Skarn type iron ore body positioning method and system based on full waveform inversion

Abstract

The present application relates to the field of geophysical exploration, and provides a skarn type iron-rich ore body positioning method and system based on full waveform inversion. The method comprises: using moving smoothing processing to separate the high and low frequencies of the acoustic curve, the magnetic susceptibility curve and the density curve, and extract the high frequency part of the acoustic curve, the high frequency part of the magnetic susceptibility curve and the high frequency part of the density curve; setting the weighting coefficients of the magnetic susceptibility curve and the density curve respectively according to the contribution degrees of different curves to the identification of iron-rich ore; fusing the high frequency part of the acoustic curve and the high frequency part of the magnetic susceptibility curve based on the weighting coefficient of the magnetic susceptibility curve to obtain a fusion curve; fusing the fusion curve and the high frequency part of the density curve based on the weighting coefficient of the density curve to obtain a pseudo-acoustic curve; constructing an initial model of iron-rich ore based on the pseudo-acoustic curve, combining with seismic data to perform full waveform inversion of the pseudo-acoustic velocity, and positioning the ore body according to the inversion result.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, and in particular to a method and system for locating skarn-type rich iron ore bodies based on full waveform inversion. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, seismic exploration of metallic minerals still relies primarily on 2D seismic data, but its exploration results are inferior to those of sedimentary minerals, especially in complex conditions with thick overburden, where the signal-to-noise ratio of seismic data is low and the imaging of strata and ore-bearing rock masses is poor. Employing a 2.5D seismic observation system with a single shot and multiple lines can achieve higher coverage times, obtain higher-quality seismic profiles, and provide more accurate stratigraphic and structural information for the inversion stage.

[0004] The full waveform inversion method, as a high-precision inversion method that has developed rapidly in recent years, has been less used in metal mines, mainly due to two limitations. First, the correctness of the initial model determines the inversion convergence speed and effect. The ore-forming environment of skarn-type rich iron ore is complex, making it difficult to accurately predict the deep geological structure. Second, metal mine logging differs from oil and gas logging, often exhibiting significant electrical and magnetic differences. Density and velocity logging data are not very useful for identifying metal ore bodies. Simply relying on velocity and density parameters without considering electrical and magnetic properties cannot accurately invert information about rich iron ore. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a method and system for locating skarn-type rich iron ore bodies based on full waveform inversion. This invention establishes an initial model of the rich iron ore and incorporates data information that highlights the rich iron ore into the inversion parameters. The full waveform inversion method is used to accurately describe the occurrence of the rich iron ore, thereby locating the ore body.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a method for locating skarn-type rich iron ore bodies based on full waveform inversion.

[0008] A method for locating skarn-type rich iron ore bodies based on full waveform inversion includes:

[0009] Acoustic wave curves, magnetic susceptibility curves, and density curves within the gravity and magnetic anomaly region are obtained. Using moving smoothing processing, the high and low frequencies of the acoustic wave curves, magnetic susceptibility curves, and density curves are separated, and the high-frequency components of the acoustic wave curve, magnetic susceptibility curve, and density curve are extracted.

[0010] Based on the contribution of different curves to the identification of rich iron ore, weighting coefficients are set for the magnetic susceptibility curve and the density curve respectively;

[0011] Based on the weighting coefficients of the magnetic susceptibility curve, the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve are fused to obtain a fused curve.

[0012] Based on the weighting coefficients of the density curve, the fusion curve is fused with the high-frequency part of the density curve to obtain the pseudo-acoustic curve.

[0013] Based on the pseudo-acoustic curve, an initial model of rich iron ore is constructed. Combined with seismic data, the full waveform of pseudo-acoustic velocity is inverted, and the ore body is located based on the inversion results.

