Gold exploration and prediction method in complex structure background based on background noise imaging
By constructing a high-resolution three-dimensional Vs model based on background noise imaging and combining it with the mineralization potential index, the exploration problem of deep gold ore bodies under complex tectonic backgrounds was solved, and the accurate location and evaluation of potential target areas were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHIFENG CHAIHULANZI GOLD MINING CO LTD
- Filing Date
- 2026-01-29
- Publication Date
- 2026-06-09
AI Technical Summary
Existing technologies are insufficient for efficiently and cost-effectively identifying and delineating deep, concealed gold deposits in complex tectonic settings, and lack methods for effectively coupling geophysical anomalies with metallogenic tectonic regularities.
A background noise-based imaging method is adopted. Noise signals are recorded through a three-dimensional receiving array, surface waves and phase velocities are extracted, a high-resolution three-dimensional Vs model is constructed, and superposition analysis is performed in combination with known mineralized structures to establish a mineralization potential index and quantify the potential of the target area.
It enables high-resolution imaging and potential evaluation of deep gold ore bodies under complex geological conditions, providing reliable guidance for prospecting target areas.
Smart Images
Figure CN122172333A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of geological exploration technology, specifically relating to a method for predicting gold deposits in complex tectonic settings based on background noise imaging. Background Technology
[0002] The formation and distribution of gold deposits, especially large and super-large gold deposits, are strictly controlled by complex metallogenic tectonic systems composed of deep faults, tectonic intersection zones, and magmatic contact zones. In complex tectonic settings such as orogenic belts and ancient craton margins, traditional geological prospecting methods have significant limitations in their ability to directly identify deep, concealed ore bodies.
[0003] Currently, the geophysical methods commonly used in exploration practice in this field mainly include the following two categories: 1. Traditional active-source seismic exploration: This method detects underground structures by artificially generating seismic waves. While it offers high resolution, it suffers from high implementation costs and low data acquisition efficiency in complex terrains and geological conditions such as mountainous and mining areas, and it struggles to obtain precise velocity structures of deep strata; 2. Gravity, magnetic, and electrical exploration: This encompasses techniques such as gravity measurement, magnetic measurement, and controlled-source audio-frequency magnetotellurics (CSAMT). These methods are sensitive to differences in lithology and electrical properties, but in complex tectonic regions, the results exhibit strong ambiguity and are insufficient in characterizing the occurrence, extension, and intersection of deep, fine structures—especially ore-controlling faults—making it difficult to directly reveal the tectonic framework closely related to mineralization.
[0004] Background noise imaging, an emerging passive source seismological technique, uses persistent background noise (such as ocean waves, wind, and human activities) as natural sources. It extracts surface wave signals through cross-correlation calculations between arrays of noise, and then inverts the three-dimensional subsurface shear wave velocity structure. This method boasts advantages such as low cost, being environmentally friendly and pollution-free, having no limitations on detection depth, and being sensitive to low-velocity anomalies (such as fractured rock zones, altered mineralization zones, and areas of subsurface fluid activity). It has been widely applied in fields such as crustal-upper mantle structure detection, volcanic activity monitoring, and the spatial distribution of fault zones.
[0005] Currently, there is no mature technical solution for systematically applying background noise imaging to the exploration and prediction of solid minerals, especially gold deposits, under complex metallogenic tectonic settings. Furthermore, existing technologies lack a complete methodology for effectively coupling high-resolution underground velocity anomalies with known metallogenic models and tectonic ore-controlling laws, and thereby quantitatively delineating prospecting target areas. Therefore, there is an urgent need to develop an exploration and prediction method that can effectively penetrate complex stratigraphic overburden, finely characterize the spatial morphology of ore-controlling structures, and achieve quantitative evaluation of three-dimensional mineralization potential for the exploration of deep, concealed gold ore bodies and target area prediction. Summary of the Invention
[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for predicting gold deposits in complex structural backgrounds based on background noise imaging.
