Geological comprehensive information analysis system and method for gold mine exploration

Abstract

The invention discloses a geological comprehensive information analysis system and method for gold mine exploration, and relates to the technical field of information analysis, and the analysis method comprises the following steps: extracting the geological type and geological features of an exploration region, and generating a corresponding collection record; carrying out quality evaluation on the geological information which is randomly collected and recorded; performing comparative analysis on any two acquisition records with the same geological type to obtain difference features, and performing deviation degree analysis on each difference feature; constructing an anomaly evaluation model and setting an anomaly identification standard; performing geological feature extraction on geological information acquired in real time in the exploration area to generate a real-time acquisition record; performing anomaly identification on the presentation condition of each geological feature; by performing multi-dimensional analysis on the geological information collected in the exploration process, errors existing in traditional single index judgment can be eliminated, quality judgment can be more accurately and effectively performed on the geological information, and the exploration efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information analysis, and specifically to a geological comprehensive information analysis system and method for gold ore exploration. Background Art

[0002] With the continuous growth of the global demand for gold ore resources, the shallow and easily-prospected ore bodies are gradually exhausted, and it has become an inevitable trend for mineral exploration to extend towards deep and concealed ore bodies. However, traditional gold ore exploration technologies face multiple technical bottlenecks: surface geophysical and geochemical methods are limited by the attenuation of detection depth. For example, the resolution of electromagnetic methods drops by more than 15% below 500m, and shallow abnormal information is difficult to effectively indicate deep ore bodies. Although downhole exploration technologies can obtain the responses of target bodies at close range, existing methods have defects such as fragmented interpretation of multi-source data and insufficient geological basis, resulting in a low success rate of deep prospecting prediction. The original data obtained during the exploration process usually contains a large amount of noise and redundant information. Traditional filtering and dimensionality reduction methods are difficult to effectively balance information retention and noise elimination, resulting in the loss of key ore-forming signals. Traditional exploration risk assessments are mostly based on expert experience or single-index threshold judgments, lacking the ability to dynamically model multi-dimensional data and unable to quantify the impact of uncertain factors on resource prediction, seriously affecting the exploration efficiency and accuracy of gold ore exploration. Summary of the Invention

[0003] The purpose of the present invention is to provide a geological comprehensive information analysis system and method for gold ore exploration to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A geological comprehensive information analysis method for gold ore exploration, the analysis method includes the following steps: Step S100: Collect geological information of the exploration area in multiple dimensions in different ways, 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: Arbitrarily select a collection record to analyze the presented feature matching situation, and evaluate the quality of the geological information in the collection record; compare and analyze any two collection records with the same geological type to obtain the differential features between the two collection records; Step S300: Extract any two collection records containing the same differential features, analyze the deviation degree of each same differential feature based on the quality assessment difference between the two collection records; construct an anomaly assessment model according to the quality assessment situation of any collection record and set anomaly recognition criteria; Step S400: Extract geological features from the geological information collected in real time in the exploration area, generate a real-time collection record and conduct feature analysis; identify anomalies of the presentation of each geological feature through an anomaly assessment model.

[0005] Further, step S100 includes the following steps: Step S101: Collect and organize the geological and mineral resources data of each channel to obtain the geological information of various geological types, extract the features of 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; the geological and mineral resources data that can be collected include mineralization information, tectonic information, wall rock alteration information, contact zone tectonic information, and dike rock information. Through sorting and analysis, ore body information, ore-forming trend geological information, and equally spaced ore-controlling geological information can be obtained; with the increase of the footage of deep drilling and the presentation of the borehole mineralization intensity information by the results of basic analysis samples, combined with the sorting of previous geological and mineral resources data, ore body information such as spatial distribution, shape, scale, and occurrence can be obtained. Step S102: Use a geological information collection device to collect the geological information of the exploration area, preprocess the geological information to obtain the effective information of the exploration area, and extract the features of the effective information to obtain several geological features of the exploration area; use a geological sampling device to collect samples in the exploration area, and identify the geological type of the exploration area by identifying the types of the collected samples; a five-directional transmitting loop device can be used for surface-to-borehole transient electromagnetic measurement. Based on the obtained ore body information, guide the layout of surface-to-borehole transient electromagnetic measurement points and the sampling of structural superposition halo measurement, and conduct geological-geophysical and geological-geochemical analyses on the obtained results to interpret the abnormal geological body information reflected by the logging curve anomalies and structural geochemical anomalies. Step S103: Summarize the geological types identified in the exploration area and the several geological features extracted to obtain a primary collection record of the geological information collection process in the exploration area; comprehensively predict the geophysical, geochemical, and geological ore body information, ore-forming law information, equally spaced ore-controlling geological information, and ore-forming trend geological information obtained from the previous work in the prediction area, evaluate the rationality of geophysical and geochemical information, and then apply reliable geophysical, geochemical, and geological information to carry out deep ore body positioning prediction and evaluate the deep prospecting space and ore-forming potential.