[0014] Furthermore, the process of acquiring the seismic data includes: under the condition of excitation by an explosive source, in the gravity and magnetic anomaly zone, in the direction perpendicular to the long axis of the gravity and magnetic anomaly, using a single shot with multiple lines, the positions of the geophone points of the survey lines are staggered in the lateral direction, and a 2.5-dimensional multi-line receiving and observation system is used to acquire seismic data and perform preprocessing.

[0015] Furthermore, the preprocessing process includes: interpolating and reconstructing the seismic data, and introducing a reconstruction threshold to evaluate the reconstruction results.

[0016] Furthermore, the preprocessing process also includes: denoising the seismic data and evaluating the denoising results using the maximum amplitude signal-to-noise ratio.

[0017] Furthermore, the preprocessing process also includes: performing 2.5-dimensional superposition. During superposition, a common reflection surface element is defined based on the number of parallel survey lines. The common reflection surface element is superimposed. After multiple iterations of the velocity, a reasonable time migration parameter is determined so that the final velocity field can approximate the velocity of the underground medium to the greatest extent, thus obtaining the pre-stack time migration profile.

[0018] Furthermore, based on the analysis of geological data, the interpretation is based on the markers of the prestack time migration profile of the iron-rich strata and the contact zone of the intrusive rock mass in the thick overburden area.

[0019] The main host strata of the rich iron ore were determined to be Carboniferous-Permian strata. The reflected wave horizons were determined by using synthetic seismic records. The reflection coefficients were obtained from well logging data. The seismic records passing through the wells were synthesized and compared with the pre-stack time migration profile to determine the geological horizons where the iron ore is located.

[0020] Furthermore, the process of performing the pseudo-acoustic velocity full waveform inversion includes:

[0021] Define the objective function for full waveform inversion:

[0022]

[0023] Where, δW=u cal -u obs u cal It is a simulated wave field, u obs It is an observation wave field;

[0024] Set the gradient update function R, which gradually decreases with the number of iterations:

[0025]

[0026] Add the result of the previous iteration to the gradient update rate:

[0027]

[0028] V n+1 =V n -τ n H -1 η n

[0029] H -1 It is the inverse of the Hessian matrix, where n represents the number of iterations, N is the total number of iterations set, and τ n It is the step size of the nth iteration, and γ is a constant;

[0030] The initial model of rich iron ore is updated iteratively. The updated initial model of rich iron ore is then used to forward model seismic data and recalculate the objective function of inversion. When the objective function value reaches the threshold, the inversion result is output; otherwise, iterative updates continue. The ore body is located based on the inversion result.

[0031] A second aspect of the present invention provides a skarn-type rich iron ore body positioning system based on full waveform inversion.

[0032] A skarn-type rich iron ore body location system based on full waveform inversion includes:

[0033] The data acquisition and separation module is configured to: acquire the acoustic wave curve, magnetic susceptibility curve and density curve in the gravity and magnetic anomaly area; use moving smoothing processing to separate the high and low frequencies of the acoustic wave curve, magnetic susceptibility curve and density curve; and extract the high frequency part of the acoustic wave curve, the high frequency part of the magnetic susceptibility curve and the high frequency part of the density curve.

[0034] The setting module is configured to set the weighting coefficients of the magnetic susceptibility curve and the density curve respectively, based on the contribution of different curves to the identification of rich iron ore.

[0035] The first fusion module is configured to: fuse the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve based on the weighting coefficients of the magnetic susceptibility curve to obtain a fused curve;

[0036] The second fusion module is configured to fuse the fusion curve with the high-frequency part of the density curve based on the weighting coefficients of the density curve to obtain the pseudo-acoustic curve.

[0037] The inversion module is configured to: construct an initial model of rich iron ore based on pseudo-acoustic curves, perform full waveform inversion of pseudo-acoustic velocity by combining seismic data, and locate the ore body based on the inversion results.

[0038] A third aspect of the present invention provides a computer-readable storage medium.

[0039] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for locating skarn-type rich iron ore bodies based on full waveform inversion as described in the first aspect above.

