Deep underground multi-physics field detection method and system

Abstract

The invention discloses a deep underground multi-physical field detection method and system, and particularly relates to the technical field of underground detection, and the method comprises the following steps: S1, obtaining seismic wave field data; s2, based on a multi-physics field original data set; s3, according to the basic characteristic parameter set; s4, calling a comprehensive feature fusion matrix; in the deep underground multi-physics field detection process, waveform, wave velocity and amplitude parameters contained in seismic wave field data and electromagnetic field intensity, resistivity and polarizability parameters contained in electromagnetic field data are collected by adopting a monitoring instrument sensor to generate a multi-physics field original data set, so that comprehensive basic data are provided for subsequent processing; based on the data set, the wave velocity and amplitude characteristics of each waveform segment of seismic wave field data and the resistivity and polarizability characteristics of electromagnetic field data are determined, a basic characteristic parameter set is obtained through integration, and key characteristics of different physical fields are accurately grasped.

Description

Technical Field

[0001] The present invention relates to the field of underground detection technology, and in particular to a deep underground multi-physical field detection method and system. Background Art

[0002] The field of underground detection technology includes a variety of geophysical detection methods such as seismic wave method, electromagnetic method, gravity method, nuclear magnetic resonance method, etc. These methods obtain information such as stratum structure, geological structure and underground resource distribution by measuring the response characteristics of different physical fields underground. Underground detection technology is widely used in oil, natural gas, mineral resource exploration, as well as engineering geological surveys, environmental geological surveys and groundwater resource assessments, providing key geological data support for related industries.

[0003] Among them, the deep underground multi-physics field detection method refers to the method of detecting deep underground geological structures and targets by comprehensively using a variety of geophysical field detection technologies. This topic covers the use of different physical fields such as seismic waves, electromagnetic waves, gravity fields, magnetic fields, etc. to combine with each other, through multi-source data acquisition, joint inversion and comprehensive interpretation, etc., to achieve high-precision detection and accurate imaging of complex geological bodies in the deep underground. Specific methods include using multi-physics field coupling models for data processing, using multiple physical field sensors to synchronously collect data, and using multi-physics field information fusion algorithms for data integration and analysis to improve the accuracy and reliability of deep underground detection.

[0004] Existing deep underground multi-physics field detection technology has shortcomings in data processing. In actual operation, its processing of multi-physics field data is not in-depth enough, and only preliminary processing is performed. It fails to fully explore the intrinsic correlation between different physical field data, resulting in insufficient data utilization. For example, when processing seismic wave field and electromagnetic field data, the weight relationship between the characteristic parameters of the two is not accurately considered, and the multi-physics field characteristic information cannot be effectively integrated. Therefore, in a complex geological environment, the geological structure identification is not accurate enough, and the target area that meets the specific geological structural characteristics cannot be accurately identified. Taking oil exploration as an example, the deviation in geological structure identification may lead to errors in the exploration direction, resulting in waste of resources and increased exploration costs; in engineering geological surveys, inaccurate geological structure judgment may also affect the project site selection and construction safety, bringing potential geological disaster risks. Summary of the Invention

[0005] The main purpose of the present invention is to provide a deep underground multi-physical field detection method and system, which can effectively solve the problem that the existing technology in deep underground multi-physical field detection is not in-depth enough in the processing of multi-physical field data, which may lead to deviation in resource extraction direction or inaccurate engineering geological stability assessment.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a deep underground multi-physical field detection method, which includes the following steps:

[0007] S1. Acquire seismic wave field data including waveform, wave velocity, and amplitude parameters, and electromagnetic field data including electromagnetic field intensity, resistivity, and polarizability parameters, collect data through monitoring instrument sensors and record the values ​​of each parameter to generate a multi-physics field original data set;

[0008] S2. Based on the original multi-physics field data set, determine the wave velocity and amplitude characteristics of each waveform segment of the seismic wave field data, determine the resistivity and polarizability characteristics of the electromagnetic field data, integrate the seismic wave characteristic parameter group and the electromagnetic wave characteristic parameter group to generate a basic characteristic parameter set;

[0009] S3. According to the basic feature parameter set, the correlation weights between seismic wave amplitude and electromagnetic field resistivity are set, other feature association weights are derived, and a feature fusion matrix is ​​constructed. The basic features of different physical fields are fused according to the weights to generate a comprehensive feature fusion matrix.

