A Method and System for Imaging Converted Waves of Metal Minerals Using Combined Active and Passive Sources and Reverse Time Migration

Abstract

This disclosure pertains to the field of exploration geophysics and presents a method and system for combined active and passive source reverse time migration imaging of metallic minerals. The method includes: performing time difference correction on active and passive source seismic data from the same subsurface structure; performing wavefield separation on the time-aligned active and passive source seismic data to reconstruct the P-wave and S-wave fields of the active source and the passive source; performing active and passive source wavefield extrapolation based on the two-way wave equation; obtaining active and passive source PS-converted wave imaging results using cross-correlation imaging conditions; and performing weighted superposition and fusion to obtain the combined active and passive source imaging results. This disclosure leverages the high resolution advantage of active sources combined with the deep detection capability of passive sources, replacing the limitations of single-source detection and achieving high-precision deep reverse time migration imaging.

Description

Technical Field

[0001] This disclosure pertains to the field of exploration geophysics, specifically relating to a method and system for imaging converted waves of metallic minerals using a combination of active and passive source reverse time migration. Background Technology

[0002] With industrial development, the demand for geophysical exploration of metallic mineral resources has increased. Metallic mineral deposits are typically found in areas with complex geological structures, exhibiting irregular ore bodies and varying depths, posing challenges to traditional seismic exploration methods. Active source seismic exploration can provide high-resolution images, but its exploration depth is relatively shallow. Furthermore, the surface conditions in metallic mineral exploration areas are usually complex, making the deployment of dense active source observation systems too costly and difficult. Passive source seismic exploration utilizes natural earthquakes or environmental noise as sources, offering advantages such as low cost and deep detection. However, the source location and vibration time are unknown, and traditional passive source imaging methods based on interferometry often fail to meet the requirements for detailed exploration when dealing with strongly scattering media. Meanwhile, reverse time migration imaging considers the elastic characteristics of seismic waves, accurately simulating complex wavefield propagation and demonstrating significant imaging effectiveness in identifying complex geological bodies.

[0003] However, in actual industrial production, problems such as insufficient energy in deep active sources, low resolution in shallow passive sources, and severe crosstalk noise in acoustic migration imaging that ignores elastic effects can cause many problems for reverse time migration imaging. Summary of the Invention

[0004] The technical problem to be solved by this disclosure is to provide a method and system for imaging converted waves of metal minerals using a combination of active and passive source reverse time migration. This method aims to combine the high-resolution capability of an active source with the deep-penetrating capability of a passive source, accurately process the converted waves through elastic wave migration imaging, effectively suppress crosstalk noise, and ultimately obtain high-resolution images of metal mineral structures, providing reliable geophysical evidence for locating deep metal ore bodies.

[0005] A method for imaging metallic minerals using a combination of active and passive source reverse-time migration, according to an embodiment of the first aspect of this disclosure, includes:

[0006] Time difference correction is performed on active source seismic data and passive source seismic data of the same subsurface structure. The time difference correction includes cross-correlation calculation of active and passive source seismic data, correction of the time difference between active and passive source seismic data, and obtaining time-aligned active source seismic data and passive source seismic data.

[0007] Wavefield separation was performed on the time-aligned active source seismic data and passive source seismic data to reconstruct the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source.

[0008] Based on the two-way wave equation, the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source are extrapolated respectively.

[0009] Using cross-correlation imaging conditions, cross-correlation was performed on the P-wave scalar wavefield and S-wave scalar wavefield obtained by extrapolation of active source wavefield and passive source wavefield, respectively, to obtain the active source PS converted wave imaging results and the passive source PS converted wave imaging results.

[0010] The active source PS converted wave imaging results and the passive source PS converted wave imaging results are weighted, superimposed, and fused to obtain the joint imaging results of active and passive sources.

[0011] Furthermore, the active source seismic data is obtained by deploying a seismic detector array in the exploration area and using an artificially controlled seismic source to record multi-component seismic waveform data.

[0012] The passive source seismic data is obtained by continuously recording passive seismic signals, including natural earthquakes and environmental noise, using the same seismic detector array.

[0013] Furthermore, time difference correction is performed on active-source and passive-source seismic data of the same subsurface structure, specifically including:

[0014] Active source seismic data is represented as a convolution of the active source source wavelet and the active source Green's function;

[0015] Passive source seismic data is represented as a convolution of the source time function and the passive source Green's function;

[0016] The active source seismic data and the passive source seismic data are subjected to short-time Fourier transform respectively. At each frequency within the preset effective frequency band, the first complex signal time series and the second complex signal time series are extracted.

[0017] For each frequency within the effective frequency band, calculate the cross-correlation function between the first complex signal time series and the second complex signal time series to obtain the cross-correlation function corresponding to multiple frequencies;

[0018] The total cross-correlation function is obtained by superimposing the cross-correlation functions corresponding to the multiple frequencies.

[0019] The maximum value of the total cross-correlation function is determined, and the time position corresponding to the maximum value is used as the time difference between the active source seismic data and the passive source seismic data.

[0020] Furthermore, wavefield separation was performed on the time-aligned active-source and passive-source seismic data to reconstruct the P-wave and S-wave fields of the active source and the passive-source, including:

[0021] The particle vibration velocity fields corresponding to the time-aligned active source seismic data and passive source seismic data are decomposed by Helmholtz, and expressed as the sum of the gradient field of the scalar potential and the curl field of the vector potential.

