Marine resource detection method, device, equipment, storage medium and product

Abstract

The invention provides a marine resource detection method, device and equipment, a storage medium and a product. The method comprises the following steps: sending an excitation instruction to a seismic source and a seabed electromagnetic transmitter to enable the seismic source to excite seismic wavelets, and sending a current signal by the seabed electromagnetic transmitter; performing signal acquisition on the seismic wavelets and the current signals by using a submarine seismic detector and a submarine electromagnetic receiver to obtain corresponding seismic sampling data and electromagnetic sampling data; constructing a joint inversion model based on cross gradient constraint according to the seismic sampling data and the electromagnetic sampling data; and carrying out iterative solution on the joint objective function of the joint inversion model, and determining an exploration result. By solving the joint model, an imaging result with high geologic structure consistency can be obtained, so that the recognition precision and reliability of exploration targets such as deep-sea oil and gas reservoirs and natural gas hydrates are effectively improved, and an accurate exploration result is obtained.

Description

Technical Field

[0001] This application relates to the field of exploration and observation technology, and in particular to a method, apparatus, equipment, storage medium and product for marine resource exploration. Background Technology

[0002] With the exploration of onshore and shallow-sea oil and gas resources largely completed, deep-sea areas have become the key region for energy exploration. In current deep-sea geophysical exploration, seismic exploration and marine controlled-source electromagnetic methods are the most commonly used techniques. Seismic exploration can provide high-resolution images of subsurface structures, but its ability to identify lithology and fluid properties is limited. Marine controlled-source electromagnetic methods, on the other hand, are sensitive to changes in formation resistivity and can effectively indicate the distribution of fluids such as oil and gas, but their accuracy is relatively low.

[0003] In existing technologies, these two methods are mostly used with time-division and independent data acquisition. However, in the deep-sea environment, severe signal interference and insufficient timing accuracy of equipment lead to a mismatch between the two types of data in time and space, resulting in significant clock deviations and location errors. This makes data fusion difficult, inversion results unreliable, and identification and exploration inaccurate. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, storage medium, and product for marine resource exploration, aiming to solve the technical problem of inaccurate exploration results in related technologies.

[0005] Firstly, this application provides a method for marine resource exploration, the method comprising: Excitation commands are sent to the earthquake source and the seabed electromagnetic transmitter, causing the earthquake source to excite a seismic wavelet and the seabed electromagnetic transmitter to emit a current signal. The earthquake source and the seabed electromagnetic transmitter are pre-deployed in the target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal.

[0006] The seismic wavelet and current signals are acquired by using a seabed seismic detector and a seabed electromagnetic receiver, respectively, to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0007] A joint inversion model is constructed based on seismic sampling data and electromagnetic sampling data, using cross-gradient constraints.

[0008] The joint objective function of the joint inversion model is solved iteratively to determine the exploration results.

[0009] In some possible implementations, seabed seismic detectors and seabed electromagnetic receivers are used to acquire seismic wavelet and current signals respectively, obtaining corresponding seismic sampling data and electromagnetic sampling data, including: The seismic wavelet is sampled and recorded using a seabed seismic detector to obtain the corresponding seismic wavefield displacement signal.

[0010] The electric and magnetic fields under current signals are measured using an underwater electromagnetic receiver, and the corresponding three-dimensional electromagnetic field is obtained.

[0011] The seismic wave field displacement signal and the three-dimensional electromagnetic field are spatiotemporally aligned to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0012] In some possible implementations, the seismic wavefield displacement signal and the three-dimensional electromagnetic field are spatiotemporally aligned to obtain corresponding seismic sampling data and electromagnetic sampling data, including: The seismic wavefield displacement signal and the three-dimensional electromagnetic field are timestamped and labeled with spatial coordinates to obtain the original seismic sampling dataset and the original electromagnetic sampling dataset.

[0013] Based on preset preprocessing rules, the original seismic sampling dataset and the original electromagnetic sampling dataset are preprocessed respectively.

[0014] The preprocessed original seismic sampling dataset and the original electromagnetic sampling dataset are interpolated to the same grid node and time resampling correction is performed to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0015] In some possible implementations, the pre-processing rules include at least one of denoising, dynamic correction and multiple suppression processing of seismic sampled data, and at least one of far reference channel denoising, tidal interference correction and time-frequency analysis processing of electromagnetic sampled data.

[0016] In some possible implementations, the earthquake source, seabed seismic detector, seabed electromagnetic transmitter, and seabed electromagnetic receiver are all arranged in an array. The joint objective function of the joint inversion model is iteratively solved to determine the exploration results, including: The joint objective function of the joint inversion model is solved iteratively to obtain the three-dimensional distribution data of P-wave velocity and resistivity of the target exploration area.

[0017] Based on preset feature combinations, the three-dimensional distribution data of each sub-region of the target exploration area are identified, and the exploration results of the target sub-region corresponding to each preset feature combination are obtained.

[0018] In some possible implementations, the joint objective function of the joint inversion model is solved iteratively to obtain the three-dimensional distribution data of the P-wave velocity and the three-dimensional distribution data of the resistivity of the target exploration area, including: The joint inversion model is iterated using the nonlinear conjugate gradient method to obtain the iterative results.

[0019] If the iteration result meets the preset termination condition or the number of iterations meets the preset threshold, the iteration result is determined as the optimal result.

