A geological comprehensive information analysis system and method for gold mine exploration

Abstract

The application discloses a geological comprehensive information analysis system and method for gold exploration, and relates to the technical field of information analysis.The analysis method comprises the following steps: extracting the geological types and geological features of an exploration area, and generating corresponding collection records; performing quality evaluation on the geological information of any collection record; performing comparison analysis on any two collection records with the same geological type, obtaining difference features, and performing deviation degree analysis on the difference features; constructing an anomaly evaluation model and setting anomaly identification standards; performing geological feature extraction on the real-time collected geological information of the exploration area, and generating real-time collection records; performing anomaly identification on the presentation of each geological feature; and through multidimensional analysis on the collected geological information in the exploration process, the errors existing in the traditional single index judgment can be eliminated, the quality of the geological information can be more accurately and effectively judged, and the exploration efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of information analysis technology, specifically a comprehensive geological information analysis system and method for gold mine exploration. Background Technology

[0002] With the continued growth in global demand for gold resources, shallow and easily detectable ore bodies are gradually being depleted, making it an inevitable trend for mineral exploration to extend towards deeper and concealed ore bodies. However, traditional gold exploration technologies face multiple technical bottlenecks: surface geophysical and geochemical methods are limited by the attenuation of detection depth, such as the electromagnetic method, where the resolution decreases by more than 15% below 500m, making it difficult for shallow anomaly information to effectively indicate deep ore bodies; although borehole exploration technology can obtain the response of the target body at close range, existing methods have defects such as fragmented interpretation of multi-source data and insufficient geological evidence, resulting in a low success rate of deep mineral exploration prediction.

[0003] The raw data obtained during the exploration process usually contains a lot of noise and redundant information. Traditional filtering and dimensionality reduction methods are difficult to effectively balance information preservation and noise elimination, resulting in the loss of key mineralization signals. Traditional exploration risk assessment is mostly based on expert experience or single indicator threshold judgment, lacking the ability to dynamically model multidimensional data and unable to quantify the impact of uncertain factors on resource prediction, which seriously affects the exploration efficiency and accuracy of gold mine exploration. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive geological information analysis system and method for gold mine exploration, in order to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for comprehensive geological information analysis in gold mine exploration, the analysis method comprising the following steps:

[0006] Step S100: Collect geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological types and geological characteristics in any collection record;

[0007] Step S200: Randomly select a collection record and analyze the feature matching situation presented to evaluate the quality of the geological information of the collection record; compare and analyze any two collection records with the same geological type to obtain the difference features between the two collection records.

[0008] Step S300: Extract any two collection records containing the same difference features, and analyze the degree of deviation for each of the same difference features based on the quality assessment difference between the two collection records; construct an anomaly assessment model and set anomaly identification criteria based on the quality assessment of any collection record.

[0009] Step S400: Extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; identify anomalies in the presentation of each geological feature through an anomaly assessment model.

[0010] Furthermore, step S100 includes the following steps:

[0011] Step S101: Collect and organize geological and mineral data from various channels to obtain geological information for various geological types. Extract features from the geological information of any geological type to obtain the corresponding feature set, and generate a feature matching database to match any geological type with the corresponding features. Through the collected geological and mineral data, including mineralization information, structural information, wall rock alteration information, contact zone structural information, and vein information, ore body information, mineralization trend geological information, and equidistant ore-controlling geological information can be obtained through organization and analysis. As the depth of deep drilling increases and the results of basic analysis samples present information on borehole mineralization intensity, combined with the organization of previous geological and mineral data, information on ore bodies such as spatial distribution, morphology, scale, and occurrence can be obtained.

[0012] Step S102: Use a geological information acquisition device to collect geological information of the exploration area. After preprocessing the geological information, obtain effective information of the exploration area, and extract features from the effective information to obtain several geological features of the exploration area. Use a geological sampling device to collect samples of the exploration area, and identify the geological type of the exploration area by identifying the type of the collected samples. A five-directional transmission loop device can be used to perform transient electromagnetic measurement of the well. Based on the obtained ore body information, guide the layout of the transient electromagnetic measurement points and the layout of the structural superimposed halo measurement samples. Perform geological-geophysical and geological-geochemical analysis on the obtained results to interpret the information of anomalous geological bodies that reflect anomalies in well logging curves and structural geochemical anomalies.

[0013] Step S103: Summarize the geological types identified in the exploration area and several extracted geological features to obtain a first collection record of the geological information collection process in the exploration area; comprehensively analyze the geophysical and geochemical information obtained from the previous work in the prediction area and the geological and ore body information, metallogenic regularity information, equidistant ore-controlling geological information, and metallogenic trend geological information obtained from deep drilling, evaluate the rationality of geophysical and geochemical information, and then use reliable geophysical and geochemical information to carry out deep ore body location prediction and evaluate the deep mineral exploration space and metallogenic potential.

[0014] Furthermore, step S200 includes the following steps:

[0015] Step S201: Set any selected collection record as the target collection record, and obtain the geological type and geological features recorded in the target collection record; arbitrarily select a geological type and compare it with the geological type in the feature matching database. If the same geological type exists in the feature matching database, extract several features matching the same geological type to obtain the feature set of the same geological type.