[0006] Further, step S200 includes the following steps: Step S201: Set an arbitrarily selected acquisition record as the target acquisition record, and obtain the geological type and geological features recorded in the target acquisition record; arbitrarily select a geological type and compare it with the geological types in the feature matching database. If there is the same geological type 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: Arbitrarily select a geological feature from the target acquisition record and compare its similarity with any one feature in the feature set of the same geological features. If the similarity between the selected geological feature and the feature is greater than or equal to the preset similarity threshold, then the selected geological feature is used as a valid feature of the same geological type; set the number of valid features corresponding to the same geological type in the target acquisition record as N eff , and obtain the effective information ratio η = N eff / N, where N is the number of features matched by the same geological type in the feature matching database; the effective information ratio indicates the number of effective information in the collected geological information for helping to identify the geological type, and the information acquisition quality can be reflected to a certain extent through the effective information ratio; Step S203: Preset corresponding quality assessment rules for the geological information corresponding to different geological types respectively, extract the geological information corresponding to any geological type in the target acquisition record and conduct quality assessment to obtain the quality characteristic value Q of any geological type. Among them, set the quality characteristic value of the i-th geological type in the target acquisition record as Q i ; The quality assessment rules include the integrity assessment of data and the accuracy assessment of data. For example, the deviation degree of the location coordinates and the deviation degree of the measurement accuracy, etc. By evaluating these data, the quality characteristic value can be obtained through the preliminary quality detection of the geological information, which can reflect the accuracy and reliability of the collected geological information; According to the formula: ; where i is a positive integer and i ∈ (1, a), a is the number of geological types included in the target acquisition record, and η i is the effective information ratio of the i-th geological type; calculate the quality assessment value Z of the target acquisition record; preset a quality assessment threshold Z th , if Z < Z th , then mark the target acquisition record as abnormal; further evaluating the information quality of the effective information on the basis of the effective information ratio can obtain the information acquisition quality more accurately; Step S204: Arbitrarily select a geological type and two acquisition records. If the selected geological type is included in both of the two selected acquisition records, respectively obtain the set of geological features corresponding to the selected geological type in the two acquisition records; arbitrarily select a geological feature from one of the sets of geological features, and perform similarity comparison with all geological features in the other set of geological features. If the obtained similarities are all less than a preset difference threshold, set the selected geological feature as a difference feature of one of the sets of geological features; perform pairwise comparison on all geological features between the two acquisition records respectively, and obtain several difference features between the two acquisition records respectively. Step S205: Arbitrarily select one of the two acquisition records, and arbitrarily select a difference feature. Compare the selected difference feature with the sets of geological features of the other acquisition records that contain the selected geological type, and count the number of times the selected difference feature appears 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 several difference features of the selected acquisition record to obtain the set of difference features of the selected acquisition record. Merely relying on the difference features extracted between two acquisition records will be accidental and cannot be used as the overall difference features for analysis. Therefore, it is necessary to analyze the occurrence frequency of the difference features to determine whether the difference features actually affect the quality of geological information acquisition.

[0007] Further, step S300 includes the following steps: Step S301: Extract the geological types where any same difference feature is located from the two acquisition records, and respectively extract the quality characteristic values and the proportion of effective information presented by the extracted geological types in the two acquisition records, to obtain the quality assessment difference Δz = Q1×η1 - Q2×η2 between the two acquisition records on the extracted geological type, where Q1 is the quality characteristic value of one of the acquisition records on the extracted geological type, Q2 is the quality characteristic value of the other acquisition record on the extracted geological type, η1 is the proportion of effective information of the acquisition record with the quality characteristic value Q1, and η2 is the proportion of effective information of the acquisition record with the quality characteristic value Q2. Step S302: Obtain the quality assessment value Z1 of the acquisition record with the quality characteristic value Q1, obtain the assessment deviation value σ = Δz / Z1 and the quality characteristic proportion δ = Q1 / Z1 of the acquisition record, obtain the number N1 of difference features included in the extracted geological type in the acquisition record, and calculate the deviation degree p = (σ×δ) / N1 of the extracted same difference feature; 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 by taking the average. ave; Based on the proportion of various geological types in the quality assessment, allocate the deviation proportion of the overall assessment value, and then evenly distribute it through the differential features included in the geological type to obtain a more accurate degree of deviation; Step S303: Set the extracted identical differential feature as the j-th identical differential feature. If the similarity between the j-th identical differential feature and any geological feature in one of the acquisition records is less than the preset differential threshold, then obtain the deviation degree p of the identical differential feature extracted from one of the acquisition records j =-(p ave ) j , otherwise p j =(p ave ) j ; The identical differential features between two acquisition records are mutual and are caused by the existence of one record and the non-existence of the other. Although the corresponding geological feature does not actually exist in one record, it has an impact on the geological information quality assessment because one has a quality improvement effect and the other has a quality reduction effect, resulting in positive and negative differences in the differential features in different acquisition records; Step S304: Arbitrarily select one acquisition record, extract each differential feature included in the selected acquisition record, and obtain the average deviation degree p of each differential feature ave ; Obtain the quality assessment value Z of the selected acquisition record, and construct an anomaly assessment model: ; where k is a positive integer and k ∈ (1, b), b is the number of differential features included in the selected acquisition record, and p k is the deviation degree of the k-th differential feature; calculate the feature assessment value Y of the selected acquisition record; the degree of difference will cause a deviation in the quality assessment of the acquisition record, so the anomaly assessment model can be used for correction, so as to more accurately identify the information anomaly situation of the acquisition record and more effectively judge whether the collected information is reliable; Step S305: If the selected acquisition record has an anomaly mark, set the anomaly assessment value Y to an anomaly assessment value; obtain the anomaly assessment values of all acquisition records with anomaly marks, and select the smallest anomaly assessment value as the anomaly identification threshold Y th .

