Deep geothermal energy micro-motion exploration method based on high and low frequency cooperation

Abstract

The invention relates to the technical field of deep geothermal energy exploration, and discloses a deep geothermal energy micro-motion exploration method, which comprises the steps of setting an exploration system and an instrument and acquiring data, processing high frequency by using an ESPAC method, processing low and medium frequency data by using an out-of-array phase shift method, and reflecting stratum information by combining high and low frequency inversion processing. Finally, the position and depth of the geothermal reservoir are judged according to the inversion result, and a basis is provided for development. The SPAC method and the out-of-array phase shift method are combined, high and low frequency information is processed separately, the defect that a traditional linear SPAC method is limited in low and medium frequency information extraction and deep detection due to the aperture problem is overcome, the longitudinal resolution is improved, a high-precision continuous frequency dispersion curve is obtained, the details of underground geology and geothermal reservoirs are accurately reflected, and the method is suitable for small-aperture linear arrangement and has a wide application prospect. And compared with a traditional circular distribution table, the device is more economical and convenient, the exploration cost is effectively reduced, and geothermal exploration has more advantages in cost control.

Description

Technical Field

[0001] The present application relates to the technical field of deep geothermal energy exploration, and in particular to a deep geothermal energy micro-motion exploration method based on high- and low-frequency coordination. Background Art

[0002] In the context of global response to climate change, geothermal energy is becoming more and more important, but traditional geothermal exploration methods have drawbacks. Artificial seismic detection is costly, and is limited by the site and environment, which may affect the surrounding environment. Although high-precision gravity measurement can provide geological information, it is subject to much interference in complex environments, and the equipment cost is high and the data processing is complex.

[0003] Micro-seismic exploration technology brings hope to geothermal exploration. It is based on micro-seismic signals naturally generated by the earth. These micro-seismic signals originate from a variety of natural and artificial sources, such as weak vibrations generated by atmospheric movement, ocean waves, and traffic activities. They carry rich underground geological structure information during the propagation process inside the earth. The surface wave component (mainly Rayleigh wave) in the micro-seismic signal has a unique dispersion characteristic. Its propagation speed varies with frequency and is closely related to the shear wave velocity of the formation. Rayleigh waves of different wavelengths can reflect geological information at different depths, which provides a theoretical basis for detecting underground formation structures and geothermal reservoirs by analyzing micro-seismic signals. However, in practical applications, the existing micro-seismic exploration technology is not accurate and complete in extracting dispersion curves, especially in low-frequency areas. The resolution is low, and it is difficult to fully and accurately reflect the characteristics of deep geological structures. The accuracy and reliability of deep detection still need to be improved. Summary of the invention

[0004] In order to solve the problem that the existing micro-seismic exploration technology is not accurate and complete in the extraction of dispersion curves in practical applications, especially in the low-frequency area, the resolution is low, and it is difficult to fully and accurately reflect the characteristics of deep geological structures, the present application provides a deep geothermal energy micro-seismic exploration method based on the coordination of high and low frequencies.

[0005] The present application provides a method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination, which adopts the following technical solutions:

[0006] A deep geothermal energy micro-vibration exploration method based on high and low frequency synergy, comprising:

[0007] S1. Data collection preparation, setting up exploration system and instruments: determine the target geothermal area, and arrange the Smart Solo node-type seismic measurement system in a linear layout mode above;

[0008] S2, data acquisition: start the instrument and automatically collect micro motion signal data according to preset parameters;

[0009] S3. Data collection and processing methods:

[0010] S31, high-frequency processing: using the extended autocorrelation method to process the linear station data, extracting the dispersion curve to invert the shear wave velocity, and extracting the high-frequency information;

[0011] S32, medium and low frequency processing process: using the out-of-array phase shift method to process seismic wave data, extract dispersion curves, and extract medium and low frequency information;

[0012] S33, high and low frequency joint inversion processing: Combine the effective frequency bands of the first two methods to reflect the formation information;

[0013] S4. Result analysis and application: Determine the location and depth of geothermal reservoirs based on the inversion results to provide a basis for geothermal energy development.

