CN111242857A - Contour line generation optimization method with geological direction characteristics - Google Patents
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Abstract
The invention provides a contour generation optimization method with geological direction characteristics, which comprises the following steps: step 1, contour image generation and preprocessing are carried out; step 2, detecting and extracting the quasi-circular contour line; step 3, determining the direction of the quasi-circular contour line by adopting a standard deviation ellipse method; and 4, determining the adjustment degree and direction of the quasi-circular isoline by adopting the size and direction of the major axis and the minor axis of the standard deviation ellipse obtained by calculation in the step 3. The contour line generation optimization method with the geological direction characteristics is applied to the field of geological exploration and the field of geological interpretation, and especially plays an important role in the process of compiling a geological equivalent map.
Description
Technical Field
The invention relates to the field of geological exploration and geological interpretation, in particular to a contour line generation optimization method with geological direction characteristics.
Background
Contour maps are a very important representation in geological interpretation systems. According to a large amount of data obtained by scientific means, the underground geological structure and geological distribution parameters are deduced, a plane geological contour map of a corresponding region is constructed, and geological forms of strata, oil layers and gas layer layers, the thickness of the strata and the like can be represented, so that the position, the form, the petroleum reserve and the like of a petroleum enrichment region are determined. The contour research mainly focuses on the generation and drawing of contours, and a relatively complete set of theories and algorithms are formed.
But the contour line variation trend in the geological contour map should have directionality, which is not considered by all mature algorithms and software at present. Directionality means that in real nature, the shape of a mountain is mostly extended in a certain direction, and the overlook view thereof appears elliptical rather than perfect circle. The circular-like contour should be adjusted to an ellipse or other reasonable shape according to the geographic environment.
Because the sampling point data is manually detected, if only a very small number of data points are sampled in a certain geographic position area, a plurality of circular contour lines taking the sampling point as the center of a circle appear in a drawn contour line graph, which does not accord with the change rule of the geological contour line. Therefore, a new contour line generation optimization method with geological direction characteristics is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a contour line generation optimization method with geological direction characteristics, which plays an important role in the process of compiling a geological equivalent map.
The object of the invention can be achieved by the following technical measures: the contour generation optimization method with the geological direction feature comprises the following steps: step 1, contour image generation and preprocessing are carried out; step 2, detecting and extracting the quasi-circular contour line; step 3, determining the direction of the quasi-circular contour line by adopting a standard deviation ellipse method; and 4, determining the adjustment degree and direction of the quasi-circular isoline by adopting the size and direction of the major axis and the minor axis of the standard deviation ellipse obtained by calculation in the step 3.
The object of the invention can also be achieved by the following technical measures:
in step 1, the contour generates data or contour data which needs to be sampled, and the contour data comprises contour sequence data of a geological contour map and image data after contour mapping.
In step 1, the contour sequence data is the trace data for each contour; the method is to calculate data on grid lines for tracking a specific contour value after geological sampling point data forms grid data through spatial interpolation.
The contour-mapped image data is a contour map formed by tracing contour sequence data, and the contour map does not need color filling for identification and detection.
In step 1, the specific method of contour image preprocessing is as follows: and drawing the contour line image according to the contour line sequence data, acquiring the contour line image to be processed, establishing a corresponding relation between the contour line and the contour line point sequence, and initializing all contour lines in the image to be processed into non-circular-like contour lines.
In step 1, the specific method for establishing the correspondence between the contour line and the equivalence point sequence is as follows: randomly selecting a pixel point on the contour line image, circularly judging whether 8 neighborhood same-color pixel points are equal to a certain equivalence point or not, and determining the corresponding relation between the contour line and an equivalence point sequence in the image; the contour marker attribute is added and this attribute for all contours is initialized to Fal se.
In step 2, an improved Hough transformation method is adopted to perform circle-like contour detection on the contour map obtained in step 1, and the detected contour is marked as a circle-like contour, and the method comprises the following steps:
A. carrying out contour line edge extraction on the image obtained in the step 1, and calculating a first-order gradient field; the result is a two-dimensional vector field, wherein the direction of the vector is the gradient direction, and the absolute value of the vector is the strength of the edge;
B. weighting Hough change projection in the gradient direction, and projecting to a two-dimensional parameter space according to different radiuses in the gradient direction;
C. performing low-pass smooth filtering on the image in the projection space to fuzzify and gather the projection points with similar positions;
D. performing morphological contraction processing on the image to separate a plurality of connected or similar circles from each other on a projection space;
E. and judging a connected region according to the result obtained by the processing, and judging the region as a circle if the area and the projection intensity are larger than a certain threshold value.
