CN109523099A - A kind of concealed orebody Prognosis Modeling method for taking Target area missing control mine index into account - Google Patents
A kind of concealed orebody Prognosis Modeling method for taking Target area missing control mine index into account Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000004393 prognosis Methods 0.000 title claims abstract description 11
- 238000012417 linear regression Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000017105 transposition Effects 0.000 claims description 2
- 230000003993 interaction Effects 0.000 claims 1
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 7
- 239000010949 copper Substances 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 229910052802 copper Inorganic materials 0.000 description 4
- 229910052759 nickel Inorganic materials 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- UCKMPCXJQFINFW-UHFFFAOYSA-N Sulphide Chemical compound [S-2] UCKMPCXJQFINFW-UHFFFAOYSA-N 0.000 description 1
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- 238000010219 correlation analysis Methods 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
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Abstract
The invention discloses a kind of concealed orebody Prognosis Modeling methods for taking Target area missing control mine index into account, it migrates to rule between the control mine index and mineralising by known area into the Target area with Target area missing control mine index, to establish a kind of concealed orebody Quantitative Prediction Model for taking Target area missing control mine index into account, it can be achieved to strengthen Target area metallogenetic data using known area's metallogenetic data, improve the accuracy and reliability of hidden orebody prediction.
Description
Technical field
The invention belongs to concealed orebody quantitative forecast fields, in particular to a kind of to take the hidden of Target area missing control mine index into account
Lie prostrate ore body Prognosis Modeling method.
Background technique
In hidden orebody prediction work, it is frequently encountered control mine shortage of data problem.There is geological exploration engineering support
Area, available certain control mine indexs, however in the Target area of deep and side of ore vein, there are individual control mine indexs can not obtain
?.This control mine index missing may cause to seriously affect to the accuracy of quantitative forecast.Tradition is towards missing ore control factor
Method only considered known area part ore control factor missing situation, to Target area ore control factor missing processing limited
System.
Summary of the invention
The object of the present invention is to provide a kind of concealed orebody Prognosis Modeling sides for taking Target area missing control mine index into account
Method, by the incidence relation using Target area missing control mine index and known control mine index, by the control mine index and mine in known area
Control law is migrated into the prediction work of the Target area of control mine index missing between change, to eliminated to a certain extent because of control
Mine index lacks bring negative effect, promotes concealed orebody three-dimensional prediction accuracy and reliability.
In order to achieve the above technical purposes, the present invention is realized especially by following technical scheme.
A kind of concealed orebody Prognosis Modeling method for taking Target area missing control mine index into account, comprising the following steps:
Step 1 establishes mapping function between known control mine index x and Target area missing control the mine index u of Target area,
Control mine index and known control mine index are lacked with quantitative correlation Target area:
U=fu(x)
Wherein x=(x1..., xn)TFor n known control mine index xiThe vector constituted, T indicate the transposition of vector；
Step 2 establishes the mineralising index pdf model for taking Target area missing control mine index into account:
Given n known control mine index xi, there is mineralising and refers in the control mine index u of vector x and the Target area missing constituted
Mark the probability density P (y | x, u) of y is defined as:
Wherein, P (y) obedience is uniformly distributed,Table
Show in x that with u, r is there are the set of the control mine index of correlationThe number of middle element,Indicate x in the mutually independent control of u
The set of mine index；
Step 3 takes the parameter Estimation of the mineralising index pdf model of Target area missing control mine index into account:
U to y, x in formula (1) are determined using local weighted linear regressioniTo y and xiTo the regression equation of u, according to recurrence
The desired value of normal distribution form probability density in equation estimator (1), is then determined using least-squares estimation in formula (1)
The variance or covariance of normal distribution form probability density, the mineralising index probability for lacking control mine index in this, as Target area are close
Spend the parameter Estimation of model；
Step 4 takes the concealed orebody quantitative forecast of Target area missing control mine index into account:
Based on the mineralising index pdf model for taking Target area missing control mine index into account for completing parameter Estimation, give pre-
The a certain volume elements v and its known control mine index x for surveying area solve the value of u and y using alternating iteration strategy, in each iteration step,
First using y as constant, minimization u, then again using u as constant, minimization y, mineralising index y of the iteration until prediction repeatedly
Until convergence.
