CN117197368A - Three-dimensional geologic model construction and reliability evaluation method based on spatial data potential field - Google Patents

Abstract

The application relates to the technical field of three-dimensional geologic model construction, and particularly discloses a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field, which comprises the following steps: taking the data source as a field source and calculating the data potential field value of each field source; converting the corresponding data potential field value into stratum potential field interpolation weight, constructing a stratum potential field model according to the data potential field weighted interpolation, tracking an equipotential surface of the stratum potential field model to obtain a three-dimensional curved surface model, and evaluating the uncertainty of the established three-dimensional curved surface model according to the relative magnitude of the total data potential field value of each point on the curved surface; the method can utilize point and line data to interpolate at the same time, and can realize quantitative evaluation of uncertainty of the curved surface model by only one calculation.

Description

Three-dimensional geologic model construction and reliability evaluation method based on spatial data potential field

Technical Field

The application relates to the technical field of three-dimensional geologic model construction, and particularly discloses a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field.

Background

The three-dimensional geologic model can intuitively and accurately express various geologic phenomena, reproduces the spatial distribution condition and the interrelation of geologic bodies, and is a basis for carrying out the work of geologic space analysis, interpretation of geologic phenomena, geologic process numerical simulation and the like. The three-dimensional implicit (implicit) modeling method of the ground horizon potential field regards the ground layer interface as an isosurface of the scalar field, avoids the self-intersection of the ground layer surface, and the established three-dimensional model can automatically meet the consistency of the geological contact relation.

The Three-dimensional geological implicit modeling (Three-Dimensional Geological Implicit Modeling) is a modeling method based on an implicit function, and the Three-dimensional geological interface model is expressed as an equivalent surface of the implicit function and can be used for simulating a boundary curved surface of a complex shape of a geological body. The implicit modeling method of the horizon potential field defines a three-dimensional geological space as a scalar function f (p), wherein f is any point p epsilon R in the three-dimensional space ⁿ Potential field values at locations, wherein the potential field values represent geologic time or relative burial depths. The series of formation interfaces to be modeled is denoted as f _k (k=1,., K), corresponds to a series of specific equipotential surfaces, i.e. satisfies the potential field condition f (p) =f _k Is a curved surface of (2); the stratum occupies the bottom and top curved surfaces f _k And f _k+1 The space between them, and thus the potential field value inside the stratum is gradually changed from bottom to top, and there are countless equipotential surfaces which are mutually disjoint inside the stratum. By extracting the equipotential surface formed by the points of the specific potential field value, the three-dimensional curved surface reconstruction work of the stratum interface can be rapidly and accurately realized. Implicit modeling, as opposed to explicit modelingThe method has higher flexibility and freedom degree, and can generate high-precision geological interface complex shapes through implicit functions without complex geometric construction and splicing by a large amount of man-machine interaction in explicit modeling.

The existing implicit modeling methods (such as a radial basis function method and a pantagri-micronaire method) cannot directly analyze the uncertainty of a three-dimensional geological model, and disturbance data is required to be subjected to random modeling for multiple times to obtain the information entropy of geological space voxels, so that a large amount of computing resources are consumed, and the influence of modeling data density on the quality of the model is difficult to accurately reflect. The data potential field is a tool for describing the spatial distribution characteristics of data, and can be used for expressing the spatial distribution characteristics of geological modeling data, so that the uncertainty of a geological model can be estimated more accurately. In the data potential field interpolation method, original data such as boundary points, boundary lines, equal depth lines and the like of a three-dimensional space geologic body are regarded as field sources, respectively generated data potential fields are researched, and the total data potential field distribution after superposition represents the distribution characteristics of all data object field sources in space; the local density of the spatial data can be better reflected through the total potential field value distribution, and the uncertainty of the interpolation curved surface model can be quantitatively evaluated, namely, the potential value of the region close to the field source is relatively higher, the uncertainty of the local model of the region is lower, and the uncertainty of the local model far away from the field source region is higher.