In the step 2, the modified Hough transform is used for weighting the projection points, namely the gradient absolute value of the projection points and the reciprocal of the logarithm of the projection radius, so that the detection accuracy of the algorithm under the condition of detecting a large circular radius change interval is improved.
The step 3 comprises the following steps:
A. obtaining the equivalent points for calculating the ellipse: if the obtained first non-circular-like contour line is a non-closed curve, selecting another non-closed contour line with the same height value from other directions, and selecting n contour points at equal intervals on the two contour lines; if the obtained first non-circular-like contour line is a closed curve, selecting n equivalent points on the contour line at equal intervals; taking the selected n points as equivalent points for calculating the ellipse;
B. determining the center of the circle, the center of the direction distribution tool, directly calculating the center of the ellipse by using the arithmetic mean center, and recording as (x)0，y0)；
C. The form of the ellipse is determined, and the formula is as follows:
wherein x isiAnd yiIs the spatial location coordinate of each element, and X and Y are the arithmetic mean center;
SDEx and SDEy are the calculated standard deviations of the ellipses; because the size of the ellipse depends on the standard deviation size, i.e., the major half axis represents the maximum standard deviation and the minor half axis represents the minimum standard deviation; calculating by using a standard deviation of X, Y on the aspect of spatial statistics to obtain a long half shaft and a short half shaft;
D. determining the direction of the ellipse, and correcting the direction of the ellipse to the north by taking the X axis as a standard; is 0 degrees, rotates clockwise and rotates by an angle theta, and the calculation formula is as follows:
wherein the content of the first and second substances,
and
is the difference in coordinates of the mean center and xy;
E. the standard deviation of the XY axis is determined, and the formula is as follows:
the function of the standard deviation is to determine the equation for the ellipse, which is as follows:
where S is a confidence value, the chi-square probability table can be queried according to the data amount.
In step 4, adjusting the equivalence points on the class circle according to the ratio of the major axis to the minor axis of the ellipse, and compressing the class circle in the minor axis direction by taking the major axis as a reference, specifically as follows:
suppose σx>σyIf the coordinates of the point on the quasi-circle are (x, y), and the coordinates of the point on the adjusted quasi-circle are (x ', y'), then x 'is x, and the calculation formula of the adjusted y' is:
at the center of the ellipse (x)0，y0) And (x ', y') clockwise rotating by theta degrees to obtain (x ', y') as a final adjusted equivalent point, and finally updating the original equivalent point sequence by the adjusted equivalent point sequence.
The invention discloses an isoline generation optimization method with geological direction characteristics, relates to detection and optimization of geological isolines, in particular to a method for automatically adjusting a similar circular isoline according to computer image processing and mathematical calculation, belongs to the fields of geological exploration and geological interpretation, and particularly applies the isoline generation optimization method to the field of geological isoline compiling. The method comprises the following steps: generating and preprocessing an isoline image; detecting and extracting the quasi-circular contour line; calculating and determining the geological directionality; and calculating and adjusting the directionality of the contour line, so that the circular-like contour line is adjusted to be a non-circular-like contour line according with the surrounding geological conditions. Computer image processing detects the circular contour and obtains the adjusting parameters through effective mathematical calculation, which are the most important two parts in the invention. The invention belongs to the fields of geological exploration and geological interpretation, and particularly plays an important role in the process of compiling and drawing a geological equivalent map.
The invention has the beneficial effects that: the quasi-circular contour line in the contour map can be automatically detected; the direction of the contour line can be automatically calculated; on the premise of not influencing the non-quasi-circular contour, the directivity of the quasi-circular contour is adjusted. The invention solves the problem of the quasi-circular contour line caused by sparse sampling points in the process of mapping the geological contour line map, and the detection and adjustment of the quasi-circular contour line are carried out on the contour line map, so that the contour line better conforms to the natural geological rule, and the problem of the quasi-circular contour line in the contour line map is greatly improved.