[0016] Step S202: Randomly select a geological feature from the target acquisition record and compare its similarity with any feature in the feature set of the same geological feature. If the similarity between the selected geological feature and the feature is greater than or equal to a preset similarity threshold, then the selected geological feature is considered a valid feature of the same geological type. The number of valid features corresponding to the same geological type in the target acquisition record is set to N. eff The percentage of valid information of the same geological type in the target acquisition record is obtained as η=N. eff / N, where N is the number of features matched in the feature matching database for the same geological type; the effective information ratio indicates the amount of effective information in the collected geological information that helps identify the geological type, and the effective information ratio can reflect the quality of information collection to a certain extent;

[0017] Step S203: Pre-set corresponding quality assessment rules for geological information corresponding to different geological types, extract geological information corresponding to any geological type in the target acquisition record and perform quality assessment to obtain the quality feature value Q of any geological type, wherein the quality feature value of the i-th geological type in the target acquisition record is set to Q. i The quality assessment rules include assessments of data integrity and accuracy, such as the degree of deviation in coordinate location and measurement accuracy. By assessing these data, preliminary quality checks of geological information can be performed to obtain quality characteristic values, reflecting the accuracy and reliability of the collected geological information. According to the formula:

[0018] ;

[0019] Where i is a positive integer and i∈(1,a), a is the number of geological types contained in the target acquisition record, and η i The effective information proportion of the i-th geological type; the quality assessment value Z of the target acquisition record is calculated; a quality assessment threshold Z is preset. th If Z < Z th If the target data collection record is not found, it will be marked as an anomaly. Based on the proportion of effective information, the information quality of the effective information will be further evaluated to obtain the information collection quality more accurately.

[0020] Step S204: Randomly select a geological type and two acquisition records. If both selected acquisition records contain the selected geological type, obtain the geological feature set corresponding to the selected geological type in each of the two acquisition records. Randomly select a geological feature from one of the geological feature sets and compare its similarity with all geological features in the other geological feature set. If the obtained similarity is less than a preset difference threshold, set the selected geological feature as a difference feature in one of the geological feature sets. Compare all geological features between the two acquisition records to obtain several difference features between the two acquisition records.

[0021] Step S205: Randomly select one of the two acquisition records and arbitrarily select a difference feature. Compare the selected difference feature with the geological feature sets of the other acquisition records containing the selected geological type. Count the number of times the selected difference feature is used as a difference feature to obtain the occurrence frequency of the selected difference feature. If the occurrence frequency is less than a preset frequency threshold, the selected difference feature is removed from the difference features of the one acquisition record to obtain the difference feature set of the one acquisition record. Relying solely on the difference features extracted between two acquisition records may be random and cannot be used as the overall difference features for analysis. Therefore, by analyzing the occurrence frequency of difference features, it is determined whether the difference features actually affect the quality of geological information acquisition.

[0022] Furthermore, step S300 includes the following steps:

[0023] Step S301: Extract the geological type containing any identical difference features from the two acquisition records, and extract the quality feature value and effective information ratio of the geological type in the two acquisition records respectively, to obtain the quality assessment difference Δz between the two acquisition records on the extracted geological type, where Q1 is the quality feature value of one acquisition record on the extracted geological type, Q2 is the quality feature value of the other acquisition record on the extracted geological type, η1 is the effective information ratio of the acquisition record with quality feature value Q1, and η2 is the effective information ratio of the acquisition record with quality feature value Q2.

[0024] Step S302: Obtain the quality assessment value Z1 of the acquisition record with quality characteristic value Q1, obtain the assessment deviation value σ=Δz / Z1 and the proportion of quality characteristics δ=Q1 / Z1 of the acquisition record, obtain the number of difference features contained in the acquisition record in terms of the extracted geological type as N1, and calculate the deviation degree p=(σ×δ) / N1 of the extracted same difference features; obtain the deviation degree of each same difference feature between any two acquisition records, and calculate the average deviation degree p of each difference feature. ave The deviation percentage of the overall assessment value is allocated based on the proportion of various geological types in the quality assessment, and then the deviation is evenly distributed according to the differences contained in the geological types, so as to obtain a more accurate degree of deviation.

[0025] Step S303: Define the extracted identical difference feature as the j-th identical difference feature. If the similarity between the j-th identical difference feature and any geological feature in one of the acquisition records is less than the preset difference threshold, then obtain the deviation p of the identical difference feature extracted from one of the acquisition records. j =-(p ave ) j Otherwise p j =(p ave ) j The identical differences between two acquisition records are reciprocal, arising from the presence of one record and the absence of the other. Although the corresponding geological features do not actually exist in one record, they both have an impact on the geological information quality assessment, as one has a quality-enhancing effect and the other has a quality-reducing effect, resulting in different positive and negative differences in the differences between different acquisition records.

[0026] Step S304: Randomly select a data collection record, extract the various differential features contained in the selected data collection record, and obtain the average deviation p of each differential feature. ave Obtain the quality assessment value Z of the selected collection records and construct an anomaly assessment model:

[0027] ;

[0028] Where k is a positive integer and k∈(1,b), b is the number of differential features contained in the selected collection records, and p k Let Y be the degree of deviation of the k-th difference feature; calculate the feature evaluation value Y of the selected collection record; the degree of difference will cause deviation in the quality evaluation of the collection record, so it can be corrected by the anomaly evaluation model, so as to more accurately identify the information anomalies of the collection record and more effectively judge whether the collected information is reliable.

