Uranium mine geophysical logging data processing method, device, equipment, medium and product
By performing interference removal, data augmentation, missing value completion, and outlier removal on uranium geophysical logging data, and combining porosity prediction and logging interpretation models, the problem of data discontinuity was solved, and the accuracy of logging data and mineral exploration efficiency were improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 2003 INST OF NUCLEAR IND
- Filing Date
- 2025-08-04
- Publication Date
- 2026-06-12
AI Technical Summary
In existing technologies, data discontinuity is prone to occur during the processing of uranium geophysical logging data, which affects the accuracy of logging data and mineral exploration.
The data is augmented by removing communication interference, white noise interference, and random impulse interference from the logging data to be processed. Missing values are detected and filled in, outliers are removed, and data normalization and lithology identification are performed using porosity prediction models and logging interpretation models. Uranium content is corrected by combining the radioactivity balance coefficient.
It improves the accuracy and completeness of well logging data, reduces lithology identification bias caused by data discontinuity, enhances the accuracy of uranium content inversion and resource assessment, and shortens the data processing cycle.
Smart Images
Figure CN120993507B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of uranium geophysical logging, and in particular to a method, apparatus, equipment, medium and product for processing uranium geophysical logging data. Background Technology
[0002] In related technologies, data discontinuity is a common problem when processing geophysical logging data for uranium deposits, which affects the accuracy of the logging data and the accuracy of subsequent mineral exploration. Therefore, there is an urgent need for a method for processing geophysical logging data for uranium deposits that can solve the problem of data discontinuity. Summary of the Invention
[0003] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for processing geophysical logging data in uranium mines, which can solve the problem of data interruption.
[0004] To achieve the above objectives, this application provides the following solution:
[0005] Firstly, this application provides a method for processing geophysical logging data from uranium deposits, including:
[0006] Each geophysical logging data in the logging dataset to be processed is sequentially subjected to communication interference removal, white noise interference removal, and random impulse interference removal operations to obtain the interference-removed logging dataset.
[0007] Data augmentation was performed on each geophysical well log data in the interference removal well log dataset to obtain the enhanced well log dataset.
[0008] Missing value detection and completion operations were performed on each geophysical logging data in the enhanced logging dataset to obtain a complete logging dataset.
[0009] Based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, outlier detection and removal operations are performed on each geophysical logging data in the complete logging dataset to obtain a normalized time-series geophysical logging dataset.
[0010] In one embodiment, after performing outlier detection and removal operations on each geophysical logging data in the completed logging dataset based on the standard deviation and mean of the geophysical logging data in the completed logging dataset to obtain a normalized time-series geophysical logging dataset, the method further includes:
[0011] For any geophysical logging data in the standardized time-series geophysical logging dataset, the geophysical logging data is input into the trained porosity prediction model to obtain the predicted core porosity data corresponding to the geophysical logging data.
[0012] Based on the geophysical logging data and the predicted core porosity data corresponding to the geophysical logging data, the lithology classification label corresponding to the geophysical logging data is obtained;
[0013] Porosity abrupt change points are obtained based on the predicted core porosity data corresponding to each geophysical logging data.
[0014] Based on the lithological classification labels and porosity abrupt change points corresponding to each geophysical logging data, lithological profile maps are obtained.
[0015] In one embodiment, the geophysical logging data includes: neutron data, gamma-ray data, and the boundaries and thicknesses of ore-bearing layers; after performing outlier detection and removal operations on each geophysical logging data in the completed logging dataset based on the standard deviation and mean of the geophysical logging data in the completed logging dataset to obtain a normalized time-series geophysical logging dataset, the method further includes:
[0016] A well logging interpretation model was constructed based on neutron and gamma-ray data from a standardized time-series geophysical well logging dataset.
[0017] Gamma logging curves were plotted based on geophysical logging data of ore-bearing layers in a standardized time-series geophysical logging dataset.
[0018] Input the gamma logging curve and the boundary and thickness of the ore-bearing layer into the logging interpretation model to obtain the area under the gamma logging curve;
[0019] The initial uranium content of the ore-bearing layer is obtained from the area under the gamma logging curve.