Accordingly, the inventors have in view of the above, provided a three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields, so as to solve the above-described problems.

Disclosure of Invention

The application aims to solve the problems that the traditional implicit modeling method cannot directly analyze the uncertainty of a three-dimensional geological model and is difficult to accurately reflect the influence of modeling data density on the quality of the model.

In order to achieve the above object, the basic scheme of the present application provides a three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields, comprising the following steps:

step S001, obtaining point and line data sources of each stratum boundary;

step S002, respectively calculating the data potential field value of each field source by using each data source as the field source through a potential function;

step S003, converting the corresponding data potential field value into stratum potential field interpolation weight, constructing a stratum potential field model according to the data potential field weighted interpolation, obtaining a three-dimensional curved surface model by tracking the equipotential surface of the stratum potential field model, and evaluating the uncertainty of the established three-dimensional curved surface model according to the relative magnitude of the total data potential field value of each point on the curved surface;

step S004, a three-dimensional entity model is built through the three-dimensional curved surface model.

Further, in the step S002, data potential field value calculation of points, data potential field value calculation of line segments, and data potential field value calculation of broken lines are included.

Further, the potential function is a continuous, smooth and finite function, isotropic or anisotropic, the potential function being a decreasing function with increasing distance between the field source and the point to be estimated.

Further, in the step S002, the potential function is as follows:

in the method, in the process of the application,the data potential field value of any field source at any point to be estimated;

m is the unit data quality;

sigma is the interaction force path or influence radius between control objects, and when the anisotropy of the influence of a point field source is considered, a non-singular positive definite matrix is used for the parameter sigmaInstead, in three-dimensional space +.>

μ is a distance index;

and the I q-p I is the distance between any field source and any point to be estimated.

Further, the data potential field value calculation of the line segment comprises calculation of a nearest distance point from any point to be estimated to the line segment and calculation of a bandwidth matrix parameter of the nearest distance point on the line segment.

Further, the data potential field value of the line segment is calculated as follows:

in the method, in the process of the application,counting potential field values for any line segment field source at any point to be estimated;

p is any point to be estimated;

q is the nearest point position of any line segment field source and any point to be estimated;

sigma is a bandwidth matrix of the nearest point position of any line segment field source and any point to be estimated;

m is the intensity of the field source of any line segment.

Further, the calculation of the data potential field value of the broken line comprises calculation of the nearest distance point from any point to be estimated to the broken line and calculation of the bandwidth matrix parameters of the nearest distance point on the broken line.

Further, in the step S003, the weight coefficient for converting the data potential field value into the formation potential field interpolation is as follows:

wherein w is _k (p) is a weight coefficient of any point to be estimated;

a data potential field generated for a field source object at any point to be estimated;

the data potential field weighted interpolation calculation mode is as follows:

wherein f (p) is a value of the potential field of the stratum bit of the weighted interpolation of the potential field of the data;

f _k is the stratigraphic interface attribute value of the field source object.

Further, the total data potential field of any point to be estimated is used for expressing the uncertainty of the curved surface model, and the calculation formula is as follows:

in the method, in the process of the application,the total potential field for any point to be estimated.

Compared with the prior art, the scheme at least has the following beneficial effects:

1. the existing implicit modeling method is to discretize boundary line of geologic body into sampling point data, then construct horizon potential field by various interpolation algorithms, in the application, the data potential field interpolation method can directly take different types of geologic data such as boundary point, boundary line, equal depth line and the like of geologic body as field sources, interpolate by weighting potential values generated by each field source in space, thus constructing horizon potential field, avoiding uncertainty introduced by discretizing boundary line and equal depth line data into sampling points and then interpolating, and having the advantages of high efficiency, accuracy and flexibility.

