Passive-active source combined geologic identification method and system constrained by geophysical space

Abstract

The application belongs to the technical field of tunnel advanced velocity field reconstruction, and specifically discloses a method and system for geologic identification of active and passive source combination under the constraint of geochemical space, which comprises the following steps: calculating the information entropy of geochemical identification, and the geochemical velocity prior and uncertainty at the position of a borehole; calculating the geochemical space extrapolation weight according to a geochemical space extrapolation function, taking the geochemical velocity prior and uncertainty at the position of the borehole as a data source, and constructing the spatial velocity prior and prior variance; respectively collecting passive source seismic data and active source seismic data in the drilling process, constructing a joint full waveform inversion function, and obtaining the inversion result including the velocity field, the geochemical constraint weight distribution, the active and passive source data fitting residual, and the interpretation uncertainty through iterative solution. The application makes up for the uncertainty and insufficient illumination of the passive source with the active source controllable waveform, guides the inversion direction with the geochemical prior constraint, and realizes the velocity field reconstruction of multi-physical field cooperation.

Description

Technical Field

[0001] This invention relates to the field of tunnel advance velocity field reconstruction technology, and in particular to a geochemical spatial constraint active and passive source joint geological identification method and system. 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] In drill-and-blast tunnel construction, unfavorable geological formations such as fault fracture zones, abrupt transitions between soft and hard rock, alteration zones, and water-rich karst are often distributed ahead of the tunnel face. Failure to accurately determine the velocity structure and geological interface location ahead can easily lead to disasters such as over-excavation, under-excavation, collapse, and sudden water or mudslides, while also causing risks related to inappropriate selection of support parameters and construction organization. Therefore, accurately reconstructing the velocity field of the surrounding rock ahead of the tunnel face is the core of advanced geological prediction and construction risk control for tunnels, and seismic full-waveform inversion is a key technology for achieving refined velocity modeling of the surrounding rock.

[0004] The borehole cuttings and dust generated during advanced drilling are in-situ rock mass products ahead of the tunnel face. Their elemental composition, oxide ratio, and mineral spectrum characteristics, among other geochemical indicators, directly reflect the lithology, alteration degree, integrity, and water-bearing fracture state of the rock mass. Furthermore, they are closely related to the seismic wave velocity of the rock mass, providing reliable geological prior constraints for seismic inversion. Current technologies mostly use geochemical cuttings data only for post-hoc interpretation and local verification, failing to effectively integrate them into the inversion modeling process, resulting in low data utilization. Simultaneously, geochemical sampling is only distributed one-dimensionally along the borehole trajectory; uniform constraints across the entire area or rigid extrapolation can easily lead to velocity field distortion and geological misjudgments. Completely discarding these samples would waste genuine in-situ geological information, lacking a reliable spatial constraint control mechanism. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a geochemical spatial constraint-based active and passive source joint geological identification method and system. This method utilizes geochemical slag powder to achieve adaptive spatial constraint of the velocity field, and relies on active source data to compensate for the uncertainties of passive source sources and insufficient illumination. This effectively reduces the ambiguity of inversion and improves the stability of velocity field reconstruction and the positioning accuracy of adverse geological interfaces.

[0006] In some implementations, the following technical solutions are adopted: A geochemical spatially constrained method for joint active and passive source geological identification includes: Slag powder samples from the area to be tested are obtained and geochemical analysis is performed to obtain geochemical feature vectors, which include element or oxide content, element ratios, mineral spectrum characteristics and geochemical anomaly indicators. Geochemical feature vectors are mapped to lithology or stratigraphic class probabilities through a geochemical identification model, and the information entropy of geochemical identification, as well as the prior and uncertainty of geochemical velocity at the borehole location, are calculated. A geochemical spatial extrapolation function is constructed, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on the geochemical spatial extrapolation function. Using the prior and uncertainty of geochemical velocity at the borehole location as data sources, spatial velocity priors and prior variances are constructed. Passive source seismic data and active source seismic data were collected separately during the drilling process and preprocessed. A joint full waveform inversion function is constructed, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term; The joint full waveform inversion function is iteratively solved to obtain inversion results including velocity field, geochemical constraint weight distribution, fitting residuals of active and passive source data, and interpretation of uncertainties.

[0007] As a further step, the information entropy of geolocation identification is calculated, specifically as follows: No. The summation of the product of the probability of each sample being identified as a lithological or stratigraphic class and the logarithmic measure of the probability uncertainty, and then normalized, yields the nth sample. Information entropy of each sample localization identification.

[0008] As a further step, the prior and uncertainty of geochemical velocity at the borehole location are calculated, specifically: No. The sum of the products of the probability of each sample being identified as each lithology or stratigraphic category and the statistical mean of the corresponding velocity for each category is used as the prior geochemical velocity at the borehole location. The uncertainty is obtained by summing the class mixture variance, model error, and sample quality error.

[0009] As a further solution, the geospatial extrapolation function is specifically as follows: ; in, Indicates the location to be estimated Spatial extrapolation weights at the location, Indicates the location to be estimated With sample position Anisotropic distance; Information entropy for localized identification, For sample quality factor, This is a geological trend consistency function.

[0010] As a further approach, using the geochemical velocity prior and uncertainty at the borehole location as the data source, a spatial velocity prior and prior variance are constructed, specifically as follows: The spatial velocity prior field is: the position to be estimated. The spatial velocity prior value is the ratio of the sum of the velocity prior values ​​of all samples after weighting them according to their respective weights to the sum of all weights. The prior variance is the sum of the spatial dispersion of the prior geochemical velocity of the surrounding area, the uncertainty of the geochemical interpretation of a single sample, and the geostatistical extrapolation variance or kriging variance.

[0011] As a further solution, it also includes: constructing the weights of the geospatial prior constraint terms in the joint full waveform inversion function based on the prior variance; the weights are obtained by normalizing the inverse of the prior variance.