[0020] Based on the optimal results, three-dimensional distribution data of P-wave velocity and resistivity in the target exploration area were obtained.

[0021] In some possible implementations, a joint inversion model is constructed based on cross-gradient constraints using seismic sampling data and electromagnetic sampling data, including: Based on the difference between the seismic sampling data and the forward modeling results of the acoustic equation, the seismic sampling data fitting term of the joint inversion model is determined.

[0022] Based on the difference between the electromagnetic sampling data and the forward modeling results of Maxwell's equations, the electromagnetic sampling data fitting term of the joint inversion model is determined.

[0023] Based on the cross gradient constraint, the functional relationship between the spatial gradient of P-wave velocity and the spatial gradient of resistivity is determined, and the cross gradient constraint term of the joint inversion model is obtained.

[0024] A joint inversion model is constructed based on the fitting terms of seismic sampling data, electromagnetic sampling data, and cross-gradient constraints.

[0025] Secondly, this application provides a marine resource detection device, the device comprising: The transmitting module sends excitation commands to the earthquake source and the seabed electromagnetic transmitter, causing the earthquake source to excite a seismic wavelet and the seabed electromagnetic transmitter to emit a current signal. The earthquake source and the seabed electromagnetic transmitter are pre-deployed in the target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal.

[0026] The acquisition module is used to acquire seismic wavelet and current signals using a seabed seismic detector and a seabed electromagnetic receiver, respectively, to obtain corresponding seismic sampling data and electromagnetic sampling data.

[0027] The module is used to construct a joint inversion model based on cross-gradient constraints from seismic sampling data and electromagnetic sampling data.

[0028] The solver module is used to iteratively solve the joint objective function of the joint inversion model to determine the exploration results.

[0029] Thirdly, this application provides a marine resource detection device, which includes a processor and a memory storing computer program instructions. The processor reads and executes the computer program instructions to implement the marine resource detection method described above.

[0030] Fourthly, this application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the marine resource exploration method described above.

[0031] Fifthly, this application provides a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the marine resource exploration method described above.

[0032] The marine resource exploration method, apparatus, equipment, storage medium, and products provided in this application control the seismic source and the seismic wavelet and current signals emitted by the seabed electromagnetic transmitter in the target exploration area, ensuring that the signals are cross-correlated and orthogonal. This firstly achieves effective separation and anti-interference of the two physical field excitations at the signal source. On this basis, corresponding seismic and electromagnetic sampling data are acquired simultaneously, and a joint inversion model is constructed by introducing cross-gradient constraints. The inversion processes of the two different physical properties are coupled at the structural level. By solving this joint model, imaging results with strong geological structure consistency can be obtained, thereby effectively improving the identification accuracy and reliability of exploration targets such as deep-sea oil and gas reservoirs and natural gas hydrates, and obtaining accurate exploration results. Attached Figure Description

[0033] This application can be better understood from the following description of specific embodiments in conjunction with the accompanying drawings, wherein: Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings, wherein the same or similar reference numerals denote the same or similar features.

[0034] Figure 1 This is a flowchart of a marine resource exploration method provided in one embodiment of this application; Figure 2 This is a flowchart of a marine resource exploration method provided in another embodiment of this application; Figure 3 This is a flowchart of a marine resource exploration method provided in another embodiment of this application; Figure 4 This is a schematic diagram of the structure of a marine resource detection device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the hardware structure of the marine resource exploration equipment provided in the embodiments of this application. Detailed Implementation

[0035] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0037] With the increasing depletion of onshore and shallow-sea oil and gas resources, deep-sea areas have become a key focus of energy exploration. Among the current mainstream marine geophysical exploration methods, seismic exploration, which uses an air gun source to generate sound waves and receive subsurface reflection signals, can provide high-resolution geological structural images and is widely used for identifying complex structures such as deep-sea basins and salt domes. However, seismic methods have limited ability to distinguish lithological differences and fluid properties, and suffer from problems such as high ambiguity and high uncertainty in reservoir prediction.

[0038] On the other hand, among specific exploration methods, the marine controlled-source electromagnetic method can be used. Based on the differences in conductivity of different strata, by emitting low-frequency electromagnetic fields to the seabed and measuring their response, it is possible to effectively identify high-resistivity oil and gas layers or low-resistivity water-bearing layers, exhibiting good fluid sensitivity. In recent years, seismic and electromagnetic data have been combined to improve exploration accuracy. However, most existing technologies adopt a time-sharing and multi-stage independent acquisition method, lacking temporal and spatial synchronization, resulting in difficulties in data matching, poor reliability of inversion results, low operational efficiency, and high costs. Furthermore, in the deep-sea environment, the difficulties in equipment deployment and retrieval, severe signal interference, and insufficient timing accuracy further restrict the application and development of multi-physics collaborative observation technology.

[0039] To address the problems of the prior art, embodiments of this application provide a method, apparatus, device, storage medium, and product for marine resource detection. The marine resource detection method provided in this application embodiment will be described first below.

[0040] Figure 1 A schematic flowchart of a marine resource exploration method according to an embodiment of this application is shown. Figure 1 As shown, the method includes the following steps: S101 to S104.