A kind of concealed orebody Prognosis Modeling method for taking Target area missing control mine index into account, the step
Three the following steps are included:
Establish u to y, xiTo y and xiTo the local weighted equation of linear regression of u, using this as given u or xiPremise
Under, In expectationGiven prediction area missing
Control the set { u of mine index sample(1)..., u(m)And corresponding mineralising index sample set { y(1)..., y(m), part
Weighed regression model indicates are as follows:
Y (u)=wbu+bu, (2)
Wherein, wuAnd buIt is obtained by solving following weighted least-squares problem:
Herein, ω (u, u(i)) it is the weight function based on Gaussian function, above-mentioned Locallinearregressionmodel y (u) is given, thenIn expectationIt is estimated asBy the above-mentioned means, further giving
The set for the control mine index sample knownBy solving following weighted least-squares problem:
X is constructed respectivelyiTo y and xiTo u regression equation:
According to formula (5a) and (5b), it is expected thatWithIt is estimated as respectively
On this basis, the variance of normal distribution form probability density in formula (1) is determined by least-squares estimation: to
Set { the x of fixed known control mine index(1)..., x(m)And Target area missing control mine index set { u(1)..., u(m), the variance about mineralising index y probability densityWith the variance for lacking control mine index u about Target areaRespectively by
Least square problem in minimization formula (6a) and (6b) is estimated are as follows:
Herein,It refers toOrIn one of them.
A kind of concealed orebody Prognosis Modeling method for taking Target area missing control mine index into account, the step
In four, a certain volume elements v in given prediction area and its known control mine index x, the mineralising index y and missing control mine index u of prediction
It is obtained by solving following optimization problem:
The mineralising index y and missing control mine index u of prediction are calculated by following formula:
Wherein,WithIt is to respectively indicate minimization formula (7a) and coefficient that (7b) final finishing obtains；
Based on formula (8a) and (8b), using the mineralising index y and missing control mine index u of the prediction of alternating iteration policy calculation:
In each iteration step, first using y as constant, minimization u, then again using u as constant, minimization y；Iteration step is executed repeatedly,
Until the mineralising index y convergence of prediction.
The present invention use by control law between the control mine index and mineralising in known area migrate to control mine index lack it is pre-
It surveys in the prediction work in area, to eliminated to a certain extent because of control mine index missing bring negative effect, promotes buried ore
Body three-dimensional prediction accuracy and reliability.
Detailed description of the invention
Fig. 1 is control mine index dFault and dFlow relation schematic diagram；
Fig. 2 is control mine index dRatio and dFlow relation schematic diagram；
Fig. 3 is control mine index dMC and dFlow relation schematic diagram；
Fig. 4 is Jinchuan Mining Area copper grade (Cu) prediction model effect picture (prediction model for taking missing control mine index into account)；
The Jinchuan Mining Area Fig. 5 nickel grade (Ni) prediction model effect picture (prediction model for taking missing control mine index into account)
Specific embodiment
The present invention the following steps are included:
(1) known control mine index x and the Target area missing using nonlinear regression method in Target area control mine index u
Between establish mapping function:
U=fu(x). (1)
To carry out the association analysis of Target area missing control mine index and known control mine index.
(2) probability for giving mineralising value y is P (y | x, u):
Assuming that known control mine index xiIt is independent from each other, and assumes at least to exist and control mine index x known to oneiWith it is pre-
Surveying area's missing control mine index u, there are correlations, then have:
All control mine index x will be given in (3)i, u conditional probability P (y | xi, u) and rewrite the known control in Zuo Zhi given prediction area
Mine index xiConditional probability, it is assumed that xiThere are correlations with u, obtain:
Conversely, if xiIt is mutually indepedent with u, then:
The probability P (y | x, u) of mineralising value y is finally obtained in the case where considering x u are as follows:
Wherein, P (y) obedience is uniformly distributed,It indicates
With u there are the set of the control mine index of correlation in x, r isThe number of middle element,Indicate x in the mutually independent control mine of u
The set of index.
(3) u to y, x in formula (1) are determined using local weighted linear regressioniTo y and xiTo the regression equation of u, according to returning
Return the expectation of normal distribution form probability density in curve estimation formula (6).It is close in order to seek normal distribution form probability in formula (6)
The variance and covariance of degree, by taking P (y | x, the u) expansion in formula (6) to counting method:
To avoid variance from appearing in denominator part, above formula is rewritten are as follows:
P- ln P (y | xi, u) and ask partial derivative to obtain:
It enables above formula be equal to 0 final finishing to obtain
WhereinWithIt is to respectively indicate coefficient w former after y and u arrangement to left side of the equal sign(·)Variation.