2. According to the potential function property of the stable active field, each stratum boundary point and line object are used as field sources, an action field exists in the surrounding space of the stratum boundary point and line object, and all position points in the field are subjected to the combined action of all field sources. The implicit modeling method based on the data potential field interpolation can characterize the joint action generated by each field source, and adopts a bandwidth matrix to control the potential attenuation speed in each direction and change the potential function simulating potential attenuation to simulate the complex space morphology of the data potential field in the process of constructing a scalar field by the implicit interpolation function; the distribution of the stratum potential field values obtained by the weighted interpolation of the data potential field is utilized, the three-dimensional geological interface expressed by the equivalent surface is extracted to construct a three-dimensional geological entity model, and the geometric modeling result is smooth and accords with the geological condition; according to the method, the uncertainty of the stratum boundary three-dimensional curved surface model can be quantitatively evaluated by single modeling according to the superposition result of the total data potential field.

3. Compared with the existing implicit modeling method, the method has the advantages that geological sampling points and line data are regarded as three-dimensional field sources, potential functions are adopted to describe the data potential fields generated by points to be estimated, and the data potential fields are used as interpolation weight coefficients to reconstruct stratum potential fields in space; and extracting a specific equipotential surface from the stratum potential field as a three-dimensional curved surface model of the stratum boundary, and quantitatively evaluating the uncertainty of the three-dimensional curved surface model through the total potential field. The method can perform interpolation by utilizing point and line data at the same time, can realize quantitative evaluation of uncertainty of the curved surface model by only one calculation, has higher efficiency compared with the calculation of information entropy by multiple random modeling of the existing implicit modeling method, and better reflects the influence of data density on uncertainty of modeling results. Therefore, the application provides a new theoretical framework and a method reference for the three-dimensional geological implicit modeling technology, and can provide a more effective and accurate solution for reconstructing a three-dimensional curved surface similar to three-dimensional point and line data.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 shows a flow diagram of a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the application;

FIG. 2 shows a schematic flow chart of a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the application;

fig. 3 shows a comparison chart of the influence condition of a point field source along with the change of a bandwidth matrix sigma and a distance index mu in a three-dimensional geological model construction and reliability evaluation method based on a spatial data potential field according to the embodiment of the application;

FIG. 4 shows a two-dimensional "epsilon-band" model diagram in a three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields, which is provided by the embodiment of the application;

FIG. 5 shows a two-dimensional epsilon in a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the application _σ -a belt "model map;

FIG. 6 shows a comparison diagram of line segment field source influence conditions along with a bandwidth matrix in a three-dimensional geological model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the application;

FIG. 7 shows a comparison diagram of the influence condition of a polyline field source along with a bandwidth matrix in a three-dimensional geological model construction and reliability evaluation method based on a spatial data potential field according to the embodiment of the application;

FIG. 8 shows a stratum top and bottom plate diagram extracted from stratum potential fields in a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the application;

fig. 9 shows a schematic diagram of uncertainty of a three-dimensional curved surface model in a three-dimensional geological model construction and reliability evaluation method based on a spatial data potential field according to an embodiment of the present application;

FIG. 10 shows a geological profile of an area;

FIG. 11 shows a schematic representation of 4 formation burial contours of a region;

FIG. 12 is a diagram showing data such as the elevation points of a surface of an area;

FIG. 13 shows a map of a stratigraphic potential field model of a region;

fig. 14 shows a three-dimensional stratigraphic solid model diagram of a region.

Detailed Description

In order to further describe the technical means and effects adopted by the present application for achieving the intended purpose, the following detailed description will refer to the specific implementation, structure, characteristics and effects according to the present application with reference to the accompanying drawings and preferred embodiments.

The three-dimensional geological implicit modeling data source has complex structure and comprises drilling holes, geological section views and stratum burial depth contour line data. The boundary points and boundary lines on each stratum need to be converted into a data format suitable for three-dimensional coordinates, namely three-dimensional points and three-dimensional broken line data objects.