[0012] As a further approach, the joint full-waveform inversion function specifically includes: Virtual shot gather waveform fitting term: For each pair of detectors (i,j), calculate the difference between the measured cross-correlation and the simulated cross-correlation, take the square integral after weighting, and then sum over all detector pairs to obtain the first scalar residual value; Active source waveform fitting term: For each source-detector combination, calculate the difference between the measured waveform and the simulated waveform, take the square after weighting, and integrate along time to obtain the corresponding waveform residual; Sum the waveform residuals of all detectors to accumulate the residuals of all receiver channels under the excitation of this source; sum them over all active sources to obtain the total residuals of all active source observations; Geomorphic velocity prior constraint: For each location x in the spatial domain, calculate the square of the difference between the current velocity model and the geomorphic prior velocity, multiply it by the constraint weight of that location, and then sum the integrals over the entire space to obtain the second scalar residual value; Geological trend constraint term: Apply the trend operator to the current velocity model at each spatial location x to obtain a quantity reflecting the degree to which the velocity field deviates from the geological trend at that location; take the square of the modulus of the quantity, and then integrate and sum it over the entire spatial domain; Smoothing regularization term: take the square of the current velocity model modulo 2, and then integrate and sum over the entire spatial domain.

[0013] In other embodiments, the following technical solutions are adopted: A geochemical spatially constrained active and passive source joint geological identification system includes: The geochemical data acquisition module is used to acquire slag powder samples from the area to be tested and perform geochemical analysis to obtain geochemical feature vectors. The geochemical feature vectors include element content, oxide content, element ratio, mineral spectrum characteristics, and geochemical anomaly indicators. The geochemical category identification module is used to map geochemical feature vectors to lithology or stratigraphic category probabilities through the geochemical identification model, calculate the information entropy of geochemical identification, and the prior and uncertainty of geochemical velocity at the borehole location; The geochemical spatial extrapolation module is used to construct a geochemical spatial extrapolation function, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. The geochemical spatial extrapolation weight is calculated based on the geochemical spatial extrapolation function, and the spatial velocity prior and prior variance are constructed using the geochemical velocity prior and uncertainty at the borehole location as the data source. The active and passive source data acquisition module is used to collect passive source seismic data and active source seismic data during the drilling process, and to perform preprocessing. The joint inversion module is used to construct a joint full waveform inversion function, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term. The joint full waveform inversion function is iteratively solved to obtain inversion results including the velocity field, geochemical constraint weight distribution, active and passive source data fitting residuals, and interpretation uncertainties.

[0014] In other embodiments, the following technical solutions are adopted: A geochemical spatially constrained active and passive source combined geological identification device, comprising: A drilling slag powder acquisition device is used to acquire slag powder samples from the area to be tested. The active and passive source data acquisition device is used to acquire passive source seismic data and active source seismic data during the drilling process in the area to be measured, respectively. The data processing device is configured as follows: Geochemical analysis was performed on the slag powder sample to obtain geochemical feature vectors, which included element or oxide content, element ratios, mineral spectrum characteristics, and geochemical anomaly indicators. Geochemical feature vectors are mapped to lithology or stratigraphic class probabilities through a geochemical identification model, and the information entropy of geochemical identification, as well as the prior and uncertainty of geochemical velocity at the borehole location, are calculated. A geochemical spatial extrapolation function is constructed, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on the geochemical spatial extrapolation function. Using the prior and uncertainty of geochemical velocity at the borehole location as data sources, spatial velocity priors and prior variances are constructed. A joint full waveform inversion function is constructed, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term. The joint full waveform inversion function is iteratively solved to obtain inversion results including the velocity field, geochemical constraint weight distribution, active and passive source data fitting residuals, and interpretation uncertainties.

[0015] As a further embodiment, the drilling slag powder acquisition device includes: a receiving box, with a water inlet at the top of the receiving box, the water inlet being connected to a water inlet pipe, and a water-passing screen structure at the bottom of the water inlet pipe; a storage screen bucket is also provided inside the receiving box, which stores multiple water-passing screens, which can automatically fall from the bottom of the storage screen bucket; a drainage trough is provided at the bottom of the receiving box, through which the water filtered out by the screens flows out. The receiving box is also equipped with cylinder fingers and an internal slide. The internal slide is connected to the external slide located outside the receiving box. The cylinder fingers can push the water screen forward to the lower part of the water inlet pipe under the push of the cylinder. When the water screen is full of slag powder, the cylinder fingers continue to push the water screen forward under the action of the cylinder, so that the water screen slides out of the receiving box from the internal slide and enters the external slide.

[0016] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, the processor being used to implement instructions; the memory being used to store multiple instructions, the instructions being adapted to be loaded and executed by the processor to perform the above-described geochemical spatial constraint-based active and passive source joint geological identification method.

[0017] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described geochemical spatial constraint active-passive source joint geological identification method.

[0018] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention introduces anisotropic geological distance, information entropy, sample quality factor and geological trend consistency to construct an adaptive spatial extrapolation weight function, which transforms one-dimensional borehole geochemical information into a three-dimensional variable-weight velocity prior field, thereby realizing strong constraints in the near area of ​​the borehole, weak constraints in the far area, strong constraints in the low-entropy stable area and weak constraints in the high-entropy uncertain area.

[0019] (2) The present invention establishes a joint objective function that includes passive source waveform fitting term, active source waveform fitting term, geochemical spatial prior constraint term, geological trend constraint term and smoothing regularization term. The active source controllable waveform compensates for the uncertainty of passive source source and insufficient illumination, and the geochemical prior constraint guides the inversion direction to realize the velocity field reconstruction of multi-physics field collaboration.

[0020] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1This is a schematic diagram of the active and passive source joint geological identification method with geochemical spatial constraints in an embodiment of the present invention; Figure 2 This is a schematic diagram of the overall structure of the slag powder sample collection device in an embodiment of the present invention; Figure 3 This is a schematic diagram of the internal structure of the slag powder sample collection device in an embodiment of the present invention; Figure 4 This is a schematic diagram of the drainage trough structure in an embodiment of the present invention; Figure 5 This is a side view of the internal structure of the receiving box according to an embodiment of the present invention; Among them, 1. finger seat, 2. cylinder finger, 3. storage tank, 4. internal slide, 5. water screen, 6. external slide, 7. base plate, 8. dual-shaft cylinder, 9. cylinder gasket, 10. mini cylinder, 11. cylinder bracket, 12. linear bearing, 13. first connecting plate, 14. drainage groove, 15. bracket, 16. anti-rotation rod, 17. little finger, 18. second connecting plate, 19. water inlet pipe. Detailed Implementation

[0022] 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 in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] 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.