[0020] The initial uranium content of the ore-bearing layer was corrected using the radioactivity balance coefficient, resulting in the corrected uranium content of the ore-bearing layer.
[0021] In one embodiment, neutron data includes neutron porosity and neutron gamma intensity; a well logging interpretation model is constructed based on neutron and gamma-ray data from a normalized time-series geophysical logging dataset, specifically including:
[0022] Response matrices for elemental characteristic peak intensities and formation elemental content were established based on neutron and gamma-ray data from a standardized time-series geophysical logging dataset.
[0023] Based on the response matrices of elemental characteristic peak intensities and the response matrices of formation elemental content, lithological characteristic parameters are obtained.
[0024] The porosity measurement value is obtained based on the negative correlation between neutron porosity and neutron gamma intensity;
[0025] An initial model is obtained by nonlinearly fusing multidimensional logging parameters based on lithological characteristic parameters and porosity measurements.
[0026] The initial model was optimized and corrected to obtain the well logging interpretation model.
[0027] In one embodiment, the step of acquiring the well logging dataset to be processed includes: using gamma logging technology to measure at various depths of the target well to obtain multiple geophysical well logging data.
[0028] In one embodiment, prior to acquiring the logging dataset to be processed, the method further includes performing instrument dead time correction and natural gamma calibration, and correcting for radioactivity imbalance.
[0029] Secondly, this application provides a uranium ore geophysical logging data processing device, comprising:
[0030] The interference removal module is used to sequentially remove communication interference, white noise interference, and random impulse interference from each geophysical logging data in the logging dataset to be processed, so as to obtain an interference-removed logging dataset.
[0031] The data augmentation module is used to augment each geophysical logging data in the interference removal logging dataset to obtain an augmented logging dataset.
[0032] The missing value completion module is used to perform missing value detection and completion operations on each geophysical logging data in the enhanced logging dataset to obtain a completed logging dataset.
[0033] The outlier removal module is used to perform outlier detection and removal operations on each geophysical logging data in the complete logging dataset based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, so as to obtain a normalized time-series geophysical logging dataset.
[0034] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the uranium ore geophysical logging data processing method described in any of the above claims.
[0035] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the uranium ore geophysical logging data processing method described in any of the preceding claims.
[0036] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the uranium ore geophysical logging data processing method described in any of the preceding claims.
[0037] According to the specific embodiments provided in this application, this application has the following technical effects:
[0038] This application provides a method, apparatus, equipment, medium, and product for processing geophysical logging data in uranium mines. Related geophysical logging data processing methods for uranium mines often suffer from data discontinuity because they do not perform missing value completion and outlier removal, thus affecting the accuracy of the logging data. This application solves the problem of data discontinuity by performing missing value completion and outlier removal operations on geophysical logging data. Attached Figure Description
[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 A flowchart illustrating a method for processing geophysical logging data in uranium deposits, provided as an embodiment of this application;
[0041] Figure 2 A flowchart illustrating an overall process for processing geophysical logging data in uranium deposits, provided in an embodiment of this application.
[0042] Figure 3 This is a flowchart of an outlier removal method for uranium ore geophysical logging data processing provided in an embodiment of this application;
[0043] Figure 4 A schematic diagram of the functional modules of a uranium ore geophysical logging data processing device provided in an embodiment of this application;
[0044] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0047] To address the problem that existing technologies do not perform missing value supplementation and outlier removal on well logging data, which can easily lead to data discontinuity and affect the accuracy of well logging data, an exemplary embodiment is provided, such as... Figure 1 As shown, a method for processing geophysical logging data in uranium deposits is provided, the specific steps of which include:
[0048] Step 201: Perform communication interference removal, white noise interference removal, and random impulse interference removal operations on each geophysical logging data in the logging dataset to be processed in sequence to obtain the interference-removed logging dataset.
