A method and system for constructing a three-dimensional geological model of a geothermal resource exploration area

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for constructing a three-dimensional geological model of a geothermal resource exploration area, comprising: obtaining the neighborhood density similarity of each point cloud microspace according to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces; obtaining the necessity of interpolation of each point cloud microspace according to the number of point cloud data in each point cloud microspace and the neighborhood density similarity; screening all point cloud microspaces according to the necessity of interpolation to obtain all microspaces to be interpolated; and obtaining an enhanced three-dimensional geological model of a geothermal resource exploration area by performing interpolation operations on all microspaces to be interpolated. The present invention eliminates the problem of missing or uneven distribution of point cloud data caused by environmental influences, and increases visibility in the process of constructing a three-dimensional geological model.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for constructing a three-dimensional geological model of a geothermal resource exploration area. Background Art

[0002] The process of scanning and imaging point clouds by laser radar (LiDAR) involves the emission and reflection of laser pulses. First, laser pulses are emitted to the target area. These pulses interact with the object and reflect back to the receiver. The laser radar records the return time of the reflected signal to calculate the distance to the target object. At the same time, it obtains the intensity information of the reflected signal to generate three-dimensional point cloud data. These point cloud data provide an important basis for subsequent applications such as terrain modeling, feature extraction and three-dimensional reconstruction. In the process of constructing point cloud data, due to the influence of the geomorphic environment of the geothermal exploration area, the corresponding rocks, soils and minerals have uneven reflection characteristics in the process of radar laser imaging three-dimensional electric cloud, which will lead to uneven point cloud density and distribution, and certain noise defects will appear in the corresponding point cloud area after imaging. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a method and system for constructing a three-dimensional geological model of a geothermal resource exploration area.

[0004] An embodiment of the present invention provides a method for constructing a three-dimensional geological model of a geothermal resource exploration area, the method comprising the following steps:

[0005] Obtain several point cloud data of geothermal resource exploration areas;

[0006] According to the point cloud data of the geothermal resource exploration area, the circumscribed cubic space of the geothermal resource exploration area is obtained; the circumscribed cubic space is divided into a number of micro spaces; according to the number of point cloud data in each micro space, the point cloud density value of each micro space is obtained; according to the point cloud density value, all micro spaces are screened to obtain all point cloud micro spaces;

[0007] According to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces, the neighborhood density similarity of each point cloud microspace is obtained; according to the number of point cloud data in each point cloud microspace and the neighborhood density similarity, the necessity of interpolation of each point cloud microspace is obtained; according to the necessity of interpolation, all point cloud microspaces are screened to obtain all microspaces to be interpolated;

[0008] By interpolating all the micro-spaces to be interpolated, an enhanced three-dimensional geological model of the geothermal resource exploration area is obtained.

[0009] Preferably, the method of obtaining the circumscribed cube space of the geothermal resource exploration area according to the point cloud data of the geothermal resource exploration area includes:

[0010] The point cloud space is constructed according to the three-dimensional coordinates, reflection intensity, and timestamp of the point cloud data. In the point cloud space, the maximum circumscribed cube of the area formed by all the point cloud data of the geothermal resource exploration area is recorded as the circumscribed cube space of the geothermal resource exploration area.

[0011] Preferably, the method of dividing the circumscribed cube space into a plurality of micro spaces includes:

[0012] Preset a partition parameter , the outer cube space of the geothermal resource exploration area is evenly divided into A cubic micro space.

[0013] Preferably, the point cloud density value of each micro space is obtained according to the amount of point cloud data in each micro space, and the specific method includes:

[0014] Preset a volume parameter , the number and volume parameters of all point cloud data in each micro space The ratio of is taken as the point cloud density value of each micro space.

[0015] Preferably, the method of screening all micro spaces according to the point cloud density value to obtain all point cloud micro spaces includes:

[0016] For any micro space, if the point cloud density value of the micro space is not 0, the micro space is recorded as a point cloud micro space.

