Method and system for detecting deep heat source mechanism of geothermal field and electronic equipment

Abstract

To provide a method for detecting a deep heat source mechanism of a geothermal field, and aiming at solving the problems of poor heat source detection accuracy and robustness caused by difficulty in obtaining direct temperature information in existing heat source detection methods.SOLUTION: A method comprises the steps of: acquiring geological data of a geothermal field of a deep heat source mechanism to be detected; performing inversion according to aeromagnetic abnormal data, and determining a Curie surface depth; calculating a temperature of a mourhue surface and a temperature distribution of a rock circle mantle in combination with seismic imaging data; calculating geothermal field deep temperature structure data as first data; obtaining temperature constraint information at different depths of the geothermal field of the deep heat source mechanism to be detected as second data; calculating a temperature fitting degree of the first data and the second data, and obtaining optimal thermal structure model parameter; and constructing a deep three-dimensional temperature structure, and determining the heat source of the geothermal field of the deep heat source mechanism to be detected.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to the technical field of geothermal resource development, and more particularly to a method, system and electronic device for exploring deep heat source mechanisms in geothermal fields. [Background technology]

[0002] Geothermal resources, as a clean energy, have the advantages of large storage, wide distribution, clean and environmentally friendly, stable and reliable, etc., and therefore have been attracting more and more attention in the process of energy structure adjustment. According to temperature, geothermal resources can be divided into high-temperature geothermal resources and medium-low-temperature geothermal resources, among which high-temperature geothermal resources have huge power generation potential, and are an effective means to address climate change, ensure energy security and sustainable development, and realize energy transformation. The deep heat source mechanism of a geothermal field directly determines whether or not there is a high-temperature geothermal resource. The traditional heat source exploration method mainly uses methods such as hydrochemistry and surface heat flow, but these methods are difficult to obtain direct temperature information, which directly restricts the development plan of geothermal resources. With the implementation of various geoscience exploration plans, the explosive increase in observation data and big data technical methods provide conditions for the invention and exploration of deep heat source mechanisms. Based on this, the present invention proposes an exploration method for the deep heat source mechanism of a geothermal field. Summary of the Invention

[0003] In order to solve the above-mentioned problems in the existing technology, that is, in order to solve the problem that the accuracy and robustness of heat source detection are not excellent because direct temperature information is difficult to obtain in the existing heat source detection method, in a first aspect of the present invention, Collect geological data of the geothermal field of the deep heat source mechanism to be explored, the geological data including thermophysical parameters of various rocks in different layers, seismic imaging data, and aeromagnetic anomaly data; Inverting the aeromagnetic anomaly data to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and calculating the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle by combining with the seismic imaging data; Calculating geothermal field deep temperature structure data according to the thermophysical parameters of each type of rock in the different strata through a geothermal field deep temperature structure model, and obtaining the first data; Based on the Curie surface depth, the temperature at the Moho surface, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, fitting is performed to obtain temperature constraint information at different depths in the geothermal field of the deep heat source mechanism to be explored, and the temperature constraint information is used as second data; Calculate the temperature fitting degree of the first data and the second data, and reserve all corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a set fitting degree threshold, and select the geothermal field deep temperature structure model with the highest occurrence probability through statistical analysis as the optimal model, and the corresponding thermophysical parameters of the optimal model are optimal thermal structure model parameters; A method for exploring the deep heat source mechanism of a geothermal field is proposed, which includes constructing a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and further determining the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, where the heat source includes a magma heat source and a non-magma heat source.