[0049] Step 202: Perform data augmentation on each geophysical logging data in the interference removal logging dataset to obtain an enhanced logging dataset, thereby enhancing the feature data in the interference removal logging dataset.
[0050] Step 203: Perform missing value detection and completion operations on each geophysical logging data in the enhanced logging dataset to obtain the completed logging dataset.
[0051] Step 204: Based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, perform outlier detection and removal operations on each geophysical logging data in the complete logging dataset to obtain a normalized time-series geophysical logging dataset.
[0052] In practical applications, the steps for acquiring the well logging dataset to be processed include: using gamma logging technology to measure at various depths in the target well to obtain multiple geophysical logging data. Here, one geophysical logging data corresponds to one depth value.
[0053] In practical applications, gamma-ray logging technology is used to measure at various depths in the target well, obtaining multiple geophysical logging data, specifically including:
[0054] Install a gamma logging device that includes a gamma logging instrument, a signal amplifier, a depth encoder, and a surface recording system. The gamma logging instrument is connected to the signal amplifier and is used to acquire geophysical logging data. The signal amplifier is used to amplify the geophysical logging data acquired by the gamma logging instrument. The signal amplifier is connected to the depth encoder and is used to encode the amplified geophysical logging data. The depth encoder is connected to the surface recording system and is used to receive the encoded geophysical logging data.
[0055] The gamma logging instrument is lowered and raised multiple times at preset depth intervals to record the natural gamma ray intensity or energy spectrum data of different well sections.
[0056] Gamma logging instruments convert gamma rays into electrical pulse signals, which are then amplified to generate a logging dataset containing depth, timestamps, and gamma intensity. During acquisition, it is crucial to ensure the instrument is centered to avoid the influence of the wellbore diameter. The stability of the gamma count rate and neutron flux data must be continuously monitored during acquisition; any abnormal fluctuations require timely instrument adjustments or repeated measurements.
[0057] In practical applications, before acquiring the logging dataset to be processed, the following steps are included: performing instrument dead-time correction and natural gamma calibration, and correcting for radioactivity imbalance. Performing instrument dead-time correction and natural gamma calibration can eliminate the influence of instrument errors on the measurement results. The radioactivity imbalance correction step can establish a regional radioactivity balance correction coefficient chart using laboratory core samples and verify the inclination data. This step can correct the influence of borehole trajectory deviation on formation positioning and the influence of environmental interference on gamma logging data, such as well diameter changes and mud density. This application, through dead-time correction, ensures the continuity and completeness of the acquired data, thereby improving data accuracy. Dead-time correction also reduces data loss due to instrument downtime, ensuring data integrity and consistency.
[0058] In practical applications, performing instrument dead time correction and natural gamma calibration specifically includes:
[0059] Using a standard signal source, the instrument's response time during gamma logging is recorded and compared with a standard value. The internal timer settings are adjusted based on the comparison results. Dead time is the response delay caused by the instrument's internal processing mechanism during measurement. The instrument's response time is measured using an external trigger signal, and the internal timer is adjusted to ensure measurement accuracy.
[0060] Calibration is performed using a standard source with known radioactivity. The standard source is placed at an appropriate distance from the instrument during gamma logging, and the response signal of the instrument during gamma logging is recorded.
[0061] The recorded response signal is compared with the standard value. If there is a deviation, the sensitivity and response threshold of the instrument during gamma logging are adjusted to ensure the accuracy of the measurement.
[0062] In practical applications, the process between steps 202 and 203 also includes: if there are two columns with the same name or the same meaning in the enhanced logging dataset, rename one of the data columns.
[0063] If duplicate data values exist in the enhanced logging dataset, delete the duplicate data values, keeping only the first record of the duplicate data.
[0064] If there are inconsistencies in the data or naming rules in the enhanced logging dataset, use the split column function to split the data values in the inconsistent data columns.
[0065] In practical applications, missing value detection and completion operations are performed on each geophysical logging data in the enhanced logging dataset to obtain a completed logging dataset, specifically including:
[0066] A fixed time window is set to check each geophysical logging data. For geophysical logging data with missing values, linear interpolation is used to obtain supplementary data to fill in the missing values.