[0024] Example 1 To address the problems of uncertainty in passive source seismic data, strong ambiguity in single seismic inversion, limited constraints of geochemical samples, and uncontrollable reliability of velocity field extrapolation in existing technologies, this study continuously collects borehole cuttings samples during drilling. Geochemical analysis is then used to obtain elemental, oxide, mineral spectra, and geochemical anomaly indices, quantifying prior information on lithology, alteration degree, rock mass integrity, and wave velocity. Simultaneously, passive source seismic data is acquired during drilling, and active source seismic observations are conducted during construction intervals. Virtual shot sets for passive sources and controllable shot sets for active sources are constructed respectively, achieving complementary advantages of the two types of seismic data.

[0025] Meanwhile, a geochemical spatial extrapolation function that takes into account geological distance attenuation, spatial trend consistency, and information entropy uncertainty is constructed. An adaptive constraint mechanism is established to achieve strong constraints in the near-field and weak constraints in the far-field, strong constraints in the low-entropy stable area and weak constraints in the high-entropy uncertain area. A reliable velocity prior field and constraint weight field are generated to solve the problem of blind extrapolation of one-dimensional geochemical information and loss of accuracy control.

[0026] Based on this, a joint full waveform inversion objective function is further constructed, which includes passive source waveform fitting term, active source waveform fitting term, geochemical prior constraint term, geological trend constraint term and smoothing regularization term. The high-precision velocity field and uncertainty distribution in front of the tunnel face are obtained by iterative solution.

[0027] Compared to traditional methods using single drilling parameters or purely passive sources, this embodiment utilizes geochemical slag powder to achieve adaptive spatial constraints on the velocity field. It relies on active source data to compensate for the uncertainties of passive source seismic sources and insufficient illumination, effectively reducing the ambiguity of inversion and improving the stability of velocity field reconstruction and the positioning accuracy of adverse geological interfaces. This can provide continuous and quantifiable velocity structure data for advance prediction and construction risk control of drill-and-blast tunnels.

[0028] Based on this, in one or more embodiments, a geochemical spatially constrained active and passive source joint geological identification method is disclosed, combining... Figure 1 Specifically, it includes the following process: S101: Obtain slag powder samples from the area to be tested and perform geochemical analysis to obtain geochemical feature vectors. Geochemical feature vectors include element content or oxide content, element ratios, mineral spectrum characteristics, and geochemical anomaly indicators.

[0029] In this embodiment, the slag powder sample can be collected manually or automatically by setting up a slag powder sample collection device at the borehole opening or the borehole return slag channel.

[0030] The collected slag powder samples were registered according to borehole depth, collection time, and sample number. The geochemical analysis device can be installed online at the rear end of the collection device or analyzed offline. Outputs include elemental content, oxide content, elemental ratios, mineral spectral characteristics, and geochemical anomaly indicators, forming a geochemical feature vector. .

[0031] During the advanced drilling process, the slag collection device collects slag according to the sampling time. Collect the k-th sample. Map the sampling time to the borehole trajectory position using a borehole depth encoder, drill rod length accumulator, advance displacement sensor, or borehole depth recording system. : , (1) in, Represents the borehole trajectory parameter equation. This represents the spatial location of the k-th sample.

[0032] Geochemical analysis of the sample yielded an example of a geochemical feature vector, as shown below: ; Where C represents oxide content, R represents elemental ratio or geochemical anomaly index, and S represents spectral characteristics.

[0033] As an example, element ratios can be selected from the following characteristics: Rb / Sr (rubidium / strontium), V / Cr (vanadium / chromium), Cu / Zn (copper / zinc), Al2O3 / Na2O (aluminum oxide / sodium oxide), MgO / CaO (magnesium oxide / calcium oxide), Ni / Co (nickel / cobalt).

[0034] Geological anomaly indices can be selected from the following indices: enrichment factor (EF), geoaccumulation index (Igeo), chemical alteration index (CIA), europium anomaly (δEu), and mineralization probability index (GMPI).

[0035] Among them, the enrichment factor is used to determine the degree of element enrichment, the geoaccumulation index is used to quantitatively evaluate the degree of heavy metal pollution in sediments, the chemical alteration index is used to quantitatively indicate the intensity of chemical weathering experienced by rocks in the source area, the europium anomaly is used to trace the source and determine the tectonic setting, and the mineralization probability index is used for mineral exploration and prediction. The higher the mineralization probability index, the more significant the comprehensive geochemical anomaly in the region.

[0036] The above-mentioned geological anomaly indices can all be calculated using existing technologies, and will not be described in detail here.

[0037] Loss on ignition is an important component of geochemical feature vectors. It can indirectly reflect information such as mineral composition related to volatiles, organic matter content, and weathering and alteration state in rocks, providing valuable geochemical constraints for subsequent lithological identification and velocity field inversion.

[0038] Depending on the engineering requirements, the above geochemical characteristics can be obtained by XRF (X-ray fluorescence spectroscopy), LIBS (laser-induced breakdown spectroscopy), near-infrared spectroscopy, Raman spectroscopy, or laboratory analysis.

[0039] S102: Map geochemical feature vectors to lithology or stratigraphic category probabilities using a geochemical identification model, calculate the information entropy of geochemical identification, and the prior and uncertainty of geochemical velocity at the borehole location.

[0040] In this embodiment, a mapping model between geochemical features and lithology or wave velocity is established based on the revealed lithology, test samples, regional geological data, or historical engineering data. This model can use random forest, support vector machine, XGBoost, neural network, Bayesian classifier, or expert rule model to output lithology probability, velocity prior, and uncertainty.

[0041] Specifically, establish a geolocation identification model. Mapping geochemical feature vectors to lithology or stratigraphic class probabilities: (2) in, Indicates the first The sample belongs to the first The probability of lithology or strata. Indicates the position of the borehole trajectory The geochemical characteristic vector of the slag powder sample, where L represents the lithology or stratigraphic category and M is the number of categories. .

[0042] This indicates that, given a localized feature vector Under the condition that it belongs to the first The probability of lithology or strata; For the first Discriminant functions for each category.

[0043] The information entropy of geolocation identification is defined as: (3) in, It is a very small positive number, for example, taking Used to prevent An error will occur if the logarithm is zero.

[0044] The smaller the value, the more stable the geolocation identification. The larger the value, the more likely the sample is located in a mixed lithology, alteration transition zone, or an area of ​​uncertain identification.

[0045] If the statistical mean of the velocity corresponding to each lithology is The prior geochemical velocity at the borehole location can then be written as: (4) Its uncertainty can be represented by the class mixture variance, model error, and sample quality error: (5) in, This represents the model error variance, which stems from the inaccurate predictions made by the geolocation identification model (such as random forest). This represents the variance of sample quality error, which stems from random factors such as insufficient sample representativeness and instrument noise.