[0041] S101: Sends excitation commands to the earthquake source and the seabed electromagnetic transmitter, causing the earthquake source to excite a seismic wavelet and the seabed electromagnetic transmitter to emit a current signal. The earthquake source and the seabed electromagnetic transmitter are pre-deployed in the target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal.

[0042] S102: The seismic wavelet and current signal are acquired by using a seabed seismic detector and a seabed electromagnetic receiver to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0043] S103: Based on seismic sampling data and electromagnetic sampling data, a joint inversion model is constructed using cross-gradient constraints.

[0044] S104: Iteratively solve the joint objective function of the joint inversion model to determine the exploration results.

[0045] In the specific implementation of S101, excitation commands are sent to the seismic source and the seabed electromagnetic transmitter. The seismic source is a device used to emit seismic waves (i.e., seismic wavelets), which generate pressure waves underground through vibration and other means. These seismic wavelets propagate along the underground medium, providing information about the underground structure. The seabed electromagnetic transmitter is a device used to emit current signals, which are used to detect the electromagnetic properties of the underground. The seismic source and the seabed electromagnetic transmitter are deployed in the target exploration area. A synchronous triggering mechanism is used to simultaneously emit the seismic wavelets and current signals. The seismic wavelets generated by the seismic source and the current signals generated by the electromagnetic transmitter are cross-correlated and orthogonal, meaning that the two signals do not overlap in frequency and waveform, ensuring that clear signals can be captured separately.

[0046] In the specific implementation of S102, seabed seismic detectors and seabed electromagnetic receivers are used to acquire seismic wavelet and current signals, respectively. The seabed seismic detectors capture and record seismic waves emitted from the earthquake source. Through these detectors, the propagation time, intensity, and other characteristics of the seismic waves can be accurately measured and recorded, thereby generating seismic sampling data. The seabed electromagnetic receiver receives current signals emitted by the seabed electromagnetic transmitter, which provide information related to subsurface conductivity and electromagnetic properties. The electromagnetic receiver obtains electromagnetic sampling data by measuring the signal's intensity, phase, and frequency. The signal acquisition process ensures that the equipment can correctly and synchronously receive both signals and can distinguish the different characteristics of the seismic wavelet and current signals at different times.

[0047] In order to obtain accurate seismic sampling data and electromagnetic sampling data, the above-mentioned S102 may include the following steps: S201 to S203.

[0048] S201: Based on the seabed seismic detector, the seismic wavelet is sampled and recorded to obtain the corresponding seismic wavefield displacement signal.

[0049] S202: Based on the measurement of electric and magnetic fields under current signals using an underwater electromagnetic receiver, the corresponding three-dimensional electromagnetic field is obtained.

[0050] S203: Spatiotemporally align the seismic wavefield displacement signal with the three-dimensional electromagnetic field to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0051] In the specific implementation of S201, the bottom seismic detector captures and records the seismic wavelet excited by the earthquake source, and performs time sampling on the received seismic wavelet signal. Each sampling point records the displacement of the seismic wave on the seabed at that moment, i.e., the wavefield displacement signal.

[0052] In the specific implementation of S202, the electric and magnetic fields generated by the current signal are measured by an underwater electromagnetic receiver. These signals are processed into three-dimensional electromagnetic field data, including electric and magnetic field components in the X, Y, and Z directions, such as the three-dimensional electric field. and three-dimensional magnetic field This allows us to obtain a comprehensive electromagnetic field distribution map.

[0053] In the specific implementation of S203, the time axis is adjusted by calculating the propagation time difference between the two signals during the alignment process. Similarly, spatially, seismic and electromagnetic data are mapped and aligned using known device locations and signal propagation models. For example, by ensuring the synchronization and consistency of the two signals through spatiotemporal alignment, the seismic wave data and the corresponding electromagnetic field data can be matched in the same coordinate system to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0054] The above-described embodiments of this application use a seabed seismic detector to sample and record seismic wavelets, obtaining the corresponding seismic wave field displacement signal. Furthermore, a seabed electromagnetic receiver measures the electric and magnetic fields under the current signal to obtain the corresponding three-dimensional electromagnetic field. The seismic wave field displacement signal and the three-dimensional electromagnetic field are then spatiotemporally aligned to obtain corresponding seismic sampling data and electromagnetic sampling data. This process acquires seismic wave field displacement signals and three-dimensional electromagnetic fields that accurately reflect resource conditions, resulting in accurate seismic sampling data and electromagnetic sampling data.

[0055] In order to accurately obtain spatiotemporally aligned seismic and electromagnetic data, the above-mentioned S203 may include the following steps: S301 to S303.

[0056] S301: Timestamp and spatial coordinate annotation are applied to the seismic wavefield displacement signal and the three-dimensional electromagnetic field to obtain the original seismic sampling dataset and the original electromagnetic sampling dataset.

[0057] S302: Based on preset preprocessing rules, preprocess the original seismic sampling dataset and the original electromagnetic sampling dataset respectively.

[0058] S303: Interpolate the preprocessed original seismic sampling dataset and the original electromagnetic sampling dataset to the same grid node, and perform time resampling correction to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0059] In the specific implementation of S301, timestamps and spatial coordinates are respectively labeled for seismic wavefield displacement signals and three-dimensional electromagnetic field signals. The timestamp is the specific time when the data was recorded, used to accurately calibrate the temporal attribute of the data; the spatial coordinates are the spatial location where the data was recorded, which can be obtained through the location of the measuring equipment and the spatial distribution of sensors. Seismic wavefield displacement signals and three-dimensional electromagnetic field data are timestamped according to the sampling time, and spatial coordinates are labeled according to the actual geographical location where the sampling was performed.