[0004] In some preferred embodiments, the method of performing inversion according to the aeromagnetic anomaly data to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored includes: The thickness of the geothermal field magnetic layer or the depth of the Curie surface is Z b Let us assume that the theoretical magnetic anomaly power spectrum Φ(k) generated by this magnetic layer is the teeth, ln(Φ(k) the )=A-βln(k)+2ln(1-e -kzb ) and Wherein, k is a wave number, A is a constant, and β is a geomagnetic fractal dimension; calculating an observed magnetic anomaly power spectrum as a first power spectrum based on the aeromagnetic anomaly data; and calculating a posterior distribution of a first parameter in the theoretical magnetic anomaly power spectrum in the first power spectrum based on the Bayes inversion theory; P(m|d) ∝ P(d|m)P(m) In this case, m represents a first parameter, d represents a first power spectrum, P(m|d) represents a posterior distribution, P(d|m) represents a likelihood distribution, and P(m) represents a priori distribution. The first parameters are A, β, Z b the a priori distribution is a mean distribution, the likelihood distribution follows a Gaussian distribution, and the posterior distribution is estimated and the first parameter is statistically calculated to determine the Curie surface depth.

[0005] In some preferred embodiments, the method for calculating the temperature of the Moho location of the geothermal field of the deep heat source mechanism to be explored includes calculating the temperature of the Moho location based on the seismic imaging data in combination with wave speed data measured under a first set of conditions for a first set of rocks, the first set of rocks including harzburgite and lherzolite, and the first set of conditions including high temperature and high pressure.

[0006] In some preferred embodiments, the temperature distribution in the lithospheric mantle is calculated by a method including obtaining the temperature distribution in the lithospheric mantle using a mantle wave speed structure model based on wave parameters measured under set second conditions for a set second type of rock, the second type of rock including mantle rock, and the second conditions including temperature and pressure.

[0007] In some preferred embodiments, a method for calculating geothermal field deep temperature structure data includes:

[0008]

number

[0009] In some preferred embodiments, the degree of temperature fitting of the first data and the second data is calculated, the method comprising: θ=1- <rmse>

[0010]

number

[0011] In a second aspect of the present invention, A data acquisition module configured to acquire geological data of a geothermal field of a deep heat source mechanism to be explored, the geological data including thermophysical parameters of various rocks in different layers, seismic imaging data, and aeromagnetic anomaly data; a data inversion module configured to perform inversion according to the aeromagnetic anomaly data, determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and calculate the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle in combination with the seismic imaging data; A temperature structure calculation module is configured to calculate geothermal field deep temperature structure data according to the thermophysical parameters of each type of rock in the different layers of the geothermal field through a geothermal field deep temperature structure model, and the calculated data is used as first data; A temperature constraint determination module is configured to perform fitting based on the Curie surface depth, the temperature at the Moho surface location, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field to obtain temperature constraint information at different depths in the geothermal field of the deep heat source mechanism to be explored, as second data; A model parameter acquisition module is configured to calculate the temperature fitting degree of the first data and the second data, reserve all corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a preset fitting degree threshold, and select the geothermal field deep temperature structure model with the highest occurrence probability through statistical analysis as the optimal model, and the corresponding thermophysical parameters of the optimal model are the optimal thermal structure model parameters; A heat source determination module is configured to construct a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and further determine the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, so that the heat source includes a magma heat source and a non-magma heat source; We propose an exploration system for the deep heat source mechanism of geothermal fields equipped with this system.

[0012] In a third aspect of the present invention, there is proposed an electronic device comprising at least one processor and a memory communicatively connected to at least one of said processors, said memory storing instructions executable by said processor, said instructions being executed by said processor and used to realise the above-mentioned method for exploration of deep heat source mechanisms in geothermal fields.

[0013] In a fourth aspect of the present invention, there is proposed a computer-readable storage medium having computer instructions stored therein, the computer instructions being executed by a computer to realize the exploration method for deep heat source mechanisms in geothermal fields as described above.

[0014] The beneficial effect of the present invention is that the accuracy and robustness of heat source detection can be improved by the present invention.