[0046] Equations (4) and (5) indicate that the geodesy constraint does not rigidly give a certain velocity value, but rather gives a velocity prior with uncertainty, providing a smooth "potential well" for the objective function and guiding gradient descent to robustly approach the global optimum.

[0047] This embodiment quantifies sample error through variance, automatically reducing the constraint strength of unreliable sample points and avoiding single misjudgments from contaminating the global velocity field. The magnitude of variance directly determines the constraint strength (strong in the near area / weak in the far area, strong in low entropy / weak in high entropy), which can match the spatial extrapolation mechanism. The output variance field is the "confidence cloud map", providing a scientific risk margin for construction decisions and avoiding blind actions. When seismic and geochemical orientations conflict, variance acts as the "arbitrator" to achieve intelligent weight allocation of multi-physics data.

[0048] S103: Construct a geochemical spatial extrapolation function, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on the geochemical spatial extrapolation function. The prior and uncertainty of geochemical velocity at the borehole location are used as data sources to construct the spatial velocity prior and prior variance.

[0049] In this embodiment, a geochemical spatial extrapolation function is constructed to extrapolate the prior geochemical velocity on the borehole trajectory to the space Ω in front of the tunnel face. The geochemical spatial extrapolation function includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term, which makes the area near the borehole and the stable geochemical identification region have strong constraints on the full waveform inversion, while the area far from the borehole or the uncertain geochemical identification region has weak constraints on the full waveform inversion.

[0050] Let the position to be estimated be x, and the sample position be x. The anisotropic distance is: (6) The sample location refers to a known location point where samples were taken in the field, providing a reference benchmark for the surrounding area; the location to be estimated is an unknown target in front of the tunnel face that needs to be predicted. The geological trend covariance matrix, which is related to the strike, dip, fault distribution, or lithological continuity direction of the strata, can be pre-defined based on geological knowledge. For example, it can be a diagonalized matrix composed of the squares of the relevant lengths along the main trend direction and the vertical trend direction.

[0051] If the unit vector of the main trend direction is known to be u, it can be expressed as a weighted distance along the trend direction and perpendicular to the trend direction: (7) in, The relevant length along the geological trend direction (i.e., the strike of strata or the direction of structural extension). The relevant length in the vertical trend direction (i.e., across strata or vertical structural direction), usually > ; This represents the position difference vector, which is the difference between the position to be estimated and the sample position; The eigenvalue of the inverse matrix in the u direction is 1 / The eigenvalue in the direction perpendicular to u is 1 / .

[0052] The geospatial extrapolation function is as follows: (8) in, For geospatial extrapolation weights, For sample quality factor, It is a quantitative value between 0 and 1, with evaluation indicators including whether the sample quantity is sufficient, the moisture content is high or low, whether it is contaminated by rock cuttings or mud, whether the particle size distribution is uniform, and the repeatability and stability of the laboratory analysis results. It is obtained by weighted scoring by experts. Information entropy for localized identification, Let x be the geological trend consistency function, when x is consistent with When located in the same lithological continuity trend or the same tectonic unit Take the larger value when both cross a known fault, interface, or discontinuity. Take the smaller value.

[0053] As an example, the geological trend consistency function is as follows: ; in, A small positive number close to 0 (such as 0.01) indicates that the extrapolation weight is very low but not completely cut off when crossing units, preserving a slight smooth transition.

[0054] Geological units are derived from prior knowledge of the geological conditions of excavated tunnels, regional geological maps, and exploration data, and are reasonably extrapolated under their guidance.

[0055] According to equation (8), the spatial velocity prior field is defined as: (9) The estimated velocity value of each borehole sample point is multiplied by its corresponding influence weight, and the sum of these values ​​is divided by the total weights to obtain the prior velocity value of the location to be estimated.

[0056] The geodesy velocity prior is given for the spatial location corresponding to the k-th sample. This represents the geospatial extrapolation weight corresponding to that location. Used to prevent the denominator from being 0.

[0057] The geochemical spatial extrapolation weights themselves incorporate modulation from multiple factors such as geological distance, information entropy, sample quality, and trend consistency. This results in amplified weights for sample points near the borehole with reliable identification, locking the prior velocity almost exactly to the measured value. When the sample points are far from the borehole or across faults, the weights are balanced and small, causing the prior velocity to revert to the global mean. This adaptive weighting mechanism ensures that the extrapolation of geochemical information is both well-founded and flexible.

[0058] The corresponding prior variance field is defined as: (10) In formula (10), the first term represents the spatial dispersion of the prior geochemical velocity of the surrounding area, the second term represents the uncertainty of the geochemical interpretation of a single sample, and the third term... This represents the variance of geostatistical extrapolation or kriging.

[0059] Constructing inversion constraint weights from prior variance: (11) (12) in, This represents the absolute weight of the localization constraint at location x. This represents the normalized relative weight of the localization constraint at location x.

[0060] As can be seen from the above formula, the smaller the prior variance, the stronger the geodesy constraint; the larger the prior variance, the weaker the geodesy constraint.

[0061] This embodiment constructs a spatial extrapolation function that integrates distance attenuation, geological trend, information entropy, and sample quality to transform discrete borehole point data into a continuous three-dimensional velocity prior field, effectively solving the problem of "representing the entire surface with points." Anisotropic distance and trend consistency functions ensure that information can be transmitted far along the stratigraphic strike, and that constraints automatically attenuate when crossing faults or lithologies, preventing velocity field distortion caused by traditional interpolation "penetrating faults." Extrapolation weights are automatically adjusted according to spatial location and reliability, with strong constraints in the near-hole area and weak constraints in the far-hole area, strong constraints in low-entropy stable zones, and weak constraints in high-entropy transition zones. Simultaneously, the output prior variance field is transformed into normalized constraint weights, providing spatially variable constraints for subsequent joint inversion and offering engineers quantifiable confidence assessment criteria.

[0062] S104: Collect passive source seismic data and active source seismic data during the drilling process, and perform preprocessing.

[0063] In this embodiment, passive source seismic data is continuously acquired during drilling; active source seismic data is acquired using a controlled source during construction breaks. The active source seismic observation data acquired during construction breaks is added as an additional data item to the joint full-waveform inversion objective function to supplement the time reference, low-frequency information, waveform repeatability, and illumination angle of the passive source seismic data. Both types of seismic data must be unified with the tunnel mileage, face location, borehole location, and geochemical sample location to the same coordinate system.