Drawings
FIG. 1 is a flow diagram of one embodiment of a method for contour generation optimization with geosteering features of the present invention;
FIG. 2 is a diagram of the results of detecting a circular-like contour in an embodiment of the present invention;
FIG. 3 is a schematic illustration of an ellipse detected and calculated for obtaining the associated geological orientation and extent in accordance with an embodiment of the present invention;
FIG. 4 is a diagram illustrating ellipse-related parameters in an embodiment of the present invention;
FIG. 5 is a diagram illustrating a non-circular-like contour after adjustment of a circular-like contour according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flow chart of the contour generation optimization method with geosteering features of the present invention.
Step 101: generating and preprocessing an isoline image; and generating data or contour line data needing sampling point by contour line, wherein the contour line data comprises contour line sequence data of the geological contour map and image data after the contour line is mapped.
The line sequence data for the geological contours is the tracking data for each contour. The method is to calculate data on grid lines for tracking a specific contour value after geological sampling point data forms grid data through spatial interpolation.
The contour-mapped image data is a contour map formed by tracing contour sequence data, and the contour map does not need color filling for identification and detection.
The specific method for preprocessing the contour image comprises the following steps: and calling the existing contour line editing software to read the contour line sequence data for drawing the contour line image, acquiring the contour line image to be processed, establishing the corresponding relation between the contour line and the contour point sequence, and initializing all contour lines in the image to be processed into the non-circular-like contour line.
The specific method for establishing the corresponding relation between the contour line and the equivalent point sequence is as follows: and (3) randomly selecting a pixel point on the contour line image, and circularly judging whether the 8-neighborhood same-color pixel points are equal to a certain equivalence point or not, so as to determine the corresponding relation between the contour line and an equivalence point sequence in the image. Adding contour mark attributes: boolean isCircle and initializes this property of all contours to False.
Step 102: detecting and extracting the quasi-circular contour line; the specific method for detecting and extracting the quasi-circular contour line comprises the following steps: and (3) carrying out circular contour line detection on the contour line graph obtained in the step (101) by adopting an improved Hough transformation method, and marking the detected contour line as a circular contour line. The following calculation steps are required:
A. contour line edge extraction is performed on the image obtained in the step 101, and a first-order gradient field is calculated. The result is a two-dimensional vector field, where the direction of the vector is the gradient direction and the absolute value of the vector is the intensity of the edge.
B. And weighting Hough change projection in the gradient direction, and projecting to a two-dimensional parameter space according to different radiuses (within a radius detection interval and a self-defined interval) in the gradient direction.
C. And performing low-pass smooth filtering on the image in the projection space to fuzzify and gather the projection points with similar positions.
D. And (3) carrying out morphological contraction processing on the image, so that a plurality of connected or similar circles are separated from each other on the projection space.
E. And judging a connected region of the result obtained by the processing, if the area and the projection intensity are larger than a region with a certain threshold value, judging the region as a circle, and modifying the Iscircle attribute to be True.
The improved Hough transformation carries out weighting processing on the projection points, namely the gradient absolute value of the projection points and the reciprocal of the logarithm of the projection radius, and improves the detection precision of the algorithm under the condition of detecting a large circular radius change interval.
Step 103: calculating and determining the geological directionality; the specific method for calculating and determining the geological directionality is as follows: the direction of the circular-like contours is determined using the standard deviation ellipse method. Through correlation calculation, an ellipse is output, and the direction and the trend of the ellipse provide reference for the extending direction and the extending degree of the similar circular contour line. The following calculation steps are required:
A. obtaining the equivalent points for calculating the ellipse: if the obtained first non-circular-like contour line is a non-closed curve, selecting another non-closed contour line with the same height value from other directions (generally taking the opposite direction), and selecting n equivalent points at equal intervals on the two contour lines; and if the obtained first non-circular contour line is a closed curve, selecting n equivalent points on the contour line at equal intervals. And taking the selected n points as equivalent points for calculating the ellipse.
B. Determining the center of the circle, the center of the direction distribution tool, directly calculating the center of the ellipse by using the arithmetic mean center, and recording as (x)0，y0)。
C. The form of the ellipse is determined, and the formula is as follows:
wherein x isiAnd yiIs the spatial location coordinate of each element, and X and Y are the arithmetic mean centers.
SDEx and SDEy are the standard deviations of the calculated ellipses. Since the size of the ellipse depends on the standard deviation size, i.e. the longer half axis represents the maximum standard deviation and the shorter half axis represents the minimum standard deviation. And calculating by using the standard deviation of X, Y to obtain the major and minor semiaxes on the basis of spatial statistics.