[0014] Preferably, the Smart Solo node-type seismic measurement system is composed of multiple autonomous node units, each node integrating independent data recording components and high-precision sensors, which can accurately capture subtle characteristics of seismic activity, cover a wide spectrum from low frequency to high frequency, support multi-channel synchronous data acquisition, and have automation capabilities, including automatic data trigger recording, seamless data transmission, and intelligent power management.

[0015] Preferably, the high frequency processing process is as follows:

[0016] Step 1: Use the extended autocorrelation method based on the SPAC method to process the linear array data and calculate the cross-correlation spectrum formula between any two points A and B in the array:

[0017]

[0018] Where M is the number of segments of the observation data of stations A and B. This formula is used to calculate the cross-correlation spectrum, which is the basis for obtaining the dispersion energy map later. By setting the appropriate aperture, performing an autocorrelation calculation for every N stations, processing the data separately in segments of every X minutes and then superimposing them, setting the frequency, and obtaining the dispersion energy map;

[0019] The average cross-correlation spectrum can be obtained by superimposing the cross-correlation spectra of all station pairs. ,

[0020] The average cross-correlation spectrum is obtained by superimposing the cross-correlation spectra of all station pairs to further assist in data analysis;

[0021] Step 2: Manually extract the dispersion curve from the dispersion energy diagram, use Y points to extract and ensure the smoothness of the curve, integrate the dispersion curves of all stations, calculate the initial velocity model, and then use surface wave analysis software to invert the shear wave velocity and extract high-frequency information.

[0022] Preferably, the medium and low frequency processing process is as follows:

[0023] Step 1: Use the cross-correlation seismic interferometry method to calculate the cross-correlation function between all stations. :

[0024] Where ⨂ represents the cross-correlation operation, G(B, S, t) and G(A, S, t) represent the Green's function from the virtual source S to the detectors B and A, respectively, and G(B, S, -t) represents the negative time signal transmitted from the virtual source S to the detector B;

[0025] Step 2: Determine the noise source according to the cross-correlation function diagram, select a suitable sub-array, take the kth source outside the sub-array as an example as a virtual source for calculation, and calculate the dispersion energy of the source and all stations in the array. :

[0026]

[0027] In the formula, and denote the true phase velocity and scanning phase velocity at position x respectively. Assuming that the layered medium below the sub-array has the same phase shift as that inside the array, the propagation path is divided into outside the array and inside the array, and we can get

[0028]

[0029] By calculating the dispersion energy through these formulas, the frequency range is set, and then the dispersion energy diagram is obtained;

[0030] Step 3: Manually extract the dispersion curve from the dispersion energy diagram, use y points for extraction and ensure the smoothness of the curve, integrate the dispersion curves of all stations, extract the medium and low frequency information, and make up for the shortcomings of the SPAC method in the low frequency band.

[0031] Preferably, the high- and low-frequency joint inversion processing process is as follows:

[0032] Step 1: Combine the effective low frequency obtained by the out-of-array phase shift method with the effective high frequency obtained by the SPAC method, place the dispersion curves obtained by the two methods in the same frequency-phase velocity domain, connect the dispersion curves extracted by the two methods under the same station one by one, connect the integrated points with a smooth curve, and obtain a complete dispersion curve from low frequency to high frequency;

[0033] Step 2: After obtaining the combined dispersion curve, calculate the initial velocity model of the dispersion curve under each station according to the empirical formula;

[0034] Step 3: Use surface wave analysis software to perform shear wave velocity inversion to obtain a more accurate shear wave velocity structure of the underground geothermal reservoir, which can reflect the stratigraphic stratification and geothermal reservoir location information.

[0035] Preferably, the empirical formula is:

[0036] The phase velocity of the dispersion curve extracted by the two methods at each station is divided by the frequency and multiplied by 0.63. The result is used as the detection depth; the phase velocity is divided by 0.88 and the result is used as the propagation velocity of the S wave in this detection depth.

[0037] In summary, this application includes the following beneficial technical effects:

[0038] 1. The present invention adopts an innovative combination of technologies to accurately obtain micro-surface wave information, break through the difficulty of extracting dispersion curves, obtain high-precision continuous dispersion curves, and accurately reflect the details of underground geology and geothermal reservoirs; compared with existing technologies, it can accurately identify the differences in strata and reservoir characteristics, provide a reliable basis for geothermal development, greatly improve exploration accuracy and reliability, and make geothermal energy exploration more accurate and deep.