[0017] Preferably, the method of obtaining the neighborhood density similarity of each point cloud microspace according to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces includes the following specific methods:

[0018] Preset a neighborhood parameter For any point cloud microspace, in the point cloud space, obtain the Euclidean distance between the centroid of the point cloud microspace and the centroids of all other point cloud microspaces, sort all other point cloud microspaces in ascending order according to the Euclidean distance, and obtain the distance point cloud microspace sequence of the point cloud microspace; sort the first point cloud microspace in the distance point cloud microspace sequence of the point cloud microspace point cloud micro-spaces, all of which serve as neighborhood point cloud micro-spaces of the point cloud micro-space;

[0019] For any neighborhood point cloud microspace of the point cloud microspace, the absolute value of the difference in point cloud density values ​​between the point cloud microspace and the neighborhood point cloud microspace is recorded as the density difference value of the neighborhood point cloud microspace; the reciprocal of the density difference value of the neighborhood point cloud microspace is recorded as the first reciprocal of the neighborhood point cloud microspace; the average of the first reciprocals of all neighborhood point cloud microspaces of the point cloud microspace is taken as the neighborhood density similarity of the point cloud microspace.

[0020] Preferably, the necessity of interpolation of each point cloud microspace is obtained according to the amount of point cloud data in each point cloud microspace and the similarity of neighborhood density, and the specific method includes:

[0021] For any point cloud micro space, a normalized value of the inverse of the product of the number of all point cloud data in the point cloud micro space and the neighborhood density similarity of the point cloud micro space is used as the interpolation necessity of the point cloud micro space.

[0022] Preferably, the method of screening all point cloud micro-spaces according to the necessity of interpolation to obtain all micro-spaces to be interpolated includes:

[0023] Preset a threshold parameter , for any point cloud differential space, if the interpolation necessity of the point cloud microspace is greater than or equal to the threshold parameter , the point cloud micro space is recorded as the micro space to be interpolated.

[0024] Preferably, the method of obtaining the enhanced three-dimensional geological model of the geothermal resource exploration area by performing interpolation operations on all micro-spaces to be interpolated includes the following specific methods:

[0025] For any microspace to be interpolated, a cubic spline curve is fitted to the microspace to be interpolated to obtain the spline curve of the microspace to be interpolated; the spline curve of the microspace to be interpolated is analyzed by using the PCA principal component analysis method to obtain the main direction of the spline curve of the microspace to be interpolated, and the main direction is used as the interpolation direction of the microspace to be interpolated; the point cloud density value of the microspace to be interpolated is used as the interpolation density of the microspace to be interpolated; the interpolation direction and interpolation density of the microspace to be interpolated are used as the interpolation attribute value of the microspace to be interpolated;

[0026] The inverse distance weighted interpolation method is used to interpolate each micro-space according to the interpolation attribute value of each micro-space to obtain an interpolated point cloud dataset; the interpolated point cloud dataset is used as an enhanced three-dimensional geological model of the geothermal resource exploration area.

[0027] The present invention also proposes a system for constructing a three-dimensional geological model of a geothermal resource exploration area, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the method for constructing a three-dimensional geological model of a geothermal resource exploration area.

[0028] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the neighborhood density similarity of each point cloud microspace according to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces; obtains the necessity of interpolation of each point cloud microspace according to the amount of point cloud data in each point cloud microspace and the neighborhood density similarity; screens all point cloud microspaces according to the necessity of interpolation to obtain all microspaces to be interpolated; obtains an enhanced three-dimensional geological model of a geothermal resource exploration area by performing interpolation operations on all microspaces to be interpolated; thereby eliminating the problem of missing or uneven distribution of point cloud data caused by environmental influences and increasing visibility during the construction of a three-dimensional geological model. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A flowchart of the steps of a method for constructing a three-dimensional geological model of a geothermal resource exploration area according to the present invention;

[0031] Figure 2 The present invention is a characteristic relationship flow chart for constructing a three-dimensional geological model of a geothermal resource exploration area. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and system for constructing a three-dimensional geological model of a geothermal resource exploration area proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0034] The following is a detailed description of a method and system for constructing a three-dimensional geological model for a geothermal resource exploration area provided by the present invention in conjunction with the accompanying drawings.

[0035] See also Figure 1 , which shows a flowchart of a method for constructing a three-dimensional geological model of a geothermal resource exploration area provided by an embodiment of the present invention, the method comprising the following steps:

[0036] Step S001: Acquire a number of point cloud data of the geothermal resource exploration area.