[0015] The present invention introduces the Curie surface depth, Moho temperature, and temperature information at depth revealed by xenoliths as data, and compared with the conventional method for determining the deep heat structure of geothermal fields based only on the surface heat flow, temperature constraints at different depths are added, and the calculation results are more accurate. The method introduces the Bayesian inversion method, and uses the above-mentioned multiple observation data as constraints to determine the model with the highest probability as the optimal model, and can also give the uncertainty of the model, effectively overcoming the problem that the calculation results have deviations due to the inaccuracy of parameters in the conventional method and the confidence level cannot be evaluated. In summary, the technical solution of the present invention can provide a more effective and accurate evaluation method for the deep heat source mechanism of geothermal fields. [Brief description of the drawings]

[0016] [Figure 1] FIG. 2 is a flow diagram of a method for exploring the deep heat source mechanism of a geothermal field according to an embodiment of the present invention. [Diagram 2] FIG. 1 is a schematic framework diagram of a geothermal field deep heat source mechanism exploration system according to one embodiment of the present invention. [Diagram 3] FIG. 1 is a schematic flow diagram of a method for exploring the deep heat source mechanism of a geothermal field according to an embodiment of the present invention. [Figure 4] FIG. 2 is a structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application according to one embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of the non-limiting embodiments with reference to the above drawings.

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention are described below in a clear and complete manner in conjunction with the drawings, of course, the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative labor are all within the scope of protection of the present invention.

[0019] The present application will be described in more detail below in conjunction with the drawings and examples. It will be understood that the specific examples described herein are merely for the purpose of illustrating the relevant invention, and are not intended to limit the invention. For ease of description, only parts related to the relevant invention are shown in the drawings.

[0020] Furthermore, where no contradiction exists, the embodiments and features of the embodiments in this application may be combined with each other.

[0021] The method for exploring the deep heat source mechanism of a geothermal field of the present invention is as shown in FIG. Collecting geological data of the geothermal field of the deep heat source mechanism to be explored, the geological data including thermophysical parameters of different rocks in different strata, seismic imaging data, and aeromagnetic anomaly data; According to the aeromagnetic anomaly data, perform inversion to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and calculate the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle by combining with the seismic imaging data; Calculating geothermal field deep temperature structure data as first data through a geothermal field deep temperature structure model according to the thermophysical parameters of each type of rock in the different strata; According to the Curie surface depth, the temperature at the Moho surface, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, fitting is performed to obtain temperature constraint information at different depths of the geothermal field of the deep heat source mechanism to be explored as second data; Calculate the temperature fitting degree of the first data and the second data, and reserve all the corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a preset fitting degree threshold, and select the geothermal field deep temperature structure model with the highest occurrence probability through statistical analysis as the optimal model, and the corresponding thermophysical parameters of the optimal model are the optimal thermal structure model parameters; The method includes a step of constructing a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and further determining the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, where the heat source includes a magma heat source and a non-magma heat source.

[0022] In order to more clearly explain the method for exploring deep heat source mechanism of geothermal field of the present invention, each step of one embodiment of the method of the present invention will be described in detail below in conjunction with FIG.

[0023] This invention comprehensively utilizes data such as earthquakes, geomagnetism, surface heat flow, and rock experiments to construct a three-dimensional temperature structure of a geothermal field, explore the deep heat source mechanism, and provide a scientific basis for evaluating the mechanism of geothermal resources and their potential, as follows:

[0024] The geological data of the geothermal field of the deep heat source mechanism to be explored is collected, and the geological data includes the thermophysical parameters of various rocks in different strata, seismic imaging data, and aeromagnetic anomaly data. In this embodiment, the existing data (geological data) of the geothermal field of the deep heat source mechanism to be explored within the block (i.e.) is comprehensively collected and organized, including seismic imaging, aeromagnetic anomaly, geothermal heat flow, rock experiment and drilling data, sedimentary layer and crustal thickness, etc. Field geological survey is carried out to measure the thermal conductivity, heat generation rate and radioactive heat radiation radius of various rocks, and statistics are made on the range values ​​of thermophysical parameters of different strata rocks.

[0025] According to the aeromagnetic anomaly data, inversion is performed to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and combined with the seismic imaging data, the temperature of the Moho surface location of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle are calculated. When the temperature rises to the Curie temperature (up to 580°C), the magnetic medium becomes paramagnetic and no magnetic anomaly occurs, and the actually observed geomagnetic anomaly is also derived from the magnetic medium above the Curie surface depth (up to 580°C isothermal surface). In this way, the Curie surface depth can be determined by magnetic anomaly observation, i.e., the depth corresponding to the 580°C isothermal surface can be determined, providing an independent constraint for calculating the temperature structure of the geothermal field.