[0022] Calculate the divergence and curl fields of the particle vibration velocity field;

[0023] Based on the divergence field and curl field, the Poisson equation satisfied by the scalar potential and the vector Poisson equation satisfied by the vector potential are solved respectively to obtain the scalar potential and the vector potential.

[0024] The P-wave velocity field is reconstructed based on the gradient of the scalar potential, and the S-wave velocity field is reconstructed based on the curl of the vector potential, thus obtaining the P-wave velocity and S-wave velocity respectively.

[0025] Furthermore, based on the two-way wave equation, the P-wave and S-wave fields of the active source are extrapolated; including:

[0026] Based on the two-way wave equation, the source and excitation time functions generate the excitation wavefield. The active source wavefield is then extrapolated to the P-wave field through forward simulated wavefield propagation, yielding the active source P-wave scalar wavefield, expressed by the formula:

[0027] ;

[0028] By extrapolating the active source wavefield back to the S-wave field, we obtain the active source S-wave scalar wavefield, expressed by the formula:

[0029] ;

[0030] in: For P-wave velocity, Let S represent the S-wave velocity, F represent the forward wave field, and B represent the reverse wave field. This refers to active source seismic data from the j-th receiving point; This represents the scalar wave field of the active source P-wave obtained by forward extrapolation from k active sources. This represents the active source S-wave scalar wavefield obtained by back-extrapolation of the k-th active source seismic data. This represents the time function of the source wavelet of an active source seismic earthquake. Represents the Dirac delta function, Let j be the position of the receiving point. For all receiving points The recorded active source seismic data are summed. This represents the gradient.

[0031] Furthermore, based on the two-way wave equation, the passive source wavefield is extrapolated to the P-wave and S-wave fields, including:

[0032] By treating all passive source seismic data from all receivers as virtual sources and performing forward extrapolation, the passive source P-wave scalar wavefield is obtained, expressed by the formula:

[0033] ;

[0034] By reversing the time of the passive source seismic data at each receiving point and simulating the wavefield propagation in reverse, the passive source S-wave scalar wavefield is obtained, expressed by the formula:

[0035] ;

[0036] in, It is the first The event in the 1st Passive source seismic data from one receiver point It is the first The event in the 1st Passive source seismic data from one receiver point The velocity used for wave field extrapolation. The total length of time recorded for earthquakes. For the first The location of each receiving point For the first The location of each receiving point Indicates the total number of receiving points. For the first The passive source P-wave scalar wavefield obtained by back-extrapolation of passive source seismic data. For the first The passive source S-wave scalar wavefield obtained by back-extrapolation of passive source seismic data.

[0037] Furthermore, the active-source PS converted-wave imaging results and the passive-source PS converted-wave imaging results are weighted, superimposed, and fused, including:

[0038] Based on the short-time Fourier transform results of time-aligned active source seismic data and passive source seismic data, the signal-to-noise ratio of active source signals and passive source signals in the time-frequency domain is calculated respectively.

[0039] Based on the signal-to-noise ratio, the weighting coefficients of the active source PS converted wave imaging results and the passive source PS converted wave imaging results are calculated separately, and then weighted and superimposed to obtain the joint imaging results of the active and passive sources.

[0040] A combined active and passive source reverse time migration metal ore converted wave imaging system according to a second aspect embodiment of this disclosure includes:

[0041] The data acquisition module is used to acquire active source seismic data and passive source seismic data of the same underground structural region;

[0042] The time difference correction module is used to perform cross-correlation calculation on the active source seismic data and the passive source seismic data, determine the time difference between the two based on the maximum value of the cross-correlation function, and perform time alignment on the active source seismic data and the passive source seismic data based on the time difference, and output the time-aligned active source seismic data and passive source seismic data.

[0043] The wavefield separation module is used to separate the wavefields of time-aligned active source seismic data and passive source seismic data, and reconstruct the P-wave and S-wave fields of the active source, as well as the P-wave and S-wave fields of the passive source.

[0044] The wavefield extrapolation module is used to extrapolate the active source wavefields of the P-wave and S-wave fields of the active source based on the two-way wave equation, to obtain the forward propagation P-wave scalar wavefield and the backward propagation S-wave scalar wavefield of the active source; at the same time, it extrapolates the passive source wavefields of the P-wave and S-wave fields of the passive source, to obtain the passive source P-wave scalar wavefield and S-wave scalar wavefield.

[0045] The cross-correlation imaging module is used to cross-correlate the P-wave scalar wave field of the active source with the S-wave scalar wave field of the active source using cross-correlation imaging conditions to obtain the active source PS converted wave imaging result; and to cross-correlate the P-wave scalar wave field of the passive source with the S-wave scalar wave field of the passive source to obtain the passive source PS converted wave imaging result.

[0046] The weighted fusion module is used to weight and superimpose the active source PS converted wave imaging results and the passive source PS converted wave imaging results to output the joint imaging results of the active and passive sources.