[0040] A fourth aspect of the present invention provides a computer device.

[0041] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for locating skarn-type rich iron ore bodies based on full waveform inversion as described in the first aspect above.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention employs a 2.5D seismic observation system for seismic exploration within anomaly zones demarcated by gravity and magnetic fields. High signal-to-noise ratio and high-fidelity seismic profiles are obtained through processing. Detailed stratigraphic and structural interpretations are performed using existing data to preliminarily infer the main rock mass characteristics under thick overburden conditions and delineate the extent of magmatic intrusion. A pseudo-acoustic curve is obtained by fusing acoustic, magnetic susceptibility, and density curves, and a full-waveform inversion initial model is constructed under the constraints of gravity and magnetic inversion results in iron-rich ore areas. During the inversion process, a gradient update function is set to adjust the parameter update rate, thereby reducing the number of iterations in the full-waveform inversion method. The iron-rich ore body is located based on the inversion results. This invention, based on obtaining high signal-to-noise ratio seismic profiles and using full-waveform inversion results, can effectively assist in the location of iron-rich ore bodies, maximizing the solution to the current problem of selecting the best iron-rich ore bodies and increasing the probability of successful deep exploration.

[0044] This invention uses a 2.5D observation system to acquire data in thick-covered areas, obtaining seismic data with a higher coverage number and a higher signal-to-noise ratio than conventional 2D seismic data, thus improving the imaging quality of mineralized surrounding rocks and providing more accurate stratigraphic and structural information for the inversion stage.

[0045] This invention utilizes the fusion of magnetic susceptibility, density curves, and acoustic curves to obtain pseudo-acoustic curves that accurately reflect information about rich iron ore. This improves the ability of full waveform inversion results for identifying rich iron ore, increases the success rate of deep mineral exploration, and constructs an initial model of rich iron ore that can preliminarily reflect the location of rich iron ore under the constraints of gravity and magnetic inversion results. An inversion update function is set to prevent overfitting and local minima, thereby improving inversion accuracy. Attached Figure Description

[0046] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0047] Figure 1 This is a flowchart illustrating the method for locating skarn-type rich iron ore bodies based on full waveform inversion, as shown in this invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0052] Example 1

[0053] This embodiment provides a method for locating skarn-type rich iron ore bodies based on full waveform inversion, including the following steps:

[0054] A method for locating skarn-type rich iron ore bodies based on full waveform inversion includes:

[0055] Acoustic wave curves, magnetic susceptibility curves, and density curves within the gravity and magnetic anomaly region are obtained. Using moving smoothing processing, the high and low frequencies of the acoustic wave curves, magnetic susceptibility curves, and density curves are separated, and the high-frequency components of the acoustic wave curve, magnetic susceptibility curve, and density curve are extracted.

[0056] Based on the contribution of different curves to the identification of rich iron ore, weighting coefficients are set for the magnetic susceptibility curve and the density curve respectively;

[0057] Based on the weighting coefficients of the magnetic susceptibility curve, the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve are fused to obtain a fused curve.

[0058] Based on the weighting coefficients of the density curve, the fusion curve is fused with the high-frequency part of the density curve to obtain the pseudo-acoustic curve.

[0059] Based on the pseudo-acoustic curve, an initial model of rich iron ore is constructed. Combined with seismic data, the full waveform of pseudo-acoustic velocity is inverted, and the ore body is located based on the inversion results.

[0060] The following section, with reference to the accompanying drawings, details a method for locating skarn-type rich iron ore bodies based on full waveform inversion, as described in this embodiment. Figure 1 As shown, it includes:

[0061] Step 1: Within the gravity and magnetic anomaly region, data is collected using a 2.5D multi-line receiving and observation system perpendicular to the long axis of the gravity and magnetic anomaly.

