Joint detection method and device based on marine seismic data and electromagnetic data, electronic equipment and storage medium

Abstract

The application discloses a kind of based on marine seismic data and the joint detection method, device, electronic equipment and storage medium of electromagnetic data.This method comprises: determining first data and second data;First data and second data are jointly inverted, and first model is obtained;According to first model, hydrate analysis is carried out.The method is by first data and second data jointly inverted, and first model is obtained, and first model is analyzed again, can reflect the distribution characteristics of natural gas hydrate from different angles, and the limitation of single geophysical method can be effectively reduced by combination, to significantly improve the interpretation accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method, apparatus, electronic device and storage medium for joint detection based on marine seismic data and electromagnetic data. Background Technology

[0002] The seabed topography in areas rich in natural gas hydrates (especially the northern continental slope of the South China Sea) is complex, and the mineralization of hydrates is irregular and unevenly distributed. Therefore, the requirements for hydrate detection differ from those for conventional oil and gas exploration, and the instruments, equipment, and technical methods used are also different.

[0003] In natural gas hydrate detection, seismic and electromagnetic exploration are both widely used geophysical exploration methods, but each has its own advantages and limitations. Seismic methods can obtain high-resolution reservoir structure data and are highly effective in identifying seafloor reflections (BSRs), amplitude gaps, wave impedance characteristics, and velocity structure characteristics, playing a crucial role in hydrate detection. However, given complex reservoir conditions, conventional seismic methods cannot simultaneously capture high-resolution information from shallow hydrate reservoirs and information on fluid accumulation and migration paths, failing to meet the technical requirements of hydrate industrialization. Controlled-source electromagnetic (CME) detection technology, based on the electrical differences between different substances, probes the internal structure of reservoirs and has excellent identification capabilities for hydrates exhibiting high resistivity anomalies. The accumulation of natural gas hydrates in marine sediments leads to changes in the microparticles of pore fluids in the hydrate ore body and adjacent strata. Microparticle movement is often accompanied by changes in ion concentration, resulting in corresponding changes in resistivity characteristics. Marine CME technology can effectively detect resistivity characteristics, making it an effective detection method for identifying the electrical characteristics of hydrates. By combining the seismic and electromagnetic physical properties of hydrates, high-resolution physical property detection of hydrate ore bodies can be achieved.

[0004] Seismic methods can obtain high-resolution reservoir structures of natural gas hydrates, and are highly effective in identifying seafloor reflections (BSRs), amplitude gaps, wave impedance characteristics, and velocity structure characteristics, playing a crucial role in hydrate detection. However, given the complex reservoir conditions, conventional seismic methods cannot simultaneously capture high-resolution information about shallow hydrate reservoirs and information about fluid accumulation and migration paths, thus failing to meet the technical requirements for hydrate industrialization. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for joint detection based on marine seismic data and electromagnetic data, in order to solve the problem that conventional seismic methods cannot meet the technical requirements of hydrate industrialization.

[0006] According to one aspect of the present invention, a joint detection method based on marine seismic data and electromagnetic data is provided, comprising: The first data and the second data are determined; the first data is seismic wave data obtained by conducting seismic exploration of the target area; the first data includes: a first sub-data and a second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form; The first model is obtained by performing a joint inversion on the first data and the second data; Hydrate analysis was performed based on the first model.

[0007] According to another aspect of the present invention, a joint detection device based on marine seismic data and electromagnetic data is provided, comprising: Data determination module: used to determine first data and second data; the first data is seismic wave data obtained by seismic exploration of the target area; the first data includes: first sub-data and second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form; First model determination module: used to perform joint inversion on the first data and the second data to obtain the first model; Analysis module: Used to perform hydrate analysis based on the first model.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the joint detection method based on marine seismic data and electromagnetic data according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the joint detection method based on marine seismic data and electromagnetic data as described in any embodiment of the present invention.