[0047] Compared with the prior art, the advantages of this disclosure are as follows:

[0048] This disclosure is used to process conventional seismic data, combining the high resolution advantage of active sources with the deep detection capability of passive sources to overcome the limitations of single-source detection, achieving high-precision deep reverse-time migration imaging. It utilizes the sensitivity of PS converted waves to reservoir fluid and lithological changes, employing converted wave information imaging to improve the identification and differentiation of geological medium interfaces. Based on a cross-correlation-based active-passive source time-frequency domain data alignment method and a signal-to-noise ratio-based adaptive weighted fusion strategy, it overcomes the shortcomings of strong randomness and low signal-to-noise ratio of passive source signals, resulting in less noise and better imaging quality in the fused imaging results. The use of the potential function method for wavefield separation and velocity modeling provides more accurate separation of complex mixed wavefields than traditional polarization filtering and velocity modeling methods, providing more reliable input data for subsequent high-precision reverse-time migration and ensuring imaging accuracy. This disclosure effectively solves the contradiction between high resolution and deep detection in deep metal mineral exploration, providing a new and effective technical means for deep mineral resource exploration, suitable for research and industrial production applications in various fields, including terrestrial and marine environments. Attached Figure Description

[0049] Figure 1 A flowchart illustrating a method for imaging metal minerals using combined active and passive source reverse time migration, provided in this embodiment of the disclosure;

[0050] Figure 2 A block diagram of a metal ore converted wave imaging system with combined active and passive source reverse time migration provided in this embodiment of the disclosure;

[0051] Figure 3 The image shows the results of the converted wave imaging of a metal ore with combined active and passive source reverse time migration in the simulation test provided in the embodiments of this disclosure. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this disclosure.

[0053] The present disclosure will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0054] Metal ore exploration in areas with complex geological structures faces challenges such as active source limitations due to insufficient energy at depth, passive source limitations due to low resolution at shallow depths, and severe crosstalk noise generated by acoustic migration imaging that ignores elastic effects.

[0055] This disclosure provides a method and system for convertible wave imaging of metal minerals using a combination of active and passive sources and reverse time migration. Compared with traditional reverse time migration imaging, this disclosure improves both imaging accuracy and detection depth by combining active and passive sources. In addition to utilizing elastic wave information, it further considers the elastic characteristics of seismic waves when using elastic wave migration imaging, enabling wavefield separation and imaging using converted waves. Converted waves are extremely sensitive to lithological changes, fluids, and fractures, giving them a unique advantage in identifying metal mineral bodies, especially mineralized zones associated with fractures and fluid activity.

[0056] See Figure 1 As shown, this disclosure provides a method for imaging metallic minerals using combined active and passive source reverse time migration, comprising:

[0057] S101, perform time difference correction on active source seismic data and passive source seismic data of the same subsurface structure. The time difference correction includes cross-correlation calculation of active and passive source seismic data, correcting the time difference between active and passive source seismic data, and obtaining time-aligned active source seismic data and passive source seismic data.

[0058] S102, Wavefield separation is performed on the time-aligned active source seismic data and passive source seismic data respectively to reconstruct the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source.

[0059] S103, based on the two-way wave equation, extrapolate the P-wave field and S-wave field of the active source and the P-wave field and S-wave field of the passive source, respectively.

[0060] S104. Using the cross-correlation imaging condition, cross-correlation is performed on the P-wave scalar wave field and S-wave scalar wave field obtained by extrapolation of active source wave field and passive source wave field, respectively, to obtain the active source PS converted wave imaging result and the passive source PS converted wave imaging result.

[0061] S105, weighted superposition and fusion of active source PS converted wave imaging results and passive source PS converted wave imaging results to obtain joint imaging results of active and passive sources.

[0062] This embodiment of the disclosure acquires seismic data from both active sources (such as controlled seismic sources) and passive sources (such as natural earthquakes or microseismic events) for the same subsurface structure. Cross-correlation techniques are used to correct the time difference between the two types of data, eliminating systematic errors caused by inconsistent excitation times and achieving data alignment in the time domain. Wavefield separation processing is then performed on the time-aligned data for each type, reconstructing the P-wave and S-wave fields of the active source and the passive source from the complex wavefield, providing clean input for subsequent imaging. Based on the two-way wave equation, wavefield extrapolation is performed on the separated P-wave and S-wave fields of the active and passive sources to simulate the wavefield propagation process in the subsurface space. Using cross-correlation imaging conditions, cross-correlation calculations are performed on the extrapolated P-wave and S-wave fields from the active and passive sources, respectively. This process aims to extract imaging information generated by wave mode conversion (P-wave to S-wave or S-wave to P-wave) at the subsurface interface, obtaining active-source PS-converted wave imaging results and passive-source PS-converted wave imaging results. The PS-converted wave imaging results obtained from the active and passive sources are then weighted and fused. By appropriately allocating weights, the advantages of both types of data are integrated to ultimately generate a high signal-to-noise ratio, high-resolution joint active-passive source imaging result.

[0063] In step S101, a seismic detector array is deployed in the target exploration area, and an artificially controlled seismic source is used to excite the seismic data, record multi-component seismic waveform data, and acquire active source seismic data.

[0064] The design of the observation system and the deployment of the seismic detector array involve pre-designing a two-dimensional or three-dimensional seismic observation system within the target exploration area, based on the geological tasks and imaging accuracy requirements. A seismic data acquisition array is constructed using seismic detectors. The deployment of the seismic detectors follows these principles: they are buried along the survey line direction (or grid nodes) at a predetermined trace spacing. A controllable seismic source (such as a hydraulically servo-controlled vibrator) is used; in this embodiment, a controllable seismic source is preferred to facilitate subsequent processing. Excitation is performed at predetermined shot point locations. Simultaneously with source excitation, all deployed multi-component seismic detectors activate recording mode to continuously record active source seismic data.