[0039] 2. The present invention combines the SPAC method with the out-of-array phase shift method to process high- and low-frequency information separately, overcoming the limitations of the traditional linear SPAC method in extracting medium- and low-frequency information and deep detection due to aperture problems. It is suitable for small-aperture linear deployment. Compared with the existing technology, it not only improves the vertical resolution, but is also more economical and convenient than the traditional circular deployment, effectively reducing the exploration cost, making geothermal exploration more advantageous in cost control, and facilitating large-scale promotion and application;

[0040] 3. The present invention uses automated instruments and real-time monitoring to ensure data quality and efficiency, reduce the risk of human error and data loss, and during processing, reasonable algorithms and parameter optimization accelerate dispersion curve extraction and inversion; compared with the existing technology, it greatly shortens the exploration time, can quickly provide accurate geological information for geothermal development, improve the overall exploration and development efficiency, assist in clean energy development and energy transformation, meet social energy needs, and promote environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of an embodiment of the present application;

[0043] Figure 2 This is a satellite image of Xiaoyangkou Town;

[0044] Figure 3 This is a satellite image of the survey line of Xiaoyangkou Town;

[0045] Figure 4 This is a partial dispersion energy diagram calculated by the SPAC method using Xiaoyangkou Town as an example in this application; in the figure:

[0046] (a) Channel 8; (b) Channel 11; (c) Channel 14; (e) Channel 17; (f) Channel 20; (g) Channel 23; (h) Channel 29; (i) Channel 32;

[0047] Figure 5 This is an integrated graph of 28 dispersion curves extracted by the SAPC method using Xiaoyangkou Town as an example in this application;

[0048] Figure 6 This is a geothermal shear wave velocity structure diagram inverted by the SAPC method using Xiaoyangkou Town as an example in this application;

[0049] Figure 7 This is a cross-correlation function diagram of the stations at both ends of the survey line of the present application taking Xiaoyangkou Town as an example, in which:

[0050] Left: Station 450150130, Station 40;

[0051] Right: Station 450150672 No. 1;

[0052] Figure 8 This is a schematic diagram of the actual calculation process of the phase shift outside the array using Xiaoyangkou Town as an example of the present application; in the figure:

[0053] Above: Schematic diagram of the first step of the calculation of the out-of-array phase shift method, where station 1 (red) is a virtual source;

[0054] Bottom: Schematic diagram of the second step of the calculation of the out-of-array phase shift method, where stations 1 and 2 (red) are virtual sources;

[0055] Fig. 9 This is a partial dispersion energy diagram calculated by the phase shift extraction method outside the array using Xiaoyangkou Town as an example in this application; in the figure:

[0056] (a) Channel 8; (b) Channel 11; (c) Channel 14; (e) Channel 17; (f) Channel 20; (g) Channel 23; (h) Channel 29; (i) Channel 32;

[0057] Fig.10 This is an integrated graph of 28 dispersion curves extracted by the out-of-array phase shift method using Xiaoyangkou Town as an example in this application;

[0058] Fig.11 This is an integrated diagram of the dispersion curves of the phase shift method (red solid line) and the SPAC method (black solid line) using Xiaoyangkou Town as an example in this application;

[0059] Fig.12 This is a docking diagram of 27 dispersion curves calculated by combining the out-of-array phase shift method with the SPAC method using Xiaoyangkou Town as an example;

[0060] Fig.13 This application takes Xiaoyangkou Town as an example and combines the geothermal shear wave velocity structure diagram based on the off-array phase shift method and the SPAC method. DETAILED DESCRIPTION

[0062] The following is combined with Figure 1-13 This application is described in further detail.

[0063] The embodiment of the present application discloses a deep geothermal energy micro-vibration exploration method based on high and low frequency coordination. The area above a known geothermal well in Xiaoyangkou, Rudong, Jiangsu is selected as the exploration target area for verification exploration, and combined with the existing geothermal exploration results of Xiaoyangkou, Rudong, Jiangsu, an analysis and demonstration is carried out.