[0010] The technical solution of this invention involves determining first data and second data. The first data, obtained by utilizing single-channel and limited multi-channel seismic profiles, describes the geometry of the seafloor-like reflector layer based on travel time and amplitude, thus depicting the distribution of natural gas hydrate reservoirs. The second data provides a more accurate characterization of the top burial depth and lateral boundaries of high-resistivity bodies. Both data provide a basis for the subsequent construction of a first model. The first and second data are then jointly inverted to obtain the first model. Hydrate analysis is performed based on the first model. This method, by jointly inverting the first and second data to obtain the first model and then analyzing it, reflects the distribution characteristics of natural gas hydrates from different perspectives. The combination of these two methods effectively reduces the limitations of single geophysical methods, thereby significantly improving the accuracy and reliability of interpretation.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a joint detection method based on marine seismic data and electromagnetic data, provided in an embodiment of the present invention; Figure 2 A schematic diagram of the deployment of the first and second devices on the seabed; Figure 3 A schematic diagram of a first model provided in an embodiment of the present invention; Figure 4 A schematic diagram of a first sub-model provided in an embodiment of the present invention; Figure 5 A schematic diagram of a second model provided in an embodiment of the present invention; Figure 6 A schematic diagram of a third model provided in an embodiment of the present invention; Figure 7 A schematic diagram of the structure of a joint detection device based on marine seismic data and electromagnetic data provided in an embodiment of the present invention; Figure 8 A schematic diagram of the structure of an electronic device for implementing the joint detection method based on marine seismic data and electromagnetic data according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] In one embodiment, Figure 1 This is a flowchart illustrating a joint detection method based on marine seismic data and electromagnetic data, provided by an embodiment of the present invention. This embodiment is applicable to the analysis of seabed hydrates. The method can be executed by a joint detection device based on marine seismic data and electromagnetic data. This device can be implemented in hardware and / or software and can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method includes: S110. Determine the first data and the second data; the first data is seismic wave data obtained by conducting seismic exploration of the target area; the first data includes: first sub-data and second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form.

[0017] Among them, the marine areas within the target region that require hydrate analysis.

[0018] Specifically, the first data is acquired through the first device; the second data is obtained by electromagnetic data acquisition through the second device.

[0019] The first device uses a 560 cubic inch BOLT gun source as its firing source, with a 5m immersion depth and a firing point spacing of 25m.

[0020] The first device includes a two-dimensional multi-channel tow cable and an OBS (submarine seismograph).

[0021] The two-dimensional multi-channel towed cable deployment is as follows: a single-sided firing and single-sided receiving observation system is adopted, with a total of 240 cables deployed for signal reception, and a channel spacing of 12.5m. To reduce the impact of the survey vessel's noise on the seismic data, the minimum offset is set at 125m, the sampling rate is 1ms, and the coverage is 60 times.

[0022] The OBS system consists of 15 units equipped with deep-sea low-frequency high-sensitivity hydrophones, with a frequency response range of 3Hz-600Hz, sensitivity ≥-160dBref1V / µPa, and a sampling rate of 1000Hz, providing excellent low-frequency acquisition performance.

[0023] The second device employs a fully towed marine controlled-source electromagnetic detection system (TTR-MCSEM).

[0024] The fully towed marine controlled-source electromagnetic detection system mainly consists of a deck terminal, an underwater electromagnetic transmitter, a towed electromagnetic acquisition station, and a bottom-mounted electromagnetic receiver (OBEM). Eleven marine electromagnetic receivers were deployed at a point spacing of 500m on the seabed at a depth of 1360m, with a survey line length of 5km. The electromagnetic transmitters emit frequency signals at 1.5Hz and 8Hz.

[0025] For example, such as Figure 2 The diagram shows the layout of the first and second devices on the seabed. As can be seen from the diagram, the transmitter of the first device, namely the surface air gun seismic source, is connected to the data collection vessel and placed in the seawater to emit transmitted waves; the electromagnetic transmitter is connected to the seismic cable and the towed electromagnetic data collection station; and the bottom-mounted electromagnetic receiver is configured inside the seabed strata.

[0026] S120. Perform joint inversion on the first data and the second data to obtain the first model.

[0027] The second set of data includes 40 electromagnetic field parameters, including the field values ​​of the electromagnetic field in the x, y, and z directions.

[0028] Specifically, the first model is obtained by inverting the first data using the first calculation model. The second model is then obtained by inverting the second data using the second calculation model. The third model is then converted in format and overlaid with the second model to obtain the first model.

[0029] The second calculation mode is the source code of the Mare2dem inversion program configured with the Occam inversion formula.

[0030] The Occam inversion algorithm is a commonly used inversion method that considers both the distribution of formation electrical parameters and minimizing the model fitting error. Typically, the relationship between formation electrical parameters (the second set of data) and formation depth is a smooth function. The Occam method defines a roughness level to represent the smoothness of the third model, addressing this reality. Therefore, the Occam inversion method can better reflect the true distribution of the subsurface medium and improve the accuracy of the inversion results. The advantages of the Occam algorithm are its simple and stable computation, its ability to effectively handle inversion problems, and its ability to control the smoothness of the third model, avoiding overfitting.