[0029] Step S305: If the selected collection record has an anomaly marker, then set the anomaly evaluation value Y as an anomaly evaluation value; obtain the anomaly evaluation values ​​of all collection records with anomaly markers, and select the anomaly evaluation value with the smallest value as the anomaly identification threshold Y. th .

[0030] Furthermore, step S400 includes the following steps:

[0031] Step S401: When a real-time acquisition record is generated for an exploration area, the geological information stored in the real-time acquisition record is extracted to determine the geological type and geological features; each geological feature is matched with various geological types to obtain a set of geological features for each geological type.

[0032] Step S402: Extract the expected feature set for each geological type from the feature matching database, compare the geological feature set of any geological type with the expected feature set, if there is an expected feature in the expected feature set that is not included in the geological feature set, then set the expected feature as the first anomalous feature; if there is a geological feature in the geological feature set that is not included in the expected feature set, and the geological feature is a difference feature in the other collected records, then set the geological feature as the second anomalous feature.

[0033] Step S403: Obtain the average deviation p when each geological feature or expected feature is used as a difference feature. ave If it belongs to the first abnormal feature, then the actual deviation degree p is obtained. ac =p ave If it belongs to the second abnormal feature, then the actual deviation degree p is obtained. ac =-p ave ;

[0034] Step S404: Evaluate the geological information in the real-time acquisition records according to preset quality assessment rules to obtain quality characteristic values ​​for various geological types and quality assessment values ​​for the real-time acquisition records; calculate the feature assessment value Y for each geological feature in the real-time acquisition records using an anomaly assessment model. ac If Y ac >Y th If an anomaly is detected, the real-time data collection record will be marked as abnormal, and an anomaly alert will be sent.

[0035] A comprehensive geological information analysis system for gold exploration, comprising a geological information analysis module, an information feature analysis module, an information risk assessment module, and an information anomaly identification module;

[0036] The geological information analysis module is used to collect geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological types and geological characteristics in any collection record;

[0037] The information feature analysis module is used to analyze the feature matching of any selected acquisition record, to assess the quality of the geological information in the acquisition record, and to compare and analyze any two acquisition records with the same geological type to obtain the difference features between the two acquisition records.

[0038] The information risk assessment module is used to extract any two collected records containing the same differences, and to analyze the degree of deviation of each of the same differences based on the quality assessment differences between the two collected records; based on the quality assessment of any collected record, an anomaly assessment model is constructed and anomaly identification standards are set.

[0039] The information anomaly identification module is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; and identify anomalies in the presentation of each geological feature through an anomaly assessment model.

[0040] Furthermore, the geological information analysis module includes a geological information acquisition unit and a geological feature matching unit;

[0041] The geological information acquisition unit is used to acquire geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; the geological feature matching unit is used to generate corresponding acquisition records for each geological information acquisition process, and match the geological types and geological characteristics in any acquisition record.

[0042] Furthermore, the information feature analysis module includes an information quality assessment unit and a differential feature extraction unit;

[0043] The information quality assessment unit is used to analyze the feature matching of any selected acquisition record and assess the quality of the geological information in the acquisition record; the difference feature extraction unit is used to compare and analyze any two acquisition records with the same geological type to obtain the difference features between the two acquisition records.

[0044] Furthermore, the information risk assessment module includes a feature deviation analysis unit and an assessment model construction unit;

[0045] The feature deviation analysis unit is used to extract any two collected records containing the same difference features, and to analyze the degree of deviation of each same difference feature based on the quality assessment difference between the two collected records; the evaluation model construction unit is used to construct an anomaly evaluation model and set anomaly identification criteria based on the quality assessment of any collected record.

[0046] Furthermore, the information anomaly identification module includes a real-time information analysis unit and an anomaly judgment and analysis unit;

[0047] The real-time information analysis unit is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records, and perform feature analysis; the anomaly judgment and analysis unit is used to identify anomalies by using an anomaly assessment model to assess the presentation of various geological features.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] 1. This invention conducts multi-dimensional analysis of geological information collected during the exploration process, analyzes the impact of various geological features on gold mine exploration results, and can get rid of the errors in traditional single-indicator judgment, so as to more accurately and effectively judge the quality of geological information and improve exploration efficiency.

[0050] 2. This invention analyzes the differences between different collection records, accurately analyzes the deviations of various difference characteristics, and effectively assesses the accuracy of geological information; it helps exploration personnel to make a more accurate judgment on the geological conditions of the area, and greatly improves the exploration efficiency of exploration personnel;

[0051] 3. This invention extracts features that affect geological information, identifies relevant information for determining the geological type of the exploration area, thereby identifying missing information in the collected geological information, making a preliminary prediction of resources, and conducting a preliminary assessment of the exploration situation through an anomaly judgment model, which can improve exploration efficiency and accuracy. Attached Figure Description

[0052] Figure 1 A schematic diagram illustrating the steps of a comprehensive geological information analysis method for gold mine exploration;

[0053] Figure 2 This is a schematic diagram of a comprehensive geological information analysis system for gold mine exploration.