[0008] Further, step S400 includes the following steps: Step S401: When a real-time acquisition record is generated by real-time acquisition of an exploration area, extract the geological type and geological features of the geological information stored in the real-time acquisition record; match each geological feature with various geological types to obtain the geological feature set of each geological type; Step S402: Extract the expected feature set of each geological type from the feature matching database, compare the geological feature set of any geological type with the expected feature set. If there are expected features in the expected feature set that are not included in the geological feature set, set the expected features as the first abnormal features. If there are geological features in the geological feature set that are not included in the expected feature set and the geological features are differential features in the remaining acquisition records, set the geological features as the second abnormal features; Step S403: Obtain the average deviation degree p when each geological feature or expected feature is a differential feature ave , if it belongs to the first abnormal feature, obtain the actual deviation degree p ac =p ave , if it belongs to the second abnormal feature, obtain the actual deviation degree p ac =-p ave ; Step S404: Evaluate the geological information in the real-time acquisition record according to the preset quality evaluation rules respectively to obtain the quality characteristic values of various geological types and the quality evaluation value of the real-time acquisition record; Calculate the feature evaluation value Y of each geological feature in the real-time acquisition record through the abnormal evaluation model ac , if Y ac >Y th , then mark the real-time acquisition record as abnormal and send an abnormal reminder.

[0009] A geological comprehensive information analysis system for gold ore exploration, 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 the geological information of the exploration area in multiple dimensions in different ways, analyze and extract the geological types and geological features of the exploration area; Generate corresponding acquisition records for each geological information acquisition process, and match the geological types and geological features in any acquisition record; The information feature analysis module is used to arbitrarily select an acquisition record to analyze the presented feature matching situation, and evaluate the quality of the geological information in the acquisition record; Compare and analyze any two acquisition records with the same geological type to obtain the differential features between the two acquisition records; The information risk assessment module is used to extract any two acquisition records containing the same differential features, analyze the deviation degree of each same differential feature based on the quality assessment difference between the two acquisition records; According to the quality assessment situation of any acquisition record, construct an abnormal evaluation model and set abnormal identification criteria; An information anomaly recognition 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 conduct feature analysis; and perform anomaly recognition on the presentation of each geological feature through an anomaly evaluation model.

[0010] Furthermore, the geological information analysis module includes a geological information collection unit and a geological feature matching unit; The geological information collection unit is used to collect geological information in multiple dimensions in the exploration area in different ways, analyze and extract the geological types and geological features of the exploration area; the geological feature matching unit is used to generate corresponding collection records for each geological information collection process, and match the geological types and geological features in any collection record.

[0011] Furthermore, the information feature analysis module includes an information quality evaluation unit and a difference feature extraction unit; The information quality evaluation unit is used to randomly select a collection record to analyze the feature matching situation presented, and evaluate the quality of the geological information in 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.

[0012] Furthermore, the information risk assessment module includes a feature deviation analysis unit and an evaluation model construction unit; The feature deviation analysis unit is used to extract any two collection records containing the same difference features, and analyze the deviation degree of each same difference feature based on the quality evaluation difference between the two collection records; the evaluation model construction unit is used to construct an anomaly evaluation model and set anomaly recognition criteria according to the quality evaluation situation of any collection record.