[0031] Furthermore, in practical applications, this algorithm can be combined with other inversion methods, such as regularization and model constraints, for optimization to obtain a more accurate and robust geophysical model.

[0032] The Mare2dem inversion program, proposed by Kerry Key, is a free code for two-dimensional anisotropic inversion, suitable for inverting audio-frequency magnetotelluric (AMT), magnetotelluric (MT), and controlled-source electromagnetic (MCSEM) data. This method solves for the relationship between electromagnetic parameters and formation depth on an iteratively optimized finite element mesh until the solution meets a specified tolerance. Adaptability is a major advantage of this algorithm; only the inversion parameter mesh needs to be defined, and the code automatically generates and optimizes a dynamic finite element mesh.

[0033] Further, the execution steps of the first calculation model are as follows: Data processing is performed on the first sub-data, and a velocity field and migration profile are established to obtain the first sub-model. Based on the first sub-model, the travel time of the P-waves in the vertical component records is obtained from the second sub-data. That is, following a top-down, layer-by-layer constraint approach, the upper-layer stratum parameters are kept constant, and the current stratum P-wave velocity is iteratively corrected layer by layer. The measured P-wave velocity is compared with the forward-modeled P-wave travel time error until convergence. When all strata have been corrected, the second sub-model is obtained. The initial shear wave velocity is determined based on the second sub-model. Then, based on the second sub-model and the initial shear wave velocity, the seismic wave propagation path is simulated using ray tracing to solve for the third time. The difference between the third time and the second time is calculated, and the difference is compared with a preset difference interval. The initial shear wave velocity is corrected based on the comparison result until all strata have been corrected, resulting in the second model.

[0034] Furthermore, the specific construction method of the first sub-model is as follows: Pre-stack migration is performed on the processed first sub-data to obtain a pre-stack depth migration profile. On the migration profile, the seafloor and various strata reflection horizons and fault structures are traced and interpreted to determine the planar distribution and burial depth of each geological interface. Stratigraphic units are divided longitudinally to construct a geometric layering framework for the subsurface strata. Pre-stack gathers from the towed cable are extracted from the first sub-data, and the velocity spectrum stacking velocity is picked point-by-point. The root mean square velocity is calculated using the Dix formula, and the initial P-wave velocity of the corresponding strata is further solved. Using the depth boundaries of each interpreted stratum as spatial constraints, the calculated initial P-wave velocity is assigned to the grid cells of the corresponding depth interval to obtain the first sub-model.

[0035] Furthermore, the third model is converted to the SEGY seismic data format to form a resistivity file based on the SGY data format.

[0036] Furthermore, the fusion of the first and second models is necessary because seismic exploration data can provide spatial distribution information of natural gas hydrates, and the seafloor reflection (BSR) of seismic profiles typically indicates the bottom boundary of natural gas hydrates. However, BSR alone cannot provide information on the top boundary of hydrates. Given that hydrate-bearing strata have higher resistivity than surrounding rocks, the marine controlled-source electromagnetic method (SEM) can accurately characterize the top burial depth and lateral boundaries of high-resistivity bodies, making it an effective supplementary method to seismic hydrate detection. The fusion of these two models can significantly improve the accuracy of natural gas hydrate exploration. Through data conversion, seismic and SEM data are simultaneously loaded, and the loaded resistivity inversion profile is fused with the seismic depth domain profile using a seismic interpretation system, providing technical support for high-precision characterization of hydrates.

[0037] The above steps enable the first model to overlay and display seismic and resistivity data under the same view, integrating them into a unified interpretation of seismic and electromagnetic data, thus improving the efficiency and reliability of the interpretation.

[0038] S130. Perform hydrate analysis based on the first model.

[0039] Specifically, the first model, such as Figure 3 As shown in the figure, the natural gas hydrates develop at a greater depth on the left, and there is also a favorable area in the middle, particularly influenced and controlled by faults, distributed between two faults. The high resistivity anomaly is located below the hydrate stability zone, and the corresponding strata have well-developed fractures, which can provide a natural gas source and migration channel for the formation of the upper hydrates.