Next to parameterWithEstimation can be obtained by minimization square error, obtain following belt restraining
Optimization problem:
In view of in real work it is difficult to ensure that control mine index xiFormula (11) can be added new in mutually indepedent problem
Constant b, indicate because of xiVariation caused by not independent, obtains following regression equation form:
On this basis, by the constraint in relaxation formula (11), following optimization problem is solved:
(4) after completing the estimation to parameter, for a certain volume elements v of Target area, mine index x is controlled known to it giving,
Based on the prediction model in formula (12), the value of u and y is solved using alternating iteration strategy.In each iteration step, it is with y first
Constant, minimization u, then again using u as constant, minimization y iterates down in this way, until predicted value y convergence.Tool
Body step is as shown in algorithm 1.Here the initial value of iterative algorithm can be obtained by the forecast of regression model that not Consideration lacks.
The prediction algorithm of 1 y of algorithm
By algorithm 1, mine index u can be controlled in known area it is known that realizing in the case where Target area control mine index u unknown
Prediction to Target area mineralising value y.
Illustrate by taking the Jinchuan Cu-ni Sulphide Deposit concealed orebody quantitative forecast of Gansu as an example.In this instance, as shown in table 1,
Ore information variable corresponding to mineralising variable (Cu, Ni) is respectively Cu_MC, Cu_Ratio, Cu_Fault, Cu_Trend, Cu_
DFlow and Ni_MC, Ni_Ratio, Ni_Fault, Ni_Trend, Ni_dFlow.Because there are Target area ore control factor Cu_
The missing of dFlow, Ni_dFlow, therefore especially realized using this patent published method and take the pre- of Target area missing ore control factor into account
Survey modeling.Specific embodiment describes according to the following steps:
1 mineralising variable of table table corresponding with ore information variable
Step 1: carrying out the correlation analysis of dFlow and the known control mine index of Target area.Pass through dFlow shown in Fig. 1-3
With other ore control factors visualization discovery dFlow and other ore control factors there are apparent correlativities.
Step 2: controlling mine index set Cu_MC, Cu_Ratio, Cu_Fault, Cu_Trend, Cu_dFlow in given known area
And Ni_MC, Ni_Ratio, Ni_Fault, Ni_Trend Cu_dFlow, Ni_dFlow and known area Cu and Ni mineralising letter
Breath respectively obtains following prediction model (table 2 and 3) by the Parameter Estimation Problem in building and solution formula (13).
The linear regression model (LRM) table of 2 dFlow_Cu predicted value of table and corresponding estimation index
Note: 1. coefficient R=0.610414；
2.F (6,103669)=10258.43, F0.05(6,103669)=2.0987, regression effect is significant.
The linear regression model (LRM) table of 3 dFlow_Ni predicted value of table and corresponding estimation index
Note: 1. coefficient R=0.602533；
2.F (6,103669)=9845.252, F0.05(6,103669)=2.0987, regression effect is significant.
Step 3: the given prediction model for taking Target area missing control mine index into account and Target area do not lack ore control factor Cu_
MC, Cu_Ratio, Cu_Fault, Cu_Trend and Ni_MC, Ni_Ratio, Ni_Fault, Ni_Trend are carried out using algorithm 1
The prediction of Target area Cu, Ni grade, it is as shown in Figures 4 and 5 to obtain prediction result.
Claims (3)
1. it is a kind of take into account Target area missing control mine index concealed orebody Prognosis Modeling method, which is characterized in that including with
Lower step:
Step 1 establishes mapping function between known control mine index x and Target area missing control the mine index u of Target area, with fixed
Measure interaction prediction area missing control mine index and known control mine index:
U=fu(x)
Wherein x=(x1..., xn)TFor n known control mine index xiThe vector constituted, T indicate the transposition of vector；
Step 2 establishes the mineralising index pdf model for taking Target area missing control mine index into account:
Given n known control mine index xi, there is mineralising index y in the control mine index u of vector x and the Target area missing constituted
Probability density P (y | x, u) is defined as:
Wherein, P (y) obedience is uniformly distributed,Table
Show in x that with u, r is there are the set of the control mine index of correlationThe number of middle element,Indicate x in the mutually independent control of u
The set of mine index；
Step 3 takes the parameter Estimation of the mineralising index pdf model of Target area missing control mine index into account:
U to y, x in formula (1) are determined using local weighted linear regressioniTo y and xiTo the regression equation of u, according to regression equation
The desired value of normal distribution form probability density in estimator (1), then determines normal state in formula (1) using least-squares estimation
The variance or covariance of distribution form probability density lack the mineralising index probability density mould of control mine index in this, as Target area
The parameter Estimation of type；
Step 4 takes the concealed orebody quantitative forecast of Target area missing control mine index into account:
Based on the mineralising index pdf model for taking Target area missing control mine index into account for completing parameter Estimation, given prediction area
A certain volume elements v and its known control mine index x, using alternating iteration strategy solve u and y value, in each iteration step, first
Using y as constant, minimization u, then again using u as constant, minimization y, mineralising index y convergence of the iteration until prediction repeatedly
Until.