[0013] Furthermore, the information anomaly recognition module includes a real-time information analysis unit and an anomaly judgment 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 conduct feature analysis; the anomaly judgment analysis unit is used to perform anomaly recognition on the presentation of each geological feature through an anomaly evaluation model.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. By conducting multi-dimensional analysis on the geological information collected during the exploration process, and analyzing the influence of each geological feature on the gold ore exploration result, the present invention can get rid of the errors existing in the traditional single-index judgment, more accurately and effectively judge the quality of geological information, and improve the exploration efficiency; 2. By analyzing the differences between different acquisition records, the present invention accurately analyzes the deviation of each difference feature, effectively evaluates the accuracy of geological information, helps exploration personnel make a more accurate judgment on the geological conditions of the area, and greatly improves the exploration efficiency of exploration personnel. 3. The present invention extracts the features affecting geological information, identifies the relevant information for judging the geological type of the exploration area, thereby can identify the information missing situation of the collected geological information, makes a preliminary prediction of the resources, and preliminarily evaluates the exploration situation through an anomaly judgment model, which can improve the exploration efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the steps of a geological comprehensive information analysis method for gold ore exploration; Figure 2 It is a schematic diagram of the structure of a geological comprehensive information analysis system for gold ore exploration; Figure 3 It is a geological map of a gold ore belt; Figure 4 It is a curve graph of the abnormal section of the three-component normalized induced electromotive force of the T1 coil in the borehole-ground transient electromagnetic measurement of the 88ZK09 borehole; Figure 5 It is an interpretation diagram of the abnormal section of the five-direction normalized induced electromotive force curve of the T1 coil of the 88ZK09 borehole. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment: As Figures 1 to 5 shown, the present invention provides a geological comprehensive information analysis method for gold ore exploration. The analysis method includes the following steps: Step S100: Collect geological information of the exploration area in multiple dimensions by different methods, analyze and extract the geological type and geological features of the exploration area; generate corresponding acquisition records for each geological information acquisition process, and match the geological type and geological features in any acquisition record; Among them, step S100 includes the following steps: Step S101: Collect and organize geological and mineral resources information 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, and generate a feature matching database to match any geological type with the corresponding features; Step S102: Use a geological information collection device to collect geological information in the exploration area. After preprocessing the geological information, obtain the effective information of the exploration area, and extract several geological features of the exploration area from the effective information; Use a geological sampling device to collect samples in the exploration area, and identify the geological type of the exploration area by identifying the types of the collected samples; Step S103: Summarize the geological type identified in the exploration area and the several geological features extracted to obtain a primary collection record of the geological information collection process in the exploration area.

[0018] Step S200: Arbitrarily select a collection record to analyze the presented feature matching situation and evaluate the quality of the geological information in the collection record; Compare and analyze any two collection records with the same geological type to obtain the differential features between the two collection records; Among them, Step S200 includes the following steps: Step S201: Set the arbitrarily 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 types in the feature matching database. If there is the same geological type 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: Arbitrarily select a geological feature from the target collection record and compare its similarity with any one 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 the preset similarity threshold, then regard the selected geological feature as a valid feature of the same geological type; Set the number of valid features corresponding to the same geological type in the target collection record as N eff , and obtain the proportion η of the valid information of the same geological type in the target collection record as η = N eff / N, where N is the number of features matched by the same geological type in the feature matching database; Step S203: Preset corresponding quality evaluation rules for the geological information corresponding to different geological types respectively. Extract the geological information corresponding to any geological type in the target collection record and conduct quality evaluation to obtain the quality characteristic value Q of any geological type. Among them, set the quality characteristic value of the i-th geological type in the target collection record as Q i ; According to the formula: ; wherein, i is a positive integer and i ∈ (1, a), a is the number of geological types included in the target acquisition record, and η i is the proportion of valid information of the i-th geological type; the quality evaluation value Z of the target acquisition record is calculated; a quality evaluation threshold Z th is preset. If Z < Z th , then the target acquisition record is marked as abnormal; Step S204: Arbitrarily select a geological type and two acquisition records. If both of the selected two acquisition records contain the selected geological type, then respectively obtain the geological feature sets corresponding to the selected geological type in the two acquisition records; arbitrarily select a geological feature from one of the geological feature sets, and perform similarity comparison with all the geological features in the other geological feature set. If the obtained similarities are all less than the preset difference threshold, then set the selected geological feature as a difference feature of one of the geological feature sets; respectively perform mutual comparison on all the geological features between the two acquisition records, and respectively obtain several difference features between the two acquisition records; Step S205: Arbitrarily 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 that contain the selected geological type, and count the number of times the selected difference feature serves as a difference feature to obtain the occurrence frequency of the selected difference feature. If the occurrence frequency is less than the preset frequency threshold, then remove the selected difference feature from the several difference features of the one acquisition record to obtain the difference feature set of the one acquisition record.