[0060] In the specific implementation of S302, the original seismic sampling dataset and electromagnetic sampling dataset need to be processed according to preset preprocessing rules to remove noise, smooth data, or perform other data normalization operations. Specific preprocessing rules can include noise filtering, data standardization, and data interpolation. Seismic sampling data can be preprocessed to filter out high-frequency noise and retain low-frequency seismic signals. For electromagnetic data, preprocessing may include correcting errors caused by inconsistent equipment precision or performing data smoothing to improve signal stability.

[0061] In the specific implementation of S303, the preprocessed raw seismic sampling dataset and the raw electromagnetic sampling dataset are mapped to the same grid node through interpolation. After interpolation, time resampling correction is performed to adjust the sampling time of different signals, so that all data points can be compared and analyzed on a unified time axis. This solves the time deviation problem caused by different device sampling frequencies or signal propagation delays, ensuring that seismic and electromagnetic data are aligned within the same time window. For example, if the sampling interval of the raw seismic data is 1 second, while the sampling interval of the electromagnetic data is 0.5 seconds, then during time resampling, the electromagnetic data will be resampled to match the sampling interval of the seismic data. Through interpolation and time resampling correction, the resulting seismic and electromagnetic sampling data will have unified spatial and temporal attributes.

[0062] The above-described implementation method of this application obtains the original seismic sampling dataset and the original electromagnetic sampling dataset by annotating the seismic wavefield displacement signal and the three-dimensional electromagnetic field with timestamps and spatial coordinates. The data are then processed according to preset preprocessing rules. The preprocessed original seismic sampling dataset and the original electromagnetic sampling dataset are interpolated to the same grid node and time resampling correction is performed to obtain the corresponding seismic sampling data and electromagnetic sampling data, thereby accurately obtaining spatiotemporally aligned seismic data and electromagnetic data.

[0063] As one implementation method, the preset preprocessing rules include at least one of denoising, dynamic correction and multiple suppression processing of seismic sampling data, and at least one of remote reference channel denoising, tidal interference correction and time-frequency analysis processing of electromagnetic sampling data.

[0064] Specifically, denoising methods include low-pass filtering, Kalman filtering, and wavelet transform. For example, low-pass filtering can remove noise with frequencies higher than a certain standard by setting a threshold, while retaining low-frequency information in the seismic signal.

[0065] Dynamic correction is used to correct errors in seismic data caused by ground motion or equipment movement. Dynamic correction can be based on algorithms of ground displacement models or use motion information provided by sensors such as inertial measurement units to correct sampled data.

[0066] Multiple suppression refers to the process of removing repeating wave signals caused by surface or subsurface reflections from seismic data. Multiple suppression can employ algorithms for identifying and filtering reflected waves, such as those based on wave velocity models or seismic wave propagation models. For example, if detected seismic data contains multiples reflected from the surface, these can be removed from the signal using multiple suppression methods, preserving information about deep seismic waves.

[0067] Far-reference channel noise reduction removes interference components from electromagnetic signals by using reference channel data that is far from the target area. For example, in electromagnetic detection, if the acquired electromagnetic signal is interfered with by surrounding electrical equipment, the noise characteristics can be estimated by acquiring the signal at a reference point far from the equipment, and then the noise can be removed using the difference method.

[0068] To remove electric field fluctuations caused by Earth's tidal variations and perform tidal interference correction, known tidal variation patterns are removed from electromagnetic data using tidal prediction models or time series analysis-based methods.

[0069] Time-frequency analysis allows for the extraction of the frequency characteristics of electromagnetic signals at different points in time. Methods include short-time Fourier transform and wavelet transform. These methods transform electromagnetic signals from the time domain to the time-frequency domain, enabling the analysis of the frequency distribution characteristics over time.

[0070] In the specific implementation of S103, a joint inversion model is constructed based on the acquired seismic and electromagnetic sampling data. The inversion model is a method of extrapolating subsurface geological characteristics from existing data, utilizing both seismic and electromagnetic data for geological inversion. In the inversion model, based on cross-gradient constraints, seismic and electromagnetic signals are combined. These constraints calculate the gradient changes of the two signals, causing the two types of data to influence each other during the inversion process, thus better reflecting the actual subsurface conditions and improving the accuracy of the inversion results. Specifically, the cross-gradient constraints calculate the gradient changes of the two signals, ensuring that the mutual influence of the two types of data is reasonably considered during the inversion process, thereby better reflecting the actual subsurface conditions. The inversion model constructed in this way can simultaneously consider the different properties of seismic waves and electromagnetic signals, providing a comprehensive subsurface structure model.

[0071] To construct an accurate joint inversion model, refer to Figure 2 The above S103 may include the following steps: S401 to S404.

[0072] S401: Based on the difference between the seismic sampling data and the forward modeling results of the acoustic equation, determine the seismic sampling data fitting term for the joint inversion model.

[0073] S402: Based on the difference between the electromagnetic sampling data and the forward modeling results of Maxwell's equations, determine the electromagnetic sampling data fitting term for the joint inversion model.