[0065] After completing the active source seismic data acquisition (or concurrently with it), the same seismic detector array is maintained, and passive source seismic data acquisition continues or is performed synchronously. In this embodiment, passive source acquisition adopts a continuous recording mode, that is, the seismic detector array does not rely on artificial source excitation, but records the naturally existing elastic wave field information in the subsurface medium at a preset high sampling rate all day or for long periods of time.

[0066] Naturally occurring elastic wavefield information includes: natural earthquake events, referring to microseismic, tectonic, or teleseismic events within a regional area, whose long-wavelength signals can penetrate deep strata; and background environmental noise, including continuous micro-vibrations caused by human activities (traffic, industry), marine activities (tides, waves), and near-surface meteorological changes. To ensure sufficient signal-to-noise ratio and signal coverage, the acquisition duration is set based on the target depth and noise field stability to ensure that enough natural earthquake events are captured and that the environmental noise cross-correlation function converges to a stable empirical Green's function.

[0067] After the data acquisition is completed, the time series data recorded continuously over a long period of time is extracted and processed to form the original passive source seismic data.

[0068] After independently acquiring active-source and passive-source seismic data, their time series references are not unified due to the fundamental differences in their excitation mechanisms. Specifically, active-source seismic data has a precisely known excitation time (i.e., a zero point), and the time axis of each record is arranged with this zero point as the reference. Passive-source data, on the other hand, uses a continuous recording mode, does not contain a clear excitation time marker, and the occurrence time of the natural earthquake event is unknown. Therefore, before joint imaging, time difference correction must be performed on the two types of data to ensure that the wavefield response reflecting the same subsurface structure is consistent on the time axis.

[0069] In some embodiments, time difference correction is performed on active-source seismic data and passive-source seismic data of the same subsurface structure, specifically including:

[0070] Active source seismic data are represented as the convolution of the active source source wavelet and the active source Green's function. The active source source wavelet is denoted as... Record wavefield data from the receiving point, active source seismic data. The expression is:

[0071] ,

[0072] in, Location of the receiving point. Location of the epicenter. The Green's function, representing the active source, describes the impulse response from the source point to the receiver point. It contains information about all wave propagation paths in the subsurface medium (such as reflection, refraction, and converted waves) and is an inherent property of subsurface structures. For convolution;

[0073] Passive source seismic data can be represented as the convolution of the source time function and the passive source Green's function. Passive source seismic data typically originates from natural earthquakes or environmental noise, and its general form can be represented as the superposition of excitation wave fields from multiple unknown sources.

[0074] ,

[0075] in, Passive source seismic data, From the station to the passive source location The Green's function, i.e., the passive source Green's function, For the source time function, Recorded as the number A passive source location.

[0076] The above reveals the essential relationship between active-source and passive-source seismic data: active-source seismic data has known source locations and known (or estimable) source wavelets, while passive-source seismic data has unknown source locations and unknown source time functions. However, both contain Green's functions reflecting the subsurface medium response. By performing cross-correlation calculations on the active and passive source data, the influence of the unknown source time function can be eliminated or suppressed, and the time difference between the Green's functions in the two types of data can be extracted. Specifically, the peak time in the cross-correlation result corresponds to the time offset between wave arrivals at the same subsurface interface in both types of data. This offset is determined by the difference in location between artificial and natural sources and the subsurface structure. Through this time difference correction, the passive-source data can be aligned with the active-source data on the time axis, thus laying the foundation for subsequent joint wavefield separation and imaging.

[0077] The active source seismic data and the passive source seismic data are subjected to short-time Fourier transform respectively. At each frequency within the preset effective frequency band, the first complex signal time series and the second complex signal time series are extracted.

[0078] For each frequency within the effective frequency band, calculate the cross-correlation function between the first complex signal time series and the second complex signal time series to obtain the cross-correlation function corresponding to multiple frequencies;

[0079] The total cross-correlation function is obtained by superimposing the cross-correlation functions corresponding to the multiple frequencies.

[0080] The maximum value of the total cross-correlation function is determined, and the time position corresponding to the maximum value is used as the time difference between the active source seismic data and the passive source seismic data.

[0081] The optimal time difference compensation for active and passive source seismic data is estimated by maximizing cross-correlation, and the data are then aligned in the time-frequency domain to correct for time differences. Since active and passive source seismic data have different frequency components and noise, time-frequency domain analysis is introduced, considering the receiver location... The information is treated as constant and simplified. Short-time Fourier transforms are performed on both active-source and passive-source seismic data: ,

[0082] ,

[0083] in, It is the first complex signal time series. It is the second complex signal time series.

[0084] Each frequency within the effective frequency band 1. Calculate the cross-correlation function. expression:

[0085]

[0086] Where * denotes complex conjugate. The cross-correlation functions of each frequency are superimposed, and the position of the maximum value of the total cross-correlation function is calculated. The estimated time difference is expressed as:

[0087] ,

[0088] Thus, time-shift corrected passive source seismic data are obtained. The expression is:

[0089] .

[0090] Using the time-aligned active-source seismic data and passive-source seismic data obtained in step S101, wavefield separation is performed on the active-source and passive-source seismic data respectively. The P-wave and S-wave fields of the active source and the passive source are reconstructed through wavefield separation. In one embodiment, this includes:

[0091] The particle vibration velocity fields corresponding to the time-aligned active source seismic data and passive source seismic data are decomposed by Helmholtz, and expressed as the sum of the gradient field of the scalar potential and the curl field of the vector potential.

[0092] Calculate the divergence and curl fields of the particle vibration velocity field;

[0093] Based on the divergence field and curl field, the Poisson equation satisfied by the scalar potential and the vector Poisson equation satisfied by the vector potential are solved respectively to obtain the scalar potential and the vector potential.