[0054] Figure 3 Geological map of the gold deposit belt;

[0055] Figure 4 This is a curve of the abnormal segment of the three-component normalized induced electromotive force of the T1 coil in the transient electromagnetic measurement of the 88ZK09 borehole well.

[0056] Figure 5 This is an interpretation diagram of the abnormal segment of the five-position normalized induced electromotive force curve of the T1 coil in the 88ZK09 borehole. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example: Figures 1 to 5 As shown, this invention provides a method for comprehensive geological information analysis in gold mine exploration. The analysis method includes the following steps:

[0059] Step S100: Collect geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological types and geological characteristics in any collection record;

[0060] Step S100 includes the following steps:

[0061] Step S101: Collect and organize geological and mineral data from various channels to obtain geological information of various geological types. Extract features from the geological information of any geological type to obtain the corresponding feature set. Generate a feature matching database to match any geological type with the corresponding features.

[0062] Step S102: Use a geological information acquisition device to collect geological information of the exploration area, preprocess the geological information to obtain effective information of the exploration area, and extract features from the effective information to obtain several geological features of the exploration area; use a geological sampling device to collect samples of the exploration area, and identify the geological type of the exploration area by identifying the type of the collected samples.

[0063] Step S103: Summarize the geological types identified in the exploration area and the extracted geological features to obtain a record of the geological information collection process in the exploration area.

[0064] Step S200: Randomly select a collection record and analyze the feature matching situation presented to evaluate the quality of the geological information of the collection record; compare and analyze any two collection records with the same geological type to obtain the difference features between the two collection records.

[0065] Step S200 includes the following steps:

[0066] Step S201: Set any selected collection record as the target collection record, and obtain the geological type and geological features recorded in the target collection record; arbitrarily select a geological type and compare it with the geological type in the feature matching database. If the same geological type exists in the feature matching database, extract several features matching the same geological type to obtain the feature set of the same geological type.

[0067] Step S202: Randomly select a geological feature from the target acquisition record and compare its similarity with any feature in the feature set of the same geological feature. If the similarity between the selected geological feature and the feature is greater than or equal to a preset similarity threshold, then the selected geological feature is considered a valid feature of the same geological type. The number of valid features corresponding to the same geological type in the target acquisition record is set to N. eff The percentage of valid information of the same geological type in the target acquisition record is obtained as η=N. eff / N, where N is the number of features matched in the feature matching database for the same geological type;

[0068] Step S203: Pre-set corresponding quality assessment rules for geological information corresponding to different geological types, extract geological information corresponding to any geological type in the target acquisition record and perform quality assessment to obtain the quality feature value Q of any geological type, wherein the quality feature value of the i-th geological type in the target acquisition record is set to Q. i According to the formula:

[0069] ;

[0070] Where i is a positive integer and i∈(1,a), a is the number of geological types contained in the target acquisition record, and η i The effective information proportion of the i-th geological type; the quality assessment value Z of the target acquisition record is calculated; a quality assessment threshold Z is preset. th If Z < Z th If so, the target acquisition record will be marked as abnormal;

[0071] Step S204: Randomly select a geological type and two acquisition records. If both selected acquisition records contain the selected geological type, obtain the geological feature set corresponding to the selected geological type in each of the two acquisition records. Randomly select a geological feature from one of the geological feature sets and compare its similarity with all geological features in the other geological feature set. If the obtained similarity is less than a preset difference threshold, set the selected geological feature as a difference feature in one of the geological feature sets. Compare all geological features between the two acquisition records to obtain several difference features between the two acquisition records.

[0072] Step S205: Randomly select one of the two acquisition records and arbitrarily select a difference feature. Compare the selected difference feature with the geological feature set of the other acquisition records containing the selected geological type. Count the number of times the selected difference feature is used as a difference feature to obtain the occurrence frequency of the selected difference feature. If the occurrence frequency is less than a preset frequency threshold, remove the selected difference feature from the difference features of the one acquisition record to obtain the difference feature set of the one acquisition record.

[0073] Step S300: Extract any two collection records containing the same difference features, and analyze the degree of deviation for each of the same difference features based on the quality assessment difference between the two collection records; construct an anomaly assessment model and set anomaly identification criteria based on the quality assessment of any collection record.

[0074] Step S300 includes the following steps:

[0075] Step S301: Extract the geological type containing any identical difference features from the two acquisition records, and extract the quality feature value and effective information ratio of the geological type in the two acquisition records respectively, to obtain the quality assessment difference Δz between the two acquisition records on the extracted geological type, where Q1 is the quality feature value of one acquisition record on the extracted geological type, Q2 is the quality feature value of the other acquisition record on the extracted geological type, η1 is the effective information ratio of the acquisition record with quality feature value Q1, and η2 is the effective information ratio of the acquisition record with quality feature value Q2.

