CN111428820A - Mine water inrush source distinguishing method based on feature selection - Google Patents
Mine water inrush source distinguishing method based on feature selection Download PDFInfo
- Publication number
- CN111428820A CN111428820A CN202010358969.1A CN202010358969A CN111428820A CN 111428820 A CN111428820 A CN 111428820A CN 202010358969 A CN202010358969 A CN 202010358969A CN 111428820 A CN111428820 A CN 111428820A
- Authority
- CN
- China
- Prior art keywords
- data set
- regression model
- ridge regression
- asso
- aquifer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Pure & Applied Mathematics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Computation (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Algebra (AREA)
- Artificial Intelligence (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a method for distinguishing a mine water inrush source based on feature selection, which comprises the steps of S1, determining an aquifer participating in modeling, collecting water samples in the aquifer, wherein the number of the water samples is at least 50, S2, testing water quality information of each group of water samples, S3, dividing a plurality of groups of water quality information into a training data set and a testing data set according to the proportion of 7: 3 by using R language, S4, establishing a first L asso regression model, S5, applying the first L asso regression model to the training data set, deleting wrong samples in the training data set to form a first data set, and S6-1 or S6-2, wherein the step S6-1 is used for establishing a second ridge regression model, the step S6-2 is used for establishing a fourth ridge regression model, and according to the method for distinguishing the mine water inrush source based on the feature selection, the method for modeling and the method for distinguishing accuracy of the classification based on the regularized L o regression model is used.
Description
Technical Field
The invention relates to the technical field of coal mine water disaster prevention and control, in particular to a method for distinguishing a mine water inrush source based on feature selection.
Background
The water inrush of the mine is one of five disasters of the coal mine, and brings threats to the safe and efficient production of the coal mine and the personal safety of workers. With the improvement of the exploitation efficiency and the deepening of the exploitation depth, the threat of water damage is increasingly serious. In the prevention stage, the water inrush warning display stage and the water damage treatment stage, the water source of water inrush is accurately determined, which is the key of the water prevention and treatment work of coal mines.
In the related art, methods for distinguishing the water inrush source include a hydrological water level method, a characteristic ion method, a mathematical analysis method and the like. The water temperature and water level method can be used for judging the initial stage of a water inrush source, and the operability and the accuracy of the judgment are both deficient under the complex condition. The characteristic ion method uses ions with strong discrimination as targets to establish a discrimination criterion. The method mainly applies the technical means of geochemistry. The defects are that the selection of the characteristic ions is difficult to be accurate, the dimensionality represented by the characteristic ions is low, and the achievable discrimination is low. Mathematical analysis methods, linear analysis methods, multivariate statistical methods, and the like. Multivariate analysis is limited by the sample. Linear analysis methods often have the problem of multivariate collinearity, resulting in instability of the model. As can be seen, the above methods all have the problem of inaccurate test results.
Disclosure of Invention
The invention provides a mine water inrush source distinguishing method based on feature selection, which can improve the detection accuracy.
The method for distinguishing the water source of the water inrush in the mine based on the feature selection comprises the steps of S1 determining an aquifer participating in modeling, collecting water samples in the aquifer, wherein the number of the water samples is at least 50, S2 testing water quality information of each group of the water samples, wherein the water quality information comprises constant element content, trace element content, pH value, total soluble solid, hardness and temperature, S3 establishing an Excel table by using a plurality of groups of the water quality information, importing the Excel table into an R language, dividing the water quality information into a training data set and a testing data set according to the proportion of 7: 3 by using the R language, S4 performing L asso regression analysis on the training data set, establishing a first L asso regression model, wherein feature parameters of the first L asso regression model are 3-6 of the water quality information, S2 applying the first L asso model to the training data set, deleting wrong samples in the training data set to form a first pass regression model, and deleting the data from the training data set to a third data set, wherein the second to the training data set, the third data set is obtained by deleting the third data set, and the fourth data set is obtained by deleting the steps S6, wherein the third data set, the third data set is obtained by deleting the third data set, and the third data set, wherein the third data set is obtained by deleting the third data set, the third data set is obtained by deleting the third data set, the third data set is obtained by deleting the third data set, the third data set of the third data set, the third data set is obtained by deleting.