[0074] S403: Based on the cross gradient constraint, determine the functional relationship between the spatial gradient of P-wave velocity and the spatial gradient of resistivity, and obtain the cross gradient constraint term of the joint inversion model.

[0075] S404: Construct a joint inversion model based on the seismic sampling data fitting term, the electromagnetic sampling data fitting term, and the cross-gradient constraint term.

[0076] In the specific implementation of S401, the forward modeling results of the acoustic wave equation are compared with actual seismic sampling data. The forward modeling results are simulated data calculated using a known subsurface medium model combined with the laws of sound wave propagation. The seismic sampling data are actual observation data obtained through seismic exploration equipment. The difference between the acoustic wave forward modeling results and the actual sampling data is calculated, and by comparing the differences, the seismic sampling data fitting term for the joint inversion model is determined.

[0077] In the specific implementation of S402, Maxwell's equations are applied to deduce the propagation and reflection of electromagnetic waves, obtaining the forward modeling results of Maxwell's equations. Electromagnetic sampling data is collected using electromagnetic detection equipment. The difference between the electromagnetic sampling data and the forward modeling results of Maxwell's equations is calculated. By comparing the differences between the two, the electromagnetic sampling data fitting term of the joint inversion model is determined.

[0078] In the specific implementation of S403, the stability and consistency of the model are improved by ensuring the interrelationship between different physical quantities (i.e., P-wave velocity and resistivity). A constraint is set to ensure that the change in P-wave velocity is correlated with the change in resistivity. For example, the functional relationship between the spatial gradient of P-wave velocity and the spatial gradient of resistivity is determined, the gradients of P-wave velocity and resistivity in space are calculated, and the cross-gradient constraint term of the joint inversion model is obtained by constraining the interrelationship between P-wave velocity and resistivity through cross-gradient constraints.

[0079] In the specific implementation of S404, a joint inversion model is constructed by combining the previously obtained seismic sampling data fitting terms, electromagnetic sampling data fitting terms, and cross-gradient constraint terms. By integrating all fitting terms and constraint terms into a unified optimization framework, for example, using seismic data and electromagnetic data as different fitting terms, and using cross-gradient constraints to ensure the constraint relationship between seismic data and electromagnetic data in terms of P-wave velocity and resistivity, a joint inversion model is obtained.

[0080] The above-described implementation method of this application determines the fitting terms of seismic sampling data and electromagnetic sampling data, and then, based on cross-gradient constraints, determines the functional relationship between the spatial gradient of P-wave velocity and the spatial gradient of resistivity, thereby obtaining the cross-gradient constraint terms of the joint inversion model and constructing a joint inversion model. Constructing an accurate joint inversion model can provide an accurate estimate of the properties of the subsurface medium.

[0081] In the specific implementation of S104, the joint objective function of the joint inversion model is solved iteratively, using optimization algorithms such as gradient descent. By continuously adjusting the parameters in the model, the value of the joint objective function is gradually reduced. After each iteration, the inversion model estimates a more accurate subsurface geological structure based on the new parameters. After multiple iterations, accurate geological structure data is obtained, thereby further confirming the exploration results.

[0082] The marine resource exploration method provided in this application controls the seismic source and the seismic wavelet and current signals emitted by the seabed electromagnetic transmitter in the target exploration area to ensure that the signals are cross-correlated and orthogonal. This firstly achieves effective separation and anti-interference of the two physical field excitations at the signal source. On this basis, the corresponding seismic and electromagnetic sampling data are acquired simultaneously, and a joint inversion model is constructed by introducing cross-gradient constraints. The inversion processes of the two different physical properties are coupled at the structural level. By solving the joint model, imaging results with strong geological structure consistency can be obtained, thereby effectively improving the identification accuracy and reliability of exploration targets such as deep-sea oil and gas reservoirs and natural gas hydrates, and obtaining accurate exploration results.

[0083] To determine areas where resources may exist, refer to Figure 3 The earthquake source, seabed seismograph, seabed electromagnetic transmitter and seabed electromagnetic receiver are all arranged in an array. The above S104 may include the following steps: S501 to S502.

[0084] S501: Iteratively solve the joint objective function of the joint inversion model to obtain the three-dimensional distribution data of P-wave velocity and resistivity of the target exploration area.

[0085] S502: Based on preset feature combinations, identify the three-dimensional distribution data of each sub-region of the target exploration area, and obtain the exploration results of the target sub-region corresponding to each preset feature combination.

[0086] In the specific implementation of S501, numerical optimization methods, such as gradient descent, Newton's method, or conjugate gradient method, are used to iteratively solve the joint objective function of the joint inversion model. In each iteration, the value of the objective function is calculated based on the current parameter values, and the model parameters are adjusted to minimize the error of the objective function, thereby obtaining the optimal solution. The iterative process continues until a predetermined convergence condition is met or the error reaches a set error range.

[0087] In the specific implementation of S502, different feature combinations represent typical characteristics of different geological units or exploration targets. For example, rock strata containing a certain type of resource may have high resistivity and low P-wave velocity, while another type of rock strata may have high resistivity and high P-wave velocity. Preset feature combinations are defined for these known characteristics. Based on these preset feature combinations, the three-dimensional data of the target exploration area is automatically identified. According to the changes in P-wave velocity and resistivity in the three-dimensional distribution data, the target area is automatically divided into several sub-regions, and each sub-region is matched with a preset feature combination. If the P-wave velocity and resistivity distribution of a certain sub-region matches the characteristics of a certain feature combination, then that sub-region is identified as a geological unit related to that feature combination, and exploration results for the target sub-regions corresponding to each preset feature combination are obtained.