[0067] In practical applications, based on the standard deviation and mean of the geophysical logging data in the completed logging dataset, outlier detection and removal are performed on each geophysical logging data point in the completed logging dataset to obtain a normalized time-series geophysical logging dataset, such as... Figure 3 As shown, it specifically includes:
[0068] Calculate the absolute value of the difference between the measured value and the mean, and determine whether the absolute value of the difference between the measured value and the mean is greater than or equal to 3 times the standard deviation. Specifically, for any geophysical logging data, taking any logging data as an example, calculate the absolute value of the difference between the measured value of this logging data and the corresponding mean value of this logging data, and determine whether this absolute value is greater than or equal to 3 times the standard deviation.
[0069] If the absolute value of the difference between the measured value and the average value of this logging data is greater than or equal to 3 times the standard deviation, then the measured value of this logging data is judged as an outlier.
[0070] The measured values identified as outliers are removed from the well logging dataset. If there are multiple outliers in the well logging dataset, the outlier data in the well logging dataset is repeatedly identified until all outliers are removed.
[0071] After outlier removal, the mean and standard deviation of the remaining data are recalculated, and the presence of new outliers is checked again. This process is repeated multiple times until no outliers are found, resulting in a normalized time-series geophysical logging dataset. Verification of the remaining data after outlier removal ensures effective outlier detection and removal.
[0072] In practical applications, the process continues until there are no outliers, resulting in a standardized time-series geophysical logging dataset. Specifically, this involves transforming the data after outlier removal to obtain dimensionally unified data.
[0073] The dimensionless data are standardized by minimax processing to obtain a normalized time-series geophysical logging dataset. Pearson correlation coefficients are calculated on the numerical values in the normalized time-series geophysical logging dataset using a sliding time window. The quality of the logging dataset is verified using the Pearson correlation coefficients.
[0074] In practical applications, after performing outlier detection and removal operations on each geophysical well logging data in the completed well logging dataset based on the standard deviation and mean of the geophysical well logging data in the completed well logging dataset to obtain a normalized time-series geophysical well logging dataset, the following steps are also included:
[0075] For any geophysical logging data in the standardized time-series geophysical logging dataset, the geophysical logging data is input into the trained porosity prediction model to obtain the predicted core porosity data corresponding to the geophysical logging data. The porosity prediction model in this application is the same as the porosity prediction model in the method, device and equipment for determining reservoir porosity based on rock physics knowledge disclosed in the patent with publication number CN114280689B.
[0076] Based on the geophysical logging data and the corresponding predicted core porosity data, lithological classification labels are obtained for the geophysical logging data; lithological classification labels are obtained by identifying the geophysical logging data and the corresponding predicted core porosity data.
[0077] Porosity abrupt change points are obtained based on the predicted core porosity data corresponding to each geophysical logging data.
[0078] Based on the lithological classification labels and porosity abrupt change points corresponding to each geophysical well logging data, lithological profiles are obtained. These profiles can be used to distinguish between ore-bearing and non-ore-bearing layers, and to determine the boundaries and thickness of ore-bearing layers.
[0079] In practical applications, the specific training process for the porosity prediction model includes:
[0080] Historical core porosity data, i.e. historical core porosity and historical lithology labels, are obtained from each geophysical logging data in the historical geophysical logging dataset and used as a benchmark for model training and validation.
[0081] A porosity prediction model was constructed using the random forest algorithm. The model was trained by supervised learning using historical geophysical logging data as input and historical core porosity data as labels. Cross-validation was then used to evaluate the model's accuracy.
[0082] In practical applications, the geophysical logging data includes: neutron data, gamma-ray data, and the boundaries and thicknesses of ore-bearing layers. After performing outlier detection and removal operations on each geophysical logging data point in the completed logging dataset based on the standard deviation and mean of the data, resulting in a standardized time-series geophysical logging dataset, the data further includes:
[0083] A well logging interpretation model was constructed based on neutron and gamma-ray data from a standardized time-series geophysical well logging dataset.