[0019] Step S300: Extract any two acquisition records that contain the same difference feature, and perform deviation degree analysis on each same difference feature based on the quality evaluation difference between the two acquisition records; construct an abnormal evaluation model and set an abnormal recognition standard according to the quality evaluation situation of any acquisition record; Among them, Step S300 includes the following steps: Step S301: Extract the geological type where any same difference feature is located from the two acquisition records, and respectively extract the quality feature value and the proportion of valid information presented by the extracted geological type in the two acquisition records, to obtain the quality evaluation difference Δz = Q1×η1 - Q2×η2 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 proportion of valid information of the acquisition record with the quality feature value Q1, and η2 is the proportion of valid information of the acquisition record with the quality feature value Q2; Step S302: Obtain the quality evaluation value Z1 of the acquisition record with the quality characteristic value Q1, obtain the evaluation deviation value σ = Δz / Z1 and the quality characteristic ratio δ = Q1 / Z1 of the acquisition record, obtain the number N1 of differential features included in the extracted geological type in the acquisition record, and calculate the deviation degree p = (σ×δ) / N1 of the extracted identical differential features; obtain the deviation degree of each identical differential feature between any two acquisition records, and calculate the average deviation degree p of each differential feature ave ; Step S303: Set the extracted identical differential feature as the j-th identical differential feature. If the similarity between the j-th identical differential feature and any geological feature in one of the acquisition records is less than the preset difference threshold, obtain the deviation degree p of the identical differential feature extracted in one of the acquisition records j = -(p ave ) j , otherwise p j =(p ave ) j ; Step S304: Arbitrarily select an acquisition record, extract each differential feature included in the selected acquisition record, and obtain the average deviation degree p of each differential feature ave ; Obtain the quality evaluation value Z of the selected acquisition record, and construct an anomaly evaluation model: ; where k is a positive integer and k ∈ (1, b), b is the number of differential features included in the selected acquisition record, p k is the deviation degree of the k-th differential feature; calculate the feature evaluation value Y of the selected acquisition record; Example: Set the quality evaluation value of the acquisition record to 80, and there are 3 differential features in the acquisition record, and the deviation degrees are 0.1, -0.2, and 0.3 respectively. Calculate the feature evaluation value Y of the acquisition record: Y = 1 / (80×0.9×1.2×0.7) = 1 / 60.48 = 0.0165; Step S305: If the selected acquisition record has an anomaly mark, set the anomaly evaluation value Y to an anomaly evaluation value; obtain the anomaly evaluation values of all acquisition records with anomaly marks, and select the smallest anomaly evaluation value as the anomaly recognition threshold Y th .

[0020] Step S400: Extract geological features from the geological information collected in real time in the exploration area, generate real-time acquisition records and perform feature analysis; identify anomalies of the presentation of each geological feature through the anomaly evaluation model; Among them, step S400 includes the following steps: Step S401: When a real-time acquisition record is generated by real-time acquisition of a prospecting area, extract the geological type and geological features from the geological information stored in the real-time acquisition record; match each geological feature 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 not included in the geological feature set in the expected feature set, set the expected feature as the first abnormal feature. If there is a geological feature not included in the expected feature set in the geological feature set, and the geological feature is a differential feature in the remaining acquisition records, set the geological feature as the second abnormal feature; Step S403: Obtain the average deviation degree p when each geological feature or expected feature is a differential feature ave , if it belongs to the first abnormal feature, obtain the actual deviation degree p ac =p ave , if it belongs to the second abnormal feature, obtain the actual deviation degree p ac =-p ave ; Step S404: Evaluate the geological information in the real-time acquisition record according to the preset quality evaluation rules respectively to obtain the quality characteristic values of various geological types and the quality evaluation value Z of the real-time acquisition record ac ; Calculate the feature evaluation value Y of each geological feature in the real-time acquisition record through the anomaly evaluation model ac , if Y ac >Y th , then mark the real-time acquisition record as abnormal and send an anomaly reminder.

[0021] A geological comprehensive information analysis system for gold ore prospecting, 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 the geological information of the prospecting area in multiple dimensions in different ways, analyze and extract the geological type and geological features of the prospecting area; generate corresponding acquisition records for each geological information acquisition process, and match the geological type and geological features in any acquisition record; The information feature analysis module is used to arbitrarily select an acquisition record to analyze the presented feature matching situation, and evaluate the quality of the geological information in the acquisition record; compare and analyze any two acquisition records with the same geological type to obtain the differential features between the two acquisition records; An information risk assessment module, which is used to extract any two acquisition records containing the same differential features, analyze the deviation degree of each same differential feature based on the quality assessment difference between the two acquisition records; construct an abnormal assessment model and set abnormal identification criteria according to the quality assessment situation of any acquisition record. An information anomaly identification module, which is used to extract geological features from the geological information collected in real time in the exploration area, generate real-time acquisition records and conduct feature analysis; identify anomalies of the presentation of each geological feature through the anomaly assessment model.

[0022] Among them, 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 collect geological information in multiple dimensions in the exploration area in different ways, analyze and extract the geological types and geological features 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 features in any acquisition record.

[0023] Among them, the information feature analysis module includes an information quality assessment unit and a differential feature extraction unit; The information quality assessment unit is used to arbitrarily select an acquisition record to analyze the feature matching situation presented, and conduct quality assessment on the geological information of the acquisition record; the differential feature extraction unit is used to compare and analyze any two acquisition records with the same geological type to obtain the differential features between the two acquisition records.

[0024] Among them, 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 acquisition records containing the same differential features, analyze the deviation degree of each same differential feature based on the quality assessment difference between the two acquisition records; the assessment model construction unit is used to construct an abnormal assessment model and set abnormal identification criteria according to the quality assessment situation of any acquisition record.

[0025] Among them, the information anomaly identification module includes a real-time information analysis unit and an anomaly judgment 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 acquisition records and conduct feature analysis; the anomaly judgment analysis unit is used to identify anomalies of the presentation of each geological feature through the anomaly assessment model.