[0088] The above-described embodiments of this application obtain three-dimensional distribution data of P-wave velocity and three-dimensional distribution data of resistivity of the target exploration area by iteratively solving the joint objective function of the joint inversion model. This allows for the identification of the three-dimensional distribution data of each sub-region of the target exploration area, obtaining the exploration results of the target sub-regions corresponding to each preset feature combination. By using the preset feature combination to identify the three-dimensional data of the exploration area, the region where resources may exist can be accurately determined.

[0089] To obtain reliable three-dimensional distribution data, the above S501 may include the following steps: S601 to S603.

[0090] S601: The joint inversion model is iterated using the nonlinear conjugate gradient method to obtain the iterative results.

[0091] S602: If the iteration result meets the preset termination condition or the number of iterations meets the preset threshold, the iteration result is determined as the optimal result.

[0092] S603: Based on the optimal results, obtain the three-dimensional distribution data of P-wave velocity and resistivity in the target exploration area.

[0093] In the specific implementation of S601, the nonlinear conjugate gradient method is used to iteratively solve the joint inversion model. Specifically, an objective function is defined to represent the error between the predicted values ​​of P-wave velocity and resistivity obtained by the model and the actual observed data. The nonlinear conjugate gradient method is used to adjust the model parameters through iterative steps to reduce this error. In each iteration, the gradient is first calculated, and then the model is updated using the gradient information, gradually approaching the optimal solution.

[0094] In the specific implementation of S602, it is determined whether the iteration result meets the preset termination condition, thereby deciding whether to determine the current iteration result as the optimal solution. As one embodiment, the preset termination condition can include two types: one is the error convergence condition, and the other is the upper limit of the number of iterations. The error convergence condition refers to the error changing less than a threshold in several consecutive iterations, reaching a predetermined tolerance range, meaning the model is accurate enough and does not need further adjustment. In this case, the iteration process can be terminated early, considering the current result to be close to the optimal solution. Another situation is that when the number of iterations reaches the maximum number of iterations, even if the error of the objective function has not reached the preset convergence criterion, the iteration is considered sufficient, the calculation stops, and the current result is taken as the optimal result.

[0095] In the specific implementation of S603, the optimal model parameters obtained through iterative solving are applied to the target exploration area to generate the final three-dimensional distribution map. Specifically, the optimal result includes the values ​​of P-wave velocity and resistivity for each sub-region, which are obtained in the aforementioned joint inversion process. By mapping these values ​​to each spatial point in the target exploration area, a three-dimensional distribution map of the P-wave velocity and resistivity of the entire target exploration area can be constructed.

[0096] The above-described implementation method of this application obtains accurate three-dimensional distribution data of P-wave velocity and resistivity in the target exploration area through iterative optimization of the nonlinear conjugate gradient method, combined with error convergence and iteration number control, thus obtaining reliable three-dimensional distribution data.

[0097] In one embodiment of this application, an integrated deep-sea joint observation network is pre-deployed. This network comprises a towed seismic source, a seabed seismic detector array, a seabed electromagnetic transmitter array, and a seabed electromagnetic receiver array, deployed in the target sea area. The seismic detectors and electromagnetic receivers are positioned on the seabed according to a predetermined grid, with a spacing of 500m to 2km between different devices. Each device has a built-in high-precision time synchronization module based on WeChat time synchronization, achieving time unification across the entire system. The seabed seismic detector array and the seabed electromagnetic receiver array are co-located, with a horizontal position deviation of less than 10 meters during deployment.

[0098] During the detection process, the seismic source and electromagnetic source are coordinated for excitation. Specifically, trigger commands are sent to the seismic source and the seabed electromagnetic transmitter, causing them to be synchronously or asynchronously excited within a preset time window. The seismic source is an air gun array with a total acoustic energy capacity of 12–24 liters and a dominant excitation frequency range of 8–80 Hz. The electromagnetic transmitter is a horizontal electric dipole, outputting a pseudo-randomly encoded current signal. The electromagnetic transmitter operates at multiple discrete frequencies including 0.1 Hz, 0.5 Hz, 1 Hz, and 5 Hz, which can be used for multi-frequency joint inversion. The pseudo-randomly encoded current signal is modulated using an M-sequence or Barker code, with a code length of not less than 31 bits. The excitation time difference between the two is... The two satisfy the cross-correlation orthogonality condition, which enables signal decoupling and anti-interference acquisition, namely: in, To excite the wavelet; It is a pseudo-random coded current signal.

[0099] Using seabed seismographs to record seismic wave field displacement signals Its sampling rate is no less than 500Hz, and its frequency band covers 0.05–100Hz; simultaneously, a three-dimensional electric field is measured using an underwater electromagnetic receiver. and magnetic field The sampling interval was 1–10 ms, and the frequency range was 0.01–10 Hz; all data were labeled with precise timestamps and spatial coordinates to form a spatiotemporally matched seismic dataset. and electromagnetic datasets .