[0010] S4. Call the comprehensive feature fusion matrix, filter the fused feature parameters according to the preset feature screening rules, identify the target area that meets the specific geological structure characteristics according to the geological structure model matching rules, and generate deep geological target identification results.

[0011] Preferably, in step S1, the multi-physics field original data set includes seismic wave field data and electromagnetic field data.

[0012] Preferably, in step S2, the basic characteristic parameter set includes a seismic wave characteristic parameter group and an electromagnetic wave characteristic parameter group.

[0013] Preferably, in step S3, the comprehensive feature fusion matrix specifically refers to a matrix generated by feature fusion matrix operation.

[0014] Preferably, in step S4, the deep geological target identification result specifically refers to the identification result of the target area that meets the specific geological structure characteristics.

[0015] In addition, the present invention also provides a detection system applied to the above-mentioned deep underground multi-physical field detection method, the system comprising:

[0016] Data acquisition and feature extraction module: collects seismic wave field data, detects the location of waveform peaks and troughs, calculates adjacent distances to determine wave velocity parameters, counts amplitude variation ranges and records maximum and minimum values; collects electromagnetic field data, measures electromagnetic field intensity changes to calculate resistivity, and records polarizability parameters; generates seismic wave characteristic parameters and electromagnetic field characteristic parameters;

[0017] Feature fusion matrix construction module: obtains the characteristic parameters of seismic wave amplitude and electromagnetic field resistivity, determines the correlation coefficient as the fusion weight based on previous detection data and geological models, sets other fusion weights according to association rules, sums the parameters according to the weights and fuses other features to construct a multi-physics field feature fusion matrix, and generates a multi-physics field feature fusion matrix;

[0018] Adaptive filtering and feedback control module: monitors the amplitude and frequency change rate of the feature fusion matrix signal in real time, determines whether the change rate exceeds the threshold to trigger the filter, compares the signal difference and updates the filter coefficient. When the signal-to-noise ratio meets the standard, the adjustment stops, otherwise the update continues to generate an optimized detection signal.

[0019] Geological structure identification module: Analyzes the optimized detection signal, uses the identification algorithm combined with the geological model to identify the deep geological structure characteristics and generate deep geological structure characteristic parameters;

[0020] Target information confirmation module: Based on the geological structure characteristic parameters, the confirmation rules are used to locate and confirm the attributes of the target and generate target information.

[0021] Preferably, the multi-physics field original data set includes seismic wave field data and electromagnetic field data.

[0022] Preferably, the basic characteristic parameter set includes a seismic wave characteristic parameter group and an electromagnetic wave characteristic parameter group.

[0023] Preferably, the comprehensive feature fusion matrix specifically refers to a matrix generated by feature fusion matrix operation. In the feature fusion matrix construction module, the calculation formula for constructing the multi-physics field feature fusion matrix is ​​calculated in the following manner:

[0024] Assume that the multi-physics field feature fusion matrix is ​​M, and its calculation formula is:

[0025] M=W1×S1+W2×S2+W3×E1+W4×E2+…+W n ×X n

[0026] Among them, W1, W2, W3, W4, ..., W n are the fusion weight values ​​corresponding to the seismic wave amplitude characteristic parameter, seismic wave velocity characteristic parameter, electromagnetic field resistivity characteristic parameter, electromagnetic field polarizability characteristic parameter and other multi-physical field characteristic parameters, and their value ranges are all [0,1], and the sum of all weight values ​​is 1. S1 is the seismic wave amplitude characteristic parameter, S2 is the seismic wave velocity characteristic parameter, E1 is the electromagnetic field resistivity characteristic parameter, E2 is the electromagnetic field polarizability characteristic parameter, and X n are other multi-physics field characteristic parameters.

[0027] Preferably, the deep geological target identification result specifically refers to the identification result of the target area that meets the specific geological structure characteristics.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. In the process of deep underground multi-physical field detection, the present invention uses monitoring instrument sensors to collect the waveform, wave velocity, and amplitude parameters contained in the seismic wave field data, and the electromagnetic field strength, resistivity, and polarizability parameters contained in the electromagnetic field data, to generate a multi-physical field original data set, providing comprehensive basic data for subsequent processing. Based on this data set, the wave velocity and amplitude characteristics of each waveform segment of the seismic wave field data and the resistivity and polarizability characteristics of the electromagnetic field data are determined, and the basic characteristic parameter set is integrated to accurately grasp the key characteristics of different physical fields.