[0062] Step 1.1: Select different line spacing, track spacing, shot spacing, and maximum offset distance to perform forward modeling of acquisition parameters. The line spacing is generally selected as 20m to 60m, the track spacing is generally selected as 10m to 40m, the shot spacing is generally selected as 2 to 4 times the track spacing, and the maximum offset distance is generally 1 to 1.2 times the depth of the target layer. The optimal acquisition parameters are determined based on the imaging quality of the ore-forming surrounding rock in the forward modeling overlay profile. The target layer refers to the target to be surveyed.

[0063] Step 1.2: During the data collection process, use an explosive source to excite the data. Select an appropriate amount of explosive and well depth. In areas with thick overburden, the amount of explosive is generally 2-4 kg and the well depth is generally 14-18 m.

[0064] Step 1.3: Data acquisition is performed using the 2.5D observation system. A multi-line deployment is adopted, with the receiver points on the survey lines staggered laterally and not on the same crossline direction. This not only multiplies the number of coverage passes but also improves the lateral resolution.

[0065] Step 2: Process the acquired seismic data;

[0066] Step 2.1: Seismic data interpolation and reconstruction. This step primarily addresses data loss caused by complex terrain and numerous large obstacles, which affects subsequent seismic data processing. An interpolation and reconstruction of the seismic data is necessary. A reconstruction threshold Q is introduced to evaluate the reconstruction results; the closer the Q value is to 0, the better the reconstruction effect. The formula for calculating the threshold Q is as follows:

[0067] Q = log 10 [||S(r,n)||2] (1)

[0068]

[0069] Where S(r,n) is the similarity function between the reconstructed signal and the original signal, the higher the similarity, the closer it is to 1, n, r represent the original data and the reconstructed data, and μ n It is the average of the original data, μ r It is the average of the data after reconstruction. and These are the variances of the original data and the reconstructed data, σ and σ, respectively. nr This represents the covariance between the original data and the reconstructed data. Generally, c1c2 = 0.67 is chosen to ensure the stability of the equation and prevent the denominator from being zero.

[0070] Step 2.2: Addressing the problem of low signal-to-noise ratio and difficulty in noise removal for seismic data with thick overburden, repeated noise parameter experiments were conducted. Appropriate denoising parameters were selected based on the amplitude, velocity, and frequency of the seismic records. To minimize damage to the effective signal caused by denoising methods, the maximum amplitude signal-to-noise ratio was used to evaluate the quality of the denoising results. The formula for calculating the maximum amplitude signal-to-noise ratio is as follows:

[0071]

[0072]

[0073] Where m and n are the scale of each dimension of the seismic data, S represents the denoised data, N represents the noisy data, and MAX... R It is the maximum value of the amplitude after denoising, and E represents the mean square error between the denoised signal and the original signal.

[0074] Step 2.3: 2.5D overlay. When overlaying, first import according to the three-dimensional observation system. At this time, the spatial distribution of the underground co-reflection points in the horizontal direction is "scattered". Therefore, it is necessary to define a surface element in the horizontal direction, which spans all parallel survey lines. The number of traces n in the surface element is the number of parallel survey lines. Underground co-reflection surface elements are defined to realize the overlay of co-reflection surface elements in order to improve the signal-to-noise ratio and suppress side waves.

[0075] Step 2.4: After multiple iterations (the first velocity pickup is processed, and if the results differ, a second velocity pickup is performed based on the first one until the result is satisfactory), a reasonable time migration parameter is determined based on the velocity with high pickup accuracy, so that the final velocity field can approximate the velocity of the underground medium to the greatest extent, and a better pre-stack time migration profile is obtained.

[0076] Step 3: Based on geological data, interpret the characteristics of the iron-rich strata and intrusive rock contact zone in the seismic profile (prestack time migration profile).

[0077] Step 3.1: Based on the analysis of geological data, it was determined that the main host strata of the rich iron ore are Carboniferous-Permian strata. The reflected wave strata were calibrated using synthetic seismic records. The reflection coefficient was obtained based on well logging data. The seismic records passing through the wells were synthesized and compared with the seismic profile to determine the geological strata where the iron ore is located.