[0026] In this embodiment, the inversion process of the Curie surface depth is as follows.

[0027] The thickness of the geothermal field magnetic layer or the depth of the Curie surface is Z b Let us assume that the theoretical magnetic anomaly power spectrum Φ(k) generated by this magnetic layer is the teeth, ln(Φ(k) the )=A-βln(k)+2ln(1-e -kzb ) (1) where k is the wave number, A is a constant, and β is the geomagnetic fractal dimension. Based on the aeromagnetic anomaly data, the first power spectrum is the observed magnetic anomaly power spectrum Φ(k) obs Calculate Based on the Bayesian inversion theory, we set the parameters m = [A, β, Z b The inversion process of [ ] is to calculate the posterior distribution of these parameters over the magnetic anomaly observations, P(m|d) ∝ P(d|m)P(m) (2) Wherein, m represents the first parameter, d represents the first power spectrum, P(m|d) represents the posterior distribution, P(d|m) represents the likelihood distribution, and P(m) represents the a priori distribution; That is, given a set of data d, the probability of obtaining model parameters m is directly proportional to the product of the probability of obtaining observed data d given the model parameters m and the model parameter distribution probability, where P(d|m) is called the "likelihood distribution" and P(m) is the "a priori distribution." In the inversion process, the prior probability of the model parameters, i.e., their degree of fitting to the data, is updated ex post by assessing the likelihood.

[0028] The a priori distribution is a mean distribution, the likelihood distribution follows a Gaussian distribution, the posterior distribution is estimated, and the distribution of the first parameter is statistically calculated to determine the Curie surface depth.

[0029] Based on seismic imaging data (including shear waves and Pn waves), combined with wave velocity data measured under a first set of conditions for a first type of rock, the temperature at the Moho location is calculated, the first type of rock being the main rock mineral components at the top of the upper mantle, including harzburgite and lherzolite, and the first conditions including high temperature and high pressure.

[0030] Based on the wave parameters measured under the set second conditions of the set second type of rock, the temperature distribution of the lithospheric mantle is obtained by a mantle wave speed structure model already obtained in seismology, where the second type of rock includes mantle rock and the second conditions include temperature and pressure.

[0031] Based on the thermophysical parameters of the rocks of different types in the different strata, a geothermal field deep temperature structure model is used to calculate the geothermal field deep temperature structure data as the first data. In this embodiment, according to the range of values ​​of the collected and measured parameters such as thermal conductivity, heat generation rate, and radioactive heat radiation radius of the rocks, the error of the geothermal field surface heat flow measurement is taken into consideration, and random sampling is performed on the model space using a uniform probability distribution sampling method according to the model space consisting of the range of values ​​and errors of these measurement parameters. Based on the randomly sampled model parameter space and the heat conduction equation (Equation 3 is the geothermal field deep temperature structure model), the geothermal field deep temperature structure T(Z i ) pre Calculate

[0032]

number

[0033] Based on the Curie surface depth, the temperature at the Moho surface, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, fitting is performed, and the second data is temperature constraint information T(Z i ) obs Get Calculate the temperature fitting degree of the first data and the second data, and reserve all corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a preset fitting degree threshold. Through statistical analysis, the geothermal field deep temperature structure model with the highest occurrence probability is determined as an optimal model, and the corresponding thermophysical parameters of the optimal model are the optimal thermal structure model parameters; In this embodiment, the temperature fitting degree of the first data and the second data is calculated as follows: θ=1- <rmse>(4)