[0030] 2. In the present invention, based on the basic characteristic parameter set, the correlation weights between the seismic wave amplitude and the electromagnetic field resistivity are set, the wave velocity characteristics in the seismic wave field data and the polarizability characteristics in the electromagnetic field data are derived, and a feature fusion matrix is ​​constructed for fusion operations to fully explore the correlation information between different physical field features and improve the efficiency of feature utilization.

[0031] 3. In the present invention, the comprehensive feature fusion matrix is ​​called, the fused feature parameters are screened according to the preset feature screening rules, and the target area is identified according to the geological structure model matching rules to generate deep geological target identification results, thereby improving the accuracy and reliability of deep underground detection. It can more accurately identify target areas that meet specific geological structure characteristics, provide more reliable geological data support for oil, natural gas, mineral resource exploration, etc., and help to accurately carry out engineering geological surveys, environmental geological surveys, and groundwater resource assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method of the present invention;

[0033] Figure 2 It is a partial system sovereignty flow chart of the present invention;

[0034] Figure 3 This is another part of the system sovereignty flow chart of the present invention

[0035] Figure 4 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0036] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0037] See also Figure 1 、 Figure 2 、 Figure 3 and Figure 4 As shown, a deep underground multi-physics field detection method includes the following steps:

[0038] S1: Acquire seismic wave field and electromagnetic field data. Seismic wave field data includes waveform, wave velocity, and amplitude parameters. Electromagnetic field data includes electromagnetic field strength, resistivity, and polarizability parameters. Data is collected through the sensors of the monitoring instrument to generate a raw data set. Based on the signals collected by the sensors, the peak and trough positions of each waveform are recorded. The distance between adjacent peaks and troughs is calculated to obtain the wave velocity. The amplitude variation range of the waveform is statistically analyzed to record the maximum and minimum amplitude values. The change of electromagnetic field strength over time is measured to calculate the resistivity and record the polarizability value. This generates a multi-physics field raw data set that covers detailed parameter information of the seismic wave field and electromagnetic field.

[0039] S2: Based on the original multi-physics field data set, the velocity, amplitude mean, and standard deviation of each waveform segment are calculated for the seismic wavefield data, and the average and rate of change of the resistivity and polarizability are calculated for the electromagnetic field data. These calculation results are integrated to obtain the seismic wave characteristic parameter group and the electromagnetic wave characteristic parameter group, and generate a basic characteristic parameter set, including the velocity and amplitude characteristics of the seismic wavefield and the resistivity and polarizability characteristics of the electromagnetic field, providing basic data for subsequent fusion processing;

[0040] S3: Based on the basic feature parameter set, the correlation coefficient between the seismic wave amplitude and the electromagnetic field resistivity is set to 0.75. The correlation coefficient between the wave velocity characteristics in the seismic wave field data and the polarizability characteristics in the electromagnetic field data is set based on the geological model theory derivation. A feature fusion matrix is ​​constructed, and the seismic wave amplitude and the electromagnetic field resistivity are weighted and summed according to the correlation coefficient. The other features are deduced in the same way. The fused comprehensive feature parameters are obtained through matrix operations, and a comprehensive feature fusion matrix is ​​generated, which contains the numerical data after the fusion of multi-physics field features, and realizes the effective integration of multi-physics field features.

[0041] S4: Call the comprehensive feature fusion matrix, set the threshold of deep feature mining to the interval where the fusion feature value is greater than 1.2 or less than 0.8, screen the fused feature parameters, and identify the target area that meets the specific geological structure characteristics based on the matching rules of the geological structure model. Generate deep geological target identification results and point out the specific location and feature parameters of potential geological structures and target information.