[0078] Step 3.2: Interpretation of the iron ore-bearing strata and the contact zone of the intrusive rocks. The Carboniferous-Permian strata under the thick overburden are generally a set of transversely discontinuous weak reflection phase axes (locally more continuous in the same direction), gradually thinning from both sides towards the middle, showing obvious differences from the Cenozoic strata and intrusive rocks. The top surface of the intrusive rock body is in contact with the upper sedimentary strata. The upper sedimentary strata are broken and migrated, and a layered reflection wave group is easily formed at the boundary of the intrusive rock body. The reflection characteristics inside the intrusive rock body are consistent, which are quite different from the reflection characteristics of the upper sedimentary strata, showing a disordered earthworm-like reflection.

[0079] Step 4: Based on the interpretation results of Step 3, construct an initial model of rich iron ore; conduct 2.5-dimensional full waveform inversion of rich iron ore based on the initial model of rich iron ore, and locate the ore body or deposit according to the inversion results.

[0080] Step 4.1: Well logging curve fusion and reconstruction. The specific steps are as follows:

[0081] Step 4.1.1: Normalize and correct the acoustic wave, magnetic susceptibility, and density curves, and eliminate outliers;

[0082] Step 4.1.2: Using motion smoothing, separate the high and low frequencies of the sound wave, magnetic susceptibility, and density curves to obtain the high-frequency and low-frequency information of each curve;

[0083] Step 4.1.3: Based on the different contributions of well logging curves to the identification of rich iron ore, set the weighting coefficient α for merging different curves. K >α ρ , where α K α is the weighting coefficient of the magnetic susceptibility curve. ρ These are the weighting coefficients for the density curve.

[0084] Step 4.1.4: First, merge the magnetic susceptibility curves. C AC For the high-frequency portion of the sound wave curve, C Ki This refers to the high-frequency portion of the magnetic susceptibility curve. This represents the average value of the magnetic susceptibility curve.

[0085] Step 4.1.5: Using the fused curve as the base value, repeat step 4.1.4 to fuse the high-frequency part of the density curve. The resulting curve is called the pseudo-acoustic curve.

[0086] Step 4.1.6: Perform cross-analysis of the magnetic susceptibility and density pseudo-acoustic curves to determine the identification ability of the pseudo-acoustic curves for rich iron ore. If the effect is not ideal, adjust the weights and repeat steps 4.1.4 and 4.1.5.

[0087] Step 4.2: Using the interpreted stratigraphic information as a framework, the framework is interpolated and extrapolated based on the constructed pseudo-acoustic curves to highlight the iron ore body, constructing an initial model of rich iron ore, and using the gravity and magnetic inversion results of the area as constraints to improve the accuracy of the initial model of rich iron ore.

[0088] Step 4.3: Perform full waveform inversion of pseudoacoustic velocity. The objective function for full waveform inversion is:

[0089]

[0090] Where, δW=u cal -u obs u cal It is a simulated wave field, u obs It is an observation wave field.

[0091] Calculating the gradient of model parameters during the inversion process To prevent overfitting, a gradient update function R is set that gradually decreases with the number of iterations.

[0092]

[0093] To prevent local minima, the result of the previous iteration is added to the gradient update rate.

[0094]

[0095] V n+1 =V n -τ n H -1 η n

[0096] H -1 It is the inverse of the Hessian matrix, where n represents the number of iterations, N is the total number of iterations set, and τ n It is the step size of the nth iteration, and γ is a constant, which can be 0.9.

[0097] By iteratively updating the model, the updated model is used to forward model the seismic data and recalculate the inversion objective function. When the objective function value reaches the threshold, the inversion result can be output; otherwise, iterative updates continue. The ore body can be located based on the inversion result.

