compare the first temperature profile data with the second temperature profile data; determine the target intersection point of the first temperature profile data and the second temperature profile data within the shallow crust, and use the target intersection point as a constraint point.

[0203] In one possible implementation, the judgment module 504 can also be used to: determine the initial intersection point of the first temperature profile data and the second temperature profile data within the shallow crust, the initial intersection point including multiple points; average the depth and temperature corresponding to the multiple initial intersection points respectively to obtain the average depth and average temperature; and use the average depth and average temperature as the depth and temperature of the target intersection point.

[0204] In one possible implementation, module 505 can also be used to: use the temperature of the constraint point as the lower boundary condition of the one-dimensional thermal steady-state conduction equation; and perform reverse calculation using the one-dimensional thermal steady-state conduction equation based on the thermal property parameters of each layer in the lithosphere layering model to obtain the surface heat flow of the target area.

[0205] The surface heat flow determination device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0206] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0207] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0208] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0209] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0210] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0211] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0212] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0213] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0214] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0215] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0216] The division of units is merely a logical functional division; 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. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0217] 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 this embodiment according to actual needs.

[0218] In addition, the functional units in the various embodiments of the present invention 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.

[0219] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0220] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0221] It should be understood that the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover but not exclude inclusion. For example, a product or device that includes a series of components is not necessarily limited to those components that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such product or device.

[0222] As used in this application, the term "module" means any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code capable of performing the functions associated with that element.

[0223] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining surface heat flow, characterized in that, include: Acquire geological information, measured geothermal flow data, and seismic wave velocity data for the target area; Based on the measured geothermal flow data and the lithosphere layering model, the first temperature profile data is determined. The lithosphere layering model is constructed based on the geological information and regional exploration results, and includes the thermal property parameters of each layer. Based on the seismic wave velocity data and the geological information, the second temperature profile data is determined; Determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range; Based on the constraint points and the lithosphere layering model, the surface heat flow of the target area is obtained through reverse heat conduction calculation.

2. The method according to claim 1, characterized in that, The determination of the first temperature profile data based on the measured geothermal flow data and the lithosphere layering model includes: Based on the geological information and regional exploration results, the lithosphere of the target area is vertically layered to obtain the lithosphere layering model; Determine the thermal properties of each layer in the lithospheric layering model, wherein the thermal properties of each layer include thermal conductivity parameters and heat generation rate parameters; Based on the measured geothermal flow data and the thermal property parameters of each layer, the first temperature profile data is calculated using a one-dimensional thermal steady-state conduction equation.

3. The method according to claim 2, characterized in that, Determining the thermal properties of each layer in the lithospheric layering model includes: Obtain test data of rock samples from the target area, including measured values ​​of thermal conductivity and the content of target elements; Based on the measured thermal conductivity values, the thermal conductivity parameters of each layer in the lithospheric layering model are determined. Based on the content of the target element, the heat generation rate parameters of each layer in the lithosphere layering model are obtained; or, Based on the seismic wave velocity data of the target area, the heat generation rate parameters of each layer in the lithospheric layering model are obtained.

4. The method according to claim 1, characterized in that, The determination of the second temperature profile data based on the seismic wave velocity data and the geological information includes: Based on the seismic wave velocity data, a three-dimensional velocity structure model of the target area is constructed; Based on the geological information, the mineral composition ratio of the upper mantle rocks in the target area is determined; Based on the three-dimensional velocity structure model, the mineral composition ratio of the upper mantle rocks, and the preset mineral elastic parameters, the second temperature profile data are determined.

5. The method according to claim 4, characterized in that, The determination of the second temperature profile data based on the three-dimensional velocity structure model, the mineral composition ratio of the upper mantle rocks, and preset mineral elastic parameters includes: The seismic wave velocity in the three-dimensional velocity structure model is subjected to hysteretic elastic correction to obtain the corrected seismic wave velocity. Based on the corrected seismic wave velocity, the mineral composition ratio of the upper mantle rocks, and the preset mineral elastic parameters, the second temperature profile data is calculated through the correlation between seismic wave velocity and temperature.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range includes: Compare the first temperature profile data with the second temperature profile data; The target intersection point of the first temperature profile data and the second temperature profile data within the shallow crust is determined, and the target intersection point is used as the constraint point.

7. The method according to claim 6, characterized in that, Determining the target intersection point of the first temperature profile data and the second temperature profile data within the shallow crust includes: Determine the initial intersection point of the first temperature profile data and the second temperature profile data within the shallow crust, wherein the initial intersection point includes multiple points; The depths and temperatures corresponding to multiple initial intersection points are averaged to obtain the average depth and average temperature. The average depth and average temperature are used as the depth and temperature of the target intersection point.

8. The method according to any one of claims 1-5, characterized in that, The calculation of surface heat flow in the target area based on the constraint points and the lithosphere layering model, using reverse heat conduction, includes: The temperature at the constraint point is used as the lower boundary condition of the one-dimensional thermal steady-state conduction equation. Based on the thermal properties of each layer in the lithospheric layering model, the surface heat flow of the target area is obtained by inverse calculation using the one-dimensional thermal steady-state conduction equation.

9. A device for determining surface heat flow, characterized in that, include: The acquisition module is used to acquire geological information, measured geothermal flow data, and seismic wave velocity data of the target area. The first determining module is used to determine the first temperature profile data based on the measured geothermal flow data and the lithosphere layering model. The lithosphere layering model is constructed based on the geological information and regional exploration results, and the lithosphere layering model includes the thermal property parameters of each layer. The second determining module is used to determine the second temperature profile data based on the seismic wave velocity data and the geological information; The judgment module is used to determine the constraint points between the first temperature profile data and the second temperature profile data within a preset depth range; The module is used to obtain the surface heat flow of the target area based on the constraint points and the lithosphere layering model through reverse heat conduction calculations.

10. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

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