[0076] Step S302: Obtain the quality assessment value Z1 of the acquisition record with quality characteristic value Q1, obtain the assessment deviation value σ=Δz / Z1 and the proportion of quality characteristics δ=Q1 / Z1 of the acquisition record, obtain the number of difference features contained in the acquisition record in terms of the extracted geological type as N1, and calculate the deviation degree p=(σ×δ) / N1 of the extracted same difference features; obtain the deviation degree of each same difference feature between any two acquisition records, and calculate the average deviation degree p of each difference feature. ave ;

[0077] Step S303: Define the extracted identical difference feature as the j-th identical difference feature. If the similarity between the j-th identical difference feature and any geological feature in one of the acquisition records is less than the preset difference threshold, then obtain the deviation p of the identical difference feature extracted from one of the acquisition records. j =-(p ave ) j Otherwise p j =(p ave ) j ;

[0078] Step S304: Randomly select a data collection record, extract the various differential features contained in the selected data collection record, and obtain the average deviation p of each differential feature. ave Obtain the quality assessment value Z of the selected collection records and construct an anomaly assessment model:

[0079] ;

[0080] Where k is a positive integer and k∈(1,b), b is the number of differential features contained in the selected collection records, and p k Let Y be the degree of deviation of the k-th difference feature; calculate the feature evaluation value Y of the selected collection records;

[0081] Example: The quality assessment value of the collected records is set to 80, and the collected records contain 3 differential features with deviations of 0.1, -0.2 and 0.3 respectively. The feature assessment value of the collected records is calculated as Y = 1 / (80 × 0.9 × 1.2 × 0.7) = 1 / 60.48 = 0.0165.

[0082] Step S305: If the selected collection record has an anomaly marker, then set the anomaly evaluation value Y as an anomaly evaluation value; obtain the anomaly evaluation values ​​of all collection records with anomaly markers, and select the anomaly evaluation value with the smallest value as the anomaly identification threshold Y. th .

[0083] Step S400: Extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; identify anomalies in the presentation of each geological feature through an anomaly assessment model.

[0084] Step S400 includes the following steps:

[0085] Step S401: When a real-time acquisition record is generated for an exploration area, the geological information stored in the real-time acquisition record is extracted to determine the geological type and geological features; each geological feature is matched with various geological types to obtain a set of geological features for each geological type.

[0086] Step S402: Extract the expected feature set for each geological type from the feature matching database, compare the geological feature set of any geological type with the expected feature set, if there is an expected feature in the expected feature set that is not included in the geological feature set, then set the expected feature as the first anomalous feature; if there is a geological feature in the geological feature set that is not included in the expected feature set, and the geological feature is a difference feature in the other collected records, then set the geological feature as the second anomalous feature.

[0087] Step S403: Obtain the average deviation p when each geological feature or expected feature is used as a difference feature. ave If it belongs to the first abnormal feature, then the actual deviation degree p is obtained. ac =p ave If it belongs to the second abnormal feature, then the actual deviation degree p is obtained. ac =-p ave ;

[0088] Step S404: Evaluate the geological information in the real-time acquisition records according to preset quality assessment rules to obtain the quality characteristic values ​​of various geological types and the quality assessment value Z of the real-time acquisition records. ac The feature evaluation value Y is calculated by using an anomaly evaluation model to obtain the geological features in the real-time acquisition records. ac If Y ac >Y th If an anomaly is detected, the real-time data collection record will be marked as abnormal, and an anomaly alert will be sent.

[0089] A comprehensive geological information analysis system for gold exploration, comprising a geological information analysis module, an information feature analysis module, an information risk assessment module, and an information anomaly identification module;

[0090] The geological information analysis module is used to collect geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological types and geological characteristics in any collection record;

[0091] The information feature analysis module is used to analyze the feature matching of any selected acquisition record, to assess the quality of the geological information in the acquisition record, and to compare and analyze any two acquisition records with the same geological type to obtain the difference features between the two acquisition records.

[0092] The information risk assessment module is used to extract any two collected records containing the same differences, and to analyze the degree of deviation of each of the same differences based on the quality assessment differences between the two collected records; based on the quality assessment of any collected record, an anomaly assessment model is constructed and anomaly identification standards are set.

[0093] The information anomaly identification module is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; and identify anomalies in the presentation of each geological feature through an anomaly assessment model.

[0094] The geological information analysis module includes a geological information acquisition unit and a geological feature matching unit.

[0095] The geological information acquisition unit is used to acquire geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; the geological feature matching unit is used to generate corresponding acquisition records for each geological information acquisition process, and match the geological types and geological characteristics in any acquisition record.

[0096] The information feature analysis module includes an information quality assessment unit and a differential feature extraction unit.

[0097] The information quality assessment unit is used to analyze the feature matching of any selected acquisition record and assess the quality of the geological information in the acquisition record; the difference feature extraction unit is used to compare and analyze any two acquisition records with the same geological type to obtain the difference features between the two acquisition records.

[0098] The information risk assessment module includes a feature deviation analysis unit and an assessment model construction unit.

[0099] The feature deviation analysis unit is used to extract any two collected records containing the same difference features, and to analyze the degree of deviation of each same difference feature based on the quality assessment difference between the two collected records; the evaluation model construction unit is used to construct an anomaly evaluation model and set anomaly identification criteria based on the quality assessment of any collected record.

[0100] The information anomaly identification module includes a real-time information analysis unit and an anomaly judgment and analysis unit.

[0101] The real-time information analysis unit is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records, and perform feature analysis; the anomaly judgment and analysis unit is used to identify anomalies by using an anomaly assessment model to assess the presentation of various geological features.