According to the method for distinguishing the water source of the mine water inrush based on the feature selection, the regularization L asso regression and ridge regression methods are used for modeling, the L asso regression method is used for feature selection in consideration of the difference of the importance of each distinguishing parameter, namely more representative data can be selected from the angle of a sample for modeling, and then ridge regression with better accuracy is used in the aspect of model parameter explanation, so that the accuracy of a model result can be improved.
According to some embodiments of the invention, after the step S2, and before the step S3, the method further comprises: and converting the content of the macroelements into equivalent concentration percentage, and converting the content of the trace elements into equivalent concentration.
According to some embodiments of the invention, after the step S6-1, the method further comprises: evaluating the accuracy of the second ridge regression model using the data of the test data set.
In some embodiments of the present invention, after the step S6-1, the method further comprises: and applying the second ridge regression model to an actual prediction and discrimination environment for verification.
According to some embodiments of the invention, after the step S6-2, the method further comprises: and evaluating the accuracy of the fourth ridge regression model by using the data of the test data set.
In some embodiments of the present invention, after the step S6-2, the method further comprises: and applying the fourth ridge regression model to an actual prediction and discrimination environment for verification.
According to some embodiments of the invention, the aquifer comprises at least one of a fourth aquifer, a coal-series sandstone aquifer, and a limestone aquifer.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flow chart of a method for distinguishing a mine water inrush source based on feature selection according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art may recognize the applicability of other processes and/or the use of other materials.
The method for distinguishing the mine water inrush source based on feature selection according to the embodiment of the invention is described below with reference to the accompanying drawings.
As shown in fig. 1, a method for distinguishing a mine water inrush source based on feature selection according to an embodiment of the present invention includes: step S1, step S2, step S3, step S4, step S5 and step S6-1 or step S6-2.
Specifically, as shown in fig. 1, step S1 is to determine an aquifer participating in modeling, where water samples are collected, the number of the water samples being at least 50 groups. It is understood that the number of water samples may be 50, 60, 70 or more. Therefore, the number of samples can be increased, and the accuracy of the model is improved.
In some embodiments of the invention, the aquifer comprises at least one of a fourth aquifer, a coal-series sandstone aquifer, and a limestone aquifer. In other words, the aquifer may include one of a fourth-line aquifer, a coal-line sandstone aquifer, and a limestone aquifer; alternatively, the aquifers may include two of a fourth aquifer, a coal-derived sandstone aquifer, and a limestone aquifer; alternatively, the aquifers include a fourth aquifer, a coal-derived sandstone aquifer, and a limestone aquifer. The fourth series aquifer, the coal series sandstone aquifer and the limestone aquifer are adopted more commonly, so that the applicability of the model can be improved. For example, in one example of the invention, the aquifers include the fourth aquifer of the north China coal mine, the coal sandstone aquifer and the limestone aquifer, and the number of water samples in each aquifer is more than 15.
As shown in fig. 1, in step S2, water quality information of each group of water samples is tested, and the water quality information includes macroelement content, trace element content, pH value, total soluble solids, hardness and temperature. It is understood that the water samples at different locations differ in macroelement content, trace element content, pH, total soluble solids, hardness and temperature, and whether water breakthrough is possible or not can be obtained by analyzing the macroelement content, trace element content, pH, total soluble solids, hardness and temperature.
As shown in fig. 1, in step S3, an Excel table is created using multiple sets of water quality information, the Excel table is imported into an R language, and the R language is used to convert the multiple sets of water quality information into a 7: the scale of 3 is divided into a training data set and a test data set. It can be understood that an Excel table can be imported into the R software, and a plurality of sets of water quality information are calculated according to the following formula 7: and 3, randomly dividing the ratio into a training data set and a testing data set, acquiring the model by using the training data set, and detecting the accuracy of the model by using the testing data set.
As shown in FIG. 1, step S4 is to perform L asso regression analysis on the training data set to establish a first L asso regression model, wherein the characteristic parameters of the first L asso regression model are 3-6 of the water quality information, the lambda value in the regression model can be adjusted to obtain the appropriate number of parameters (generally 3-6) and the lowest error rate.