[0034]

number

[0035] The present invention selects those whose temperature fitting degree is greater than the set fitting degree threshold (the temperature fitting degree preferred in the present invention is 0.8), performs statistical analysis on the model parameters that satisfy the temperature fitting degree, and takes the one with the highest occurrence probability as the optimal model parameter (specifically, reserves all geothermal field deep temperature structure models (i.e., Equation 3) whose temperature fitting degree is greater than the set fitting degree threshold, performs normal distribution calculation on the temperature fitting degree of the reserved models, and takes the geothermal field deep temperature structure model corresponding to the maximum probability calculated by the normal distribution as the optimal model, and the thermophysical parameters (including thermal conductivity, heat generation rate, and radioactive heat radiation radius) corresponding to the optimal model as the optimal thermal structure model parameters), and determines the uncertainty of the model parameters according to the variance of the model parameter distribution that satisfies the fitting degree, which is used for subsequent result reliability analysis.

[0036] Based on the optimal thermal structure model parameters, a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored is constructed, and the heat source of the geothermal field of the deep heat source mechanism to be explored is determined according to the depth temperature field distribution, where the heat source includes a magma heat source and a non-magma heat source.

[0037] In this embodiment, the optimal thermal structure model parameters are used to construct a three-dimensional temperature structure in the deep part of the geothermal field (i.e., the optimal thermal structure model parameters are substituted into equation (3) to obtain the three-dimensional temperature structure in the deep part of the geothermal field). Depending on the deep temperature field distribution, the deep heat source mechanism in the geothermal field is identified, and depending on whether a sudden change has occurred in the geothermal gradient, it is determined whether the heat source is magmatic or non-magmatic.

[0038] The present invention introduces the Curie surface depth, Moho temperature, and temperature information at depth revealed by xenoliths as data, and compared with the conventional method for determining the deep thermal structure of geothermal fields based only on the surface heat flow, temperature constraints at different depths are added, and the calculation results are more accurate. As a method, the Bayesian inversion method is introduced, and the above-mentioned multiple observation data are used as constraints to determine the model with the highest probability as the optimal model, and the uncertainty of the model can be given. The problem of deviations in the calculation results and inability to evaluate the confidence level due to the inaccuracy of parameters in the conventional method is effectively overcome. In summary, the technical solution of the present invention can provide a more effective and accurate evaluation method for the deep heat source mechanism of geothermal fields.

[0039] The second embodiment of the present invention is a system for detecting deep heat source mechanism in a geothermal field, as shown in FIG. A data acquisition module 100 configured to acquire geological data of a geothermal field of a deep heat source mechanism to be explored, the geological data including thermophysical parameters of various rock types in different strata, seismic imaging data, and aeromagnetic anomaly data; a data inversion module 200 configured to perform inversion according to the aeromagnetic anomaly data, determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and calculate the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle in combination with the seismic imaging data; A temperature structure calculation module 300 is configured to calculate geothermal field deep temperature structure data as first data through a geothermal field deep temperature structure model according to the thermophysical parameters of each type of rock in the different geothermal layers; A temperature constraint determination module 400 configured to perform fitting based on the Curie surface depth, the temperature at the Moho surface, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, and obtain temperature constraint information at different depths of the geothermal field of the deep heat source mechanism to be explored as second data; A model parameter acquisition module 500 is configured to calculate the temperature fitting degree of the first data and the second data, reserve all corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a preset fitting degree threshold, and select the geothermal field deep temperature structure model with the highest occurrence probability through statistical analysis as the optimal model, and the corresponding thermophysical parameters of the optimal model are the optimal thermal structure model parameters; A heat source determination module 600 is configured to construct a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and further determine the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, so that the heat source includes a magma heat source and a non-magma heat source; Equipped with.

[0040] Those skilled in the art will clearly understand that for convenience and simplification of description, the specific operating processes and related explanations of the system described above may refer to the corresponding processes in the method embodiments described above and will not be described repeatedly here.

[0041] It should be noted that the exploration system for deep heat source mechanism of geothermal fields according to the above embodiment is only illustratively described by distributing each of the above functional modules, and in actual application, the above functions can be assigned to different functional modules as necessary to be completed, that is, the modules or steps in the embodiments of the present invention can be re-decomposed or combined, for example, the modules in the above embodiment can be merged into one module or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps in the embodiments of the present invention are merely for distinguishing each module or step, and are not to be considered as inappropriately limiting the present invention.