[0100] The seismic data was then subjected to denoising, dynamic correction, and multiple suppression; the electromagnetic data underwent far-reference denoising, tidal interference correction, and time-frequency analysis. Next, based on a unified geographic coordinate system, the seismic and electromagnetic receiver points were interpolated to a common grid node, followed by time resampling correction using a high-precision clock residual model. in, It is the time difference dynamically estimated by temperature compensation and time drift fitting algorithm, which ensures that the two types of data are strictly aligned in the spatial and temporal domains.

[0101] Furthermore, a system based on longitudinal wave velocity is established. With resistivity The joint objective function for the inversion parameters is as follows: in, , which are the model parameters on a logarithmic scale; For the forward modeling operator of the acoustic wave equation; For the forward operands of Maxwell's equations in the frequency domain, and This is a regularization parameter. A normalized cross-gradient structure constraint term is used to enhance the spatial structure consistency between the two datasets. An iterative solution using the nonlinear conjugate gradient method is employed to output high-resolution velocity-resistivity joint imaging results.

[0102] Acoustic forward modeling operator The solution employs a second- or fourth-order finite-difference method in the time domain, with a spatial grid accuracy of at least 10 μm. Maxwell's equations are solved in the frequency domain using forward modeling and discretized using the finite-volume method, supporting anisotropic conductivity models. Regularization parameters in the cross-gradient constraint term are also considered. and The determination is made dynamically using the L-curve method or generalized cross-validation.

[0103] Combining regional geological structure, lithological prior information, and well logging data, the results obtained from the joint inversion were analyzed. and The model undergoes cross-validation and anomaly extraction; it identifies low-velocity-high-resistivity combination features to determine natural gas hydrate enrichment areas, or high-velocity-high-resistivity anomalies to predict oil and gas reservoir distribution, generating target suggestion maps for deep-sea resource exploration.

[0104] The method described in this application is applicable to deep-sea areas with water depths greater than 500 meters, and is used for the detection and monitoring of natural gas hydrates, oil and gas reservoirs, or submarine landslides. The joint observation system supports periodic repeated observations and time-shifted seismic-electromagnetic joint analysis for assessing reservoir dynamic changes. By controlling the coordinated excitation of seismic sources and electromagnetic emission sources through a unified timing system, and synchronously recording response signals by seabed seismic detectors and electromagnetic receivers, the system ensures a high degree of temporal and spatial matching between seismic wavefield and electromagnetic field data, significantly improving data fusion quality. A pseudo-random coding staggered excitation strategy is employed to separate seismic and electromagnetic signals temporally while maintaining spatiotemporal correlation, effectively avoiding mutual interference and facilitating subsequent signal decoupling and separation processing.

[0105] Based on the marine resource detection method provided in the above embodiments, this application also provides specific implementation methods of the marine resource detection device. Please refer to the following embodiments.

[0106] First see Figure 4 The marine resource detection device 400 provided in this application embodiment includes the following modules: The transmitting module 401 is used to send excitation commands to the earthquake source and the seabed electromagnetic transmitter, so that the earthquake source excites a seismic wavelet and the seabed electromagnetic transmitter emits a current signal. The earthquake source and the seabed electromagnetic transmitter are pre-deployed in the target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal.

[0107] The acquisition module 402 is used to acquire seismic wavelet and current signals using a seabed seismic detector and a seabed electromagnetic receiver, respectively, to obtain corresponding seismic sampling data and electromagnetic sampling data.

[0108] Module 403 is used to construct a joint inversion model based on cross-gradient constraints using seismic sampling data and electromagnetic sampling data.

[0109] The solver module 404 is used to iteratively solve the joint objective function of the joint inversion model to determine the exploration results.

[0110] As one implementation of this application, the acquisition module 402 includes: The sampling unit is used to sample and record the seismic wavelet based on the seabed seismic detector to obtain the corresponding seismic wavefield displacement signal.

[0111] The measurement unit is used to measure the electric and magnetic fields under the current signal based on the seabed electromagnetic receiver, and obtain the corresponding three-dimensional electromagnetic field.

[0112] The alignment unit is used to perform spatiotemporal alignment of the seismic wave field displacement signal and the three-dimensional electromagnetic field to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0113] As one implementation of this application, the alignment unit includes: The annotation sub-unit is used to annotate the seismic wavefield displacement signal and the three-dimensional electromagnetic field with timestamps and spatial coordinates, so as to obtain the original seismic sampling dataset and the original electromagnetic sampling dataset.

[0114] The preprocessing subunit is used to preprocess the original seismic sampling dataset and the original electromagnetic sampling dataset respectively based on preset preprocessing rules.

[0115] The correction sub-unit is used to interpolate the preprocessed original seismic sampling dataset and the original electromagnetic sampling dataset to the same grid node and perform time resampling correction to obtain the corresponding seismic sampling data and electromagnetic sampling data.

[0116] As one implementation of this application, the solver module 404 includes: The solver unit is used to iteratively solve the joint objective function of the joint inversion model to obtain the three-dimensional distribution data of the P-wave velocity and the three-dimensional distribution data of the resistivity in the target exploration area.

[0117] The identification unit is used to identify the three-dimensional distribution data of each sub-region of the target exploration area based on preset feature combinations, and to obtain the exploration results of the target sub-region corresponding to each preset feature combination.