As shown in FIG. 1, step S5 is to apply the first L asso regression model to the training data set, delete the erroneous samples in the training data set to form the first data set, and the first data set only contains data with the characteristic parameters.
As shown in FIG. 1, the method for distinguishing the mine water inrush source based on the feature selection according to the embodiment of the invention comprises a step S6-1 or a step S6-2, wherein the step S6-1 is as follows: performing ridge regression training on the first data set to obtain a first ridge regression model, applying the first ridge regression model to the first data set, deleting wrong samples in the first data set to form a second data set, performing ridge regression training on the second data set, and establishing a second ridge regression model. It can be understood that whether the data in the first data set is correct or not can be detected by using the first ridge regression model, and the wrong data can be deleted in time, so that the wrong data can be prevented from interfering the accuracy of the model result, and meanwhile, the accuracy of the model result can be improved by using the new correct second data set to obtain the final second ridge regression model with higher accuracy.
As shown in FIG. 1, step S6-2 includes performing L asso regression analysis on the first data set, building a second L asso regression model, applying a second L asso regression model to the first data set, removing erroneous samples from the first data set to form a third data set, performing a training of ridge regression on the third data set, building a third ridge regression model, applying the third ridge regression model to the third data set, removing erroneous samples from the third data set to form a fourth data set, performing a training of ridge regression on the fourth data set, building a fourth ridge regression model, it is understood that using the second L asso regression model can detect whether the data in the first data set are correct, and remove erroneous data in time to avoid the erroneous data from interfering with the accuracy of the model results, while using the new correct third data set to obtain the third ridge regression model, then using the third ridge regression model can detect whether the data in the third data set are correct, and remove erroneous data in time to avoid the accuracy of the new correct data set from interfering with the accuracy of the fourth ridge regression model, and further using the accuracy of the fourth ridge regression model to obtain a final ridge regression model.
For example, in one example of the invention, a method for distinguishing a mine water inrush source based on feature selection comprises the following steps:
step S1: determining aquifers participating in modeling, and collecting water samples in the aquifers, wherein the number of the water samples is at least 50;
step S2: testing the water quality information of each group of water samples, wherein the water quality information comprises the content of macroelements, the content of trace elements, the pH value, total soluble solids, hardness and temperature;
step S3: establishing an Excel table by utilizing a plurality of groups of water quality information, importing the Excel table into an R language, and enabling the plurality of groups of water quality information to be 7: 3 into a training data set and a test data set;
step S4, L asso regression analysis is carried out on the training data set, a first L asso regression model is established, and the characteristic parameters of the first L asso regression model are 3-6 of the water quality information;
step S5, applying the first L asso regression model to the training data set, deleting the erroneous samples in the training data set to form a first data set, and the first data set only contains data with characteristic parameters, and
step S6-1: performing ridge regression training on the first data set to obtain a first ridge regression model, applying the first ridge regression model to the first data set, deleting wrong samples in the first data set to form a second data set, performing ridge regression training on the second data set, and establishing a second ridge regression model.
For example, in another example of the present invention, a method for distinguishing a mine water inrush source based on feature selection includes:
step S1: determining aquifers participating in modeling, and collecting water samples in the aquifers, wherein the number of the water samples is at least 50;
step S2: testing the water quality information of each group of water samples, wherein the water quality information comprises the content of macroelements, the content of trace elements, the pH value, total soluble solids, hardness and temperature;
step S3: establishing an Excel table by utilizing a plurality of groups of water quality information, importing the Excel table into an R language, and enabling the plurality of groups of water quality information to be 7: 3 into a training data set and a test data set;
step S4, L asso regression analysis is carried out on the training data set, a first L asso regression model is established, and the characteristic parameters of the first L asso regression model are 3-6 of the water quality information;
step S5, applying the first L asso regression model to the training data set, deleting the erroneous samples in the training data set to form a first data set, and the first data set only contains data with characteristic parameters, and
step S6-2, performing L asso regression analysis on the first data set, establishing a second L asso regression model, applying the second L asso regression model to the first data set, deleting the wrong samples in the first data set to form a third data set, performing ridge regression training on the third data set, establishing a third ridge regression model, applying the third ridge regression model to the third data set, deleting the wrong samples in the third data set to form a fourth data set, performing ridge regression training on the fourth data set, and establishing a fourth ridge regression model.