[0064] Reference Figure 1 , a deep geothermal energy micro-seismic exploration method based on high and low frequency synergy, including:

[0065] S1. Data collection preparation, setting up exploration system and instruments: determine the target geothermal area, and arrange the Smart Solo node-type seismic measurement system on it using a linear layout method.

[0066] S2. Data collection: Start the instrument and automatically collect micro-motion signal data according to preset parameters.

[0067] S3. Data collection and processing methods:

[0068] S31. High-frequency processing: The extended autocorrelation method is used to process the linear station data, extract the dispersion curve, invert the shear wave velocity, and extract the high-frequency information.

[0069] S32, medium and low frequency processing process: Use the off-array phase shift method to process seismic wave data, extract dispersion curves, and extract medium and low frequency information.

[0070] S33, high and low frequency joint inversion processing: Combine the effective frequency bands of the first two methods to reflect the formation information.

[0071] S4. Result analysis and application: Determine the location and depth of geothermal reservoirs based on the inversion results to provide a basis for geothermal energy development.

[0072] Exploration preparation stage

[0073] Determine the target area and instrument selection: Select the area above the known geothermal well in Xiaoyangkou, Rudong, Jiangsu as the exploration target area, such as Figure 2As shown in the figure, the Smart Solo node-type seismic measurement system of Shenzhen Mianyuan Intelligent Technology Co., Ltd. is used. The system consists of multiple autonomous node units, integrates independent data recording components and high-precision sensors, can capture subtle characteristics of seismic activity, covers low-frequency to high-frequency spectrum, supports multi-channel synchronous data acquisition, and has automation capabilities (automatic data trigger recording, seamless data transmission, intelligent power management, etc.). Its rugged shell can adapt to harsh environments.

[0074] Design observation system: 40 Smart Solo node-type seismometers are used to lay out a linear survey line above the target area, with a station spacing of 100m, a total survey line length of 4km, an observation time of 14 days, and an estimated observation depth of 2km.

[0075] Acquisition parameter setting and implementation: The acquisition period is from July 12 to July 26, 2023. The instruments are numbered from 1 to 40 from the coastline (northeast) to the land (southwest). The instrument number is 1 (450150130) near the coast, and the instrument number is 40 (450150672) near the land end. The instrument was left to stand for two weeks and then recovered. The output obtained the initial seismic data, such as Figure 2 shown.

[0076] High frequency processing based on SPAC method

[0077] The extended autocorrelation method is used to process the linear station data. The aperture is set to 1400m. An autocorrelation calculation is performed every 15 stations. The autocorrelation calculation of stations 1 to 15 will obtain the dispersion energy map under station 8. A total of 28 calculations are performed. The data are processed separately in segments of 10 minutes each and then superimposed. The processing and superposition of long-term observation data can amplify the effective results and suppress interference. The frequency is set to 0.1~8Hz. Finally, a total of 28 dispersion energy maps are obtained. The dispersion energy maps of some stations are selected as follows Figure 4

[0078] The formula for calculating the cross-correlation spectrum between any two points A and B in the array is:

[0079]

[0080] Where M is the number of segments of the observation data of stations A and B. This formula is used to calculate the cross-correlation spectrum, which is the basis for the subsequent dispersion energy map. The average cross-correlation spectrum can be obtained by superimposing the cross-correlation spectra of all station pairs. ,

[0081]

[0082] The average cross-correlation spectrum is obtained by superimposing the cross-correlation spectra of all station pairs, and the data is further assisted in analysis. The dispersion curve is manually extracted from the dispersion energy diagram, 10 points are used for extraction and the curve is smooth, the dispersion curves of all stations are integrated, the initial velocity model is calculated, and then the surface wave analysis software (such as Program3.30) is used to perform shear wave velocity inversion and extract high-frequency information. However, this method has limited resolution in the low-frequency region.

[0083] After obtaining the dispersion energy map, the dispersion curve is manually extracted from the dispersion energy map. In the SPAC method, 10 points are used to extract the dispersion curve. Due to the different stratigraphic structures under different stations, the extracted dispersion curves will be slightly different, but the dispersion curves need to be kept as smooth as possible during extraction. The dispersion curves of each station (station 8 to station 34) are extracted and integrated to obtain Figure 5 .