[0064] The data preprocessing process is as follows: For passive source data, the following steps are performed: mean removal, trend removal, abnormal impulse removal, 1-bit normalization, spectral whitening, bandpass filtering, cross-correlation, short-window overlay, and virtual shot set construction.

[0065] For active source data, trigger correction, bad path removal, geometric assignment, filtering, deconvolution, source consistency correction, and waveform normalization are performed.

[0066] S105: Construct a joint full waveform inversion function, which includes: virtual shot gather waveform fitting term, active source waveform fitting term, geochemical velocity prior constraint term, geological trend constraint term, and smoothing regularization term.

[0067] Specifically, the passive source seismic waveform fitting term is as follows: For seismic data from passive sources of drilling noise, source estimation can be directly performed on continuous passive source records followed by fitting, or cross-correlation or virtual shot gathers can be used for fitting. To reduce the uncertainty of source timing, cross-correlation is preferred. (13) Given the velocity model m and the equivalent source model, calculate the simulation cross-correlation.

[0068] The passive source waveform fitting term is defined as: (14) Where P is the selected set of detector pairs, For time windows, frequency bands, or credibility weights. Let i represent the measured cross-correlation function of the i,j pairs of detectors. For the time-domain passive source seismic signal recorded by the i-th detector, This represents the time shift recorded by the j-th detector. The following signal, This represents the fitting residual term of the passive source virtual shot gather waveform. This represents the time window / band / confidence weighting function for passive source cross-correlation fitting. This represents the simulated cross-correlation function calculated under the current velocity model m.

[0069] This embodiment transforms construction noise into valuable constraint information by using passive source seismic waveform fitting terms, enabling low-cost, all-weather continuous observation without requiring additional construction time. It eliminates the uncertainty caused by unknown source location and excitation time through cross-correlation, constructing an equivalent virtual shot set. Simultaneously, it provides mid-to-low frequency velocity background constraints, providing stable support for large-scale velocity structures. By complementing the high-precision constraints of active sources, it jointly reduces the ambiguity of inversion and improves the reliability of velocity field reconstruction.

[0070] The specific terms for fitting active source seismic waveforms during construction gaps are: During breaks in drill-and-blast tunnel construction, active source seismic acquisition can be performed using hammer impact, drop hammer, mechanical seismic source, pneumatic seismic source, or electromagnetic seismic source. Active sources have a definite trigger time and high repeatability, and can supplement passive source signals with time reference, low-frequency information, and illumination angle.

[0071] Let the active source observation data be The simulated data is Then the active source waveform fitting term is: (15) in, For active source collection, For detector assembly, The active source data weights are added as additional data terms to the joint objective function. Together with the passive source waveform fitting terms, they constrain the velocity model, effectively supplementing the missing time reference, low-frequency information, and limited illumination angle of the passive source, thus improving imaging quality in deep and shadowed areas. The passive source constrains large-scale background trends, while the active source is sensitive to velocity interface details. Together, they give the velocity field both a macroscopic framework and a fine structure, while suppressing artifacts that might be introduced by a single data source.

[0072] The specific prior constraints of geomorphic space are as follows: Geospatial prior constraints are used to limit the inversion velocity model. Approximate within the localized prior confidence region : (16) in, This represents the prior spatial velocity field value at position x. This represents the prior constraints of geospatialization.

[0073] The velocity model is a numerical description of the spatial distribution of seismic wave propagation velocity (usually P-wave velocity) in the rock mass in front of the tunnel face. The inversion process of the velocity model is a conventional technique and will not be described in detail here.

[0074] The prior constraints in geomorphic space are not hard constraints of fixed strength, but rather are determined by... The system employs spatially variable soft constraints. The constraint strength automatically adjusts spatially, with strong constraints in the borehole vicinity and low-entropy stable zones, and weak constraints in areas far from the borehole, high-entropy transition zones, and fault-crossing regions. When there is a reasonable deviation between the geological structure indicated by seismic data and geochemical priors, the soft constraints allow the model to deviate moderately, prioritizing the fitting of waveform data. This ensures that the inversion is both guided and remains open to new information.

[0075] The geological trend constraints and smoothing constraints are as follows: To ensure the velocity field conforms to geostatistics and spatial trends, geological trend constraints can be constructed: (17) in, As a spatial trend operator, it can impose a weaker penalty along bedding, strike, or structural extension directions, and a stronger penalty on vertical trend directions or cross-structural directions.

[0076] The regular smoothing regularization term can be written as: (18) This represents the spatial gradient vector of the velocity field m at position x.

[0077] Combining equations (14) to (18), the joint objective function of this embodiment is obtained as follows: (19) in, and The weights are for passive and active source data, respectively. , and These are the weights of geochemical prior constraints, geological trend constraints, and smoothing constraints, respectively. and The weights are for passive and active source data, respectively. , and These are the weights of geochemical prior constraints, geological trend constraints, and smoothing constraints, respectively.

[0078] If active source data is not collected during construction, then it can be ordered that... =0; if active source data is obtained during construction breaks, then >0, thus incorporating the active source waveform fitting term as an additional data term into the inversion.

[0079] Equation (19) differs from inversions that use only passive source seismic data and geochemical constraints; its active source term... It can provide controllable source constraints for velocity models, reducing inversion ambiguities caused by uncertainties in the passive source source mechanism.

[0080] S106: Iteratively solve the joint full waveform inversion function to obtain inversion results including velocity field, geochemical constraint weight distribution, fitting residuals of active and passive source data, and interpretation of uncertainties.

[0081] In this embodiment, the gradient of the joint objective function can be written as: (20) in and It can be calculated using the adjoint state method.

[0082] Update the model using gradient descent, conjugate gradient, or L-BFGS: (twenty one) in, Step size, The identity matrix, conjugate gradient approximation, or inverse Hessian approximation from Limited-memory BFGS can be used. Iteration stops after satisfying the residual rate of change and model rate of change thresholds, yielding the final velocity field. .

[0083] The velocity field is solved iteratively using equations (20) to (21). The inversion results include the velocity field, geochemical constraint weight distribution, fitting residuals of active / passive source data, and interpretation uncertainties.

[0084] In this embodiment, lithology and alteration degree are identified based on geochemical features to obtain the velocity prior along the borehole trajectory. This velocity prior is extrapolated to the spatial velocity prior field ahead of the drilling face using a geological trend function and information entropy weights. Simultaneously, passive source seismic data are cross-correlated and superimposed to form a virtual shot gather; active source seismic records are obtained using hammer-driven or mechanical sources during construction intervals. The passive source cross-correlation fitting term, the active source waveform fitting term, and the geochemical spatial prior constraint term are combined to form the objective function, and the forward velocity field is obtained through iterative inversion.