[0084] Gamma logging curves were plotted based on geophysical logging data of ore-bearing layers in a standardized time-series geophysical logging dataset.
[0085] Input the gamma logging curve and the boundary and thickness of the ore-bearing layer into the logging interpretation model to obtain the area under the gamma logging curve;
[0086] The initial uranium content of the ore-bearing layer is obtained from the area under the gamma logging curve.
[0087] The initial uranium content of the ore-bearing layer was corrected using the radioactivity balance coefficient, resulting in the corrected uranium content of the ore-bearing layer.
[0088] In practical applications, neutron data includes neutron porosity and neutron gamma intensity. A well logging interpretation model is constructed based on neutron and gamma-ray data from a normalized time-series geophysical logging dataset, specifically including:
[0089] Response matrices for elemental characteristic peak intensities and formation elemental content were established based on neutron and gamma-ray data from a standardized time-series geophysical logging dataset.
[0090] Based on the response matrices of elemental characteristic peak intensities and the response matrices of formation elemental content, lithological characteristic parameters are obtained.
[0091] Porosity measurements were obtained based on the negative correlation between neutron porosity and neutron gamma intensity. This negative correlation reflects the difference in the physical mechanisms of neutron porosity and neutron gamma intensity; specifically, one increases with increasing porosity while the other decreases. This is manifested as a negative correlation between neutron porosity and neutron gamma intensity. The pore structure was delineated using this negative correlation (increased gamma intensity in high hydrogen index formations), and the porosity measurements of the pore structure were extracted.
[0092] An initial model is obtained by nonlinearly fusing multidimensional logging parameters based on lithological characteristic parameters and porosity measurements. A machine learning algorithm is then introduced to perform nonlinear feature fusion of multidimensional logging parameters using lithological characteristic parameters and porosity measurements to obtain the initial model.
[0093] The initial model was optimized and corrected to obtain the well logging interpretation model.
[0094] In practical applications, the geophysical logging data processing method for uranium deposits also includes generating digital reports and visualization charts containing uranium content distribution, lithological profile, and porosity data based on uranium content calculation results and lithological profiles.
[0095] The overall process of the uranium ore geophysical logging data processing method provided in this application is as follows: Figure 2 As shown, the process includes: S1: Performing instrument dead time correction and natural gamma calibration, and making corrections; S2: Acquiring geophysical logging data multiple times using gamma logging to obtain a logging dataset; S3: Preprocessing the data in the logging dataset to obtain a standardized time-series geophysical logging dataset; S4: Identifying lithological profiles in the geophysical logging dataset using a porosity prediction model and lithology identification algorithm; S5: Calculating the uranium content in the ore-bearing layer using a logging interpretation model, and correcting the uranium content calculation results using a radioactivity balance coefficient; S6: Generating a digital report and visualization charts containing uranium content distribution, lithological profile, and porosity data based on the uranium content calculation results and lithological profiles. This application solves the problem of data discontinuity in existing technologies, which affects the accuracy of logging data.
[0096] This application ensures that the collected data is continuous and complete, thereby improving data accuracy, ensuring data integrity and consistency, thus enhancing detection efficiency and accuracy. It can avoid lithological identification deviations caused by data discontinuity, ensure the continuity of uranium geophysical logging data processing, improve data reliability, and reduce the impact of invalid data on calculation results after removal. It can also improve the accuracy of uranium content inversion and resource assessment, shorten the data processing cycle, and improve the efficiency and accuracy of data processing, thus solving the problems mentioned in the background art.
[0097] Natural gamma calibration, by accurately measuring the gamma rays emitted by rocks, can more accurately determine rock type and uranium content, ensuring the reliability of detection results. This application uses natural gamma calibration to help instruments more accurately identify the uranium content in rocks, thereby improving detection efficiency and accuracy. By supplementing missing values, it can avoid lithological identification deviations caused by data discontinuity, ensuring the continuity of uranium ore geophysical logging data processing. At the same time, complete uranium ore geophysical logging data can improve the accuracy of gamma logging quantitative interpretation, reduce errors in porosity prediction and geophysical parameter calculation, and ensure the compliance of output results through data correction, which can significantly shorten the overall data processing cycle.