[0084] The dispersion curves of all stations extracted by the SPAC method have a high degree of fitting, and the highest phase velocity of the extracted dispersion curves is around 800m / s. Then, all the obtained dispersion curves are used to calculate the initial velocity model below them. Taking station No. 13 as an example, the dispersion curve extracted by the station (black dotted line) is divided by the phase velocity and multiplied by 0.63 according to the empirical formula, and the phase velocity is divided by 0.88, which is considered to be the propagation velocity of the S wave in this detection depth.

[0085] After the initial velocity model is established using the dispersion curve extracted by spatial autocorrelation (SPAC), the shear wave velocity is inverted using a software package. After the above operations are completed for all stations, the shear wave velocity inversion results of the underground structure under the survey line can be obtained, such as Figure 6 .

[0086] Reference Figure 7 The collected continuous seismic wave data are cross-correlated using the cross-correlation seismic interferometry method to calculate the cross-correlation function between all stations. :

[0087]

[0088] Among them, ⨂ represents the cross-correlation operation, G(B, S, t) and G(A, S, t) represent the Green's function from the virtual source S to the detectors B and A respectively, and G(B, S, -t) represents the negative time signal from the virtual source S to the detector B. There are 40 stations recording the background noise this time. The cross-correlation calculation is performed for all stations, and a total of 780 calculations are required to obtain 780 cross-correlation functions. The positive and negative branches of the cross-correlation function diagram can reflect the source signal strength on both sides of the survey line. The positive and negative branches in the cross-correlation function of the linear array represent the noise sources from two different ends respectively. The stations at both ends of the survey line are selected to draw the cross-correlation function diagram. The seismic wave data are all concentrated in the negative branch, and the imaging quality is good, which means that the noise source of the background noise is concentrated outside the survey line. Normally, the method of superimposing the positive and negative branches is used to eliminate the uneven influence of the noise source. However, since the positive branch seismic wave data is not effective and the imaging is not obvious, it means that it does not carry enough effective information. Therefore, the calculation of the positive branch will be ignored in the subsequent cross-correlation phase shift calculation, and the method of calculating the negative branch alone will be used.

[0089] After obtaining the cross-correlation function of all stations, a station outside the sub-array is selected as the source. It is assumed that the surface wave is excited from the source and scattered by the underground medium and then received by the sub-array. The aperture of the sub-array is set to 1200m (r=600m, j=6), so the number of stations in the array is 13 ( ), the number of stations outside the array is 27 ( ). When setting up substations, it is necessary to reserve the initial out-of-array stations for simulating earthquake sources. Therefore, the initial substation array numbers are set from station 2 to station 14. Figure 8 Station 1 is used as a virtual source to perform the first calculation to obtain the dispersion energy diagram under station 8; then the sub-array is displaced as a whole, and the sub-array for the second calculation is from station 3 to station 15, using stations 1 and 2 as virtual sources to calculate and superimpose them respectively; the dispersion energy diagram under station 9 is obtained, as shown in Fig. 9 As shown. The calculations were performed sequentially. The last calculation was for sub-arrays from station 28 to station 40. Stations 1 to 27 were calculated and superimposed as virtual sources to obtain the dispersion energy diagram under station 34. A total of 27 calculations were performed. Theoretically, as the sub-array moves toward the end, the more stations are used as virtual sources, the more superimposed calculation data, the more interference from other seismic waves can be eliminated, and the better the presentation of the dispersion energy diagram. Fig. 9 The variation of the dispersion image is consistent with the theory, proving that the out-of-array phase shift method is suitable for use in this linear survey line.

[0090] Take the source outside the sub-array as an example as a virtual source to calculate the dispersion energy of this source and all stations in the array :

[0091] .

[0092] Set the frequency range to 0.1-2 Hz and obtain the dispersion energy diagram.

[0093] After calculation by the phase shift method outside the array, the dispersion curve is manually extracted. Since the dispersion energy is concentrated in 0.1~1.5Hz, 8 points are used to extract the dispersion curve this time to ensure the smoothness of the extracted dispersion curve. After extraction, all dispersion curves are integrated to obtain Fig.10 , extract mid- and low-frequency information and make up for the shortcomings of the SPAC method in the low-frequency band.