[0098] Example 2

[0099] This embodiment provides a skarn-type rich iron ore body positioning system based on full waveform inversion.

[0100] The data acquisition and separation module is configured to: acquire the acoustic wave curve, magnetic susceptibility curve and density curve in the gravity and magnetic anomaly area; use moving smoothing processing to separate the high and low frequencies of the acoustic wave curve, magnetic susceptibility curve and density curve; and extract the high frequency part of the acoustic wave curve, the high frequency part of the magnetic susceptibility curve and the high frequency part of the density curve.

[0101] The setting module is configured to set the weighting coefficients of the magnetic susceptibility curve and the density curve respectively, based on the contribution of different curves to the identification of rich iron ore.

[0102] The first fusion module is configured to: fuse the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve based on the weighting coefficients of the magnetic susceptibility curve to obtain a fused curve;

[0103] The second fusion module is configured to fuse the fusion curve with the high-frequency part of the density curve based on the weighting coefficients of the density curve to obtain the pseudo-acoustic curve.

[0104] The inversion module is configured to: construct an initial model of rich iron ore based on pseudo-acoustic curves, perform full waveform inversion of pseudo-acoustic velocity by combining seismic data, and locate the ore body based on the inversion results.

[0105] It should be noted that the data acquisition and separation module, setting module, first fusion module, second fusion module, and inversion module described above are the same examples and application scenarios implemented in the steps of Embodiment 1, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0106] Example 3

[0107] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the skarn-type rich iron ore body location method based on full waveform inversion as described in Embodiment 1 above.

[0108] Example 4

[0109] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the skarn-type rich iron ore body location method based on full waveform inversion as described in Embodiment 1 above.

[0110] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0111] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating skarn-type rich iron ore bodies based on full waveform inversion, characterized in that, include: Acoustic wave curves, magnetic susceptibility curves, and density curves within the gravity and magnetic anomaly region are obtained. Using moving smoothing processing, the high and low frequencies of the acoustic wave curves, magnetic susceptibility curves, and density curves are separated, and the high-frequency components of the acoustic wave curve, magnetic susceptibility curve, and density curve are extracted. Based on the contribution of different curves to the identification of rich iron ore, weighting coefficients are set for the magnetic susceptibility curve and the density curve, respectively, wherein the weighting coefficient of the magnetic susceptibility curve is greater than the weighting coefficient of the density curve. Based on the weighting coefficients of the magnetic susceptibility curve, the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve are fused to obtain a fused curve. Based on the weighting coefficients of the density curve, the fusion curve is fused with the high-frequency part of the density curve to obtain the pseudo-acoustic curve. Based on the pseudo-acoustic wave curve, an initial model of rich iron ore is constructed. Combined with seismic data, a full waveform inversion of pseudo-acoustic velocity is performed. The ore body is located based on the inversion results. The process of performing the full waveform inversion of pseudo-acoustic velocity includes: Define the objective function for full waveform inversion: in, , It is a simulated wave field. It is an observation wave field; Set the gradient update function R, which gradually decreases with the number of iterations: Add the result of the previous iteration to the gradient update rate: It is the inverse of the Hessian matrix, where n represents the iteration number and N is the set total number of iterations. It is the step size of the nth iteration. It is a constant; The initial model of rich iron ore is updated iteratively. The updated initial model of rich iron ore is then used to forward model seismic data and recalculate the objective function of inversion. When the objective function value reaches the threshold, the inversion result is output; otherwise, iterative updates continue. The ore body is located based on the inversion result.

2. The method for locating skarn-type rich iron ore bodies based on full waveform inversion according to claim 1, characterized in that, The process of acquiring the earthquake data includes: under the condition of excitation by an explosive source, in the gravity and magnetic anomaly zone, in the direction perpendicular to the long axis of gravity and magnetic anomaly, using a single shot with multiple lines, the positions of the geophone points of the survey lines are staggered in the lateral direction, and a 2.5-dimensional multi-line receiving and observation system is used to acquire earthquake data and perform preprocessing.