[0102] Example of transient electromagnetic operation in a borehole: A five-directional transmitting loop device is used for measurement. Five transmitting frames are set up around the borehole to collect data. Figure 3 As shown; the instrument used is the PROTEM transient electromagnetic instrument manufactured by GEONICS, Canada. The instrument model is PROTEM57, which includes a 57 transmitter, a 57 receiver and a BH43-3 well three-component transient electromagnetic probe. The instrument is powered by a 5kW Honda Yamaha generator. According to the five-directional transmission loop device, data is collected from the transmission frame centered on the borehole.

[0103] Based on a comprehensive analysis of the target depth, surrounding rock electrical parameters, and the performance of the transmission system, the side length of the transmission loop was ultimately determined to be 600m. The transient electromagnetic measurement in the well uses a five-directional transmission loop device, with five 600m×600m transmission coils set up around the borehole to obtain information on geological structure and mineralization characteristics at a depth of 2000m.

[0104] For transient electromagnetic sounding of the borehole, the receiving probe is located inside the borehole, with a detection range of 200m centered on the wellhead. To ensure a satisfactory signal-to-noise ratio, a base frequency of 25Hz was selected for transmission, and the detection range extends 200m outward from the wellhead. Based on the geological characteristics of borehole 88ZK09, the 1650m–2150m range was initially identified as an anomaly zone, and the 0–1650m range as a background zone. Measurement points were spaced 1m apart in the anomaly zone and 5m apart in the background zone. Single-point acquisition time ranged from 0.0881ms to 6.978ms, with 20 sampling channels and a transmission current of 15A.

[0105] First, a 600×600m transmitting frame, designated T1, was set up centered on the borehole. Then, transmitting frames T2, T3, T4, and T5 were set up and data was collected at each frame. Transmitting frames T2, T3, T4, and T5 were located to the north, east, south, and west of transmitting frame T1, respectively. Based on geological logging of 88ZK09, the 1650–2150m range was preliminarily identified as an anomaly zone, and the 0–1650m range as a background zone. Measurements were taken at 1m intervals in the anomaly zone and at 5m intervals in the background zone. XYZ three-component data were collected at each point in all five azimuths, and the measurement results were used to plot curves. Based on the measurement results from the five transmitting frames, normalized induced electromotive force curves (EMF) curves were plotted for each XYZ channel in different azimuths, varying with depth. This curve represents the normalized current and receiving area, with units of nV / m²A. The amplitude of a single curve reflects the strength distribution of the secondary field at different depths at the same moment after transmission is cut off. Both the horizontal and vertical axes of the curves are linear coordinates.

[0106] The normalized induced electromotive force (EMF) curves of the three components in the abnormal section of the T1 coil in the 88ZK09 borehole show some differences: the X and Y component curves are similar in shape, with large fluctuations and amplitudes between 0 nv / (A·m2) and 300 nv / (A·m2); the Z component amplitude is between 0 nv / (A·m2) and 80 nv / (A·m2); each curve is divided into early, middle, and late phases according to the number of sampling channels: channels 1-7 are defined as the early phase, with acquisition times of 0.0881ms to 0.3144ms; channels 8-14 are defined as the middle phase, with acquisition times of 0.3956ms to 1.6360ms; channels 15-20 are defined as the late phase, with acquisition times of 2.081ms to 6.978ms; the amplitude of the seven middle-phase curves is higher than that of the seven early-phase curves, and the late-phase curves gradually decay to zero, such as... Figure 4 As shown.

[0107] like Figure 5As shown, 6 to 13 channels of the X component, characterized by large fluctuations and high amplitude, from the five-directional coil induced electromotive force curves were analyzed. The T1 coil is located at borehole 88ZK09, and the surrounding four-directional coils exhibit similar curve characteristics, suggesting that the alteration zone and ore body of borehole 88ZK09 extend stably along the strike and dip within a 200m extension beyond the borehole. The borehole structural alteration and mineralization description results were overlaid with the T1 coil curves; the curve segments corresponding to the alteration zone and ore body were designated as mineralization indicator segments, i.e., borehole depths of 1750m to 2060m. Curve segments with similar characteristics from the surrounding four-directional coils were designated as their respective mineralization indicator segments. The mineralization indicator segment of the T3 coil logging curve, with known shallow alteration zones and ore bodies, extends from 1650m to 1970m and connects with the alteration zone of the T1 coil, indicating that the alteration zone extends along both lines. The spatial distribution of the alteration zone along the azimuth of the T1 and T3 coils; the dip angle of the alteration zone inferred from the profile of the T1 and T3 coils is basically consistent with the known alteration zone occurrence, indicating that the logging results can reflect the characteristics of the alteration zone; the mineralization indicator section of the induced electromotive force curves of the deep T5 coil and the strike T2 and T4 coils can be used as the inferred spatial location of the alteration zone and ore body occurrence 200m outward from the azimuth of the corresponding coil from 88ZK09; the depth of the abnormal section of the T2 and T4 coils is basically consistent with that of the T1 coil, that is, 1750m~2060m, which respectively represent the predicted occurrence depth of the alteration zone 200m outward from 88ZK09 to the north and south; the depth of the abnormal section of the logging curve of the T5 coil is 1840m~2150m, which represents the predicted deep extension of the alteration zone 200m outward from 88ZK09 to the west;

[0108] The results of transient electromagnetic measurements in the well show that the mineralization indicator section of the induced electromotive force curve of the deep T5 coil still has similar characteristics to the mineralization indicator section of the T1 coil logging curve of borehole 88ZK09. It is inferred that the deep alteration zone and ore body characteristics are similar to those of the exposed parts. Based on geological understanding such as mineralization regularity, trend extrapolation and equidistant ore control, it is speculated that the thick ore body exposed by borehole 88ZK09 is the front end of the deep rich ore section.