[0094] The effective low frequency obtained by the out-of-array phase shift method is combined with the effective high frequency obtained by the SPAC method, and the dispersion curves obtained by the two methods are placed in the same frequency-phase velocity domain. The dispersion curves extracted by the two methods under the same station are connected one by one, and the integrated points are connected with a smooth curve to obtain a complete dispersion curve from low frequency to high frequency. Fig.11 .

[0095] from Fig.11 It can be seen that the overall trend of the two is the same, with complete continuity, intersecting within 0.5s to 1.2s, with partial differences, and no connection faults in phase velocity, so the conditions for inverting the underground velocity structure are met. Therefore, the dispersion curves extracted by the two methods under the same station are docked one by one. Specifically, the 8 points when the dispersion curve is extracted by the topology phase shift method and the 10 points when the dispersion curve is extracted by the SPAC method are integrated in the same time-velocity domain, and the 18 points are connected with a smooth curve. In this way, a continuous and smooth dispersion curve from low frequency to high frequency is obtained. The dispersion curves extracted by the two methods for each station are docked one by one and integrated. Fig.12 ,

[0096] After obtaining the combined dispersion curve, the initial velocity model of the dispersion curve under each station is calculated according to the empirical formula (the phase velocity of the dispersion curve extracted by the two methods at each station is divided by the frequency and multiplied by 0.63 as the detection depth; the phase velocity is divided by 0.88 as the wave propagation velocity in this detection depth).

[0097] Surface wave analysis software is then used to perform shear wave velocity inversion to obtain a more accurate shear wave velocity structure of the underground geothermal reservoir, which can reflect information such as stratification and geothermal reservoir location.

[0098] The dispersion curves based on the splicing of the two methods are obtained, and the initial velocity model is calculated, and the shear wave velocity structure is inverted through the software package. Taking station 8 as the 0-meter starting point, the difference calculation is performed for each station, and the geothermal velocity structure of Xiaoyangkou, Rudong County, Jiangsu Province can be obtained, such as Fig.13 shown.

[0099] Combining SPAC and the phase shift method outside the array to extract the dispersion curve, the inverted formation detection depth is about 2800m. The proven geothermal wells in the Xiaoyangkou area of ​​Rudong include the Jinhadao geothermal well, geothermal well 1, geothermal well 2 and geothermal well 3. The aquifer of geothermal well 2 is located at 2105.40-2406.5m, which is consistent with the inversion results. Fig.13 The middle D layer The velocity structure of the location is consistent with the low velocity anomaly; the Jinge Island geothermal well is located less than 2 km from the geothermal well 3, which is consistent with the Fig.13 The middle D layer The velocity structure of the local area is consistent with the low speed, which verifies the reliability of the results of the method applied.

[0100] Comparison and verification with other methods: Xu Peifen and others used micro-motion technology to detect underground geothermal reservoirs in Wujiang City, Jiangsu Province. The velocity structure inversion results showed S-wave low-velocity anomalies in the uniform bedrock layer. The low-velocity anomaly morphology of the inversion results of the geothermal reservoir velocity structure in this paper is similar, but Xu Peifen's team used a single SPAC method and a different wiring method (continuous nested triangle wiring was used to approximate circular wiring, and the observation aperture was approximately 693m). This application achieved a similar detection depth through linear layout combined with SPAC and off-array phase shift method, and obtained the final results that were consistent with the actual geological data, verifying the feasibility and advantages of the method of this application.

[0101] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, which may refer to mechanical connection or electrical connection, or internal communication between two components, or direct connection. "upper", "lower", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may change;

[0102] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0103] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0104] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A deep geothermal energy micro-vibration exploration method based on high and low frequency coordination, characterized in that: include: S1. Data collection preparation, setting up exploration system and instruments: determine the target geothermal area, and arrange the Smart Solo node-type seismic measurement system in a linear layout mode above; S2, data acquisition: start the instrument and automatically collect micro motion signal data according to preset parameters; S3. Data collection and processing methods: S31, high-frequency processing: using the extended autocorrelation method to process the linear station data, extracting the dispersion curve to invert the shear wave velocity, and extracting the high-frequency information; S32, medium and low frequency processing process: using the out-of-array phase shift method to process seismic wave data, extract dispersion curves, and extract medium and low frequency information; S33, high and low frequency joint inversion processing: Combine the effective frequency bands of the first two methods to reflect the formation information; S4. Result analysis and application: Determine the location and depth of geothermal reservoirs based on the inversion results to provide a basis for geothermal energy development.