The embodiment provides a three-dimensional geologic model construction and reliability evaluation method based on a spatial data potential field, and the embodiment is as shown in fig. 1 and 2: the method mainly comprises the aspects of data potential field calculation, stratum potential field interpolation, equipotential surface tracking, three-dimensional solid modeling and the like, and specifically comprises the following steps:

1. and calculating a data potential field. By using the concept of using 'field' as the medium for non-contact interaction between objects in physics and using the concept and theoretical framework of data potential field, the field description method is introduced into abstract digital domain space to realize formal description of interaction between data objects. Known data spaceIn (a), there is a data set d= { x containing N objects ₁ ,x ₂ ,…,x _N Each object corresponds to a point or line of a certain quality in space, which is used as a field source to generate an action field around, and all the position points in the field are subjected to the joint action of the whole data object field sources.

1. And calculating the data potential field of the point. Given spaceThe point data object q in (1) assuming that the potential value generated by object q at position point p is +.>Then->The following should be satisfied: (1) and (2)>Is a continuous, smooth and finite function; (2) and (2)>Is a single-valued decreasing function of q, p inter-point distance q-p. When q-p=0, ++>Up to a maximum, when ||q-p|→infinity, |>

The function forms meeting the above criteria can define the potential function of the data field, and the potential derivative field function is adopted to simulate the attenuation situation of the potential in the data field in the embodiment. The potential derived field potential function is:

wherein m is the unit data quality and m is more than or equal to 0, which represents the intensity of the field source, and for the boundary point field source, it is assumed that m=1;

sigma is the interaction force range or influence radius between control objects, and sigma epsilon (0, + -infinity), called influence factor, when sigma is smaller, the influence radius of the data object is shorter; as σ increases, the radius of influence increases;

μ is a distance index and μ∈N.

The spatial distribution of the field source influence is mainly dependent on the interaction force path between the objects, and the correlation with the value of the distance index μ is not obvious. Where the sigma parameter is isotropic, the parameter sigma may be a non-singular positive definite matrix when considering the anisotropy of the point field source effectInstead, in three-dimensional space +.>Typically the matrix Σ can be reduced to a diagonal matrix, i.e. with different σ parameter values for each direction, and the value on the off-diagonal is 0 to represent influencing features in different directions, thus making the data potential field more universally applicable. The case where the field source point influence varies with the bandwidth matrix Σ and the distance index μ is shown in fig. 3.

In FIG. 3, the bandwidth matrix of FIG. 3 (a) isAs can be seen from FIG. 3 (a), the equivalent surface ++under the wideband matrix and the distance index μ is 2>The potential field is uniformly attenuated to the surrounding space along with the increase of the distance by taking the source point as the center, and the equipotential surface is in a sphere shape.

The bandwidth matrix of FIG. 3 (b) isAs can be seen from FIG. 3 (b), the equivalent surface ++under the wideband matrix and the distance index μ is 6>In the method, the potential field uniformly attenuates to the surrounding space with the increase of the distance by taking the source point as the center, but the attenuation speed becomes high due to the increase of the distance index, and the equipotential surface is in a sphere shape.

The bandwidth matrix of FIG. 3 (c) isAs can be seen from FIG. 3 (c), the equivalent surface ++under the wideband matrix and the distance index μ is 2>In this case, the potential field attenuates to the surrounding space with increasing distance about the source point, but the attenuation speed in the Y direction becomes slow. Similarly, increasing the elements on the diagonal of the matrix slows down the potential field decay in the corresponding direction, whereas increases the potential field decay in the opposite direction, and the equipotential surfaces are elliptical.

The bandwidth matrix of FIG. 3 (d) isAs can be seen from FIG. 3 (d), the equivalent surface ++under the wideband matrix and the distance index μ is 2>In the method, the potential field takes a source point as a center, decays to the surrounding space along with the increase of the distance, and the decays in the X-Y direction have mutual influence.