[0040] The resistivity anomaly and seismic reflection anomaly characteristics obtained from the high-frequency inversion results in the first model are in good agreement with those of the seismic reflection anomaly, which can provide important electrical information for the study of the distribution of natural gas hydrates and free gas in the study area.

[0041] Optionally, a joint inversion is performed on the first data and the second data to obtain the first model, including steps A1-A3: Step A1: Invert the first data to obtain the second model; the second model is used to characterize the relationship between the emitted wave velocity and the depth.

[0042] Specifically, the first sub-data is processed to establish a velocity field and migration profile, resulting in the first sub-model. Based on the first sub-model, the travel time of P-waves in the vertical components of the second sub-data is obtained. Following a top-down, layer-by-layer constraint approach, the upper-layer strata parameters are kept constant, and the P-wave velocity in the current stratum is iteratively corrected layer by layer. The measured P-wave velocity is compared with the forward-modeled P-wave travel time error until convergence. Once all strata have been corrected, the second sub-model is obtained. The initial shear wave velocity is determined based on the second sub-model. Then, based on the second sub-model and the initial shear wave velocity, the seismic wave propagation path is simulated using ray tracing to solve for the third time. The difference between the third time and the second time is calculated and compared with a preset difference interval. The initial shear wave velocity is corrected based on the comparison results until all strata have been corrected, resulting in the second model.

[0043] Step A2: Invert the second data to obtain the third model; the third model is used to characterize the relationship between electrical parameters and formation depth.

[0044] Specifically, the amplitude, phase, logarithm of amplitude, and polarization ellipse parameters of the electromagnetic field are calculated from the second data to obtain the first array of parameters. A first matrix is ​​determined based on the second data, and the amplitude, phase, logarithm of amplitude, and polarization ellipse parameters of the electromagnetic field are calculated from the first matrix to obtain the first matrix parameters. The first array of parameters and the first matrix parameters are then inverted according to the second calculation model to obtain the third model.

[0045] Step A3: Convert the format of the third model and overlay the data with the second model to obtain the first model.

[0046] Specifically, the third model is converted into SEGY seismic data format, and after conversion, it is overlaid with the second model to obtain the first model.

[0047] Optionally, the first data can be inverted to obtain the second model, including steps B1-B3: Step B1: Establish the velocity field and migration profile based on the first sub-data to obtain the first sub-model; the first sub-model can characterize the relationship between P-wave and depth.

[0048] Specifically, the first sub-dataset is processed and pre-stack migrated to obtain a pre-stack depth migration profile. On the migration profile, the seafloor and various strata reflection horizons and fault structures are traced and interpreted to determine the planar distribution and burial depth of each geological interface. Stratigraphic units are divided longitudinally to construct a geometric layering framework for the subsurface strata. Pre-stack gathers from the towed cable are extracted from the first sub-dataset, and the velocity spectrum stacking velocity is picked point-by-point. The root mean square velocity is calculated using the Dix formula, and the initial P-wave velocity of the corresponding strata is further solved. Using the interpreted strata depth boundaries as spatial constraints, the calculated initial P-wave velocity is assigned to the grid cells of the corresponding depth interval. The stratigraphic spatial geometry and initial P-wave velocity data are integrated to obtain the first sub-model.

[0049] The data processing includes at least one of the following: preprocessing, noise suppression, ghost wave suppression, and multiple wave suppression.

[0050] The preprocessing process involves decompiling the first sub-data, converting the field binary records into standard seismic trace data, performing geometric positioning correction on the standard seismic data, and then performing amplitude compensation and phase consistency correction. After the correction is completed, bad traces are removed and unused shots are cut off.

[0051] Among them, noise suppression involves using FK filtering, multi-channel coherent filtering and other methods to separate various linear interferences and random noises in the first sub-data, and to remove invalid interference waves brought by the ship hull and sea waves while protecting the effective reflected signal.

[0052] Among them, ghost wave suppression is based on the depth of the seismic source and cable deployment to construct a ghost wave operator. By separating and deconvolving the wavefield up and down, the ghost wave interference formed by secondary reflection from the sea surface in the first sub-data is eliminated, thus repairing the distorted wavelet and the missing spectrum. The ghost wave is formed when the sea surface is a strong free reflection interface, and the downward propagating source wavelet and the reflected wave reaching the sea surface will undergo secondary reflection from the sea surface.

[0053] Among them, the multiple wave suppression is to predict the sea surface related multiple wave field using the SRME wave equation, adaptively subtract the predicted multiple waves from the first sub-data, remove the false phase axis of the interface reciprocating reflection, and retain the effective single reflection of the formation.