[0094] The P-wave velocity field is reconstructed based on the gradient of the scalar potential, and the S-wave velocity field is reconstructed based on the curl of the vector potential, thus obtaining the P-wave velocity and S-wave velocity respectively.

[0095] This step employs a potential function method based on Helmholtz decomposition for wavefield separation. Specifically, it includes the following steps:

[0096] Helmholtz decomposition of the velocity field of a particle vibration:

[0097] According to the vector field decomposition theorem, any continuously differentiable vector field that approaches zero at infinity can be uniquely decomposed into the sum of an irrotational field (the gradient of the scalar potential) and a divergence-free field (the curl of the vector potential). The time-aligned particle vibration velocity field can be represented by Helmholtz decomposition as the sum of the gradient field of the scalar potential and the curl field of the vector potential. Applying the divergence operator and curl operator to the particle vibration velocity field respectively yields the corresponding divergence field and curl field. According to the identities in vector analysis, the scalar potential and the divergence field satisfy the Poisson equation, and the vector potential and the curl field satisfy the vector Poisson equation, expressed as:

[0098] ,

[0099] The above equation is the Poisson equation satisfied by the scalar potential. For scalar potential, For divergence fields, For the Laplace operator.

[0100] ,

[0101] The above equation is a vector potential. Satisfying the vector Poisson equation, It is a curl field.

[0102] According to the definition of Helmholtz decomposition, scalar potential Find the gradient to obtain the pure P-wave velocity field; for the vector potential Calculate the curl to obtain the pure transverse wave velocity field.

[0103] Based on the above-described solution process for the pure P-wave velocity field and the pure S-wave velocity field, the time-aligned active source seismic data and passive source seismic data are processed to obtain the P-wave velocity field and S-wave velocity field of the active source, as well as the P-wave velocity field and S-wave velocity field of the passive source.

[0104] In some embodiments, in step S103, the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source are extrapolated based on the two-way wave equation, respectively; including extrapolating the active source wave field based on the P-wave and S-wave fields of the active source; and extrapolating the passive source wave field based on the P-wave and S-wave fields of the passive source.

[0105] Extrapolation of the active source wavefield for the P-wave and S-wave fields based on the two-way wave equation; including:

[0106] Based on the two-way wave equation, the source and excitation time functions generate the excitation wavefield. The active source wavefield is then extrapolated to the P-wave field through forward simulated wavefield propagation, yielding the active source P-wave scalar wavefield, expressed by the formula:

[0107] ;

[0108] By extrapolating the active source wavefield back to the S-wave field, we obtain the active source S-wave scalar wavefield, expressed by the formula:

[0109] ;

[0110] in: For P-wave velocity, Let S represent the S-wave velocity, F represent the forward wave field, and B represent the reverse wave field. This refers to active source seismic data from the j-th receiving point; This represents the scalar wave field of the active source P-wave obtained by forward extrapolation from k active sources. This represents the active source S-wave scalar wavefield obtained by back-extrapolation of the k-th active source seismic data. This represents the time function of the source wavelet of an active source seismic earthquake. Represents the Dirac delta function, Let j be the position of the receiving point. For all receiving points The recorded active source seismic data are summed. This represents the gradient.

[0111] Extrapolation of the active source wavefield based on the two-way wave equation for the P-wave and S-wave fields includes:

[0112] By treating all passive source seismic data from all receivers as virtual sources and performing forward extrapolation, the passive source P-wave scalar wavefield is obtained, expressed by the formula:

[0113] ;

[0114] By reversing the time of the passive source seismic data at each receiving point and simulating the wavefield propagation in reverse, the passive source S-wave scalar wavefield is obtained, expressed by the formula:

[0115] ;

[0116] in, It is the first The event in the 1st Passive source seismic data from one receiver point It is the first The event in the 1st Passive source seismic data from one receiver point The velocity used for wave field extrapolation. The total length of time recorded for earthquakes. For the first The location of each receiving point For the first The location of each receiving point Indicates the total number of receiving points. For the first The passive source P-wave scalar wavefield obtained by back-extrapolation of passive source seismic data. For the first The passive source S-wave scalar wavefield obtained by back-extrapolation of passive source seismic data.

[0117] In one embodiment, using cross-correlation imaging conditions, the P-wave scalar wavefield and S-wave scalar wavefield obtained by extrapolation of the active source wavefield and the passive source wavefield are cross-correlated to obtain the active source PS converted wave imaging result and the passive source PS converted wave imaging result. The specific implementation process is as follows:

[0118] The process includes cross-correlation of the active source P-wave scalar wavefield obtained by extrapolating the active source wavefield with the active source S-wave scalar wavefield, and cross-correlation of the passive source P-wave scalar wavefield obtained by extrapolating the passive source wavefield with the passive source S-wave scalar wavefield. Specifically, the PS-converted wave imaging of the active source is completed by cross-correlation of the positively extrapolated active source P-wave scalar wavefield and the active source S-wave scalar wavefield; the PS-converted wave imaging of the passive source is completed by cross-correlation of the positively extrapolated passive source P-wave scalar wavefield and the passive source S-wave scalar wavefield.

[0119] At the imaging point At this point, calculate the scalar wave field of the active source P-wave. and active source S-wave scalar wave field The product of these two components, followed by integration over time, gives the expression for active-source PS converted-wave imaging:

[0120] ,

[0121] in, It is the record length. The total number of events triggered by the active source. It is an index variable used to sum the active source events; This is the result of active source PS converted wave imaging.