2. The data potential field of the line segment. When calculating the data potential field of the line segment field source, firstly, the coordinate of the nearest distance point from the point to be estimated to the line segment object needs to be determined, then the bandwidth matrix parameter is set, and finally, the data potential field generated by the line object is determined through the potential function. Assume that there is a segment object in spaceThe potential value generated by the derivative field potential function simulation at the point p to be estimated can be defined as the line segment +.>The data potential field value generated at q at the point nearest to p, whose formula can be rewritten by formula (1):

in which the bandwidth matrix Σ is a line segmentBandwidth matrix of nearest point q of upper distance p, line segment field source +.>The intensity m of (c) is defined as:

therefore, the line segment data potential field calculation turns into two problems, namely, solving the spatial position p to the lineSegment(s)Is defined by the closest distance point q and the set line segment +.>The bandwidth matrix parameters of the q points can be used for calculating the data potential field value. Space arbitrary point p to a line segment +.>The shortest distance of (2) may use a vector method, defining the parameter ρ as follows:

in which θ is a vectorVector->An included angle between the two.

When p is atWhen the projection in the direction is on the extension line of the endpoint a, ρ is less than or equal to 0; when p is +.>Projection in the direction is +.>Internally, 0<ρ<1, a step of; when p is +.>When the projection in the direction is on the extension line of the endpoint b, ρ is more than or equal to 1; special cases such as point on line segment, point on endpoint, point on line segment extension, etc. are also applicable, and need not be discussed separately.

Based on the difference of p values, p-to-line segment can be calculatedThe coordinates of the closest point q on the map are shown in the following formula:

the position point p is to a line segmentIs converted to a distance q-p between p and the nearest distance point q.

When the bandwidth matrix sigma on the line segment is kept consistent, that is, the line segment object is provided with a unified bandwidth matrix, the bandwidth matrix of any point on the line segment is the same, which is called an epsilon-band model, and the equipotential line form generated by the two-dimensional line segment object is shown in fig. 4. And when the two end points and the middle point adopt different bandwidth matrixes, epsilon is formed _σ Band "model, which is more flexible than the" epsilon-band "model, as shown in fig. 5.

For three-dimensional line segments“ε _σ The band model requires the endpoint bandwidth matrix Σ to be set first _a Sum sigma _b And a correlation matrix between the endpoints +.>And->If the endpoints are independent of each other, then->And->All are zero matrices, the total endpoint bandwidth matrix is +.>The four sub-matrixes are all third-order square matrixes.

Memory matrixFrom this, a bandwidth matrix for any point q on the line segment is calculated, as shown in the following equation:

Σ _q ＝E·W·E ^T (6)

using sigma _q By replacing Σ in the above formula (2), "ε" can be obtained _σ The band model, the line segment field source influence condition as a function of the bandwidth matrix, is shown in fig. 6.

In FIG. 6, the bandwidth matrix of FIG. 6 (a) isThe model is an epsilon-band model, and as can be seen from FIG. 6 (a), under the broadband matrix and model, the isosurface +.>In the method, the bandwidth matrixes of all the line segments are consistent, the data potential field takes the line segment as the peak center, decays to the surrounding space along with the increase of the distance, the middle part of the equipotential surface is cylindrical, and the two ends of the equipotential surface are hemispherical.

The bandwidth matrix of FIG. 6 (b) is

The model is epsilon _σ The band model, under which the isosurface is found in FIG. 6 (b)In the method, the potential field takes a line segment as a peak center, decays to the surrounding space along with the increase of the distance, the equipotential surface is inwards sunken in the middle of the line segment, and the two ends of the equipotential surface are hemispherical.

The bandwidth matrix of FIG. 6 (c) is

The model is epsilon _σ The band model, under which the isosurface is found in FIG. 6 (c), is the wideband matrix and modelIn the method, the potential field takes a line segment as a peak center, decays to the surrounding space along with the increase of the distance, the equipotential surface is inwards sunken in the middle of the line segment, and the decay speed of the potential field at the end point a is obviously slowed down.