[0085] When active source earthquakes, passive source earthquakes, and geochemical priors all show a decrease in velocity or anomalies in a certain area, it can be determined that there is a high probability of a fracture zone, alteration zone, or water-rich anomaly in that area. When an isolated anomaly appears in a certain data source and neither geochemical constraints nor another seismic data source support it, its interpretation weight is reduced, thereby reducing misjudgments caused by a single observation.

[0086] This embodiment utilizes borehole cuttings geochemical information to spatially extrapolate and constrain the velocity field. It also supplements the source uncertainty and insufficient illumination of the passive source inversion by using active source seismic data. This can improve the stability of the velocity field reconstruction, the positioning accuracy of faults and soft-hard transition interfaces, and provide continuous and quantifiable velocity structure data for advanced geological prediction and construction risk control of drill-and-blast tunnels.

[0087] Example 2 In one or more embodiments, a geochemical spatially constrained active and passive source joint geological identification system is disclosed, comprising: The geochemical data acquisition module is used to acquire slag powder samples from the area to be tested and perform geochemical analysis to obtain geochemical feature vectors. The geochemical feature vectors include element content, oxide content, element ratio, mineral spectrum characteristics, and geochemical anomaly indicators. The geochemical category identification module is used to map geochemical feature vectors to lithology or stratigraphic category probabilities through the geochemical identification model, calculate the information entropy of geochemical identification, and the prior and uncertainty of geochemical velocity at the borehole location; The geochemical spatial extrapolation module is used to construct a geochemical spatial extrapolation function, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. The geochemical spatial extrapolation weight is calculated based on the geochemical spatial extrapolation function, and the spatial velocity prior and prior variance are constructed using the geochemical velocity prior and uncertainty at the borehole location as the data source. The active and passive source data acquisition module is used to collect passive source seismic data and active source seismic data during the drilling process, and to perform preprocessing. The joint inversion module is used to construct a joint full waveform inversion function, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term. The joint full waveform inversion function is iteratively solved to obtain inversion results including the velocity field, geochemical constraint weight distribution, active and passive source data fitting residuals, and interpretation uncertainties.

[0088] It should be noted that the specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0089] Example 3 In one or more embodiments, a geochemical spatially constrained active and passive source combined geological identification device is disclosed, specifically comprising: A drilling slag powder acquisition device is used to acquire slag powder samples from the area to be tested. The active and passive source data acquisition device is used to acquire passive source seismic data and active source seismic data during the drilling process in the area to be measured, respectively. The data processing device is configured as follows: Geochemical analysis was performed on the slag powder sample to obtain geochemical feature vectors, which included elemental content, oxide content, elemental ratios, mineral spectrum characteristics, and geochemical anomaly indicators. Geochemical feature vectors are mapped to lithology or stratigraphic class probabilities through a geochemical identification model, and the information entropy of geochemical identification, as well as the prior and uncertainty of geochemical velocity at the borehole location, are calculated. A geochemical spatial extrapolation function is constructed, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on the geochemical spatial extrapolation function. Using the prior and uncertainty of geochemical velocity at the borehole location as data sources, spatial velocity priors and prior variances are constructed. A joint full waveform inversion function is constructed, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term. The joint full waveform inversion function is iteratively solved to obtain inversion results including the velocity field, geochemical constraint weight distribution, active and passive source data fitting residuals, and interpretation uncertainties.

[0090] The specific implementation method of the above process is exactly the same as that in Example 1.

[0091] The structure of the drilling slag powder acquisition device in this embodiment includes: a receiving box, with a water inlet at the top of the receiving box, the water inlet being connected to a water inlet pipe, and a water-passing screen structure at the bottom of the water inlet pipe; a storage screen bucket is also provided inside the receiving box, which stores multiple water-passing screens, which can automatically fall from the bottom of the storage screen bucket; a drainage trough is provided at the bottom of the receiving box, through which the water filtered out of the screens flows out.

[0092] The receiving box is also equipped with cylinder fingers and an internal slide. The internal slide is connected to the external slide located outside the receiving box. The cylinder fingers can push the water screen forward to the lower part of the water inlet pipe under the push of the dual-shaft cylinder. When the water screen is full of a set amount of slag powder, the water screen continues to be pushed forward under the action of the mini cylinder, so that the water screen slides out of the receiving box from the internal slide and enters the external slide.

[0093] As a specific implementation method, combined with Figure 2 , Figure 3 and Figure 5 The drilling slag powder collection device specifically includes: The receiving mechanism is used to receive the slag-water mixture discharged from the advanced drilling rig, separate the slag powder from the water, and collect the separated slag powder in the water-passing screen 5. The conveying mechanism is used to push the water-passing screen 5 to the external slide 6 for sampling after the preset amount is received, and then return to push the next empty water-passing screen to the receiving position; The receiving and transporting mechanisms work together to collect the drilling slag powder.

[0094] The receiving mechanism specifically includes: a water receiving funnel, a receiving box, a water inlet pipe 19, a drainage trough 14, a water-passing screen 5, and a storage screen bucket 3. The water receiving funnel is installed below the borehole outlet of the advanced drilling rig, and its lower end is connected to the water inlet at the top of the receiving box via a water pipe, ensuring that the slag-water mixture falls directly into the receiving box. The water inlet is connected to the water inlet pipe 19, and a water-passing screen 5 is installed at the bottom of the water inlet pipe 19. The receiving box also contains a storage screen bucket 3, which stores multiple water-passing screens that automatically fall from the bottom of the storage screen bucket 3. Figure 4 The bottom of the receiving box is provided with a drainage trough 14 and the internal slide 4 is provided with a water leakage hole. The water inlet pipe 19 flows into the water screen 5, the slag powder remains on the screen, the filtrate is filtered out from the side wall of the screen, discharged into the bottom drainage trough 14 through the leakage hole, and flows out of the receiving box along the slide direction.

[0095] The receiving box is also equipped with a cylinder finger 2 and an internal slide 4. The internal slide 4 is connected to the external slide 6 located outside the receiving box. The cylinder finger 2 can push the water screen 5 forward to the lower part of the water inlet pipe 19 under the push of the cylinder. When the water screen 5 is filled with a set amount of slag powder, the cylinder finger continues to push the water screen 5 forward under the action of the cylinder, so that the water screen 5 slides out of the receiving box from the internal slide 4 and enters the external slide 6.