[0122] The same set of passive source data is used for the forward and reverse extrapolation of the passive source converted wave. The imaging expression for the passive source converted wave is as follows:

[0123] ,

[0124] in, For passive source S-wave scalar wave field, For passive source P-wave scalar wave field, The results are from passive source PS converted wave imaging.

[0125] In one embodiment, the active-source PS converted wave imaging results and the passive-source PS converted wave imaging results are weighted, superimposed, and fused, including:

[0126] Based on the short-time Fourier transform results of time-aligned active source seismic data and passive source seismic data, the signal-to-noise ratio of active source signals and passive source signals in the time-frequency domain is calculated respectively.

[0127] Based on the signal-to-noise ratio, the weighting coefficients of the active source PS converted wave imaging results and the passive source PS converted wave imaging results are calculated separately, and then weighted and superimposed to obtain the joint imaging results of the active and passive sources.

[0128] Specifically, the active source PS converted wave imaging results and the passive source PS converted wave imaging results are fused using a weighted superposition method, with the weight ratio determined based on the signal-to-noise ratio of the active and passive sources.

[0129] The signal-to-noise ratio is estimated through the energy ratio in the time-frequency domain. The time-frequency signal is either an active or passive source. The time-frequency signal is noise. , Given the signal weights for the active and passive sources, the signal-to-noise ratio is calculated as follows:

[0130] ,

[0131] Then calculate the signal weights of the active source and the passive source respectively.

[0132] The signal weights of the active sources are calculated using the following formula:

[0133] ,

[0134] The signal weight of a passive source is calculated using the following formula:

[0135] ,

[0136] in, For the signal-to-noise ratio of the active source, The signal-to-noise ratio of the passive source.

[0137] Joint imaging equation of active and passive sources:

[0138] ;

[0139] ;

[0140] in, The results are from combined imaging of the primary and passive sources.

[0141] when At that time, joint imaging relies more on the high resolution of the active source; when In this case, joint imaging relies more on the deep-penetration capabilities of passive sources.

[0142] On the other hand, see Figure 2 As shown, this disclosure provides a metal ore converted wave imaging system based on combined active and passive source reverse time migration, comprising the following functional modules: a data acquisition module, a time difference correction module, a wavefield separation module, a wavefield extrapolation module, a cross-correlation imaging module, and a weighted fusion module. These modules work together to complete the entire process from raw data input to final fused imaging output.

[0143] The data acquisition module, serving as the system's front-end input unit, is used to acquire active-source and passive-source seismic data from the same underground structural region. In a specific example, active-source seismic data can be obtained by artificially generating seismic sources (such as explosives or controlled seismic sources) on the surface or in wells and received by an array of seismic detectors deployed on the surface or in wells; passive-source seismic data can be continuous or triggered records of natural earthquakes, mining tremors, or environmental noise data. This data acquisition module is responsible for standardizing and initially organizing these two types of data for subsequent processing.

[0144] The time difference correction module receives raw active-source and passive-source seismic data. Since the excitation times of active-source and passive-source seismic data are usually unknown or asynchronous, an unknown time difference exists between them. The core function of the time difference correction module is to eliminate this time difference, achieving time domain alignment between the two types of data. The specific processing includes: First, cross-correlation calculations are performed on the active-source and passive-source seismic data. To improve the accuracy and noise resistance of the time difference estimation, the data can be transformed to the frequency domain, and the cross-correlation function of each frequency component is calculated within the effective frequency band. Then, the cross-correlation results of all frequencies are superimposed to obtain a total cross-correlation function. Subsequently, the maximum value of this total cross-correlation function is found; the time offset corresponding to this maximum value is the time difference between the two types of data. Finally, based on the calculated relative time difference, a total time shift is performed on one of the data streams (such as passive-source seismic data), thereby outputting time-aligned active-source and passive-source seismic data.

[0145] The wavefield separation module receives time-aligned active-source and passive-source seismic data. It performs wavefield separation on the two types of data to reconstruct their respective P-wave and S-wave fields. In a preferred embodiment, a potential function method is used for wavefield separation. This method is based on the Helmholtz decomposition principle, decomposing the particle vibration velocity field into an irrotational scalar potential field and a divergence-free vector potential field. Specifically, the divergence and curl fields of the particle vibration velocity field in the data are first calculated; then, the P-wave and S-wave potential fields are obtained by solving the Poisson equation (for scalar potential) and the vector Poisson equation (for vector potential); finally, the P-wave and S-wave fields in physical space are reconstructed by calculating the gradient or curl of the potential fields. The module outputs four sets of data: active-source P-wave field, active-source S-wave field, passive-source P-wave field, and passive-source S-wave field.

[0146] The wavefield extrapolation module is used to extrapolate the active source wavefields of the P-wave and S-wave fields of the active source based on the two-way wave equation, to obtain the forward propagation P-wave scalar wavefield and the backward propagation S-wave scalar wavefield of the active source; at the same time, it extrapolates the passive source wavefields of the P-wave and S-wave fields of the passive source, to obtain the passive source P-wave scalar wavefield and S-wave scalar wavefield.

[0147] The cross-correlation imaging module is used to cross-correlate the P-wave scalar wave field of the active source with the S-wave scalar wave field of the active source using cross-correlation imaging conditions to obtain the active source PS converted wave imaging result; and to cross-correlate the P-wave scalar wave field of the passive source with the S-wave scalar wave field of the passive source to obtain the passive source PS converted wave imaging result.