The bandwidth matrix of FIG. 6 (d) is

The model is epsilon _σ The band model, under which the isosurface is found in FIG. 6 (d), is the wideband matrix and modelIn the method, the potential field takes a line segment as a peak center, decays to the surrounding space along with the increase of the distance, the trend of the equipotential surface sinking in the middle of the line segment is slowed down, and the decay speed of the potential field at the end point a is obviously slowed down.

3. A data potential field of the polyline. If a source of a polyline object field is used in the interpolation process, the potential generated at the point p to be estimated can be defined as the data potential field generated at the point q closest to p on the polyline L. The calculation of the data potential field of the polyline object mainly comprises the steps of solving the nearest distance point q from the point p to be estimated to the polyline L and setting the bandwidth matrix parameters of the point q on the polyline, and then the spatial distribution of the data potential field can be calculated by using a potential derivative function.

The shortest distance from the point p to the polyline L is obtained based on the minimum value of the shortest distance from the point to each line segment on the polyline, and the steps are as follows:

(1) using vector method to calculate each line segment L from the point p to be estimated to the broken line L ₁ ,L ₂ ,…,L _n The shortest distance of (d) ₁ ,d ₂ ,…,d _n ；

(2) Comparison d) ₁ ,d ₂ ,…,d _n Take the minimum value d _min ＝min(d ₁ ,d ₂ ,…,d _n ) I.e. the shortest distance from the point to the polyline.

Thus, the shortest distance between the point p and the folding line L can be obtained, and the point q closest to the point p on the folding line is obtained through the shortest distance. The intensity m of the folded field source L is defined as:

wherein q is _i Is the ith node on the fold line. And (3) setting bandwidth matrix parameters as in formula (6), and calculating data potential field distribution as in formula (2). The attenuation of the potential by the fold line is shown in figure 7.

In FIG. 7, the bandwidth matrix of FIG. 7 (a) isThe model is an epsilon-band model, and as can be seen from FIG. 7 (a), under the broadband matrix and model, the isosurface +.>In the method, the bandwidth matrixes of all the line segments are consistent, the data potential field takes the line segment as the peak center, decays to the surrounding space along with the increase of the distance, the middle part of the equipotential surface is cylindrical, and all the folding points are hemispherical.

The bandwidth matrix of FIG. 7 (b) is

The model is epsilon _σ The band model, as can be seen from FIG. 7 (b), under which the isosurface isIn the method, the bandwidth matrixes of all the line segments are consistent, the potential field takes the line segment as the peak center, the potential field decays to the surrounding space along with the increase of the distance, the equipotential surface is concave inwards at the part between all the folding points, and all the folding points are hemispherical.

2. And (5) interpolating the stratum potential field. The horizon potential field is used to represent the geologic formation environment and is a scalar function f (p) in three dimensions, and the horizon potential field attribute value may be the horizon number, or its corresponding geologic age, relative burial depth value, etc. Arbitrary formation interface f _k Corresponding to an equipotential surface, i.e. a series of points f (p) =f of the same horizon potential field value _k A curved surface is formed, and the stratum is positioned at two adjacent interfaces f _k And f _k+1 Between them. According to the definition of the ground horizon potential field, two arbitrary equipotential surfaces do not intersect, nor are they tangential. Thus, a formation potential field model may be constructed by defining a set of values as potential field attribute values for different formation interfaces and weighting interpolation from the data potential fields. Assuming that N field source objects exist in the space, the data potential field generated by the kth field source object at the point p to be estimated isThe weight coefficient for converting the corresponding data potential field value into the formation potential field interpolation is:

after obtaining the weight parameters of the formation potential field interpolation, interpolation calculation of the formation potential field f (p) can be performed, namely:

wherein f _k Is the stratigraphic interface attribute value of the kth field source object. The formation potential field model of the space grid point can be obtained through calculation of the formula.