[0042] By comprehensively collecting multiple parameters of the seismic wave field and electromagnetic field, and calculating the statistical values ​​of characteristics such as wave velocity, amplitude, resistivity, and polarizability in detail, a basic feature parameter set containing rich information is generated to provide a solid data foundation for subsequent processing. The correlation coefficient is set to construct a feature fusion matrix to achieve effective integration of multi-physical field characteristics, give full play to the advantages of different physical field characteristics, and explore the inherent connections between them, so that the fused feature parameters can more comprehensively reflect the characteristics of the underground geological structure. The depth feature mining threshold is set to filter the fused feature parameters, and the target area is accurately identified according to the geological structure model matching rules, which effectively improves the accuracy of deep underground detection, accurately points out the specific location and feature parameters of potential geological structures and target information, and provides more reliable and accurate geological data support for related industries, helping to carry out resource exploration, engineering geological surveys and other tasks efficiently.

[0043] In step S1, the multi-physics field original data set covers seismic wave field data and electromagnetic field data, which comprehensively reflect the different physical characteristics of the underground geological structure. The seismic wave field data provides information on the structure and construction of the strata, and the electromagnetic field data reflects the electrical characteristics of the strata. The combination of the two can characterize the underground geological conditions from multiple dimensions. Through the fusion of multi-physics field data, complex geological bodies can be identified more accurately, and the accuracy and reliability of deep underground detection can be improved, laying a solid foundation for subsequent geological target identification and analysis, and helping related industries to carry out their work efficiently.

[0044] In step S2, the basic characteristic parameter set includes a seismic wave characteristic parameter group and an electromagnetic wave characteristic parameter group; the characteristic information of seismic waves and electromagnetic waves are recorded separately to form a systematic and comprehensive basic characteristic parameter set, which facilitates targeted analysis and processing of data from different physical fields, provides sufficient basis for subsequent fusion, and helps to more accurately detect and analyze underground geological structures.

[0045] In step S3, the comprehensive feature fusion matrix specifically refers to the matrix generated by the feature fusion matrix operation;

[0046] The comprehensive feature fusion matrix is ​​generated through specific feature fusion matrix operations, which can fuse the basic feature parameters of different physical fields according to the set correlation weights to achieve effective integration of multi-physical field feature information. This integration method can fully explore the intrinsic connection between seismic waves and electromagnetic field characteristics, so that the fused feature parameters can more comprehensively reflect the comprehensive characteristics of underground geological structures, providing more accurate and representative data support for subsequent geological target identification, thereby improving the accuracy and reliability of deep underground detection.

[0047] In step S4, the deep geological target identification result specifically refers to the identification result of the target area that meets the specific geological structure characteristics.

[0048] In addition, the present invention also provides an intelligent management system applied to the above-mentioned deep underground multi-physical field detection method, the system comprising:

[0049] Data acquisition and feature extraction module: collects seismic wave field data, detects the peak and trough positions of each waveform, calculates the distance between adjacent peaks and troughs to determine the wave velocity parameters, and simultaneously counts the waveform amplitude variation range and records the maximum and minimum amplitude values; collects electromagnetic field data, measures the change of electromagnetic field intensity over time, calculates the resistivity parameters, and records the electromagnetic field polarization rate parameters; generates seismic wave amplitude characteristic parameters, seismic wave velocity characteristic parameters, electromagnetic field resistivity characteristic parameters, and electromagnetic field polarization rate characteristic parameters;

[0050] Feature fusion matrix construction module: obtains seismic wave amplitude characteristic parameters and electromagnetic field resistivity characteristic parameters, determines the correlation coefficient as the fusion weight value based on the analysis results of previous detection data and the theoretical derivation of the geological model, sets the corresponding fusion weight according to the association rules between other physical field characteristics, performs weighted sum operation on the seismic wave amplitude characteristic parameters and the electromagnetic field resistivity characteristic parameters according to the weight, integrates other basic features, and constructs a multi-physical field feature fusion matrix; generates a multi-physical field feature fusion matrix;

[0051] Adaptive filtering and feedback control module: Real-time monitoring of the amplitude and frequency change rate of the signal corresponding to the multi-physics field feature fusion matrix. When the change rate of the signal amplitude exceeds the upper limit of the common noise amplitude change rate obtained based on the statistics of previous detection data, it is judged that there is noise interference, triggering the intelligent adaptive filter, comparing the difference between the current signal and the filter output signal, and updating the filter coefficient to minimize the difference between the filtered signal and the original signal. When the signal-to-noise ratio of the filtered signal reaches the preset optimization standard, the adjustment is stopped, otherwise the update continues; thus generating the optimized multi-physics field detection signal;

[0052] Geological structure identification module: Analyzes the optimized multi-physics field detection signals, uses the preset geological structure identification algorithm combined with the deep underground geological model to identify the geological structure characteristics of the deep underground; and generates deep geological structure characteristic parameters;

[0053] Target information confirmation module: Based on the characteristic parameters of deep geological structures, target information confirmation rules are used to accurately locate and confirm the attributes of underground resource exploration targets or geological structure research objects; and to generate underground resource exploration target information or geological structure research target information.