[0148] The weighted fusion module receives active-source PS converted-wave imaging results and passive-source PS converted-wave imaging results. It then performs weighted superposition and fusion of the two imaging results to fully utilize their respective advantages and output a joint active-passive source imaging result.

[0149] See Figure 3The image shown is an image of the converted wave imaging results of metallic minerals obtained through simulated testing using a combination of active and passive source reverse time migration. It can be seen that the horizontal axis X represents the horizontal distance, ranging from 0 to 1000 meters; the vertical axis Z represents the depth, also ranging from 0 to 1000 meters. The entire profile covers complete subsurface structural information from shallow to deep layers, and the imaging results are represented by grayscale levels indicating the intensity of converted wave reflection energy. In the shallow region at depths of 0 to 300 meters, the imaging results primarily demonstrate the high-resolution advantage of active source seismic data. High-precision stratigraphic characterization: clear and continuous stratigraphic reflection interfaces are visible in the profile, with rich stratigraphic details, enabling the differentiation of thin mineralized layers or alteration zones. This is attributed to the high-frequency components of the active source wavelet and the precise time-frequency analysis after short-time Fourier transform, allowing for high-fidelity reconstruction of shallow structural morphology (such as faults and unconformities). The ore body exhibits a clear response: a strong amplitude anomaly zone appears within a range of 200-350 meters in the X direction and a depth of 150-250 meters. Verification with borehole data confirms that this anomaly corresponds to a known skarn-type metallic ore body. The ore body boundary is clear, with a distinct contact relationship with the surrounding rock, demonstrating the sensitive response of PS converted waves to lithological interfaces. In the deep region at depths of 300 to 800 meters, the imaging results primarily demonstrate the deep-penetration capabilities of passive source seismic data. Deep structural outlines: Although the resolution of deep imaging is somewhat reduced compared to shallower areas due to high-frequency signal attenuation, it still clearly delineates the outlines of large deep structures, such as the top interface of concealed rock masses and deep fault zones. At a depth of 450-600 meters in the X direction and a depth of 500-700 meters, a set of gently dipping, strongly reflective zones is observed, presumably altered and fractured zones formed by deep magmatic hydrothermal activity.

[0150] Thanks to the signal-to-noise ratio weighted fusion strategy of passive source data, the effective reflection signals that were originally submerged in noise at depth are effectively enhanced. Through time-frequency domain signal-to-noise ratio estimation, the weight of passive source imaging results in depth is correspondingly increased, ensuring the reliability of deep geological bodies in the joint imaging results.

[0151] In the overlapping transition zone at depths of 300-400 meters, the weighted fusion module adaptively adjusts the weights based on the local signal-to-noise ratio, resulting in a smooth transition between shallow high-resolution information and deep structural information, without obvious splicing traces or energy abrupt changes. From the surface (0 meters) to a depth of 1000 meters, the imaging results maintain good continuity and consistency. In particular, in the ultra-deep region at depths of 800-1000 meters, although the energy is weaker, the deep reflection characteristics at the Moho discontinuity or lithosphere scale can still be faintly traced, providing valuable geophysical evidence for deep mineral exploration prediction. Figure 3The effectiveness of the proposed active-passive source combined reverse time migration imaging method has been fully verified. Through key steps such as time difference correction, wavefield separation, two-way wave extrapolation, and weighted fusion, this method successfully achieves the organic unity of high resolution of active sources and deep penetration characteristics of passive sources, providing strong technical support for "deep exploration and blind detection" in metal mines.

[0152] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for imaging metallic minerals using combined active and passive source reverse time migration, characterized in that, The method includes: Time difference correction is performed on active source seismic data and passive source seismic data of the same subsurface structure. The time difference correction includes cross-correlation calculation of active and passive source seismic data, correction of the time difference between active and passive source seismic data, and obtaining time-aligned active source seismic data and passive source seismic data. Wavefield separation was performed on the time-aligned active source seismic data and passive source seismic data to reconstruct the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source. Based on the two-way wave equation, the P-wave and S-wave fields of the active source and the P-wave and S-wave fields of the passive source are extrapolated respectively. Using cross-correlation imaging conditions, cross-correlation was performed on the P-wave scalar wavefield and S-wave scalar wavefield obtained by extrapolation of active source wavefield and passive source wavefield, respectively, to obtain the active source PS converted wave imaging results and the passive source PS converted wave imaging results. The active source PS converted wave imaging results and the passive source PS converted wave imaging results are weighted, superimposed, and fused to obtain the joint imaging results of active and passive sources.

2. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 1, characterized in that, The active source seismic data is obtained by deploying a seismic detector array in the exploration area and using an artificially controlled seismic source to record multi-component seismic waveform data. The passive source seismic data is obtained by continuously recording passive seismic signals, including natural earthquakes and environmental noise, using the same seismic detector array.

3. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 1, characterized in that, Time difference correction is performed on active-source and passive-source seismic data of the same subsurface structure, specifically including: Active source seismic data is represented as a convolution of the active source source wavelet and the active source Green's function; Passive source seismic data is represented as a convolution of the source time function and the passive source Green's function; The active source seismic data and the passive source seismic data are subjected to short-time Fourier transform respectively. At each frequency within the preset effective frequency band, the first complex signal time series and the second complex signal time series are extracted. For each frequency within the effective frequency band, calculate the cross-correlation function between the first complex signal time series and the second complex signal time series to obtain the cross-correlation function corresponding to multiple frequencies; The total cross-correlation function is obtained by superimposing the cross-correlation functions corresponding to the multiple frequencies. The maximum value of the total cross-correlation function is determined, and the time position corresponding to the maximum value is used as the time difference between the active source seismic data and the passive source seismic data.

4. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 1, characterized in that, Wavefield separation was performed on the time-aligned active-source and passive-source seismic data to reconstruct the P-wave and S-wave fields of the active source and the passive-source, including: The particle vibration velocity fields corresponding to the time-aligned active source seismic data and passive source seismic data are decomposed by Helmholtz, and expressed as the sum of the gradient field of the scalar potential and the curl field of the vector potential. Calculate the divergence field and curl field of the particle vibration velocity field; Based on the divergence field and curl field, the Poisson equation satisfied by the scalar potential and the vector Poisson equation satisfied by the vector potential are solved respectively to obtain the scalar potential and the vector potential. The P-wave velocity field is reconstructed based on the gradient of the scalar potential, and the S-wave velocity field is reconstructed based on the curl of the vector potential, thus obtaining the P-wave velocity and S-wave velocity respectively.

5. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 4, characterized in that, Extrapolation of the active source wavefield for the P-wave and S-wave fields based on the two-way wave equation; including: Based on the two-way wave equation, the source and excitation time functions generate the excitation wavefield. The active source wavefield is then extrapolated to the P-wave field using a forward simulated wavefield propagation method, yielding the active source P-wave scalar wavefield, expressed by the following formula: ； By extrapolating the active source wavefield back to the S-wave field, we obtain the active source S-wave scalar wavefield, expressed by the formula: ； in: For P-wave velocity, Let S represent the S-wave velocity, F represent the forward wave field, and B represent the reverse wave field. This refers to active source seismic data from the j-th receiving point; This represents the scalar wave field of the active source P-wave obtained by forward extrapolation from k active sources. This represents the active source S-wave scalar wavefield obtained by back-extrapolation of the k-th active source seismic data. This represents the time function of the source wavelet of an active source seismic earthquake. Represents the Dirac delta function, Let j be the position of the receiving point. To sum the active source seismic data recorded at all receiver points j, This represents the gradient.

6. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 4, characterized in that, Passive source wavefield extrapolation based on the two-way wave equations for the P-wave and S-wave fields includes: By treating all passive source seismic data from all receivers as virtual sources and performing forward extrapolation, the passive source P-wave scalar wavefield is obtained, expressed by the formula: ； By reversing the time of the passive source seismic data at each receiving point and simulating the wavefield propagation in reverse, the passive source S-wave scalar wavefield is obtained, expressed by the formula: ； in, It is the first The event in the 1st Passive source seismic data from one receiver point It is the first The event in the 1st Passive source seismic data from one receiver point The velocity used for wave field extrapolation. The total length of time recorded for earthquakes. For the first The location of each receiving point For the first The location of each receiving point Indicates the total number of receiving points. For the first The passive source P-wave scalar wavefield obtained by back-extrapolation of passive source seismic data. For the first The passive source S-wave scalar wavefield obtained by back extrapolation of passive source seismic data.

7. The method for imaging metal minerals using combined active and passive source reverse time migration according to claim 1, characterized in that, The active-source PS converted-wave imaging results and the passive-source PS converted-wave imaging results are weighted, superimposed, and fused, including: Based on the short-time Fourier transform results of time-aligned active source seismic data and passive source seismic data, the signal-to-noise ratio of active source signals and passive source signals in the time-frequency domain is calculated respectively. Based on the signal-to-noise ratio, the weighting coefficients of the active source PS converted wave imaging results and the passive source PS converted wave imaging results are calculated separately, and then weighted and superimposed to obtain the joint imaging results of the active and passive sources.

8. A combined active and passive source reverse time migration system for converted wave imaging of metallic minerals, characterized in that, include: The data acquisition module is used to acquire active source seismic data and passive source seismic data of the same underground structural region; The time difference correction module is used to perform cross-correlation calculation on the active source seismic data and the passive source seismic data, determine the time difference between the two based on the maximum value of the cross-correlation function, and perform time alignment on the active source seismic data and the passive source seismic data based on the time difference, and output the time-aligned active source seismic data and passive source seismic data. The wavefield separation module is used to separate the wavefields of time-aligned active source seismic data and passive source seismic data, and reconstruct the P-wave and S-wave fields of the active source, as well as the P-wave and S-wave fields of the passive source. The wavefield extrapolation module is used to extrapolate the active source wavefields of the P-wave and S-wave fields of the active source based on the two-way wave equation, to obtain the forward propagation P-wave scalar wavefield and the backward propagation S-wave scalar wavefield of the active source; at the same time, it extrapolates the passive source wavefields of the P-wave and S-wave fields of the passive source, to obtain the passive source P-wave scalar wavefield and S-wave scalar wavefield. The cross-correlation imaging module is used to cross-correlate the P-wave scalar wave field of the active source with the S-wave scalar wave field of the active source using cross-correlation imaging conditions to obtain the active source PS converted wave imaging result; and to cross-correlate the P-wave scalar wave field of the passive source with the S-wave scalar wave field of the passive source to obtain the passive source PS converted wave imaging result. The weighted fusion module is used to weight and superimpose the active source PS converted wave imaging results and the passive source PS converted wave imaging results to output the joint imaging results of the active and passive sources.

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