According to the method for distinguishing the water source of the mine water inrush based on the feature selection, the regularization L asso regression and ridge regression methods are used for modeling, the L asso regression method is used for feature selection in consideration of the difference of the importance of each distinguishing parameter, namely more representative data can be selected from the angle of a sample for modeling, and then ridge regression with better accuracy is used in the aspect of model parameter explanation, so that the accuracy of a model result can be improved.
According to some embodiments of the invention, after step S2, and before step S3, the method further comprises: the content of the macroelements is converted to the percentage of the equivalent concentration, and the content of the trace elements is converted to the equivalent concentration (step S2-1). Therefore, the calculation difficulty can be reduced, the calculation efficiency is improved, and the calculation time is saved.
According to some embodiments of the invention, after step S6-1, the method further comprises: and evaluating the accuracy of the second ridge regression model by using the data of the test data set. Therefore, the accuracy of the data of the test data set to the second ridge regression model can be utilized, and the model is adaptively modified through the detection result, so that the reliability of the detection result can be further improved.
In some embodiments of the present invention, after step S6-1, the method further comprises: and applying the second ridge regression model to the actual prediction and discrimination environment for verification. Therefore, the accuracy of the environment on the second ridge regression model can be judged by using actual prediction, and the reliability of the detection result can be further improved by adaptively modifying the model through the detection result.
According to some embodiments of the invention, after step S6-2, the method further comprises: and evaluating the accuracy of the fourth ridge regression model by using the data of the test data set. Therefore, the accuracy of the data of the test data set to the fourth ridge regression model can be utilized, and the model is adaptively modified through the detection result, so that the reliability of the detection result can be further improved.
In some embodiments of the present invention, after step S6-2, the method further comprises: and applying the fourth ridge regression model to the actual prediction and discrimination environment for verification. Therefore, the accuracy of the environment to the fourth ridge regression model can be judged by using actual prediction, and the reliability of the detection result can be further improved by adaptively modifying the model through the detection result.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. A mine water inrush source distinguishing method based on feature selection is characterized by comprising the following steps:
step S1: determining an aquifer participating in modeling, and collecting water samples in the aquifer, wherein the number of the water samples is at least 50;
step S2: testing the water quality information of each group of water samples, wherein the water quality information comprises the content of macroelements, the content of trace elements, the pH value, total soluble solids, hardness and temperature;
step S3: establishing an Excel table by utilizing a plurality of groups of water quality information, importing the Excel table into an R language, and enabling the plurality of groups of water quality information to be in a 7: 3 into a training data set and a test data set;
step S4, performing L asso regression analysis on the training data set, and establishing a first L asso regression model, wherein the characteristic parameters of the first L asso regression model are 3-6 of the water quality information;
step S5, applying the first L asso regression model to the training data set, deleting erroneous samples in the training data set to form a first data set, and the first data set only contains data with the feature parameters, and
step S6-1 or step S6-2,
wherein the step S6-1 is: performing ridge regression training on the first data set to obtain a first ridge regression model, applying the first ridge regression model to the first data set, deleting wrong samples in the first data set to form a second data set, performing ridge regression training on the second data set, establishing a second ridge regression model,
the step S6-2 includes performing L asso regression analysis on the first data set, creating a second L asso regression model, applying the second L asso regression model to the first data set, deleting erroneous samples in the first data set to form a third data set, performing ridge regression training on the third data set, creating a third ridge regression model, applying the third ridge regression model to the third data set, deleting erroneous samples in the third data set to form a fourth data set, performing ridge regression training on the fourth data set, and creating a fourth ridge regression model.
2. The method for distinguishing mine water inrush source of claim 1, wherein after the step S2 and before the step S3, the method further comprises: and converting the content of the macroelements into equivalent concentration percentage, and converting the content of the trace elements into equivalent concentration.
3. The method for distinguishing the mine water inrush source according to claim 1, wherein after the step S6-1, the method further comprises: evaluating the accuracy of the second ridge regression model using the data of the test data set.