[0026] Embodiment of the surface-to-borehole transient electromagnetic method: The five-direction transmitting loop device is used for measurement. Five transmitting frames are arranged respectively centered on the borehole, and data acquisition work is carried out respectively, such as Figure 3As shown in the figure; the instrument uses the PROTEM transient electromagnetic instrument produced by GEONICS Company in Canada, with the instrument model being PROTEM57, including a 57-type transmitter, a 57-type receiver, and a BH43-3 borehole three-component transient electromagnetic probe. The instrument power supply uses a 5kW Honda Yamaha generator. According to the five-direction transmitting loop device, data is collected for the transmitting frame centered on the borehole respectively; Based on the comprehensive analysis of the target body depth, the electrical parameters of the surrounding rock, and the performance of the transmitting system, etc., it is finally determined that the side length of the transmitting loop for this time is 600m; the surface-borehole transient electromagnetic measurement uses a five-direction transmitting loop device, and five 600m×600m transmitting coils are respectively arranged centered on the borehole, and information such as the geological structure and mineralization characteristics at a depth of 2000m can be obtained; For surface-borehole transient electromagnetic sounding, the receiving probe is located in the borehole, and the detection range is within 200m centered on the wellhead. On the premise of meeting the signal-to-noise ratio, the fundamental frequency of this transmission is selected as 25Hz. On the premise of meeting the signal-to-noise ratio, the fundamental frequency of this transmission is selected as 25Hz, and the detection range is extrapolated 200m centered on the wellhead; according to the geological characteristics of the 88ZK09 borehole, it is preliminarily determined that 1650m to 2150m is the abnormal section, and 0 to 1650m is the background area. The measuring point spacing in the abnormal section is 1m, and the 5m point spacing is adopted for measurement in the background area. The single-point acquisition time is 0.0881ms to 6.978ms, the sampling channels are 20 channels, and the transmitting current is 15A; First, a 600×600m transmitting frame is arranged centered on the borehole, numbered T1. Secondly, the T2, T3, T4, and T5 transmitting frames are respectively arranged, and data collection work is carried out respectively. The T2, T3, T4, and T5 transmitting frames are respectively located in the north, east, south, and west directions of the T1 transmitting frame. Through the geological logging work of 88ZK09, it is preliminarily determined that 1650 to 2150m is the abnormal section, and the area between 0 and 1650m is the background area. The measuring point spacing in the abnormal section is 1m for measurement, and the 5m point spacing is adopted for measurement in the background section. XYZ three-component data are collected for each point in the five directions, and the result curves are drawn through the measurement results; through the measurement results of the five-direction transmitting frames, the normalized induced electromotive force curves of each channel of XYZ in different directions with the change of depth are drawn, that is, the current is normalized to the receiving area, unit: nv / m2A. The amplitude of a single curve reflects the strength distribution of the secondary field at different depths at the same moment after the transmitter is turned off. Both the horizontal and vertical coordinates of the curve adopt linear coordinates; The three-component normalized induced electromotive force curve patterns of the abnormal section of the T1 coil in borehole 88ZK09 drilling show differences: the X and Y component curve patterns are relatively similar, with large fluctuations, and the amplitudes are between 0 nv / (A·m2) and 300 nv / (A·m2); the amplitude of the Z component is between 0 nv / (A·m2) and 80 nv / (A·m2); each channel curve is divided into early, middle, and late stage signals according to the number of sampling channels: channels 1 to 7 are defined as the early stage, and the acquisition time is 0.0881ms to 0.3144ms; channels 8 to 14 are defined as the middle stage, and the acquisition time is 0.3956ms to 1.6360ms; channels 15 to 20 are defined as the late stage, and the acquisition time is 2.081ms to 6.978ms; the amplitudes of the 7-channel curves in the middle stage are higher than those of the 7-channel curves in the early stage, and the late stage curves gradually decay to zero, as Figure 4 shown.

[0027] As Figure 5 shown, take the signals of channels 6 to 13 of the X component with large fluctuations and high amplitudes of the induced electromotive force curve of the five-azimuth coil for analysis; the T1 coil is located at the borehole position of 88ZK09, and the curve characteristics of the surrounding four-azimuth coils are similar to it. It is speculated that the altered zone and ore body of borehole 88ZK09 are stably extended within 200m of the borehole extrapolation along the strike and dip; overlay the description results of the borehole structural alteration mineralization with the T1 coil curve, and the curve segments corresponding to the altered zone and ore body are used as the ore-forming indication segments, that is, the hole depth is 1750m to 2060m; the curve segments with similar characteristics of the surrounding four-azimuth coils are used as their respective ore-forming indication segments; the depth of the ore-forming indication segment of the T3 coil logging curve of the shallow known altered zone and ore body is 1650m to 1970m, which is connected to the altered zone of the T1 coil, and the spatial distribution of the altered zone along the azimuth of the connection line of the two coils can be inferred; it is inferred that the apparent dip angle of the altered zone in the cross-section of the connection line of the T1 and T3 coils is basically the same as the occurrence of the known altered zone, indicating that the logging results can reflect the characteristics of the altered zone; the ore-forming indication segments of the induced electromotive force curves of the deep T5 coil and the strike T2 and T4 coils can be used as the speculated spatial positions of the occurrence of the altered zone and ore body extrapolated 200m from 88ZK09 to the corresponding coil azimuth. The depths of the abnormal sections of the T2 and T4 coils are basically the same as those of the T1 coil, that is, 1750m to 2060m, which respectively represent the predicted occurrence depths of the altered zone extrapolated 200m north and south from 88ZK09. The depth of the abnormal section of the T5 coil logging curve is 1840m to 2150m, indicating the deep predicted extension part of the altered zone extrapolated 200m west from 88ZK09; The results of the surface-to-borehole transient electromagnetic measurement show that the ore-forming indication segment of the induced electromotive force curve of the deep T5 coil still has similar characteristics to the ore-forming indication segment of the T1 coil logging curve in borehole 88ZK09 drilling. It is inferred that the characteristics of the deep altered zone and ore body are similar to those of the exposed parts; based on geological understandings such as ore-forming laws, trend extrapolation, and equally spaced ore control, it is speculated that the thick and large ore body exposed by borehole 88ZK09 is the front ore head part of the deep rich ore section.