[0054] Step B2: Based on the first sub-model, the first time is inverted and corrected according to a preset order to obtain the second sub-model; the first time is the propagation time of the transmitted wave obtained from the second sub-data along a preset path to the second monitoring device.

[0055] The first time is also known as the longitudinal wave travel time.

[0056] The preset order is from top to bottom.

[0057] Specifically, based on the first sub-model, the travel time of the P-waves in the formation recorded by the vertical component is obtained from the second sub-data. That is, the first step is to follow the top-down layer-by-layer constraint approach, keep the upper formation parameters unchanged, and iteratively correct the current formation P-wave velocity layer by layer. The measured P-wave velocity is compared with the forward modeling P-wave travel time error until convergence. When all formations have been corrected, the second sub-model is obtained.

[0058] Step B3: Based on the second sub-model, the second model is obtained by correcting it using the second time; the second time is the time when the emitted wave obtained from the second sub-data enters the formation and undergoes waveform conversion, and is then collected by the second monitoring device.

[0059] The second time is also called the converted wave travel time.

[0060] Specifically, the initial shear wave velocity is determined based on the second sub-model. Then, based on the second sub-model and the initial shear wave velocity, the seismic wave propagation path is simulated using ray tracing forward modeling to solve for the third time. The difference between the third time and the second time is calculated, and the difference is compared with a preset difference interval. The initial shear wave velocity is corrected based on the comparison results until all strata are corrected, thus obtaining the second model.

[0061] Furthermore, the specific correction steps are as follows: if the difference is within the preset difference range, the preset shear wave velocity is considered to meet the requirements, and the initial shear wave velocity of the next formation is corrected; if the difference is outside the preset difference range, the preset shear wave velocity is considered to not meet the requirements, so it is corrected, and then verified until the requirements are met, and then the initial shear wave velocity of the next formation is corrected.

[0062] For example, such as Figure 4 The image shown is a schematic diagram of the first sub-model. Figure 5 This is a schematic diagram of the second model. As can be seen from the red circle in the diagram, the offset profile after correction using the second sub-data shows clearer texture details.

[0063] Optionally, a second model is obtained by correcting the second sub-model through a second time step, including steps C1-C2: Step C1: Perform forward modeling based on the second sub-model and the preset shear wave velocity to obtain the third time.

[0064] The preset shear wave velocity is based on the determined stratigraphic structure and precise P-wave velocity field in the second sub-model, and is calculated using regional geological experience, adjacent well data, or empirical ratios derived from tow cable velocity conversion, and is used as the shear wave velocity of each stratigraphic layer.

[0065] Specifically, based on the second sub-model and the initial transverse wave velocity, ray tracing is performed according to Snell's law to delineate the complete propagation trajectory of the PS converted wave. The propagation time of the entire ray path is solved by integration to obtain the theoretical converted wave travel time corresponding to each stratum interface, i.e., the third time.

[0066] Among them, PS converted wave: The source emits a P longitudinal wave downwards and it enters the interface of the underground rock strata. The waveform is converted and reflected back as an S transverse wave, which is called PS converted wave.

[0067] Step C2: Compare the third time and the second time, and correct the preset shear wave velocity based on the comparison results to obtain the second model; the preset shear wave velocity is the shear wave velocity of each stratum generated based on the second sub-model.

[0068] Specifically, the third time is compared with the second time. If the difference is within the preset difference range, the preset shear wave velocity is considered to meet the requirements, and the initial shear wave velocity of the next formation is corrected. If the difference is outside the preset difference range, the preset shear wave velocity is considered to not meet the requirements, so it is corrected. After correction, it is verified until the requirements are met, and then the initial shear wave velocity of the next formation is corrected. This process continues until all formations are corrected, and the second model is obtained.

[0069] The correction is as follows: if the difference is less than the left boundary of the preset difference interval, the transverse wave velocity is increased; if the difference is greater than the right boundary of the preset difference interval, the transverse wave velocity is decreased.

[0070] Optionally, the second data can be inverted to obtain the third model, including steps D1-D2: Step D1: Determine the first array parameters and the first matrix parameters based on the second data; the first array parameters are the parameters of the electromagnetic data; the first matrix parameters are the difference parameters of the first matrix; the first matrix is ​​the sensitivity matrix of the electric field.