[0054] In addition, the multi-physics field original data set includes seismic wave field data and electromagnetic field data; the basic feature parameter set includes a seismic wave feature parameter group and an electromagnetic wave feature parameter group; the comprehensive feature fusion matrix specifically refers to a matrix generated by feature fusion matrix operations;

[0055] In the Feature Fusion Matrix Construction module, the calculation formula for constructing the multi-physics feature fusion matrix is ​​calculated as follows:

[0056] Assume that the multi-physics field feature fusion matrix is ​​M, and its calculation formula is:

[0057] M=W1×S1+W2×S2+W3×E1+W4×E2+…+W n ×X n

[0058] Among them, W1, W2, W3, W4, ..., W n are the fusion weight values ​​corresponding to the seismic wave amplitude characteristic parameter, seismic wave velocity characteristic parameter, electromagnetic field resistivity characteristic parameter, electromagnetic field polarizability characteristic parameter and other multi-physical field characteristic parameters, and their value ranges are all [0,1], and the sum of all weight values ​​is 1. S1 is the seismic wave amplitude characteristic parameter, S2 is the seismic wave velocity characteristic parameter, E1 is the electromagnetic field resistivity characteristic parameter, E2 is the electromagnetic field polarizability characteristic parameter, and X n are other multi-physics field characteristic parameters;

[0059] It is assumed that only two physical field characteristic parameters are considered: seismic wave amplitude (S1) and electromagnetic field resistivity (E1), and their fusion weight values ​​are assumed to be W1 and W2 respectively, and W1+W2=1.

[0060] Assumptions:

[0061] Seismic wave amplitude characteristic parameter S1 = 100 units;

[0062] The characteristic parameter of electromagnetic field resistivity E1 = 200 units;

[0063] The fusion weight value W1=0.6 (indicates that the seismic wave amplitude feature accounts for 60% of the weight in the fusion process);

[0064] The fusion weight value W2=0.4 (indicating that the electromagnetic field resistivity feature accounts for 40% of the weight in the fusion process).

[0065] According to the formula:

[0066] M=W1×S1+W2×E1

[0067] Substituting the values:

[0068] M=0.6×100+0.4×200

[0069] Calculation steps:

[0070] Calculate the weighted part of the seismic wave amplitude characteristic parameter: 0.6×100=60;

[0071] Calculate the weighted part of the electromagnetic field resistivity characteristic parameter: 0.4×200=80;

[0072] Add the two parts: 60 + 80 = 140.

[0073] Therefore, the multiphysics feature fusion matrix M = 140 units.

[0074] This result indicates that the eigenvalue after weighted fusion is 140 units. This value comprehensively considers the two characteristic parameters of seismic wave amplitude and electromagnetic field resistivity and their corresponding weights, and can be used for subsequent geological structure analysis or target information confirmation.

[0075] The deep geological target identification results specifically refer to the identification results of target areas that meet specific geological structural characteristics.

[0076] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A deep underground multi-physics field detection method, characterized in that: The following steps are involved: S1. Acquire seismic wave field data including waveform, wave velocity, and amplitude parameters, and electromagnetic field data including electromagnetic field intensity, resistivity, and polarizability parameters, collect data through monitoring instrument sensors and record the values ​​of each parameter to generate a multi-physics field original data set; S2. Based on the original multi-physics field data set, determine the wave velocity and amplitude characteristics of each waveform segment of the seismic wave field data, determine the resistivity and polarizability characteristics of the electromagnetic field data, integrate the seismic wave characteristic parameter group and the electromagnetic wave characteristic parameter group to generate a basic characteristic parameter set; S3. According to the basic characteristic parameter set, the correlation weights between the seismic wave amplitude and the electromagnetic field resistivity are set, the correlation weights between the velocity characteristics in the seismic wave field data and the polarizability characteristics in the electromagnetic field data are derived, and a feature fusion matrix is ​​constructed. The basic features of different physical fields are fused according to the weights to generate a comprehensive feature fusion matrix. S4. Call the comprehensive feature fusion matrix, filter the fused feature parameters according to the preset feature screening rules, identify the target area that meets the specific geological structure characteristics according to the geological structure model matching rules, and generate deep geological target identification results.