[0042] An electronic device according to a third embodiment of the present invention comprises at least one processor and at least one memory communicatively connected to the processor, wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to realize the above-mentioned method for exploring deep heat source mechanisms in geothermal fields.

[0043] A fourth embodiment of the present invention provides a computer-readable storage medium having computer instructions stored therein, the computer instructions being executed by a computer to realize the above-mentioned method for exploring deep heat source mechanisms in geothermal fields.

[0044] Those skilled in the art will clearly understand that for convenience and simplification of description, the specific operating processes and related descriptions of the electronic devices and readable storage media described above may refer to the corresponding processes in the above method examples, and will not be described repeatedly here.

[0045] Reference is now made to Figure 4, which shows a structural diagram of a computer system of a server suitable for implementing the method, system and device embodiments of the present application. The server shown in Figure 4 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present application.

[0046] As shown in Fig. 4, the computer system comprises a central processing unit (CPU) 401 capable of performing various appropriate operations and processes according to programs stored in a read only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the system operation are further stored. The CPU 401, the ROM 402 and the RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0047] The following members, that is, an input unit 406 including a keyboard, a mouse, etc., an output unit 407 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc., a storage unit 408 including a hard disk, etc., and a communication unit 409 including, for example, a network interface card such as a LAN (Local Area Network) card and a modem, are connected to the I / O interface 405. The communication unit 409 executes communication processing via a network such as the Internet. A driver 410 is also connected to the I / O interface 405 as necessary. A removable medium 411, for example, a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the driver 410 as necessary, and a computer program read from the driver 410 is thereby installed in the storage unit 408 as necessary.

[0048] In particular, according to an embodiment of the present disclosure, the process described with reference to the flowchart above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product having a computer program carried on a computer-readable medium, the computer program having program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication unit 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, the above functions defined in the method of the present application are performed. Note that the computer-readable medium according to the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above. The computer-readable storage medium may include, for example, but is not limited to, an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage components, magnetic storage devices, or any combination of the above. In the present invention, a computer readable storage medium may be a tangible medium that contains or stores any program, which may be used in or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer readable signal medium may be a data signal carrying computer readable program code, which may be included in baseband or transmitted as part of a carrier wave.Such transmitted data signals may take a number of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may transmit, transmit, or transport a program for use in or in conjunction with an instruction execution system, apparatus, or device. The program code contained in the computer-readable medium may be transported in any suitable medium, including, but not limited to, wireless, wire, optical cable, RF (radio frequency), or the like, or any suitable combination of the above.

[0049] Computer program code for carrying out the operations of the present invention is written in one or more program design languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and further including conventional procedural program design languages ​​such as "C" or similar program design languages. The program code may run entirely on the user computer, partially on the user computer, as one independent software package, partially on the user computer and partially on a remote computer, or entirely on a remote computer or server. In the context of a remote computer, the remote computer may be connected to the user computer by any type of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., connected by the Internet using an Internet Service Provider).

[0050] The flowcharts and block diagrams in the drawings illustrate possible system architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowcharts or block diagrams may represent a module, program segment, or part of code, which includes executable instructions for implementing one or more predetermined logic functions. It should be noted that in some alternative implementations, the functions depicted in the boxes may occur in a different order than depicted in the drawings. For example, two consecutively illustrated boxes may actually be essentially executed in parallel, and they may be executed in a reverse order according to the functions involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, may be implemented in a system based on dedicated hardware that performs a predetermined function or operation, or may be implemented in a combination of dedicated hardware and computer instructions.

[0051] Additionally, the terms "first," "second," etc. are used merely to distinguish between similar objects and are not intended to describe or indicate a particular order or chronological order.

[0052] The term "comprises" or any other similar term means a non-exclusive inclusion, such that a process, method, article, or apparatus / device comprising a list of elements may include those elements as well as other elements not expressly stated, or may further include elements inherent to the process, method, article, or apparatus / device.