D. Determining the direction of the ellipse, taking the X axis as a reference, setting the due north (12-point direction) as 0 degree, rotating clockwise and rotating the angle theta, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
and
is the difference in coordinates between the mean center and xy.
E. The standard deviation of the XY axis is determined, and the formula is as follows:
the function of the standard deviation is to determine the equation of the ellipse, which is generally as follows:
where S is a confidence value, and a Chi-square probability Table (Table) can be queried according to the data amount.
Step 104: and calculating and adjusting the directivity of the quasi-circular contour line.
The specific method for calculating and adjusting the directionality of the contour line is as follows: the size and direction of the major and minor axes of the standard deviation ellipse calculated in step 103 are used to determine the degree and direction of adjustment of the circle-like contour.
And adjusting the equivalent points on the similar circle according to the ratio of the major axis to the minor axis of the ellipse, and compressing the similar circle in the direction of the minor axis by taking the major axis as a reference in order to avoid intersection of the contour lines. The method comprises the following specific steps:
suppose σx>σyIf the coordinates of the point on the quasi-circle are (x, y), and the coordinates of the point on the adjusted quasi-circle are (x ', y'), then x 'is x, and the calculation formula of the adjusted y' is:
at the center of the ellipse (x)0，y0) And (x ', y') clockwise rotating by theta degrees to obtain (x ', y') as a final adjusted equivalent point, and finally updating the original equivalent point sequence by the adjusted equivalent point sequence.
In one embodiment of the present invention, the method comprises the following steps:
step 1: generating and preprocessing an isoline image; and generating data or contour line data needing sampling point by contour line, wherein the contour line data comprises contour line sequence data of the geological contour map and image data after the contour line is mapped.
The line sequence data for the geological contours is the tracking data for each contour. The method is to calculate data on grid lines for tracking a specific contour value after geological sampling point data forms grid data through spatial interpolation.
The contour-mapped image data is a contour map formed by tracing contour sequence data, and the contour map does not need color filling for identification and detection.
The specific method for preprocessing the contour image comprises the following steps: and calling the existing contour line editing software to read the contour line sequence data for drawing the contour line image, acquiring the contour line image to be processed, establishing the corresponding relation between the contour line and the contour point sequence, and initializing all contour lines in the image to be processed into the non-circular-like contour line.
The specific method for establishing the corresponding relation between the contour line and the equivalent point sequence is as follows: and (3) randomly selecting a pixel point on the contour line image, and circularly judging whether the 8-neighborhood same-color pixel points are equal to a certain equivalence point or not, so as to determine the corresponding relation between the contour line and an equivalence point sequence in the image. Adding contour mark attributes: boolean isCircle and initializes this property of all contours to False.
Step 2: detecting and extracting the quasi-circular contour line; the specific method for detecting and extracting the quasi-circular contour line comprises the following steps: and (3) carrying out circular contour line detection on the contour line graph obtained in the step (1) by adopting an improved Hough transformation method, and marking the detected contour line as a circular contour line as shown in a figure 2. The following calculation steps are required: contour edge extraction is performed on the image obtained in step 1 as described in claim 1, and a first order gradient field is calculated. The result is a two-dimensional vector field, wherein the direction of the vector is the gradient direction, and the absolute value of the vector is the strength of the edge; weighting Hough change projection in the gradient direction, and projecting to a two-dimensional parameter space according to different radiuses (within a radius detection interval and a self-defined interval) in the gradient direction; performing low-pass smooth filtering on the image in the projection space to fuzzify and gather the projection points with similar positions; performing morphological contraction processing on the image to separate a plurality of connected or similar circles from each other on a projection space; and judging a connected region of the result obtained by the processing, if the area and the projection intensity are larger than a region with a certain threshold value, judging the region as a circle, and modifying the Iscircle attribute to be True.
The improved Hough transformation carries out weighting processing on the projection points, namely the gradient absolute value of the projection points and the reciprocal of the logarithm of the projection radius, and improves the detection precision of the algorithm under the condition of detecting a large circular radius change interval.