2. The deep underground multi-physics field detection method according to claim 1, characterized in that: In step S1, the multi-physics field original data set includes seismic wave field data and electromagnetic field data.

3. The deep underground multi-physics field detection method according to claim 1, characterized in that: In step S2, the basic characteristic parameter set includes a seismic wave characteristic parameter group and an electromagnetic wave characteristic parameter group.

4. The deep underground multi-physics field detection method according to claim 1, characterized in that: In step S3, the comprehensive feature fusion matrix specifically refers to a matrix generated by feature fusion matrix operation.

5. The deep underground multi-physics field detection method according to claim 1, characterized in that: In step S4, the deep geological target identification result specifically refers to the identification result of the target area that meets the specific geological structure characteristics.

6. A detection system applied to the deep underground multi-physical field detection method according to any one of claims 1 to 5, characterized in that: The system includes: Data acquisition and feature extraction module: collects seismic wave field data, detects the location of waveform peaks and troughs, calculates adjacent distances to determine wave velocity parameters, counts amplitude variation ranges and records maximum and minimum values; collects electromagnetic field data, measures electromagnetic field intensity changes to calculate resistivity, and records polarizability parameters; generates seismic wave characteristic parameters and electromagnetic field characteristic parameters; Feature fusion matrix construction module: obtains the characteristic parameters of seismic wave amplitude and electromagnetic field resistivity, determines the correlation coefficient as the fusion weight based on previous detection data and geological models, sets other fusion weights according to association rules, sums the parameters according to the weights and fuses other features to construct a multi-physics field feature fusion matrix, and generates a multi-physics field feature fusion matrix; Adaptive filtering and feedback control module: monitors the amplitude and frequency change rate of the feature fusion matrix signal in real time, determines whether the change rate exceeds the threshold to trigger the filter, compares the signal difference and updates the filter coefficient. When the signal-to-noise ratio meets the standard, the adjustment stops, otherwise the update continues to generate an optimized detection signal. Geological structure identification module: Analyzes the optimized detection signal, uses the identification algorithm combined with the geological model to identify the deep geological structure characteristics and generate deep geological structure characteristic parameters; Target information confirmation module: Based on the geological structure characteristic parameters, the confirmation rules are used to locate and confirm the attributes of the target and generate target information.

7. The intelligent management system for the deep underground multi-physical field detection method according to claim 6, characterized in that: The multi-physics field original data set includes seismic wave field data and electromagnetic field data.

8. The intelligent management system for the deep underground multi-physical field detection method according to claim 6, characterized in that: The basic characteristic parameter set includes a seismic wave characteristic parameter group and an electromagnetic wave characteristic parameter group.

9. The intelligent management system for the deep underground multi-physical field detection method according to claim 6, characterized in that: The comprehensive feature fusion matrix specifically refers to a matrix generated by feature fusion matrix operations. In the feature fusion matrix construction module, the calculation formula for constructing the multi-physics field feature fusion matrix is ​​calculated in the following manner: Assume that the multi-physics field feature fusion matrix is ​​M, and its calculation formula is: M=W1×S1+W2×S2+W3×E1+W4×E2+…+W n ×X n Among them, W1, W2, W3, W4, ..., W n are the fusion weight values ​​corresponding to the seismic wave amplitude characteristic parameter, seismic wave velocity characteristic parameter, electromagnetic field resistivity characteristic parameter, electromagnetic field polarizability characteristic parameter and other multi-physical field characteristic parameters, and their value ranges are all [0,1], and the sum of all weight values ​​is 1. S1 is the seismic wave amplitude characteristic parameter, S2 is the seismic wave velocity characteristic parameter, E1 is the electromagnetic field resistivity characteristic parameter, E2 is the electromagnetic field polarizability characteristic parameter, and X n are other multi-physics field characteristic parameters.

10. The intelligent management system for the deep underground multi-physical field detection method according to claim 6, characterized in that: The deep geological target identification result specifically refers to the identification result of the target area that meets the specific geological structure characteristics.

Images