[0053] Up to now, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the drawings, but as those skilled in the art can easily understand, the protection scope of the present invention is of course not limited to these specific embodiments. On the premise that it does not deviate from the principle of the present invention, those skilled in the art can make equivalent modifications or replacements to the relevant technical features, and the technical solutions after these modifications or replacements all fall within the protection scope of the present invention.< / rmse> < / rmse>

Claims

1. Collect geological data of the geothermal field of the deep heat source mechanism to be explored, the geological data including thermophysical parameters of various rocks in different layers, seismic imaging data, and aeromagnetic anomaly data; Inversion is performed according to the aeromagnetic anomaly data to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle are calculated by combining the data with the seismic imaging data; According to the aeromagnetic anomaly data, inversion is performed to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, the method being as follows: The thickness of the geothermal field magnetic layer or the depth of the Curie surface is Z ｂ Let us assume that the theoretical magnetic anomaly power spectrum Φ(k) generated by this magnetic layer is ｔｈｅ teeth, Φ(Φ) ｔｈｅ CHAβBY(A)20(10). －ｋｚｂ )であり、 In this, k is the wave number, A is a constant, and β is the geomagnetic fractal dimension. calculating an observed magnetic anomaly power spectrum as a first power spectrum based on the aeromagnetic anomaly data; Calculate a posterior distribution of a first parameter in the theoretical magnetic anomaly power spectrum on the first power spectrum based on the Bayes inversion theory; P(m|d)∝P(d|m)P(m) In this case, m represents a first parameter, d represents a first power spectrum, P(m|d) represents a posterior distribution, P(d|m) represents a likelihood distribution, and P(m) represents an a priori distribution, and the first parameters are A, β, Z ｂ and The a priori distribution is a mean distribution, the likelihood distribution follows a Gaussian distribution, the posterior distribution is estimated, and the first parameter is statistically calculated to determine a Curie surface depth; Calculating geothermal field deep temperature structure data as first data through a geothermal field deep temperature structure model based on the thermophysical parameters of each type of rock in the different geothermal layers; Perform fitting based on the Curie surface depth, the temperature at the Moho surface location, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, and obtain, as second data, temperature constraint information at different depths in the geothermal field of the deep heat source mechanism to be explored; Calculate the temperature fitting degree of the first data and the second data, and reserve all the geothermal field deep temperature structure models whose temperature fitting degree is greater than a preset fitting degree threshold. Through statistical analysis, the geothermal field deep temperature structure model with the highest occurrence probability is determined as the optimal model, and the corresponding thermophysical parameters of the optimal model are the optimal thermal structure model parameters. Construct a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and determine the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, and the heat source includes a magma heat source and a non-magma heat source; A method for exploring deep heat source mechanisms in geothermal fields, comprising:

2. The method for exploring a deep heat source mechanism in a geothermal field, as described in claim 1, further comprising the steps of: calculating a temperature of the Moho surface location of the geothermal field of the deep heat source mechanism to be explored; and calculating the temperature of the Moho surface location based on the seismic imaging data in combination with wave speed data measured under a first set condition for a first type of rock; the first type of rock includes harzburgite and lherzolite; and the first condition includes high temperature and high pressure.

3. The temperature distribution in the lithospheric mantle is calculated using the following methods: The method for exploring deep heat source mechanisms in geothermal fields, as described in claim 2, characterized in that the temperature distribution of the lithospheric mantle is obtained by a mantle wave speed structure model based on wave parameters measured under set second conditions for a set second type of rock, the second type of rock including mantle rock, and the second conditions including temperature and pressure.

4. The deep temperature structure data of the geothermal field is calculated as follows: [0010] and Among them, T(z) represents the geothermal field deep temperature structure data, and Q ｓ is the surface heat flow, and T ｓ is the surface temperature, and A ０ is the surface heat generation rate, K is the thermal conductivity coefficient, and l ｒａｄ is the radiative heat radiation radius, and T ｒ 3. The method for exploring deep geothermal field heat source mechanism according to claim 2, wherein: σ is the mantle adiabatic temperature, Z represents the depth, and erf represents the error function.