[0028] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for analyzing geological comprehensive information for gold mine exploration, characterized in that: The analytical method comprises the following steps: Step S100: adopt different methods to collect geological information of the exploration area in multiple dimensions, analyze and extract the geological type and geological characteristics of the exploration area; generate corresponding collection records for each geological information collection process, and match the geological type and geological characteristics in any collection record; Step S200: arbitrarily select a collection record to analyze the feature matching presented, and perform quality assessment on 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 acquisition records containing the same difference features, and analyze the degree of deviation of each of the same difference features based on the quality assessment difference between the two acquisition records; construct an anomaly assessment model and set an anomaly recognition standard according to the quality assessment of any acquisition record; Step S400: extract geological features from the geological information collected in real time in the survey area, generate real-time collection records and perform feature analysis; identify anomalies based on the presentation of each geological feature through an anomaly assessment model.

2. The method for analyzing geological comprehensive information for gold mine exploration according to claim 1, characterized in that: The 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 a corresponding feature set, and generate a feature matching database to match any geological type with corresponding features; Step S102: using a geological information acquisition device to acquire geological information of the survey area, pre-processing the geological information to obtain effective information of the survey area, and extracting features from the effective information to obtain several geological features of the survey area; using a geological sampling device to acquire samples of the survey area, and obtaining the geological type of the survey area by identifying the types of the acquired samples; Step S103: Summarize the geological types identified in the survey area and the extracted geological features to obtain a collection record of the geological information collection process in the survey area.

3. A method for analyzing geological comprehensive information for gold mine exploration according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: setting an arbitrarily selected acquisition record as a target acquisition record, obtaining the geological type and geological features recorded in the target acquisition record; arbitrarily selecting a geological type to compare with the geological type in the feature matching database, if the same geological type exists in the feature matching database, extracting several features matching the same geological type to obtain a feature set of the same geological type; Step S202: randomly select a geological feature from the target acquisition record, and compare the 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, the selected geological feature is used as 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 effective information proportion 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: Preset corresponding quality assessment rules for geological information corresponding to different geological types, extract the geological information corresponding to any geological type in the target acquisition record and perform quality assessment to obtain the quality characteristic value Q of any geological type, wherein the quality characteristic value of the i-th geological type in the target acquisition record is set to Q i ; According to the formula: ; Wherein, i is a positive integer and i∈(1,a), a is the number of geological types contained in the target acquisition record, η i is the effective information proportion of the i-th geological type; calculate the quality assessment value Z of the target acquisition record; preset a quality assessment threshold Z th , if Z<Z th , then the target collection record is marked as abnormal; Step S204: arbitrarily select a geological type and two acquisition records. If the selected geological type is included in both of the selected acquisition records, then obtain the geological feature sets corresponding to the selected geological type in the two acquisition records respectively; arbitrarily select a geological feature from one of the geological feature sets, and compare the similarity with all geological features in the other geological feature set. If the obtained similarities are less than a preset difference threshold, then set the selected geological feature as a difference feature of the one of the geological feature sets; compare all the geological features between the two acquisition records respectively, and obtain a number of difference features between the two acquisition records respectively; Step S205: arbitrarily 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 remaining acquisition records containing the selected geological type, count the number of times the selected difference feature is used as the difference feature, and 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 plurality of difference features of the one of the acquisition records to obtain the difference feature set of the one of the acquisition records.