[0037] Specifically, we first need to collect some point cloud data of the geothermal resource exploration area. The specific process is as follows:

[0038] Using ground-based laser radar to transmit laser beams to the geothermal resource exploration area and receive reflected signals, a number of point cloud data of the geothermal resource exploration area are generated, each point cloud data includes its three-dimensional coordinates, reflection intensity, and timestamp;

[0039] Among them, the scanning frequency of the lidar is set between 100HZ and 2000HZ; median filtering is used to remove noise caused by environmental interference or equipment errors; multiple scanning positions are used to align the point cloud through feature matching and iterative nearest point algorithm to ensure seamless connection of point cloud data at different locations.

[0040] It should be noted that median filtering, feature matching and iterative closest point algorithm are existing technologies and will not be described in detail in this embodiment.

[0041] So far, several point cloud data of the geothermal resource exploration area have been obtained through the above method.

[0042] Step S002: According to the point cloud data of the geothermal resource exploration area, the circumscribed cube space of the geothermal resource exploration area is obtained; the circumscribed cube space is divided into a plurality of micro spaces; according to the amount of point cloud data in each micro space, the point cloud density value of each micro space is obtained; according to the point cloud density value, all micro spaces are screened to obtain all point cloud micro spaces.

[0043] It should be noted that in the process of obtaining point cloud data of geothermal resource exploration areas, due to the influence of the main structure of the geothermal environment, some areas of the point cloud data present low density and uneven distribution, which leads to the influence of some defects in the modeling results; in order to solve this problem, the point cloud data is enhanced, and the specific enhancement method is point cloud interpolation; therefore, the point cloud data of the geothermal resource exploration area can be divided into several micro spaces in the point cloud space, and then according to the distribution of the point cloud data, the necessary point cloud micro spaces required for interpolation are obtained.

[0044] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the circumscribed cube space of the geothermal resource exploration area according to the point cloud data of the geothermal resource exploration area is:

[0045] The point cloud space is constructed according to the three-dimensional coordinates, reflection intensity, and timestamp of the point cloud data. In the point cloud space, the maximum circumscribed cube of the area formed by all the point cloud data of the geothermal resource exploration area is recorded as the circumscribed cube space of the geothermal resource exploration area.

[0046] Preferably, in some implementations of the embodiments of the present invention, the specific method of dividing the circumscribed cube space into a plurality of micro spaces is:

[0047] Preset a partition parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation;

[0048] The circumscribed cubic space of the geothermal resource exploration area is evenly divided into A cubic micro space.

[0049] The volume of each micro-space is the volume of the external cube space of the geothermal resource exploration area. ; Each micro space includes several point cloud data.

[0050] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the point cloud density value of each micro space according to the amount of point cloud data in each micro space is:

[0051] Preset a volume parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation;

[0052] The number and volume parameters of all point cloud data in each micro space The ratio of is used as the point cloud density value of each micro space;

[0053] The specific formula is:

[0054] ;

[0055] In the formula, Indicates Point cloud density value of a micro space; Indicates The total number of all point cloud data in a microspace; Indicates the preset volume parameters.

[0056] It should be noted that The larger the point cloud density value of a micro-space is, the lower the possibility of uneven point cloud distribution or uneven density in the local area due to factors such as reflection differences of materials in the environment, and the higher the possibility that the point cloud data of this part is normal.

[0057] Preferably, in some implementations of the embodiments of the present invention, since some microspaces do not have point cloud data when dividing the circumscribed cubic space of the geothermal resource exploration area, these microspaces do not need to participate in the new calculations necessary for interpolation; therefore, all microspaces are screened according to the point cloud density value, and the specific method for obtaining all point cloud microspaces is:

[0058] For any micro space, if the point cloud density value of the micro space is not 0, the micro space is recorded as a point cloud micro space; if the point cloud density value of the micro space is 0, the micro space is recorded as a noise micro space.

[0059] So far, all point cloud microspaces are obtained through the above method.

[0060] Step S003: According to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces, the neighborhood density similarity of each point cloud microspace is obtained; according to the number of point cloud data in each point cloud microspace and the neighborhood density similarity, the necessity of interpolation of each point cloud microspace is obtained; according to the necessity of interpolation, all point cloud microspaces are screened to obtain all microspaces to be interpolated.