3. The method for locating skarn-type rich iron ore bodies based on full waveform inversion according to claim 2, characterized in that, The preprocessing process includes: interpolating and reconstructing the seismic data, and introducing a reconstruction threshold to evaluate the reconstruction results.

4. The method for locating skarn-type rich iron ore bodies based on full waveform inversion according to claim 2, characterized in that, The preprocessing process also includes: denoising the seismic data and evaluating the denoising results using the maximum amplitude signal-to-noise ratio.

5. The method for locating skarn-type rich iron ore bodies based on full waveform inversion according to claim 2, characterized in that, The preprocessing process also includes: performing 2.5-dimensional superposition. During superposition, a common reflection surface element is defined based on the number of parallel survey lines. The common reflection surface element is superimposed. After multiple iterations of the velocity, a reasonable time migration parameter is determined so that the final velocity field can approximate the velocity of the underground medium to the greatest extent, thus obtaining the pre-stack time migration profile.

6. The method for locating skarn-type rich iron ore bodies based on full waveform inversion according to claim 2, characterized in that, Based on the analysis of geological data, the interpretation is based on the characteristics of the pre-stack time migration profile of the iron-rich strata and the contact zone of the intrusive rock mass in the thick overburden area. The main host strata of the rich iron ore were determined to be Carboniferous-Permian strata. The reflected wave horizons were determined by using synthetic seismic records. The reflection coefficients were obtained from well logging data. The seismic records passing through the wells were synthesized and compared with the pre-stack time migration profile to determine the geological horizons where the iron ore is located.

7. A skarn-type rich iron ore body positioning system based on full waveform inversion, characterized in that, include: The data acquisition and separation module is configured to: acquire the acoustic wave curve, magnetic susceptibility curve and density curve in the gravity and magnetic anomaly area; use moving smoothing processing to separate the high and low frequencies of the acoustic wave curve, magnetic susceptibility curve and density curve; and extract the high frequency part of the acoustic wave curve, the high frequency part of the magnetic susceptibility curve and the high frequency part of the density curve. The setting module is configured to: set weighting coefficients for the magnetic susceptibility curve and the density curve respectively according to the contribution of different curves to the identification of rich iron ore, wherein the weighting coefficient of the magnetic susceptibility curve is greater than the weighting coefficient of the density curve. The first fusion module is configured to: fuse the high-frequency part of the acoustic wave curve and the high-frequency part of the magnetic susceptibility curve based on the weighting coefficients of the magnetic susceptibility curve to obtain a fused curve; The second fusion module is configured to fuse the fusion curve with the high-frequency part of the density curve based on the weighting coefficients of the density curve to obtain the pseudo-acoustic curve. The inversion module is configured to: construct an initial model of rich iron ore based on pseudo-acoustic curves, perform full-waveform pseudo-acoustic velocity inversion in conjunction with seismic data, and locate the ore body based on the inversion results; the process of performing full-waveform pseudo-acoustic velocity inversion includes: Define the objective function for full waveform inversion: in, , It is a simulated wave field. It is an observation wave field; Set the gradient update function R, which gradually decreases with the number of iterations: Add the result of the previous iteration to the gradient update rate: It is the inverse of the Hessian matrix, where n represents the iteration number and N is the set total number of iterations. It is the step size of the nth iteration. It is a constant; The initial model of rich iron ore is updated iteratively. The updated initial model of rich iron ore is then used to forward model seismic data and recalculate the objective function of inversion. When the objective function value reaches the threshold, the inversion result is output; otherwise, iterative updates continue. The ore body is located based on the inversion result.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for locating skarn-type rich iron ore bodies based on full waveform inversion as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for locating skarn-type rich iron ore bodies based on full waveform inversion as described in any one of claims 1-6.

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