The data potential field theory takes each spatial point and line data object as a field source, and quantitatively describes the influence of the field source on a spatial point to be estimated by defining a potential function of the field. Based on data field interpolation, original data (three-dimensional point objects, line segment objects and broken line objects) such as sampling points and sampling lines on a geological interface are all regarded as field sources, respectively generated data potential fields are researched, and the distribution of stratum potential fields after weighted interpolation based on the data potential fields reflects the joint action of the data objects on the points to be estimated in space. According to the interpolation results of the three-dimensional points and the linear field sources, the reference equipotential surfaces of the stratum boundary, namely the three-dimensional curved surface models of the stratum top and bottom plates, can be tracked and obtained. As shown in fig. 8, the three-dimensional curved surface model of the top and bottom surfaces of the stratum, which is the equipotential surfaces of the complex morphology, can be tracked based on the stratum potential field.

In fig. 8, (a) is a group i top plate; (b) is a group i backplane; (c) is a group ii backplane; (d) is a group iii backplane; (e) is a group IV backplane.

3. Uncertainty analysis of geological interfaces. In this embodiment, the total data potential field is used to represent the uncertainty of the three-dimensional curved surface model of the stratum boundary. In general, where data is denser, the uncertainty of the model is lower; the more sparse the model, the higher the uncertainty. The data potential fields generated by the field sources decrease with increasing distance, and the total data potential fields may reflect the density distribution of the spatial data.

On the three-dimensional curved surface of the stratum boundary extracted in this embodiment, the total data potential field value of each point may reflect the uncertainty of the modeling result, that is, where the total data potential field value is larger on the curved surface, the uncertainty of the modeling result is lower at that place. The total data potential field of any point to be estimated pThe calculation formula of (2) is as follows：

The total data potential field value reflects the space data density condition and simultaneously reflects the uncertainty of the model, so that the uncertainty of the established three-dimensional curved surface model can be evaluated according to the relative magnitude of the total data potential field value of each point on the curved surface. Figure 9 shows the uncertainty evaluation of four aqueous set top and bottom three-dimensional surfaces.

In fig. 9, (a) is the uncertainty of group i top plates; (b) uncertainty for group i backplanes; (c) uncertainty for group II backplanes; (d) uncertainty for group III backplanes; (e) is the uncertainty of group IV backplanes.

4. And (5) constructing a three-dimensional entity model. After the three-dimensional curved surface model of each stratum boundary is obtained, a corresponding three-dimensional geological entity model can be constructed in a three-dimensional space. Solid models are an important three-dimensional representation in geologic modeling, which is generally surrounded by top, bottom and sides. The bottom plate of each stratum and the bottom plate of the previous stratum respectively correspond to the bottom constraint and the top constraint of the stratum, and a three-dimensional geological entity model can be built under the combined constraint of the digital elevation model DEM. In this embodiment, a three-dimensional curved surface shape with a complex formation boundary can be reconstructed, and the top and bottom surfaces thereof can be reconstructed by extracting equipotential surfaces of the formation potential field.

In the implementation of the present application, taking geological conditions of a certain region as an example:

step S001, acquiring data sources of stratum in the region: the geological section of the region is shown in fig. 10, the contour lines of the burial depths of 4 strata are shown in fig. 11, and the data of the earth surface Gao Chengdian and the like are shown in fig. 12.

In step S002, the data potential field values of the respective field sources are calculated by the potential function using the respective data sources as field sources.

Step S003, using data potential field interpolation method, using ε for cross-section stratum boundary line _σ -adopting an epsilon-strip line source model for a stratum buried depth contour line and adopting a point source model for a surface elevation pointAnd carrying out weighted interpolation on the data potential fields. The stratum potential field model of the region is constructed as shown in fig. 13, and a three-dimensional curved surface model is obtained by tracking the equipotential surface of the stratum potential field model.

Step S004, a three-dimensional solid model is built through the three-dimensional curved surface model, and the three-dimensional stratum solid model of the region is built as shown in fig. 14.