5. The degree of temperature fitting of the first data and the second data is calculated, as a method thereof, θ=1-<RMSE>, [0025] and In it, θ represents the temperature fitting degree, < > represents normalization, and T(Z ｉ ) ｏｂｓ represents the temperature constraint information at different depths of the geothermal field of the deep heat source mechanism to be explored, and T(Z ｉ ) ｐｒｅ represents the geothermal field deep temperature structure data, and n represents the number of data points. The method for exploring deep heat source mechanism of a geothermal field according to claim 4.

6. A data acquisition module is configured to acquire geological data of a geothermal field of a deep heat source mechanism to be explored, the geological data including thermophysical parameters of various rock types in different strata, seismic imaging data, and aeromagnetic anomaly data; a data inversion module configured to perform inversion according to the aeromagnetic anomaly data, determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, and calculate the temperature of the Moho surface of the geothermal field of the deep heat source mechanism to be explored and the temperature distribution of the lithospheric mantle in combination with the seismic imaging data; According to the aeromagnetic anomaly data, inversion is performed to determine the Curie surface depth of the geothermal field of the deep heat source mechanism to be explored, the method being as follows: The thickness of the geothermal field magnetic layer or the depth of the Curie surface is Z ｂ Let us assume that the theoretical magnetic anomaly power spectrum Φ(k) generated by this magnetic layer is ｔｈｅ teeth, Φ(Φ) ｔｈｅ CHAβBY(A)20(10). －ｋｚｂ )であり、 In this, k is the wave number, A is a constant, and β is the geomagnetic fractal dimension. calculating an observed magnetic anomaly power spectrum as a first power spectrum based on the aeromagnetic anomaly data; Calculate a posterior distribution of a first parameter in the theoretical magnetic anomaly power spectrum on the first power spectrum based on the Bayes inversion theory; P(m|d)∝P(d|m)P(m) In this case, m represents a first parameter, d represents a first power spectrum, P(m|d) represents a posterior distribution, P(d|m) represents a likelihood distribution, and P(m) represents an a priori distribution, and the first parameters are A, β, Z ｂ and The a priori distribution is a mean distribution, the likelihood distribution follows a Gaussian distribution, the posterior distribution is estimated, and the first parameter is statistically calculated to determine a Curie surface depth; A temperature structure calculation module is configured to calculate geothermal field deep temperature structure data as first data through a geothermal field deep temperature structure model according to the thermophysical parameters of each type of rock in the different geothermal layers; A temperature constraint determination module is configured to perform fitting based on the Curie surface depth, the temperature at the Moho surface location, the temperature distribution of the lithospheric mantle, and the mantle xenoliths in the geothermal field, and obtain temperature constraint information at different depths of the geothermal field of the deep heat source mechanism to be explored as second data; A model parameter acquisition module configured to calculate the temperature fitting degree of the first data and the second data, reserve all corresponding geothermal field deep temperature structure models when the temperature fitting degree is greater than a preset fitting degree threshold, and determine the geothermal field deep temperature structure model with the highest occurrence probability through statistical analysis as an optimal model, and the corresponding thermophysical parameters of the optimal model are optimal thermal structure model parameters; A heat source determination module is configured to construct a deep three-dimensional temperature structure of the geothermal field of the deep heat source mechanism to be explored based on the optimal thermal structure model parameters, and further determine the heat source of the geothermal field of the deep heat source mechanism to be explored according to the depth temperature field distribution, so that the heat source includes a magma heat source and a non-magma heat source; A system for exploring deep heat source mechanisms in geothermal fields, comprising:

7. 1. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor, The memory stores instructions executable by the processor, and the instructions are executed by the processor to realize the exploration method for deep heat source mechanisms in geothermal fields according to any one of claims 1 to 5.

1. An electronic device comprising:

8. A computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being executed by the computer to realize the exploration method for deep heat source mechanisms in geothermal fields according to any one of claims 1 to 5. A computer-readable storage medium comprising:

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