[0098] This application improves data reliability by removing outliers. Outliers may be caused by instrument noise, environmental interference, or human error. Removing them reduces the impact of invalid data on calculation results and improves the accuracy of uranium content inversion and resource assessment. When establishing porosity prediction models, outliers can distort the data distribution pattern. Removing them can more accurately reflect the correlation of formation parameters and enhance the applicability of interpretation standards. By automatically screening outliers, manual intervention can be reduced, the data processing cycle can be shortened, and the efficiency and accuracy of data processing can be improved.
[0099] This application eliminates short pauses caused by data processing during high-speed sampling through dead-time correction, ensuring continuous and complete data acquisition and thus improving data accuracy. Dead-time correction also reduces data loss due to instrument downtime, optimizes data processing, and ensures data integrity and consistency. Natural gamma calibration helps the instrument more accurately identify the uranium content in rocks, thereby improving detection efficiency and accuracy.
[0100] Correcting uranium content using the radioactivity balance coefficient can effectively correct gamma-ray measurement biases caused by differences in uranium migration. When the radioactivity balance coefficient is not equal to 1, the uranium content measured directly by gamma spectroscopy will deviate from the true value. The balance coefficient correction can eliminate this systematic error. The balance coefficient correction can significantly improve the accuracy of reserve calculation. The trend of balance coefficient change can reflect the redox environment and mineralization process of uranium deposits, providing geochemical basis for mineral exploration.
[0101] Based on the same inventive concept, this application also provides a uranium ore geophysical logging data processing device for implementing the uranium ore geophysical logging data processing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the uranium ore geophysical logging data processing device provided below can be found in the limitations of the uranium ore geophysical logging data processing method described above, and will not be repeated here.
[0102] In one exemplary embodiment, such as Figure 4 As shown, a uranium ore geophysical logging data processing device is provided, comprising:
[0103] The interference removal module is used to sequentially remove communication interference, white noise interference, and random impulse interference from each geophysical logging data in the logging dataset to be processed, so as to obtain an interference-removed logging dataset.
[0104] The data augmentation module is used to augment each geophysical logging data in the interference removal logging dataset to obtain an augmented logging dataset.
[0105] The missing value completion module is used to perform missing value detection and completion operations on each geophysical logging data in the enhanced logging dataset to obtain a completed logging dataset.
[0106] The outlier removal module is used to perform outlier detection and removal operations on each geophysical logging data in the complete logging dataset based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, so as to obtain a normalized time-series geophysical logging dataset.
[0107] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores uranium geophysical logging data processing data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a uranium geophysical logging data processing method.
[0108] Those skilled in the art will understand that Figure 5 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.
[0109] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.
[0110] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.
[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0112] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0113] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0115] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for processing geophysical logging data in uranium deposits, characterized in that, The method for processing geophysical logging data from uranium deposits includes: Each geophysical logging data in the logging dataset to be processed is sequentially subjected to communication interference removal, white noise interference removal, and random impulse interference removal operations to obtain the interference-removed logging dataset. Data augmentation was performed on each geophysical well log data in the interference removal well log dataset to obtain the enhanced well log dataset. Missing value detection and completion operations were performed on each geophysical logging data in the enhanced logging dataset to obtain a complete logging dataset. Based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, outlier detection and removal operations are performed on each geophysical logging data in the complete logging dataset to obtain a normalized time-series geophysical logging dataset. For any geophysical logging data in the standardized time-series geophysical logging dataset, the geophysical logging data is input into the trained porosity prediction model to obtain the predicted core porosity data corresponding to the geophysical logging data. Based on the geophysical logging data and the predicted core porosity data corresponding to the geophysical logging data, the lithology classification label corresponding to the geophysical logging data is obtained; Porosity abrupt change points are obtained based on the predicted core porosity data corresponding to each geophysical logging data. Based on the lithological classification labels and porosity abrupt change points corresponding to each geophysical logging data, lithological profile maps are obtained.