[0071] Specifically, the amplitude value, phase value, logarithmic value of amplitude, and polarization ellipse parameters of electromagnetic field are calculated for the second data to obtain the first array parameters; the first matrix is ​​determined based on the second data, and the amplitude value, phase value, logarithmic value of amplitude, and polarization ellipse parameters of electromagnetic field are calculated for the first matrix to obtain the first matrix parameters.

[0072] Furthermore, the first array parameters and the first matrix parameters are shown in Table 1.

[0073] Table 1 Parameters Furthermore, the first array parameters include: The first parameter, namely E with respect to resistivity The derivative form is: ; The second parameter, namely the amplitude, can be expressed as: ; The expression for the third parameter, namely the logarithmic value of the amplitude, is: ; The fourth parameter, i.e., the phase expression, is: ; The fifth parameter, namely the polarization ellipse parameter, is the value of the electric field component that needs to be used. and real and imaginary parts Its expression is: ; ; ; in, , , , , .

[0074] Furthermore, the real part of the electric field component E' contained in the first matrix is... The imaginary part is The parameters of the first matrix include: The expression for the amplitude parameter is: ; The expression for the logarithmic amplitude is: ; The expression for the phase in the first moment and first matrix is: ; The values ​​of the polarization ellipse parameters in the first matrix require the electric field components. and real and imaginary parts Substituting the values, its expression becomes: + ; + ; in, , , , Step D2: Perform forward and inverse operations on the first array parameters and the first matrix parameters to obtain the third model.

[0075] Specifically, the third model is obtained by inverting the first array parameters and the first matrix parameters according to the second calculation mode.

[0076] Further, the specific implementation steps are as follows: Based on the first array parameters and the first matrix parameters, a function relationship is written and embedded in the Mare2dem subroutine `convert_to_data_format`. This subroutine is called by the forward and inversion programs in Mare2dem. When forward modeling is performed, after selecting the differential function, the original frequency data and the differential data will form a new first array parameter file; during inversion, a new sensitivity matrix first matrix parameter file will also be formed. Once the first array parameters and the first matrix parameters containing the differential data are ready, the Occam inversion program is called to perform the inversion.

[0077] For example, inversion is performed on electromagnetic data with a transmission frequency of 8 Hz, such as... Figure 6 As shown, electromagnetic data can effectively reflect the reservoir boundaries, with the upper boundary approximately 100m from the seabed and the lower boundary approximately 200m, covering a horizontal range of about 1km. Electromagnetic data exhibits good resolution of the upper interface of hydrates and is also quite sensitive to lateral variations.

[0078] Optionally, the first array parameters are determined based on the second data, including steps E1-E6: Step E1: Differentiate the second data to obtain the first parameter.

[0079] The second data can be represented as: .

[0080] Specifically, in the second data, each electric field component corresponds to the resistivity. By taking the derivative, we obtain the first parameter.

[0081] The first parameter can be expressed as: .

[0082] Step E2: Calculate the amplitude of the second data to obtain the second parameter.

[0083] Specifically, the amplitude of each electric field component is calculated based on the imaginary and real parts of the second data to obtain the second parameter.

[0084] The second parameter can be expressed as:

[0085] Step E3: Take the logarithm of the second parameter to obtain the third parameter.

[0086] Specifically, the third parameter is obtained by performing a logarithmic calculation with base 10 on the second parameter.

[0087] The third parameter can be expressed as:

[0088] Step E4: Determine the phase change based on the second data to obtain the fourth parameter.

[0089] Specifically, the phase is calculated based on the imaginary and real parts of each electric field component in the second data to obtain the fourth parameter.

[0090] The fourth parameter can be expressed as:

[0091] Step E5: Determine the fifth parameter based on the second data.

[0092] Specifically, two electric field components are taken from the second data, and the polarization ellipse parameters are calculated based on the two electric field components to obtain the fifth parameter.

[0093] The fifth parameter can be represented as: ; ; ; in, , , , ,

[0094] Step E6: Combine the first parameter, second parameter, third parameter, fourth parameter and fifth parameter to obtain the first array of parameters.

[0095] Specifically, the first, second, third, fourth, and fifth parameters are organized to obtain the first array of parameters.