4. The method for analyzing geological comprehensive information for gold mine exploration according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: extracting the geological type where any identical difference feature is located from the two acquisition records, respectively extracting the quality characteristic value and the effective information proportion of the geological type presented in the two acquisition records, and obtaining the quality assessment difference Δz=Q1×η1-Q2×η2 between the two acquisition records on the extracted geological type, wherein Q1 is the quality characteristic value of one of the acquisition records on the extracted geological type, Q2 is the quality characteristic value of the other acquisition record on the extracted geological type, η1 is the effective information proportion of the acquisition record with the quality characteristic value of Q1, and η2 is the effective information proportion of the acquisition record with the quality characteristic value of Q2; Step S302: Obtain the quality assessment value Z1 of the acquisition record with the quality characteristic value Q1, obtain the assessment deviation value σ=Δz / Z1 and the quality characteristic proportion δ=Q1 / Z1 of the acquisition record, obtain the number of difference characteristics contained in the extracted geological type in the acquisition record as N1, and calculate the deviation degree p=(σ×δ) / N1 of the extracted same difference characteristics; obtain the deviation degree of each same difference characteristic between any two acquisition records, and calculate the average value to obtain the average deviation degree p of each difference characteristic ave ; Step S303: Set the extracted identical difference feature as the jth identical difference feature. If the similarity between the jth identical difference feature and any geological feature in one of the acquisition records is less than a preset difference threshold, then obtain the deviation degree p of the identical difference feature extracted in one of the acquisition records. j =-(p ave ) j , otherwise p j =(p ave ) j ; Step S304: randomly select a collection record, extract the various difference features contained in the selected collection record, and obtain the average deviation degree p of each difference feature. ave ; Get the quality assessment value Z of the selected collection record and build an abnormal assessment model: ; Where k is a positive integer and k∈(1,b), b is the number of difference features contained in the selected collection records, and p k is the deviation degree of the kth difference feature; the feature evaluation value Y of the selected collection record is calculated; Step S305: If the selected collection record has an abnormal mark, set the abnormal assessment value Y as an abnormal assessment value; obtain the abnormal assessment values ​​of all collection records with abnormal marks, select the abnormal assessment value with the smallest value and set it as the abnormal identification threshold Y th .

5. The method for analyzing geological comprehensive information for gold mine exploration according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: when a real-time acquisition record is generated for a survey area, the geological type and geological features are extracted from the geological information stored in the real-time acquisition record; each geological feature is matched with various geological types to obtain a set of geological features for each geological type; Step S402: extracting an expected feature set for each geological type from a feature matching database, performing feature comparison between a geological feature set of any geological type and the expected feature set, and if the expected feature set contains an expected feature that is not contained in the geological feature set, setting the expected feature as a first abnormal feature; if the geological feature set contains a geological feature that is not contained in the expected feature set, and the geological feature is a differential feature in the remaining acquisition records, setting the geological feature as a second abnormal feature; Step S403: Obtain the average deviation degree p of each geological feature or expected feature 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 record according to the preset quality evaluation rules to obtain the quality characteristic values ​​of various geological types and the quality evaluation value Z of the real-time acquisition record. ac Calculate each geological feature in the real-time acquisition record through the anomaly assessment model to obtain a feature assessment value Y ac , if Y ac >Y th , the real-time collection record is marked as abnormal and an abnormal reminder is sent.

6. A geological comprehensive information analysis system for gold mine exploration, used to execute a geological comprehensive information analysis method for gold mine exploration according to any one of claims 1 to 5, 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 in different ways, analyze and extract the geological type and geological characteristics of the exploration area; Generate corresponding collection records for each geological information collection process, and match the geological type and geological characteristics in any collection record; The information feature analysis module is used to select any acquisition record to analyze the feature matching presented, and to perform quality assessment on the geological information of the acquisition record; to compare and analyze any two acquisition records of the same geological type, and to obtain the difference features between the two acquisition records; The information risk assessment module is used to extract any two collection records containing the same difference features, and based on the quality assessment difference between the two collection records, analyze the degree of deviation of each of the same difference features; according to the quality assessment of any collection record, build an anomaly assessment model to set anomaly identification standards; 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 based on the presentation of each geological feature through an anomaly assessment model.

7. A geological comprehensive information analysis system for gold mine exploration according to claim 6, 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 adopt different methods to perform multi-dimensional acquisition of geological information in the exploration area, analyze and extract the geological type and geological characteristics of the exploration area; the geological characteristic matching unit is used to generate corresponding acquisition records for each geological information acquisition process, and match the geological type and geological characteristics in any acquisition record.

8. The comprehensive geological information analysis system for gold mine exploration according to claim 6 is characterized by: 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 presented and perform quality assessment on the geological information of the collection record; the difference feature extraction unit is used to compare and analyze any two collection records of the same geological type to obtain the difference features between the two collection records.

9. The comprehensive geological information analysis system for gold mine exploration according to claim 6, characterized in that: The information risk assessment module includes a characteristic 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 feature, and based on the quality assessment difference between the two collection records, perform a deviation degree analysis on each of the same difference features; the evaluation model construction unit is used to construct an abnormal evaluation model and set an abnormal identification standard based on the quality assessment of any collection record.

10. A geological comprehensive information analysis system for gold mine exploration according to claim 6, characterized in that: The information anomaly identification module includes a real-time information analysis unit and an anomaly judgment 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 analysis unit is used to identify anomalies based on the presentation of each geological feature through an anomaly assessment model.

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