[0061] It should be noted that the interpolation method of point cloud data not only solves the problem of low density in some point cloud areas, but also solves the uneven point cloud caused by low density, which in turn affects the purpose of modeling results; choosing to perform predictive analysis on the point cloud structure refers to determining the main geothermal structure characteristics of the low-density area of ​​the point cloud, and performing interpolation processing based on this characteristic; that is, geothermal areas usually have a unique terrain structure, which has its own main direction, and in this terrain structure area, there are rocks, soils, and minerals that have uneven reflection characteristics in the process of radar laser imaging three-dimensional electric clouds. At this time, by analyzing and extracting its main direction in the point cloud space structure, it is possible to perform prediction processing based on the main direction, and construct the interpolation area on the predicted main direction, so as to achieve the purpose of accurate interpolation based on geothermal structure characteristics.

[0062] 1. Obtain the neighborhood density similarity of each point cloud microspace.

[0063] It should be noted that due to the influence of geothermal structure, the number of point cloud data in different point cloud microspaces will be different; that is, in the hillside area where geothermal is more concentrated, the corresponding heat diffusion is more, and the point cloud data in the formed point cloud microspace is denser; in general geothermal ground areas, due to the relatively simple structure, the point cloud density in the formed point cloud microspace is lower; therefore, the necessity of interpolation is not determined by the point cloud density value in each point cloud microspace, but by the overall amount of point cloud microspaces in its multi-neighborhood; the more abnormal the point cloud density in the point cloud microspace is compared with the density value of the point cloud microspace in other neighborhoods, the more abnormal the corresponding point cloud density or distribution is, and the higher the necessity of interpolation is.

[0064] Preferably, in some implementations of the embodiments of the present invention, according to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces, a calculation method for obtaining the neighborhood density similarity of each point cloud microspace is:

[0065] Preset a neighborhood parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation;

[0066] For any point cloud microspace, in the point cloud space, obtain the Euclidean distance between the centroid of the point cloud microspace and the centroids of all other point cloud microspaces, sort all other point cloud microspaces in ascending order according to the Euclidean distance, and obtain the distance point cloud microspace sequence of the point cloud microspace; sort the centroids of the point cloud microspace in the distance point cloud microspace sequence of the point cloud microspace point cloud micro-spaces, all of which serve as neighborhood point cloud micro-spaces of the point cloud micro-space;

[0067] For any neighborhood point cloud microspace of the point cloud microspace, the absolute value of the difference between the point cloud density values ​​of the point cloud microspace and the neighborhood point cloud microspace is recorded as the density difference value of the neighborhood point cloud microspace; the reciprocal of the density difference value of the neighborhood point cloud microspace is recorded as the first reciprocal of the neighborhood point cloud microspace; the average of the first reciprocals of all neighborhood point cloud microspaces of the point cloud microspace is taken as the neighborhood density similarity of the point cloud microspace;

[0068] The specific formula is:

[0069] ;

[0070] In the formula, Indicates The neighborhood density similarity of each point cloud microspace; Indicates the preset neighborhood parameters; Indicates Point cloud density value of a point cloud micro space; Indicates The point cloud microspace Point cloud density value of the neighborhood point cloud micro space; Indicates taking the absolute value; Represents the preset hyperparameters. This implementation presets , used to prevent the denominator from being 0.

[0071] It should be noted that when The closer the value is to 0, the greater the similarity of the neighborhood density, which means that the area where the point cloud microspace point is located is a normal point cloud area, such as the ground area of ​​a geothermal three-dimensional model; conversely, when the value is smaller, it proves that the features between this point cloud microspace and the point cloud microspaces of the surrounding neighborhoods are less similar, the more abnormal the represented value is, and the lower the similarity of the corresponding neighborhood density is.

[0072] At this point, the neighborhood density similarity of each point cloud microspace is obtained.

[0073] 2. Obtain the necessity of interpolation for each point cloud microspace.

[0074] It should be noted that due to the influence of geothermal environment material reflection conditions, the higher the degree of noise influence in the process of generating point cloud data by lidar, firstly because its density value is low, that is, the number of point cloud data in the point cloud microspace is small; secondly, the density distribution corresponding to the point cloud microspace is relatively abnormal, that is, the more abnormal the point cloud distribution is, the higher the necessity of interpolation is.