[0007] The objective of this invention is achieved through the following technical solution: a method for predicting gold deposits in complex tectonic backgrounds based on background noise imaging, comprising the following steps: Step 1: Using passive source seismic technology based on natural background noise, a three-dimensional receiving array with variable station density is deployed in the exploration area to continuously record and preprocess the noise signal. Then, cross-correlation and superposition are performed, and the Green's function between stations is extracted. Subsequently, surface waves, phase velocities, and dispersion curves are extracted. Through three-dimensional surface wave tomography, a high-resolution three-dimensional Vs model can be constructed from tens of meters near the surface to several kilometers underground. Step 2: Based on the obtained 3D Vs model, calculate and identify low-velocity and high-velocity anomaly zones, perform superposition analysis of velocity anomalies with known fault and magma contact mineralization structures, and delineate velocity anomaly-structure coupling zones; establish a mineralization potential index based on multiple factors such as velocity anomaly amplitude, anomaly volume, and structural intersection angle. Step 3: Delineate high-potential target areas based on the potential index threshold, and output the area and anomaly type information on the GIS platform.
[0008] In step one, the preprocessing includes detrending, instrument response removal, filtering, and noise reduction.
[0009] In step two, establishing the mineralization potential index specifically includes the following steps: Extract each 3D mesh cell (or prediction cell). i Multiple quantitative evaluation indicators (including abnormal speed amplitude) , structural intersection density and distance from the rock mass To eliminate dimensions and make all indicators comparable, range standardization is used to standardize the values of each indicator. Mapping to the [0,1] interval yields the standardized score. The positive and negative indicators are as follows: Positive indicators: ; Negative indicators: ; in, For unit i In indicators j Standardized score; The value of the index is dimensionless. The choice between positive or negative index depends on the geological correlation between the original value of the index and the "mineralization favorability": if the index value (abnormal intensity, mineralization density) is larger, it directly represents that the mineralization conditions are more favorable, then positive standardization is used; if the index value (distance to fault, rock mass mineralization element) is smaller, it represents that the conditions are more favorable, then negative standardization is used. The final weights of each indicator are determined using a combination of subjective and objective weighting methods. : ① Subjective weight ( ): Determined through the Analytic Hierarchy Process (AHP) and expert judgment; ② Objective weight ( ): Calculated using the improved CRITIC method, which is based on the standard deviation of the index data. Correlation coefficient between indicators Calculate information content And thus obtain ; ③Combined weights: ,in This is the balance coefficient (usually taken as 0.5); Based on this mineralization potential index ( MPI ) Calculation, for each three-dimensional mesh element i The comprehensive mineralization potential index is calculated using the weighted summation formula: ; The initial three-dimensional potential model is obtained, with a value range of [0,1]. Finally, to quantify the impact of uncertainty on the results of key parameters (outlier threshold, weights), Monte Carlo simulation and uncertainty quantification were performed; a total of [number] operations were conducted. N Sub-Monte Carlo simulation; For the k The simulation ( k = 1 to N ): A set of sample values is randomly selected from the distribution of each parameter. Using this new set of parameters, a set of three-dimensional values is repeatedly calculated. MPI Data body Collect N values for each unit i across all simulations: Calculate the average potential: .
[0010] The beneficial effects of this invention are: 1) This invention combines low-cost, pollution-free background noise imaging with multi-band layered inversion technology to achieve simultaneous high-resolution imaging of shallow details and deep structures; 2) Geophysical anomalies are directly mapped to mineralization control factors through a velocity anomaly-tectonic coupling model; 3) Multi-factor weighted scoring and uncertainty quantification are introduced to provide objective and comparable numerical basis for target area delineation; 4) This invention can evaluate mineralization potential in three-dimensional space, delineate prospecting target areas, and realize the prediction of concealed gold ore bodies. Attached Figure Description
[0011] Figure 1 This is a flowchart of three-dimensional shear wave velocity imaging; Figure 2 This is a mineralization exploration delineation map; where A is the location map of the survey line within the delineated area; and B is the apparent shear wave velocity structure map below the survey line. Detailed Implementation
[0012] The present invention will now be described in detail with reference to the accompanying drawings. Example
[0013] A method for predicting gold deposits in complex tectonic backgrounds based on background noise imaging, comprising the following steps: Step 1: As Figure 1 As shown, in the exploration area of Songshan District, Chifeng City, Inner Mongolia Autonomous Region, passive source seismic technology based on natural background noise was used. By deploying a three-dimensional receiving array with variable station density in the exploration area, the noise signal was continuously recorded and preprocessed (including detrending, instrument response reduction, filtering, and denoising). Then, cross-correlation and superposition were performed, and the Green's function between stations was extracted. Subsequently, the surface wave dispersion curve was extracted, and a high-resolution three-dimensional shear wave velocity (Vs) model from near the surface to a depth of several kilometers was constructed through three-dimensional surface wave tomography.