[0096] The conveying mechanism specifically includes: an outer slide rail 6, an inner slide rail 4, a dual-axis cylinder 8, a cylinder finger 2, a finger seat 1, a mini cylinder 10, a first connecting plate 13, a second connecting plate 18, an anti-rotation rod 16, and a linear bearing 12. The internal slide 4 is installed at the bottom of the receiving box, with a bracket 15 underneath it. The bracket 15 is connected to the base plate 7 to support the slide. The internal slide 4 guides the inlet and outlet of the water-passing screen. The external slide 6 is fixed to one side of the receiving box and connects with the internal slide 4 to hold the water-passing screen that has been filled. The end of the dual-shaft cylinder 8 is connected to the cylinder finger 2 through the finger seat 1. The front end of the cylinder finger contacts the water-passing screen in an arc shape to match the shape of the water-passing screen and is used to push the water-passing screen. The cylinder finger 2 is connected to the finger... The base 1 is connected to the cylinder, and the cylinder finger is provided with a second connecting plate 18 to connect with the little finger 17 for stable contact with the water screen 5 and thus stable pushing; the cylinder finger can push the water screen 5 forward to the bottom position of the inlet under the action of the dual-shaft cylinder 8; the bottom of the dual-shaft cylinder 8 is provided with an anti-rotation rod 16 to prevent the dual-shaft cylinder 8 from rotating; the bottom of the dual-shaft cylinder 8 is provided with a cylinder pad 9 to support the cylinder, and a first connecting plate 13 is provided to connect the cylinder pad 9 to the base plate 7.

[0097] The mini cylinder 10, supported by the cylinder bracket 11, pushes the dual-axis cylinder 8 forward, thereby continuing to push the water-passing screen with the material in place forward, sliding through the inner slide rail 4 to the outer slide rail 6. The linear bearing 12 is installed between the first connecting plate 13 and the slide rail to ensure the smoothness of the pushing action.

[0098] The front of the receiving box is equipped with a transparent acrylic door, which makes it easy to observe the accumulation of slag powder on the internal screen. The front door is connected to the main body of the receiving box by a hinge and is equipped with a button-type round pin for locking.

[0099] The receiving box is equipped with an openable cover on the top, and a straight connector is provided at the top of the cover for connecting the water inlet pipe 19; the side wall of the receiving box is bolted to the bottom plate 7 and is equipped with a handle for easy overall handling.

[0100] The specific working process of the borehole slag powder acquisition device in this embodiment is as follows: (1) Fix the water receiving funnel directly below the borehole outlet of the advanced drilling machine, and connect the lower end of the water receiving funnel to the straight connector (water inlet) at the top of the material receiving box with a hose. (2) Place the first empty water-passing screen on the slide inside the receiving box, so that its opening faces the bottom of the water inlet pipe outlet; or, push the water-passing screen in the storage screen bucket to the position facing the bottom of the water inlet pipe outlet by using a dual-shaft cylinder and cylinder fingers, close the front door and lock it. (3) Start the advanced drilling machine. During the drilling process, the slag-water mixture discharged enters the receiving box through the water receiving funnel and water pipe, and falls into the water-passing screen. (4) The water in the mixture flows into the bottom of the receiving box through the screen and is discharged out of the box through the drainage trough 14; the slag powder is trapped in the water-passing screen. (5) When the slag powder inside the water-passing screen is observed to accumulate to the set capacity (or according to the drilling depth), stop the drilling machine feed, but keep the flushing water flowing for a period of time to allow the slag powder in the bucket to settle fully and the water to be drained. (6) Start the mini cylinder. The mini cylinder drives the dual-shaft cylinder to move forward, thereby pushing the water screen forward and pushing it along the inner slide to the outer slide. (7) The piston rod returns to the initial position; at this time, the next empty water screen is placed in the receiving position of the internal slide by manual or automatic cylinder, and the drilling machine feed and flushing are started; (8) Repeat steps (3) to (7) until slag powder collection is completed at all predetermined depths or borehole locations; (9) After all drilling is completed, the operators will remove the numbered water-passing screens from the external slide in sequence, seal and preserve them for geochemical analysis.

[0101] Example 4 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the active and passive source joint geological identification method for geochemical spatial constraints as described in Embodiment 1.

[0102] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any other conventional processor.

[0103] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0104] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0105] Example 5 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the geochemical spatial constraint active and passive source joint geological identification method described in Embodiment 1.

[0106] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A geochemical spatially constrained method for joint active and passive source geological identification, characterized in that, include: Slag powder samples from the area to be tested are obtained and geochemical analysis is performed to obtain geochemical feature vectors, which include element or oxide content, element ratios, mineral spectrum characteristics and geochemical anomaly indicators. Geochemical feature vectors are mapped to lithology or stratigraphic class probabilities through a geochemical identification model, and the information entropy of geochemical identification, as well as the prior and uncertainty of geochemical velocity at the borehole location, are calculated. A geochemical spatial extrapolation function is constructed, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on geochemical spatial extrapolation functions. Spatial velocity priors and prior variances are constructed using geochemical velocity priors and uncertainties at borehole locations as data sources. Passive source seismic data and active source seismic data were collected separately during the drilling process and preprocessed. A joint full waveform inversion function is constructed, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term; The joint full waveform inversion function is iteratively solved to obtain inversion results including velocity field, geochemical constraint weight distribution, fitting residuals of active and passive source data, and interpretation of uncertainties.

2. The geochemical spatially constrained active and passive source joint geological identification method as described in claim 1, characterized in that, The information entropy of geodesy identification is calculated as follows: No. The summation of the product of the probability of each sample being identified as a lithological or stratigraphic class and the logarithmic measure of the probability uncertainty, and then normalized, yields the nth sample. Information entropy of each sample localization identification.

3. The geochemical spatially constrained method for joint active and passive source geological identification as described in claim 1, characterized in that, The prior and uncertainty of geochemical velocity at the borehole location are calculated as follows: No. The sum of the products of the probability of each sample being identified as each lithology or stratigraphic category and the statistical mean of the corresponding velocity for each category is used as the prior geochemical velocity at the borehole location. The uncertainty is obtained by summing the class mixture variance, model error, and sample quality error.