2. The method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination according to claim 1 is characterized in that: The Smart Solo node-based seismic measurement system consists of multiple autonomous node units. Each node integrates independent data recording components and high-precision sensors, which can accurately capture subtle characteristics of seismic activity, cover a wide spectrum from low frequency to high frequency, support multi-channel synchronous data acquisition, and have automation capabilities, including automatic data trigger recording, seamless data transmission, and intelligent power management.

3. The method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination according to claim 1 is characterized in that: The high frequency processing process is as follows: Step 1: Use the extended autocorrelation method based on the SPAC method to process the linear array data and calculate the cross-correlation spectrum formula between any two points A and B in the array: Where M is the number of segments of the observation data at stations A and B. This formula is used to calculate the cross-correlation spectrum, which is the basis for the subsequent dispersion energy map. By setting the appropriate aperture, performing autocorrelation calculations for every N stations, superimposing the data after individual processing every X minutes, and setting the frequency, a dispersion energy map is obtained; The cross-correlation spectra of all station pairs are superimposed to obtain the average cross-correlation spectrum , The average cross-correlation spectrum is obtained by superimposing the cross-correlation spectra of all station pairs to further assist in data analysis; Step 2: Manually extract the dispersion curve from the dispersion energy diagram, use Y points to extract and ensure the smoothness of the curve, integrate the dispersion curves of all stations, calculate the initial velocity model, and then use surface wave analysis software to invert the shear wave velocity and extract high-frequency information.

4. The method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination according to claim 1 is characterized in that: The medium and low frequency processing process is as follows: Step 1: Use the cross-correlation seismic interferometry method to calculate the cross-correlation function between all stations. : Where ⨂ represents the cross-correlation operation, G(B, S, t) and G(A, S, t) represent the Green's function from the virtual source S to the detectors B and A, respectively, and G(B, S, -t) represents the negative time signal transmitted from the virtual source S to the detector B; Step 2: Determine the noise source according to the cross-correlation function diagram, select a suitable sub-array, take the kth source outside the sub-array as an example as a virtual source for calculation, and calculate the dispersion energy of the source and all stations in the array. : In the formula, and denote the true phase velocity and scanning phase velocity at position x respectively. Assuming that the layered medium below the sub-array has the same phase shift as that inside the array, the propagation path is divided into outside the array and inside the array, and we can get By calculating the dispersion energy through these formulas, the frequency range is set, and then the dispersion energy diagram is obtained; Step 3: Manually extract the dispersion curve from the dispersion energy diagram, use y points for extraction and ensure the smoothness of the curve, integrate the dispersion curves of all stations, extract the medium and low frequency information, and make up for the shortcomings of the SPAC method in the low frequency band.

5. The method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination according to claim 1 is characterized in that: The high- and low-frequency joint inversion process is as follows: Step 1: Combine the effective low frequency obtained by the out-of-array phase shift method with the effective high frequency obtained by the SPAC method, place the dispersion curves obtained by the two methods in the same frequency-phase velocity domain, connect the dispersion curves extracted by the two methods under the same station one by one, connect the integrated points with a smooth curve, and obtain a complete dispersion curve from low frequency to high frequency; Step 2: After obtaining the combined dispersion curve, calculate the initial velocity model of the dispersion curve under each station according to the empirical formula; Step 3: Use surface wave analysis software to perform shear wave velocity inversion to obtain a more accurate shear wave velocity structure of the underground geothermal reservoir, which can reflect the stratigraphic stratification and geothermal reservoir location information.

6. The method for deep geothermal energy micro-vibration exploration based on high and low frequency coordination according to claim 5 is characterized in that: The empirical formula is: The phase velocity of the dispersion curve extracted by the two methods at each station is divided by the frequency and multiplied by 0.

63. The result is used as the detection depth; the phase velocity is divided by 0.88 and the result is used as the propagation velocity of the S wave in this detection depth.

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