[0014] The specific operation process is as follows: ① Based on the geological structure and the target exploration depth, a three-dimensional receiving array with variable station density will be deployed. A high-density array (50-200 meters between adjacent stations) will be used above key structural zones (known faults, rock mass contact zones), while a lower-density array (200-800 meters between adjacent stations) will be used in the peripheral areas. Simultaneous continuous observations will be conducted using a three-component seismometer, with a recording duration of no less than 30 days, to ensure sufficient acquisition of low-frequency environmental noise signals.
[0015] ② The acquired continuous waveform data undergoes standardized preprocessing, including detrending, instrument response removal, filtering, and denoising. Subsequently, the noise cross-correlation function between all station pairs is calculated, and the stable surface wave Green's function signal is enhanced through linear or phase-weighted superposition. Rayleigh surface waves are extracted from the superimposed cross-correlation signals, and the phase velocity dispersion curve of each station pair path is obtained using time-frequency analysis (multiple filtering method).
[0016] ③ Layered inversion is performed using the extracted dispersion curves. Considering the sensitivity differences between different frequency bands, high-frequency data is used to invert the fine structure of the shallow layer, while low-frequency data is used to invert the velocity framework of the deep layer. This yields one-dimensional shear wave velocity (Vs) profiles below the path for each station. All one-dimensional Vs profiles are used as path constraints and projected onto a unified three-dimensional mesh system (mesh size approximately 100 m). An inversion algorithm based on surface wave ray tracing at different frequencies and wavelet transform with sparse constraints is used for imaging, ultimately generating a continuous, high-resolution three-dimensional Vs structure model.
[0017] Step 2: Based on the obtained 3D Vs model, the background velocity distribution of the region is calculated according to the model, and significant low-velocity anomaly zones (usually indicating rock fracturing, alteration, or fluid enrichment) and high-velocity anomaly zones (usually indicating dense rock masses or rigid basements) are identified according to a set threshold (deviation from background value ±10%). Subsequently, the extracted velocity anomalies are overlaid with known metallogenic tectonic models (digitized deep faults, magma contact zones, and fracturing / alteration zones) in 3D space. The spatial relationship between low-velocity anomalies and key structures (faults) is analyzed, including intersections, consistency of extension directions, and strike angles, thereby delineating the "velocity anomaly-tectonic coupling zone" and providing a core geological framework for quantitative evaluation.
[0018] Subsequently, each 3D mesh cell (or prediction cell) is extracted. i Multiple quantitative evaluation indicators (including abnormal speed amplitude) , structural intersection density and distance from the rock mass To eliminate dimensions and make all indicators comparable, range standardization is used to standardize the values of each indicator. Mapping to the [0,1] interval yields the standardized score. The positive and negative indicators are as follows: Positive indicators: ; Negative indicators: ; in, For unit i In indicators j Standardized score; The value of the index is dimensionless. The choice between positive or negative index depends on the geological correlation between the original value of the index and the "mineralization favorability": if the index value (abnormal intensity, mineralization density) is larger, it directly represents that the mineralization conditions are more favorable, then positive standardization is used; if the index value (distance to fault, rock mass mineralization element) is smaller, it represents that the conditions are more favorable, then negative standardization is used. The final weights of each indicator are determined using a combination of subjective and objective weighting methods. : ① Subjective weight ( ): Determined through the Analytic Hierarchy Process (AHP) and expert judgment; ② Objective weight ( ): Calculated using the improved CRITIC method, which is based on the standard deviation of the index data. Correlation coefficient between indicators Calculate information content And thus obtain ; ③Combined weights: ,in This is the balance coefficient (usually taken as 0.5); Based on this mineralization potential index ( MPI ) Calculation, for each three-dimensional mesh element i The comprehensive mineralization potential index is calculated using the weighted summation formula: ; The initial three-dimensional potential model is obtained, with a value range of [0,1]. Finally, to quantify the impact of uncertainty on the results of key parameters (outlier threshold, weights), Monte Carlo simulation and uncertainty quantification were performed; a total of [number] operations were conducted. N Sub-Monte Carlo simulation; For the k The simulation ( k = 1 to N ): A set of sample values is randomly selected from the distribution of each parameter. Using this new set of parameters, a set of three-dimensional values is repeatedly calculated. MPI Data body Collect N values for each unit i across all simulations: Calculate the average potential: .
[0019] Step 3: Based on the potential index threshold (>0.8), delineate high-potential target areas and output area and anomaly type information on the GIS platform to provide objective and comparable numerical basis for drilling decisions (see...). Figure 2 ).