4. The geochemical spatially constrained active and passive source joint geological identification method as described in claim 1, characterized in that, The geochemical spatial extrapolation function is specifically as follows: ； in, Indicates the location to be estimated Spatial extrapolation weights at the location, Indicates the location to be estimated With sample position Anisotropic distance; Information entropy for localized identification, For sample quality factor, This is a geological trend consistency function.

5. The geochemical spatially constrained method for joint active and passive source geological identification as described in claim 1, characterized in that, Using the geochemical velocity prior and uncertainty at the borehole location as the data source, a spatial velocity prior and prior variance are constructed, specifically as follows: The spatial velocity prior field is: the position to be estimated. The spatial velocity prior value is the ratio of the sum of the velocity prior values ​​of all samples after weighting them according to their respective weights to the sum of all weights. The prior variance is the sum of the spatial dispersion of the prior geochemical velocity of the surrounding area, the uncertainty of the geochemical interpretation of a single sample, and the geostatistical extrapolation variance or kriging variance.

6. The geochemical spatially constrained method for joint active and passive source geological identification as described in claim 5, characterized in that, Also includes: The weights of the geospatial prior constraint terms in the joint full waveform inversion function are constructed based on the prior variance. The weights are obtained by normalizing the inverse of the prior variance.

7. The geochemical spatially constrained method for joint active and passive source geological identification as described in claim 1, characterized in that, The joint full waveform inversion function specifically includes: Virtual shot gather waveform fitting term: For each pair of detectors (i,j), calculate the difference between the measured cross-correlation and the simulated cross-correlation, take the square integral after weighting, and then sum over all detector pairs to obtain the first scalar residual value; Active source waveform fitting term: For each source-detector combination, calculate the difference between the measured waveform and the simulated waveform, take the square after weighting, and integrate along time to obtain the corresponding waveform residual; Sum the waveform residuals of all detectors to accumulate the residuals of all receiver channels under the excitation of this source; sum them over all active sources to obtain the total residuals of all active source observations; Geomorphic velocity prior constraint: For each location x in the spatial domain, calculate the square of the difference between the current velocity model and the geomorphic prior velocity, multiply it by the constraint weight of that location, and then sum the integrals over the entire space to obtain the second scalar residual value; Geological trend constraint term: Apply the trend operator to the current velocity model at each spatial location x to obtain a quantity reflecting the degree to which the velocity field deviates from the geological trend at that location; take the square of the modulus of the quantity, and then integrate and sum it over the entire spatial domain; Smoothing regularization term: take the square of the current velocity model modulo 2, and then integrate and sum over the entire spatial domain.

8. A geochemical spatially constrained active and passive source joint geological identification system, characterized in that, include: The geochemical data acquisition module is used to acquire slag powder samples from the area to be tested and perform geochemical analysis to obtain geochemical feature vectors. The geochemical feature vectors include element content, oxide content, element ratio, mineral spectrum characteristics, and geochemical anomaly indicators. The geochemical category identification module is used to map geochemical feature vectors to lithology or stratigraphic category probabilities through the geochemical identification model, calculate the information entropy of geochemical identification, and the prior and uncertainty of geochemical velocity at the borehole location; Geochemical spatial extrapolation module is used to construct geochemical spatial extrapolation function, which includes distance attenuation term, geological trend consistency term, sample quality factor and information entropy modulation term; Geochemical spatial extrapolation weights are calculated based on geochemical spatial extrapolation functions. Spatial velocity priors and prior variances are constructed using geochemical velocity priors and uncertainties at borehole locations as data sources. The active and passive source data acquisition module is used to collect passive source seismic data and active source seismic data during the drilling process, and to perform preprocessing. The joint inversion module is used to construct a joint full waveform inversion function, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term; The joint full waveform inversion function is iteratively solved to obtain inversion results including velocity field, geochemical constraint weight distribution, fitting residuals of active and passive source data, and interpretation of uncertainties.

9. A geochemical spatially constrained active and passive source combined geological identification device, characterized in that, include: A drilling slag powder acquisition device is used to acquire slag powder samples from the area to be tested. The active and passive source data acquisition device is used to acquire passive source seismic data and active source seismic data during the drilling process in the area to be measured, respectively. The data processing device is configured as follows: Geochemical analysis was performed on the slag powder sample to obtain geochemical feature vectors, which included element or oxide content, element ratios, mineral spectrum characteristics, and geochemical anomaly indicators. Geochemical feature vectors are mapped to lithology or stratigraphic class probabilities through a geochemical identification model, and the information entropy of geochemical identification, as well as the prior and uncertainty of geochemical velocity at the borehole location, are calculated. A geochemical spatial extrapolation function is constructed, which includes a distance attenuation term, a geological trend consistency term, a sample quality factor, and an information entropy modulation term. Geochemical spatial extrapolation weights are calculated based on geochemical spatial extrapolation functions. Spatial velocity priors and prior variances are constructed using geochemical velocity priors and uncertainties at borehole locations as data sources. A joint full waveform inversion function is constructed, which includes: a virtual shot gather waveform fitting term, an active source waveform fitting term, a geochemical velocity prior constraint term, a geological trend constraint term, and a smoothing regularization term; The joint full waveform inversion function is iteratively solved to obtain inversion results including velocity field, geochemical constraint weight distribution, fitting residuals of active and passive source data, and interpretation of uncertainties.

10. The geochemical spatially constrained active and passive source combined geological identification device as described in claim 9, characterized in that, The drilling slag powder acquisition device includes: a receiving box, with a water inlet at the top of the receiving box, the water inlet being connected to a water inlet pipe, and a water-passing screen structure at the bottom of the water inlet pipe; a storage screen bucket is also provided inside the receiving box, which stores multiple water-passing screens, which can automatically fall from the bottom of the storage screen bucket; a drainage trough is provided at the bottom of the receiving box, through which the water filtered out by the screens flows out. The receiving box is also equipped with a cylinder finger and an internal slide. The internal slide is connected to the external slide located outside the receiving box. The cylinder finger can push the water screen forward to the lower part of the water inlet pipe under the push of the cylinder. When the water screen is full of a set amount of slag powder, the cylinder finger continues to push the water screen forward under the action of the cylinder, so that the water screen slides out of the receiving box from the internal slide and enters the external slide.

11. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as the geochemical spatial constraint active and passive source joint geological identification method according to any one of claims 1-7.

12. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of a terminal device and executed by the geochemical spatial constraint active and passive source joint geological identification method according to any one of claims 1-7.

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