2. a kind of concealed orebody Prognosis Modeling side for taking Target area missing control mine index into account according to claim 1
Method, which is characterized in that the step three the following steps are included:
Establish u to y, xiTo y and xiTo the local weighted equation of linear regression of u, using this as given u or xiUnder the premise of, In expectationGiven prediction area missing
Control mine index sample set { u(1)..., u(m)And corresponding mineralising index sample set { y(1)..., y(m), office
Portion's weighed regression model indicates are as follows:
Y (u)=wbu+bu, (2)
Wherein, wuAnd buIt is obtained by solving following weighted least-squares problem:
Herein, ω (u, u(i)) it is the weight function based on Gaussian function, above-mentioned Locallinearregressionmodel y (u) is given, thenIn expectationIt is estimated asBy the above-mentioned means, further giving
The set for the control mine index sample knownBy solving following weighted least-squares problem:
X is constructed respectivelyiTo y and xiTo u regression equation:
According to formula (5a) and (5b), it is expected thatWithIt is estimated as respectively
On this basis, the variance of normal distribution form probability density in formula (1) is determined by least-squares estimation: being given
Set { the x for the control mine index known(1)..., x(m)And Target area missing control mine index set { u(1)..., u(m), it closes
In the variance of mineralising index y probability densityWith the variance for lacking control mine index u about Target areaRespectively by minimization
Least square problem in formula (6a) and (6b) is estimated are as follows:
Herein,It refers toOrIn one of them.
3. a kind of concealed orebody Prognosis Modeling side for taking Target area missing control mine index into account according to claim 1
Method, which is characterized in that in the step four, a certain volume elements v in given prediction area and its known control mine index x, prediction
Mineralising index y and missing control mine index u are obtained by solving following optimization problem:
The mineralising index y and missing control mine index u of prediction are calculated by following formula:
Wherein,WithIt is to respectively indicate minimization formula (7a) and coefficient that (7b) final finishing obtains；
Based on formula (8a) and (8b), using the mineralising index y and missing control mine index u of the prediction of alternating iteration policy calculation: every
In a iteration step, first using y as constant, minimization u, then again using u as constant, minimization y；Iteration step is executed repeatedly, until
Until the mineralising index y convergence of prediction.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040076944A1 (en) * | 2002-08-22 | 2004-04-22 | Ibex Process Technology, Inc. | Supervised learning in the presence of null data |
| CN101706589A (en) * | 2009-12-07 | 2010-05-12 | 中国科学院新疆生态与地理研究所 | Geographic tuple based quantitative prediction method of ore concentration areas |
| CN102147934A (en) * | 2011-04-19 | 2011-08-10 | 中南大学 | Geological interface three-dimensional morphological analysis method based on TIN (triangulated irregular network) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040076944A1 (en) * | 2002-08-22 | 2004-04-22 | Ibex Process Technology, Inc. | Supervised learning in the presence of null data |
| CN101706589A (en) * | 2009-12-07 | 2010-05-12 | 中国科学院新疆生态与地理研究所 | Geographic tuple based quantitative prediction method of ore concentration areas |
| CN102147934A (en) * | 2011-04-19 | 2011-08-10 | 中南大学 | Geological interface three-dimensional morphological analysis method based on TIN (triangulated irregular network) |
Non-Patent Citations (2)
| Title |
|---|
| JIN CHEN 等: "A new method about three-dimensional quantitative prediction of concealed ore bodies", 《2011 INTERNATIONAL CONFERENCE ON REMOTE SENSING》 * |
| 黄继先 等: "基于 GWＲ 的丁家山铅锌矿控矿因素影响研究", 《地质学刊》 * |
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| CN113159321A (en) * | 2021-04-07 | 2021-07-23 | 中南大学 | Bayes inference method for fracture surface morphology under gravity constraint |
| CN113159321B (en) * | 2021-04-07 | 2022-05-20 | 中南大学 | Bayes inference method for fracture surface morphology under gravity constraint |
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