[0020] like Figure 2 ( Figure 2 In section A, the pink markers form a measurement line delineating the range where the threshold is greater than 0.8. Figure 2 As shown in Figure B (apparent shear wave velocity profile along the survey line), multiple structures are distributed below the profile, two of which are nearly vertical and extend deeply, reaching at least -300 meters above sea level. Borehole data shows that three boreholes on the northwest side of the survey line discovered gold veins at elevations between -100 and -200 meters. Boreholes on the southeast side of the survey line show a 400-meter-thick overburden layer below, which is composed of mineralized volcanic rocks. The final results of this scheme match well with the strata revealed by the boreholes, accurately distinguishing the velocity difference interfaces between limestone, siltstone, and gneiss. This indicates that these two nearly vertical low-velocity channels are ore-controlling structures with considerable length and scale, and their strike is basically perpendicular to the survey line direction. This effectively verifies the feasibility of this scheme, which can serve as a basis and guide for delineating high-potential target areas for metal mineral exploration.
[0021] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.
Claims
1. A method for predicting gold deposits in complex tectonic settings based on background noise imaging, characterized in that... Includes the following steps: Step 1: Using passive source seismic technology based on natural background noise, a three-dimensional receiving array with variable station density is deployed in the exploration area to continuously record and preprocess the noise signal. Then, cross-correlation and superposition are performed, and the Green's function between stations is extracted. Subsequently, the dispersion curve and surface wave velocity are extracted. Through three-dimensional surface wave tomography, a high-resolution three-dimensional Vs model can be constructed from tens of meters near the surface to several kilometers underground. Step 2: Based on the obtained 3D Vs model, calculate and identify low-velocity and high-velocity anomaly zones, perform superposition analysis of velocity anomalies with known fault and magma contact mineralization structures, and delineate velocity anomaly-structure coupling zones; establish a mineralization potential index based on multiple factors such as velocity anomaly amplitude, anomaly volume, and structural intersection angle. Step 3: Delineate high-potential target areas based on the potential index threshold, and output the area and anomaly type information on the GIS platform.
2. The gold deposit exploration prediction method based on background noise imaging in complex tectonic backgrounds according to claim 1, characterized in that: In step one, the preprocessing includes detrending, instrument response removal, filtering, and noise reduction.
3. The gold deposit exploration prediction method based on background noise imaging in complex tectonic backgrounds according to claim 1, characterized in that: In step two, establishing the mineralization potential index specifically includes the following steps: Extract each 3D mesh cell (or prediction cell). i Multiple quantitative evaluation indicators (including abnormal speed amplitude) , structural intersection density and distance from the rock mass To eliminate dimensions and make all indicators comparable, range standardization is used to standardize the values of each indicator. Mapping to the [0,1] interval yields the standardized score. The positive and negative indicators are as follows: Positive indicators: ; Negative indicators: ; in, For unit i In indicators j Standardized score; The value of the index is dimensionless. The choice between positive or negative index depends on the geological correlation between the original value of the index and the "mineralization favorability": if the index value (abnormal intensity, mineralization density) is larger, it directly represents that the mineralization conditions are more favorable, then positive standardization is used; if the index value (distance to fault, rock mass mineralization element) is smaller, it represents that the conditions are more favorable, then negative standardization is used. The final weights of each indicator are determined using a combination of subjective and objective weighting methods. : ① Subjective weight ( ): Determined through the Analytic Hierarchy Process (AHP) and expert judgment; ② Objective weight ( ): Calculated using the improved CRITIC method, which is based on the standard deviation of the index data. Correlation coefficient between indicators Calculate information content And thus obtain ; ③Combined weights: ,in This is the balance coefficient (usually taken as 0.5); Based on this mineralization potential index ( MPI ) Calculation, for each three-dimensional mesh element i The comprehensive mineralization potential index is calculated using the weighted summation formula: ； The initial three-dimensional potential model is obtained, with a value range of [0,1]. Finally, to quantify the impact of uncertainty on the results of key parameters (outlier threshold, weights), Monte Carlo simulation and uncertainty quantification were performed; a total of [number] operations were conducted. N Sub-Monte Carlo simulation; For the k The simulation ( k = 1 to N ): A set of sample values is randomly selected from the distribution of each parameter. Using this new set of parameters, a set of three-dimensional values is repeatedly calculated. MPI Data body Collect N values for each unit i across all simulations: Calculate the average potential: 。