4. The method for distinguishing the mine water inrush source according to the feature selection as claimed in claim 3, wherein after the step S6-1, the method further comprises: and applying the second ridge regression model to an actual prediction and discrimination environment for verification.
5. The method for distinguishing the mine water inrush source according to claim 1, wherein after the step S6-2, the method further comprises: and evaluating the accuracy of the fourth ridge regression model by using the data of the test data set.
6. The method for distinguishing the mine water inrush source according to claim 5, wherein after the step S6-2, the method further comprises: and applying the fourth ridge regression model to an actual prediction and discrimination environment for verification.
7. The method of claim 1, wherein the aquifer comprises at least one of a fourth aquifer, a coal-derived sandstone aquifer, and a limestone aquifer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010358969.1A CN111428820A (en) | 2020-04-29 | 2020-04-29 | Mine water inrush source distinguishing method based on feature selection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010358969.1A CN111428820A (en) | 2020-04-29 | 2020-04-29 | Mine water inrush source distinguishing method based on feature selection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111428820A true CN111428820A (en) | 2020-07-17 |
Family
ID=71554976
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010358969.1A Withdrawn CN111428820A (en) | 2020-04-29 | 2020-04-29 | Mine water inrush source distinguishing method based on feature selection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111428820A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112185484A (en) * | 2020-10-13 | 2021-01-05 | 华北科技学院 | AdaBoost model-based water quality characteristic mineral water classification method |
-
2020
- 2020-04-29 CN CN202010358969.1A patent/CN111428820A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112185484A (en) * | 2020-10-13 | 2021-01-05 | 华北科技学院 | AdaBoost model-based water quality characteristic mineral water classification method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108122066B (en) | Method and device for determining reservoir lithology | |
| CN108897066B (en) | Carbonate rock crack density quantitative prediction method and device | |
| CN109993459B (en) | Method for identifying water bursting source of complex multi-aquifer mine | |
| CN111222683A (en) | PCA-KNN-based comprehensive grading prediction method for TBM construction surrounding rock | |
| CN108804849B (en) | Rock mechanical parameter evaluation method based on structural complexity | |
| CN103278616B (en) | A kind of multiple-factor method of soil corrosivity Fast Evaluation | |
| CN105117550A (en) | Product multidimensional correlation-oriented degradation failure modeling method | |
| CN101769147B (en) | Method for evaluating oilfield fireflood scheme | |
| CN110210084B (en) | Method and system for judging landslide possibility based on time fusion mechanical change | |
| CN103678938B (en) | A kind of degradation model consistency check method of space-oriented shape and range of error | |
| CN111428820A (en) | Mine water inrush source distinguishing method based on feature selection | |
| CN109779622B (en) | Method and device for characterizing low-efficiency water injection zone of oil reservoir in ultrahigh water cut period | |
| CN115358285A (en) | Method, device and equipment for selecting key geological parameters of block to be surveyed | |
| CN112215307B (en) | Method for automatically detecting signal abnormality of earthquake instrument by machine learning | |
| CN106919650A (en) | A kind of textural anomaly detection method of increment parallel type Dynamic Graph | |
| CN113109228B (en) | Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling | |
| CN114021620A (en) | Electrical submersible pump fault diagnosis method based on BP neural network feature extraction | |
| CN103970129B (en) | Control valve viscous detection method | |
| CN112257336B (en) | Mine water inrush source distinguishing method based on feature selection and support vector machine model | |
| CN110632191B (en) | Transformer chromatographic peak qualitative method and system based on decision tree algorithm | |
| CN112257763A (en) | Mine water inrush source distinguishing method based on feature selection and AdaBoost model | |
| CN114611832B (en) | Seawater intrusion prediction method based on Bayesian multi-model set pair analysis | |
| CN112668872B (en) | Drilling block falling depth reduction method based on comprehensive similarity evaluation | |
| CN115545112A (en) | Method for automatically identifying and processing large amount of sewage real-time automatic monitoring data | |
| CN114185785A (en) | Natural language processing model test case reduction method for deep neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200717 |
|
| WW01 | Invention patent application withdrawn after publication |