[0096] The technical solution of this embodiment determines first data and second data. The first data, obtained by using single-channel and a small number of multi-channel seismic profiles, describes the geometry of the seafloor-like reflector layer based on travel time and amplitude, thus describing the distribution of natural gas hydrate reservoirs. The second data more accurately characterizes the top burial depth and lateral boundaries of high-resistivity bodies. Both provide a basis for the subsequent construction of the first model. The first and second data are jointly inverted to obtain the first model. Hydrate analysis is then performed based on the first model. This method, by jointly inverting the first and second data to obtain the first model and then analyzing it, reflects the distribution characteristics of natural gas hydrates from different perspectives. The combination of these two methods effectively reduces the limitations of a single geophysical method, thereby significantly improving the accuracy and reliability of interpretation.

[0097] Figure 7This is a schematic diagram of a combined detection device based on marine seismic and electromagnetic data, provided as an embodiment of the present invention. This embodiment is applicable to the analysis of seafloor hydrates. The combined detection device based on marine seismic and electromagnetic data can be implemented in hardware and / or software, and can be configured in any electronic device with network communication capabilities. Figure 7 As shown, the device includes: a data determination module 210, a first model determination module 220, and an analysis module 230, wherein: Data determination module 210: used to determine first data and second data; the first data is seismic wave data obtained by conducting seismic exploration of the target area; the first data includes: first sub-data and second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form; First model determination module 220: used to perform joint inversion on the first data and the second data to obtain the first model; Analysis module 230: Used for hydrate analysis based on the first model.

[0098] Optionally, the first model determination module 220 includes: The second model determination unit is used to invert the first data to obtain the second model; the second model is used to characterize the relationship between the velocity and depth of the emitted wave. The third model determination unit is used to invert the second data to obtain the third model; the third model is used to characterize the relationship between electrical parameters and formation depth. The first model determination module is used to convert the format of the third model and then overlay the data with the second model to obtain the first model.

[0099] Optionally, the second model determining unit includes: The first sub-model determines the sub-units: it is used to establish the velocity field and migration profile based on the first sub-data to obtain the first sub-model; the first sub-model can characterize the relationship between P-wave and depth; The second sub-model determination sub-unit is used to perform inversion correction on the first time according to a preset order based on the first sub-model to obtain the second sub-model; the first time is the propagation time of the transmitted wave obtained from the second sub-data along a preset path to reach the second monitoring device; The second model determines the sub-unit: it is used to correct the second sub-model through the second time to obtain the second model; the second time is the time when the emitted wave obtained from the second sub-data enters the formation and undergoes waveform conversion, and is collected by the second monitoring device.

[0100] Optionally, the second model determines the sub-units, specifically for: The third time is obtained by performing forward modeling based on the second sub-model and the preset shear wave velocity; The second model is obtained by comparing the third and second time periods and correcting the preset shear wave velocity based on the comparison results. The preset shear wave velocity is the shear wave velocity of each stratum generated based on the second sub-model.

[0101] Optionally, the third model determining unit includes: Parameter determination subunit: used to determine the first array parameters and the first matrix parameters based on the second data; the first array parameters are the parameters of the electromagnetic data; the first matrix parameters are the difference parameters of the first matrix; the first matrix is ​​the sensitivity matrix of the electric field; The third model determines the sub-unit: used to perform forward and inverse operations on the first array parameters and the first matrix parameters to obtain the third model.

[0102] Optionally, parameters determine the sub-unit, specifically used for: Differentiating the second data yields the first parameter; The amplitude of the second data is calculated to obtain the second parameter; Take the logarithm of the second parameter to obtain the third parameter; The phase change is determined based on the second data, and the fourth parameter is obtained. The fifth parameter is determined based on the second data; The first, second, third, fourth, and fifth parameters are combined to obtain the first array of parameters.

[0103] The joint detection device based on marine seismic data and electromagnetic data provided in this embodiment of the invention can execute the joint detection method based on marine seismic data and electromagnetic data provided in any of the above embodiments of the invention. It has the corresponding functions and beneficial effects of executing the joint detection method based on marine seismic data and electromagnetic data. For detailed process, please refer to the relevant operations of the joint detection method based on marine seismic data and electromagnetic data in the foregoing embodiments.

[0104] In one embodiment, Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of the present invention, such as... Figure 8The diagram illustrates a schematic representation of an electronic device 10 that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0105] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor 11, and the computer program is executed by the at least one processor 11 to enable the at least one processor 11 to perform the method provided by the present invention.

[0106] The processor 11 can perform various appropriate actions and processes based on a computer program stored in the read-only memory (ROM) 12 or a computer program loaded from the storage unit 18 into the random access memory (RAM) 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0108] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the methods provided in this invention.