2. The method for processing uranium ore geophysical logging data according to claim 1, characterized in that, The geophysical logging data includes: neutron data, gamma-ray data, and the boundaries and thicknesses of ore-bearing layers; after performing outlier detection and removal operations on each geophysical logging data in the completed logging dataset based on the standard deviation and mean of the geophysical logging data in the completed logging dataset to obtain a normalized time-series geophysical logging dataset, it also includes: A well logging interpretation model was constructed based on neutron and gamma-ray data from a standardized time-series geophysical well logging dataset. Gamma logging curves were plotted based on geophysical logging data of ore-bearing layers in a standardized time-series geophysical logging dataset. Input the gamma logging curve and the boundary and thickness of the ore-bearing layer into the logging interpretation model to obtain the area under the gamma logging curve; The initial uranium content of the ore-bearing layer is obtained from the area under the gamma logging curve. The initial uranium content of the ore-bearing layer was corrected using the radioactivity balance coefficient, resulting in the corrected uranium content of the ore-bearing layer.
3. The method for processing uranium ore geophysical logging data according to claim 2, characterized in that, Neutron data includes neutron porosity and neutron gamma intensity; a well logging interpretation model is constructed based on neutron and gamma-ray data from a normalized time-series geophysical logging dataset, specifically including: Response matrices for elemental characteristic peak intensities and formation elemental content were established based on neutron and gamma-ray data from a standardized time-series geophysical logging dataset. Based on the response matrices of elemental characteristic peak intensities and the response matrices of formation elemental content, lithological characteristic parameters are obtained; The porosity measurement value is obtained based on the negative correlation between neutron porosity and neutron gamma intensity; The initial model is obtained by nonlinearly fusing multidimensional logging parameters based on lithological characteristic parameters and porosity measurements. The initial model was optimized and corrected to obtain the well logging interpretation model.
4. The method for processing uranium ore geophysical logging data according to claim 1, characterized in that, The steps for obtaining the well logging dataset to be processed include: using gamma logging technology to measure at various depths in the target well to obtain multiple geophysical well logging data.
5. The method for processing uranium ore geophysical logging data according to claim 4, characterized in that, Before acquiring the logging dataset to be processed, the process also includes: performing instrument dead time correction and natural gamma calibration, and correcting for radioactivity imbalance issues.
6. A uranium ore geophysical logging data processing device, characterized in that, The uranium ore geophysical logging data processing device includes: The interference removal module is used to sequentially remove communication interference, white noise interference, and random impulse interference from each geophysical logging data in the logging dataset to be processed, so as to obtain an interference-removed logging dataset. The data augmentation module is used to augment each geophysical logging data in the interference removal logging dataset to obtain an augmented logging dataset. The missing value completion module is used to perform missing value detection and completion operations on each geophysical logging data in the enhanced logging dataset to obtain a completed logging dataset. The outlier removal module is used to perform outlier detection and removal operations on each geophysical logging data in the complete logging dataset based on the standard deviation and mean of the geophysical logging data in the complete logging dataset, so as to obtain a normalized time-series geophysical logging dataset. For any geophysical logging data in the standardized time-series geophysical logging dataset, the geophysical logging data is input into the trained porosity prediction model to obtain the predicted core porosity data corresponding to the geophysical logging data. Based on the geophysical logging data and the predicted core porosity data corresponding to the geophysical logging data, the lithology classification label corresponding to the geophysical logging data is obtained; Porosity abrupt change points are obtained based on the predicted core porosity data corresponding to each geophysical logging data. Based on the lithological classification labels and porosity abrupt change points corresponding to each geophysical logging data, lithological profile maps are obtained.
7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the uranium ore geophysical logging data processing method according to any one of claims 1-5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the uranium ore geophysical logging data processing method as described in any one of claims 1-5.
9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the uranium ore geophysical logging data processing method as described in any one of claims 1-5.