And step 3: calculating and determining the geological directionality; the specific method for calculating and determining the geological directionality is as follows: the direction of the circular-like contours is determined using the standard deviation ellipse method. By means of correlation calculation, an ellipse is output, as shown in fig. 3, whose direction and trend provide a reference for the extension direction and extension degree of the circle-like contour. The following calculation steps are required:
A. obtaining the equivalent points for calculating the ellipse: if the obtained first non-circular-like contour line is a non-closed curve, selecting another non-closed contour line with the same height value from other directions (generally taking the opposite direction), and selecting n equivalent points at equal intervals on the two contour lines; and if the obtained first non-circular contour line is a closed curve, selecting n equivalent points on the contour line at equal intervals. And taking the selected n points as equivalent points for calculating the ellipse.
B. Determining the center of the circle, the center of the direction distribution tool, directly calculating the center of the ellipse by using the arithmetic mean center, and recording as (x)0，y0)。
C. The form of the ellipse is determined, and the formula is as follows:
wherein x isiAnd yiIs the spatial location coordinate of each element, and X and Y are the arithmetic mean centers.
SDEx and SDEy are the standard deviations of the calculated ellipses as shown in fig. 4. Since the size of the ellipse depends on the standard deviation size, i.e. the longer half axis represents the maximum standard deviation and the shorter half axis represents the minimum standard deviation. And calculating by using the standard deviation of X, Y to obtain the major and minor semiaxes on the basis of spatial statistics.
D. Determining the direction of the ellipse, taking the X axis as a reference, setting the due north (12-point direction) as 0 degree, rotating clockwise, and rotating by an angle θ, as shown in fig. 4, and calculating the formula as follows:
wherein the content of the first and second substances,
and
is the difference in coordinates between the mean center and xy.
E. The standard deviation of the XY axis is determined, and the formula is as follows:
the function of the standard deviation is to determine the equation of the ellipse, which is generally as follows:
where S is a confidence value, and a Chi-square probability Table (Table) can be queried according to the data amount.
And 4, step 4: and calculating and adjusting the directivity of the quasi-circular contour line. The specific method for calculating and adjusting the directionality of the contour line is as follows: and (4) determining the adjustment degree and direction of the circular-like contour line by adopting the size and direction of the major axis and the minor axis of the standard deviation ellipse obtained by calculation in the step (3).
And adjusting the equivalent points on the similar circle according to the ratio of the major axis to the minor axis of the ellipse, and compressing the similar circle in the direction of the minor axis by taking the major axis as a reference in order to avoid intersection of the contour lines. The method comprises the following specific steps:
suppose σx>σyIf the coordinates of the point on the quasi-circle are (x, y), and the coordinates of the point on the adjusted quasi-circle are (x ', y'), then x 'is x, and the calculation formula of the adjusted y' is:
at the center of the ellipse (x)0，y0) As the center, (x ', y') rotates clockwise by θ degrees to obtain (x ", y"), which is used as the final adjusted equivalence point, and finally the original equivalence point sequence is updated by the adjusted equivalence point sequence, and the adjusted contour line is shown in fig. 5.
Finally, it should be noted that: it should be understood that the above-described embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the scope of the invention.
Claims (10)
1. The method for generating and optimizing the contour line with the geological direction characteristic is characterized by comprising the following steps of:
step 1, contour image generation and preprocessing are carried out;
step 2, detecting and extracting the quasi-circular contour line;
step 3, determining the direction of the quasi-circular contour line by adopting a standard deviation ellipse method;
and 4, determining the adjustment degree and direction of the quasi-circular isoline by adopting the size and direction of the major axis and the minor axis of the standard deviation ellipse obtained by calculation in the step 3.
2. The method for optimizing the generation of a contour with a geostationary feature of claim 1 wherein in step 1, the contour generation requires sampling point data or contour data, the contour data comprising contour sequence data of a geological contour map and contour mapped image data.
3. The method for optimizing generation of contours with geosteering features of claim 2 wherein in step 1, the contour sequence data is the trace data for each contour; the method is to calculate data on grid lines for tracking a specific contour value after geological sampling point data forms grid data through spatial interpolation.
4. The method for optimizing generation of contour lines with geosteering features of claim 2 wherein in step 1 the contour mapped image data is a contour map formed by tracing contour sequence data, the contour map requiring no color filling for ease of identification and detection.