The application uses the data potential field interpolation algorithm, takes the stratum demarcation point and line as the field source, constructs the stratum potential field to express the stratum interface, tracks the specific equipotential surface of the stratum potential field, and can build the three-dimensional curved surface model and the three-dimensional geological entity model of the stratum boundary. Compared with the existing implicit modeling method, the stratum potential field modeling method based on data potential field interpolation can directly use stratum boundary line data in the interpolation process, and uncertainty of a stratum boundary three-dimensional curved surface model can be quantitatively evaluated through single modeling.

The present application is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present application.

Claims (9)

1. The three-dimensional geologic model construction and reliability evaluation method based on the spatial data potential field is characterized by comprising the following steps:

step S001, obtaining point and line data sources of each stratum boundary;

step S002, respectively calculating the data potential field value of each field source by using each data source as the field source through a potential function;

step S003, converting the corresponding data potential field value into stratum potential field interpolation weight, constructing a stratum potential field model according to the data potential field weighted interpolation, obtaining a three-dimensional curved surface model by tracking the equipotential surface of the stratum potential field model, and evaluating the uncertainty of the established three-dimensional curved surface model according to the relative magnitude of the total data potential field value of each point on the curved surface;

step S004, a three-dimensional entity model is built through the three-dimensional curved surface model.

2. A three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields according to claim 1, characterized in that in said step S002, data potential field value calculation of points, data potential field value calculation of line segments and data potential field value calculation of broken lines are included.

3. A method of three-dimensional geologic model construction and reliability evaluation based on spatial data potential fields according to claim 2, characterized in that said potential function is a continuous, smooth and finite function, isotropic or anisotropic, said potential function being a decreasing function with increasing distance from the field source to the point to be estimated.

4. A three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields according to claim 3, characterized in that in said step S002 the potential functions of said points are as follows:

in the method, in the process of the application,the data potential field value of any field source at any point to be estimated;

m is the unit data quality;

sigma is the interaction force path or influence radius between control objects, and when the anisotropy of the influence of a point field source is considered, a non-singular positive definite matrix is used for the parameter sigmaInstead, in three-dimensional space +.>

μ is a distance index;

and the I q-p I is the distance between any field source and any point to be estimated.

5. A method of three-dimensional geologic model construction and reliability evaluation based on spatial data potential fields as claimed in any of claims 2-4, wherein the data potential field value calculation of the line segment comprises calculation of nearest distance points from any point to be estimated to the line segment and calculation of bandwidth matrix parameters of nearest distance points on the line segment.

6. The three-dimensional geologic model construction and reliability evaluation method based on spatial data potential field as set forth in claim 5, wherein the data potential field value of a line segment is calculated as follows:

in the method, in the process of the application,counting potential field values for any line segment field source at any point to be estimated;

p is any point to be estimated;

q is the nearest point position of any line segment field source and any point to be estimated;

sigma is a bandwidth matrix of the nearest point position of any line segment field source and any point to be estimated;

m is the intensity of the field source of any line segment.

7. The three-dimensional geologic model construction and reliability evaluation method based on space data potential field according to claim 6, wherein the data potential field value calculation of the broken line comprises the calculation of the nearest distance point from any point to be estimated to the broken line and the calculation of the bandwidth matrix parameter of the nearest distance point on the broken line.

8. The method for three-dimensional geologic model construction and reliability evaluation based on spatial data potential field according to claim 1, wherein in said step S003, the weight coefficient of the conversion of data potential field value into the interpolation of stratum potential field is as follows:

wherein w is _k (p) is a weight coefficient of any point to be estimated;

a data potential field generated for a field source object at any point to be estimated;

the data potential field weighted interpolation calculation mode is as follows:

wherein f (p) is a value of the potential field of the stratum bit of the weighted interpolation of the potential field of the data;

f _k is the stratigraphic interface attribute value of the field source object.

9. The three-dimensional geologic model construction and reliability evaluation method based on spatial data potential fields according to claim 8, wherein the calculation formula of the total data potential field of any point to be estimated is as follows:

in the method, in the process of the application,the total potential field for any point to be estimated.