[0109] In some embodiments, the methods provided herein may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0110] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of this invention, a computer-readable storage medium stores computer instructions that are used to cause a processor to execute and implement the method provided by this invention.

[0113] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method provided according to embodiments of the present invention. A computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0114] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0115] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0116] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0117] This invention also provides a computer program product, including a computer program that, when executed by a processor, can implement the methods provided in any embodiment of this application.

[0118] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0119] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0120] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A joint detection method based on marine seismic data and electromagnetic data, characterized in that, include: Determine the first and second data points; The first data is seismic wave data obtained by conducting seismic exploration on the target area; The first data includes: a first sub-data and a second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form; The first model is obtained by performing a joint inversion on the first data and the second data; Hydrate analysis was performed based on the first model.

2. The method according to claim 1, characterized in that, The joint inversion of the first data and the second data to obtain the first model includes: The first data is inverted to obtain the second model; the second model is used to characterize the relationship between the velocity and depth of the emitted wave. The second data is inverted to obtain the third model; the third model is used to characterize the relationship between electrical parameters and formation depth. The third model is format-converted and then overlaid with the second model to obtain the first model.

3. The method according to claim 2, characterized in that, The process of inverting the first data to obtain the second model includes: Based on the first sub-data, a velocity field and migration profile are established to obtain the first sub-model; the first sub-model can characterize the relationship between P-waves and depth. Based on the first sub-model, the first time is inverted and corrected in a preset order to obtain the second sub-model; the first time is the propagation time of the transmitted wave obtained from the second sub-data along a preset path to reach the second monitoring device; The second model is obtained by correcting the second sub-model using the second time; the second time is the time when the emitted wave, obtained from the second sub-data, enters the formation and undergoes waveform conversion, and is then collected by the second monitoring device.

4. The method according to claim 3, characterized in that, The process of correcting the second sub-model through a second time step to obtain the second model includes: The third time is obtained by performing forward modeling based on the second sub-model and the preset shear wave velocity; The second model is obtained by comparing the third time and the second time, and correcting the preset shear wave velocity based on the comparison results; the preset shear wave velocity is the shear wave velocity of each stratum generated based on the second sub-model.

5. The method according to claim 2, characterized in that, The process of inverting the second data to obtain the third model includes: The first array parameters and the first matrix parameters are determined based on the second data; the first array parameters are the parameters of the electromagnetic data; the first matrix parameters are the difference parameters of the first matrix; the first matrix is ​​the sensitivity matrix of the electric field. The first array parameters and the first matrix parameters are subjected to forward and inverse operations to obtain the third model.

6. The method according to claim 5, characterized in that, The parameters of the first array are determined based on the second data, including: Differentiating the second data yields the first parameter; The amplitude of the second data is calculated to obtain the second parameter; Taking the logarithm of the second parameter yields the third parameter; Based on the second data, the phase change is determined, and the fourth parameter is obtained; The fifth parameter is determined based on the second data; The first parameter, the second parameter, the third parameter, the fourth parameter, and the fifth parameter are combined to obtain the first array of parameters.

7. A joint detection device based on marine seismic data and electromagnetic data, characterized in that, include: The data determination module is used to determine the first data and the second data. The first data is seismic wave data obtained by conducting seismic exploration on the target area; The first data includes: a first sub-data and a second sub-data; the first sub-data is a single-phase seismic pressure wave signal after the transmitted wave propagates through seawater; the second sub-data is a seismic pressure wave signal after the transmitted wave propagates through seawater; the second data is data obtained by collecting changes in the electromagnetic field of the target area, and the second data is recorded in complex form; The first model determination module is used to perform joint inversion on the first data and the second data to obtain the first model; The analysis module is used to perform hydrate analysis based on the first model.

8. The apparatus according to claim 7, characterized in that, The first model determination module includes: The second model determination unit is used to invert the first data to obtain the second model; the second model is used to characterize the relationship between the transmitted wave velocity and depth. The third model determination unit is used to invert the second data to obtain the third model; the third model is used to characterize the relationship between electrical parameters and formation depth. The first model determination unit is used to perform format conversion on the third model and then overlay the data with the second model to obtain the first model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the joint detection method based on marine seismic data and electromagnetic data as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the joint detection method based on marine seismic data and electromagnetic data as described in any one of claims 1-6.

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