5. The method for optimizing the generation of the contour line with the geological direction characteristic as claimed in claim 1, wherein in step 1, the specific method for preprocessing the contour line image is as follows: and drawing the contour line image according to the contour line sequence data, acquiring the contour line image to be processed, establishing a corresponding relation between the contour line and the contour line point sequence, and initializing all contour lines in the image to be processed into non-circular-like contour lines.
6. The method for generating and optimizing the contour line with the geological direction feature as claimed in claim 5, wherein in step 1, the specific method for establishing the correspondence between the contour line and the sequence of the equivalence points is as follows: randomly selecting a pixel point on the contour line image, circularly judging whether 8 neighborhood same-color pixel points are equal to a certain equivalence point or not, and determining the corresponding relation between the contour line and an equivalence point sequence in the image; a contour marker attribute is added and this attribute for all contours is initialized to False.
7. The method for optimizing contour generation with geologic direction characteristics of claim 1, wherein in step 2, using improved Hough transform method, performing circle-like contour detection on the contour map obtained in step 1, and marking the detected contour as a circle-like contour, comprises:
A. carrying out contour line edge extraction on the image obtained in the step 1, and calculating a first-order gradient field; the result is a two-dimensional vector field, wherein the direction of the vector is the gradient direction, and the absolute value of the vector is the strength of the edge;
B. weighting Hough change projection in the gradient direction, and projecting to a two-dimensional parameter space according to different radiuses in the gradient direction;
C. performing low-pass smooth filtering on the image in the projection space to fuzzify and gather the projection points with similar positions;
D. performing morphological contraction processing on the image to separate a plurality of connected or similar circles from each other on a projection space;
E. and judging a connected region according to the result obtained by the processing, and judging the region as a circle if the area and the projection intensity are larger than a certain threshold value.
8. The method for generating and optimizing the contour line with the geological direction characteristic as claimed in claim 7, wherein in step 2, the modified Hough transform is used for weighting the projection points, wherein the weighting is respectively the absolute value of the gradient of the projection points and the reciprocal of the logarithm of the projection radius, and the detection accuracy of the algorithm is improved under the condition of detecting a large circular radius change interval.
9. The method for optimizing the generation of a contour line with a geological direction feature as claimed in claim 1, wherein the step 3 comprises:
A. obtaining the equivalent points for calculating the ellipse: if the obtained first non-circular-like contour line is a non-closed curve, selecting another non-closed contour line with the same height value from other directions, and selecting n contour points at equal intervals on the two contour lines; if the obtained first non-circular-like contour line is a closed curve, selecting n equivalent points on the contour line at equal intervals; taking the selected n points as equivalent points for calculating the ellipse;
B. determining the center of the circle, the center of the direction distribution tool, directly calculating the center of the ellipse by using the arithmetic mean center, and recording as (x)0，y0)；
C. The form of the ellipse is determined, and the formula is as follows:
wherein x isiAnd yiIs each timeThe spatial location coordinates of the elements, X and Y being the arithmetic mean center;
SDEx and SDEy are the calculated standard deviations of the ellipses; because the size of the ellipse depends on the standard deviation size, i.e., the major half axis represents the maximum standard deviation and the minor half axis represents the minimum standard deviation; calculating by using a standard deviation of X, Y on the aspect of spatial statistics to obtain a long half shaft and a short half shaft;
D. determining the direction of the ellipse, taking the X axis as a standard, setting the north and the south as 0 degree, rotating clockwise and setting the rotation angle as theta, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
and
is the difference in coordinates of the mean center and xy;
E. the standard deviation of the XY axis is determined, and the formula is as follows:
the function of the standard deviation is to determine the equation for the ellipse, which is as follows:
where S is a confidence value, the chi-square probability table can be queried according to the data amount.
10. The method for optimizing the generation of contour lines with geological direction features as claimed in claim 9, wherein in step 4, the contour points on the class circle are adjusted according to the ratio of the major axis to the minor axis of the ellipse, and in order to avoid intersection of contour lines, the class circle is compressed in the minor axis direction by taking the major axis as a reference, specifically as follows:
suppose σx>σyIf the coordinates of the point on the quasi-circle are (x, y), and the coordinates of the point on the adjusted quasi-circle are (x ', y'), then x 'is x, and the calculation formula of the adjusted y' is:
at the center of the ellipse (x)0，y0) And (x ', y') clockwise rotating by theta degrees to obtain (x ', y') as a final adjusted equivalent point, and finally updating the original equivalent point sequence by the adjusted equivalent point sequence.
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