[0109] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for comprehensive geological information analysis in gold deposit exploration, characterized in that: The analytical method includes the following steps: Step S100: Collect geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological types and geological characteristics in any collection record; Step S200: Randomly select a collection record and analyze the feature matching situation presented to evaluate the quality of the geological information of the collection record; compare and analyze any two collection records with the same geological type to obtain the difference features between the two collection records. Step S300: Extract any two collection records containing the same difference features, and analyze the degree of deviation for each of the same difference features based on the quality assessment difference between the two collection records; construct an anomaly assessment model and set anomaly identification criteria based on the quality assessment of any collection record. Specifically, it includes: Step S301: Extract the geological type containing any identical difference features from the two acquisition records, and extract the quality feature value and effective information ratio of the geological type in the two acquisition records respectively, to obtain the quality assessment difference Δz between the two acquisition records on the extracted geological type, where Q1 is the quality feature value of one acquisition record on the extracted geological type, Q2 is the quality feature value of the other acquisition record on the extracted geological type, η1 is the effective information ratio of the acquisition record with quality feature value Q1, and η2 is the effective information ratio of the acquisition record with quality feature value Q2. Step S302: Obtain the quality assessment value Z1 of the acquisition record with quality characteristic value Q1, obtain the assessment deviation value σ=Δz / Z1 and the proportion of quality characteristics δ=Q1 / Z1 of the acquisition record, obtain the number of difference features contained in the acquisition record in terms of the extracted geological type as N1, and calculate the deviation degree p=(σ×δ) / N1 of the extracted same difference features; obtain the deviation degree of each same difference feature between any two acquisition records, and calculate the average deviation degree p of each difference feature. ave ; Step S303: Define the extracted identical difference feature as the j-th identical difference feature. If the similarity between the j-th identical difference feature and any geological feature in one of the acquisition records is less than the preset difference threshold, then obtain the deviation p of the identical difference feature extracted from one of the acquisition records. j =-(p ave ) j Otherwise p j =(p ave ) j ; Step S304: Randomly select a data collection record, extract the various differential features contained in the selected data collection record, and obtain the average deviation p of each differential feature. ave Obtain the quality assessment value Z of the selected collection records and construct an anomaly assessment model: ； Where k is a positive integer and k∈(1,b), b is the number of differential features contained in the selected collection records, and p k Let Y be the degree of deviation of the k-th difference feature; calculate the feature evaluation value Y of the selected collection records; Step S305: If the selected collection record has an anomaly marker, then set the anomaly evaluation value Y as an anomaly evaluation value; obtain the anomaly evaluation values ​​of all collection records with anomaly markers, and select the anomaly evaluation value with the smallest value as the anomaly identification threshold Y. th ; Step S400: Extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; identify anomalies in the presentation of each geological feature through an anomaly assessment model. Specifically, it includes: Step S401: When a real-time acquisition record is generated for an exploration area, the geological information stored in the real-time acquisition record is extracted to determine the geological type and geological features; each geological feature is matched with various geological types to obtain a set of geological features for each geological type. Step S402: Extract the expected feature set for each geological type from the feature matching database, compare the geological feature set of any geological type with the expected feature set, if there is an expected feature in the expected feature set that is not included in the geological feature set, then set the expected feature as the first anomalous feature; if there is a geological feature in the geological feature set that is not included in the expected feature set, and the geological feature is a difference feature in the other collected records, then set the geological feature as the second anomalous feature. Step S403: Obtain the average deviation p when each geological feature or expected feature is used as a difference feature. ave If it belongs to the first abnormal feature, then the actual deviation degree p is obtained. ac =p ave If it belongs to the second abnormal feature, then the actual deviation degree p is obtained. ac =-p ave ; Step S404: Evaluate the geological information in the real-time acquisition records according to preset quality assessment rules to obtain the quality characteristic values ​​of various geological types and the quality assessment value Z of the real-time acquisition records. ac The feature evaluation value Y is calculated by using an anomaly evaluation model to obtain the geological features in the real-time acquisition records. ac If Y ac >Y th If an anomaly is detected, the real-time data collection record will be marked as abnormal, and an anomaly alert will be sent.

2. The geological comprehensive information analysis method for gold mine exploration according to claim 1, characterized in that: Step S100 includes the following steps: Step S101: Collect and organize geological and mineral data from various channels to obtain geological information of various geological types. Extract features from the geological information of any geological type to obtain the corresponding feature set. Generate a feature matching database to match any geological type with the corresponding features. Step S102: Use a geological information acquisition device to collect geological information of the exploration area, preprocess the geological information to obtain effective information of the exploration area, and extract features from the effective information to obtain several geological features of the exploration area; use a geological sampling device to collect samples of the exploration area, and identify the geological type of the exploration area by identifying the type of the collected samples. Step S103: Summarize the geological types identified in the exploration area and the extracted geological features to obtain a record of the geological information collection process in the exploration area.