[0075] Preferably, in some implementations of the embodiments of the present invention, according to the amount of point cloud data in each point cloud micro space and the neighborhood density similarity, the specific method for obtaining the necessity of interpolation of each point cloud micro space is:

[0076] For any point cloud micro-space, a normalized value of the inverse of the product of the number of all point cloud data in the point cloud micro-space and the neighborhood density similarity of the point cloud micro-space is used as the interpolation necessity of the point cloud micro-space;

[0077] The specific formula is:

[0078] ;

[0079] In the formula, Indicates Necessity of interpolation of point cloud microspaces; Indicates The number of all point cloud data in a point cloud microspace; Indicates The neighborhood density similarity of each point cloud microspace; represents the linear normalization function.

[0080] At this point, the necessity of interpolation of each point cloud microspace is obtained.

[0081] Preferably, in some implementations of the embodiments of the present invention, all point cloud micro-spaces are screened according to the necessity of interpolation, and a specific method for obtaining all micro-spaces to be interpolated is:

[0082] Preset a threshold parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation;

[0083] For any point cloud differential space, if the interpolation necessity of the point cloud microspace is greater than or equal to the threshold parameter , the point cloud micro space is recorded as the micro space to be interpolated.

[0084] So far, all micro-spaces to be interpolated are obtained through the above method.

[0085] Step S004: Obtain an enhanced three-dimensional geological model of the geothermal resource exploration area by performing interpolation operations on all micro-spaces to be interpolated.

[0086] It should be noted that by interpolating the point cloud data set in the geothermal resource exploration area, an enhancement effect is achieved, eliminating the problem of certain defects in the modeling results caused by the irregular distribution of point cloud data due to environmental factors, thereby achieving the purpose of accurate model construction.

[0087] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the enhanced three-dimensional geological model of the geothermal resource exploration area by performing interpolation operations on all micro-spaces to be interpolated is:

[0088] For any microspace to be interpolated, a cubic spline curve is fitted to the microspace to be interpolated to obtain the spline curve of the microspace to be interpolated; the spline curve of the microspace to be interpolated is analyzed by using the PCA principal component analysis method to obtain the main direction of the spline curve of the microspace to be interpolated, and the main direction is used as the interpolation direction of the microspace to be interpolated; the point cloud density value of the microspace to be interpolated is used as the interpolation density of the microspace to be interpolated; the interpolation direction and interpolation density of the microspace to be interpolated are used as the interpolation attribute value of the microspace to be interpolated;

[0089] The inverse distance weighted interpolation method is used to interpolate each micro-space according to the interpolation attribute value of each micro-space to obtain an interpolated point cloud dataset; the interpolated point cloud dataset is used as an enhanced three-dimensional geological model of the geothermal resource exploration area.

[0090] Among them, cubic spline curve fitting, PCA principal component analysis method, and inverse distance weighted interpolation method are existing technologies, and this implementation will not be described in detail here.

[0091] See also Figure 2 , which shows a characteristic relationship flow chart for constructing a three-dimensional geological model for a geothermal resource exploration area;

[0092] Through the above steps, a method for constructing a three-dimensional geological model of a geothermal resource exploration area is completed.

[0093] The present invention also proposes a system for constructing a three-dimensional geological model of a geothermal resource exploration area, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for constructing a three-dimensional geological model of a geothermal resource exploration area described in steps S001 to S004 are implemented.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a three-dimensional geological model of a geothermal resource exploration area, characterized in that: The method comprises the following steps: Obtain several point cloud data of geothermal resource exploration areas; According to the point cloud data of the geothermal resource exploration area, the circumscribed cubic space of the geothermal resource exploration area is obtained; the circumscribed cubic space is divided into a number of micro spaces; according to the number of point cloud data in each micro space, the point cloud density value of each micro space is obtained; according to the point cloud density value, all micro spaces are screened to obtain all point cloud micro spaces; According to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces, the neighborhood density similarity of each point cloud microspace is obtained; according to the number of point cloud data in each point cloud microspace and the neighborhood density similarity, the necessity of interpolation of each point cloud microspace is obtained; according to the necessity of interpolation, all point cloud microspaces are screened to obtain all microspaces to be interpolated; By interpolating all the micro-spaces to be interpolated, an enhanced three-dimensional geological model of the geothermal resource exploration area is obtained.

2. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The specific method of obtaining the circumscribed cube space of the geothermal resource exploration area according to the point cloud data of the geothermal resource exploration area includes: The point cloud space is constructed according to the three-dimensional coordinates, reflection intensity, and timestamp of the point cloud data. In the point cloud space, the maximum circumscribed cube of the area formed by all the point cloud data of the geothermal resource exploration area is recorded as the circumscribed cube space of the geothermal resource exploration area.

3. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The specific method of dividing the circumscribed cube space into a plurality of micro spaces includes: Preset a partition parameter , the outer cube space of the geothermal resource exploration area is evenly divided into A cubic micro space.

4. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The specific method of obtaining the point cloud density value of each micro space according to the amount of point cloud data in each micro space is as follows: Preset a volume parameter , the number and volume parameters of all point cloud data in each micro space The ratio of is taken as the point cloud density value of each micro space.

5. The method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The specific method of screening all micro spaces according to the point cloud density value to obtain all point cloud micro spaces includes: For any micro space, if the point cloud density value of the micro space is not 0, the micro space is recorded as a point cloud micro space.

6. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The specific method of obtaining the neighborhood density similarity of each point cloud microspace according to the difference in point cloud density values ​​between each point cloud microspace and its surrounding point cloud microspaces is as follows: Preset a neighborhood parameter For any point cloud microspace, in the point cloud space, obtain the Euclidean distance between the centroid of the point cloud microspace and the centroids of all other point cloud microspaces, sort all other point cloud microspaces in ascending order according to the Euclidean distance, and obtain the distance point cloud microspace sequence of the point cloud microspace; sort the first point cloud microspace in the distance point cloud microspace sequence of the point cloud microspace point cloud micro-spaces, all of which serve as neighborhood point cloud micro-spaces of the point cloud micro-space; For any neighborhood point cloud microspace of the point cloud microspace, the absolute value of the difference in point cloud density values ​​between the point cloud microspace and the neighborhood point cloud microspace is recorded as the density difference value of the neighborhood point cloud microspace; the reciprocal of the density difference value of the neighborhood point cloud microspace is recorded as the first reciprocal of the neighborhood point cloud microspace; the average of the first reciprocals of all neighborhood point cloud microspaces of the point cloud microspace is taken as the neighborhood density similarity of the point cloud microspace.

7. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The necessity of interpolation of each point cloud microspace is obtained according to the amount of point cloud data in each point cloud microspace and the similarity of neighborhood density, and the specific method includes: For any point cloud micro space, a normalized value of the inverse of the product of the number of all point cloud data in the point cloud micro space and the neighborhood density similarity of the point cloud micro space is used as the interpolation necessity of the point cloud micro space.

8. The method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The method of screening all point cloud micro-spaces according to the necessity of interpolation to obtain all micro-spaces to be interpolated includes: Preset a threshold parameter , for any point cloud differential space, if the interpolation necessity of the point cloud microspace is greater than or equal to the threshold parameter , the point cloud micro space is recorded as the micro space to be interpolated.

9. A method for constructing a three-dimensional geological model of a geothermal resource exploration area according to claim 1, characterized in that: The method of obtaining the enhanced three-dimensional geological model of the geothermal resource exploration area by performing interpolation operations on all micro-spaces to be interpolated includes the following specific methods: For any microspace to be interpolated, a cubic spline curve is fitted to the microspace to be interpolated to obtain the spline curve of the microspace to be interpolated; the spline curve of the microspace to be interpolated is analyzed by using the PCA principal component analysis method to obtain the main direction of the spline curve of the microspace to be interpolated, and the main direction is used as the interpolation direction of the microspace to be interpolated; the point cloud density value of the microspace to be interpolated is used as the interpolation density of the microspace to be interpolated; the interpolation direction and interpolation density of the microspace to be interpolated are used as the interpolation attribute value of the microspace to be interpolated; The inverse distance weighted interpolation method is used to interpolate each micro-space according to the interpolation attribute value of each micro-space to obtain an interpolated point cloud dataset; the interpolated point cloud dataset is used as an enhanced three-dimensional geological model of the geothermal resource exploration area.

10. A system for constructing a three-dimensional geological model of a geothermal resource exploration area, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for constructing a three-dimensional geological model of a geothermal resource exploration area as described in any one of claims 1-9 are implemented.

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