3. The geological comprehensive information analysis method for gold mine exploration according to claim 2, characterized in that: Step S200 includes the following steps: Step S201: Set any selected collection record as the target collection record, and obtain the geological type and geological features recorded in the target collection record; arbitrarily select a geological type and compare it with the geological type in the feature matching database. If the same geological type exists in the feature matching database, extract several features matching the same geological type to obtain the feature set of the same geological type. Step S202: Randomly select a geological feature from the target acquisition record and compare its similarity with any feature in the feature set of the same geological type. If the similarity between the selected geological feature and the feature is greater than or equal to a preset similarity threshold, then the selected geological feature is considered a valid feature of the same geological type. The number of valid features corresponding to the same geological type in the target acquisition record is set to N. eff The percentage of valid information of the same geological type in the target acquisition record is obtained as η=N. eff / N, where N is the number of features matched in the feature matching database for the same geological type; Step S203: Pre-set corresponding quality assessment rules for geological information corresponding to different geological types, extract geological information corresponding to any geological type in the target acquisition record and perform quality assessment to obtain the quality feature value Q of any geological type, wherein the quality feature value of the i-th geological type in the target acquisition record is set to Q. i According to the formula: ； Where i is a positive integer and i∈(1,a), a is the number of geological types contained in the target acquisition record, and η i The effective information proportion of the i-th geological type; the quality assessment value Z of the target acquisition record is calculated; a quality assessment threshold Z is preset. th If Z < Z th If so, the target acquisition record will be marked as abnormal; Step S204: Randomly select a geological type and two acquisition records. If both selected acquisition records contain the selected geological type, obtain the geological feature set corresponding to the selected geological type in each of the two acquisition records. Randomly select a geological feature from one of the geological feature sets and compare its similarity with all geological features in the other geological feature set. If the obtained similarity is less than a preset difference threshold, set the selected geological feature as a difference feature in one of the geological feature sets. Compare all geological features between the two acquisition records to obtain several difference features between the two acquisition records. Step S205: Randomly select one of the two acquisition records and arbitrarily select a difference feature. Compare the selected difference feature with the geological feature set of the other acquisition records containing the selected geological type. Count the number of times the selected difference feature is used as a difference feature to obtain the occurrence frequency of the selected difference feature. If the occurrence frequency is less than a preset frequency threshold, remove the selected difference feature from the difference features of the one acquisition record to obtain the difference feature set of the one acquisition record.

4. A comprehensive geological information analysis system for gold mine exploration, used to execute the comprehensive geological information analysis method for gold mine exploration as described in any one of claims 1-3, characterized in that: The analysis system includes a geological information analysis module, an information feature analysis module, an information risk assessment module, and an information anomaly identification module; The geological information analysis module is used to collect geological information of the exploration area in multiple dimensions using different methods, and to analyze and extract the geological types and geological characteristics of the exploration area. For each geological information collection process, a corresponding collection record is generated, and the geological type and geological characteristics in any collection record are matched; The information feature analysis module is used to arbitrarily select a collection record to analyze the feature matching situation and to evaluate the quality of the geological information of the collection record; and to compare and analyze any two collection records with the same geological type to obtain the difference features between the two collection records. The information risk assessment module is used to extract any two collection records containing the same difference features, analyze the degree of deviation of each same difference feature based on the quality assessment difference between the two collection records, and construct an anomaly assessment model and set anomaly identification standards according to the quality assessment of any collection record. The information anomaly identification module is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records and perform feature analysis; and identify anomalies in the presentation of each geological feature through an anomaly assessment model.

5. A comprehensive geological information analysis system for gold mine exploration according to claim 4, characterized in that: The geological information analysis module includes a geological information acquisition unit and a geological feature matching unit; The geological information acquisition unit is used to acquire geological information of the exploration area in multiple dimensions using different methods, analyze and extract the geological types and geological characteristics of the exploration area; the geological feature matching unit is used to generate corresponding acquisition records for each geological information acquisition process, and match the geological types and geological characteristics in any acquisition record.

6. The geological comprehensive information analysis system for gold mine exploration according to claim 4, characterized in that: The information feature analysis module includes an information quality assessment unit and a difference feature extraction unit; The information quality assessment unit is used to arbitrarily select a collection record to analyze the feature matching situation and assess the quality of the geological information of the collection record; the difference feature extraction unit is used to compare and analyze any two collection records with the same geological type to obtain the difference features between the two collection records.

7. The geological integrated information analysis system for gold mine exploration according to claim 4, characterized in that: The information risk assessment module includes a feature deviation analysis unit and an assessment model construction unit; The feature deviation analysis unit is used to extract any two collection records containing the same difference features, and to analyze the degree of deviation of each same difference feature based on the quality assessment difference between the two collection records; the evaluation model construction unit is used to construct an anomaly evaluation model and set anomaly identification criteria based on the quality assessment of any collection record.

8. A comprehensive geological information analysis system for gold mine exploration according to claim 4, characterized in that: The information anomaly identification module includes a real-time information analysis unit and an anomaly judgment and analysis unit; The real-time information analysis unit is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time collection records, and perform feature analysis; the anomaly judgment and analysis unit is used to identify anomalies in the presentation of various geological features through an anomaly evaluation model.

Images