[0118] As one implementation of this application, the solving unit includes: The iterative sub-unit is used to iterate the joint inversion model using the nonlinear conjugate gradient method to obtain the iterative results.

[0119] The sub-unit is used to determine the optimal result when the iteration result meets the preset termination condition or the number of iterations meets the preset threshold.

[0120] The determination of sub-units is also used to obtain three-dimensional distribution data of P-wave velocity and resistivity in the target exploration area based on the optimal results.

[0121] As one implementation of this application, construction module 403 includes: The defined element is used to determine the seismic sampling data fitting term for the joint inversion model based on the difference between the seismic sampling data and the forward modeling results of the acoustic equation.

[0122] The unit is also used to determine the electromagnetic sampling data fitting term of the joint inversion model based on the difference between the electromagnetic sampling data and the forward modeling results of Maxwell's equations.

[0123] The element is also used to determine the functional relationship between the spatial gradient of P-wave velocity and the spatial gradient of resistivity based on cross-gradient constraints, thereby obtaining the cross-gradient constraint term of the joint inversion model.

[0124] The building unit is used to construct a joint inversion model based on the fitting terms of seismic sampling data, electromagnetic sampling data, and cross-gradient constraint terms.

[0125] Each module in the marine resource detection device provided in this application embodiment can implement each step in the above-mentioned marine resource detection method and achieve the corresponding effect. For the sake of brevity, it will not be described in detail here.

[0126] Figure 5 A schematic diagram of the structure of the marine resource detection hardware provided in an embodiment of this application is shown.

[0127] The marine resource exploration equipment may include a processor 501 and a memory 502 storing computer program instructions.

[0128] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0129] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is non-volatile solid-state memory.

[0130] The memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, a memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the marine resource exploration method according to any embodiment of this disclosure.

[0131] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the marine resource exploration methods in the above embodiments.

[0132] In one example, the marine resource detection device may also include a communication interface 503 and a bus 510. Wherein, for example... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0133] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0134] Bus 510 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0135] Furthermore, in conjunction with the methods for marine resource exploration described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the marine resource exploration methods described in the above embodiments.

[0136] This application also provides a computer program product, including a computer program that, when executed, implements any of the methods for marine resource exploration described in the above embodiments.

[0137] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0138] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0139] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0140] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can also be implemented by an FPGA performing the specified functions or actions, or can be implemented by a combination of an FPGA and computer instructions.

[0141] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method of marine resource exploration, characterized by, The method comprises: sending a firing instruction to a seismic source and a seabed electromagnetic transmitter to make the seismic source fire a seismic wavelet and the seabed electromagnetic transmitter emit a current signal; the seismic source and the seabed electromagnetic transmitter are pre-deployed in a target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal; collecting signals of the seismic wavelet and the current signal by using a seabed seismic receiver and a seabed electromagnetic receiver respectively to obtain corresponding seismic sampling data and electromagnetic sampling data; constructing a joint inversion model based on a cross-gradient constraint according to the seismic sampling data and the electromagnetic sampling data; iteratively solving a joint objective function of the joint inversion model to determine an exploration result.

2. The method of claim 1, wherein, The method of collecting signals of the seismic wavelet and the current signal by using the seabed seismic receiver and the seabed electromagnetic receiver respectively to obtain corresponding seismic sampling data and electromagnetic sampling data comprises: sampling and recording the seismic wavelet based on the seabed seismic receiver to obtain corresponding seismic wave field displacement signals; measuring electric field and magnetic field under the current signal based on the seabed electromagnetic receiver to obtain corresponding three-dimensional electromagnetic field; spatiotemporally aligning the seismic wave field displacement signals and the three-dimensional electromagnetic field to obtain corresponding seismic sampling data and electromagnetic sampling data.

3. The method of claim 2, wherein, The method of spatiotemporally aligning the seismic wave field displacement signals and the three-dimensional electromagnetic field to obtain corresponding seismic sampling data and electromagnetic sampling data comprises: annotating time stamps and spatial coordinates of the seismic wave field displacement signals and the three-dimensional electromagnetic field to obtain an original seismic sampling data set and an original electromagnetic sampling data set; preprocessing the original seismic sampling data set and the original electromagnetic sampling data set respectively based on a preset preprocessing rule; interpolating the preprocessed original seismic sampling data set and the original electromagnetic sampling data set to the same grid node and performing time resampling correction to obtain corresponding seismic sampling data and electromagnetic sampling data.

4. The method of claim 3, wherein, The preset preprocessing rule comprises at least one of denoising, moveout correction and multiple wave suppression processing of seismic sampling data and at least one of far reference trace denoising, tidal interference correction and time-frequency analysis processing of electromagnetic sampling data.

5. The method of claim 2, wherein, The seismic source, the seabed seismic receiver, the seabed electromagnetic transmitter and the seabed electromagnetic receiver are arranged in an array; the method of iteratively solving the joint objective function of the joint inversion model to determine an exploration result comprises: iteratively solving the joint objective function of the joint inversion model to obtain three-dimensional distribution data of P-wave velocity and three-dimensional distribution data of resistivity of the target exploration area; identifying the three-dimensional distribution data of each sub-region of the target exploration area based on a preset feature combination to obtain an exploration result of a target sub-region corresponding to each preset feature combination.

6. The method of claim 5, wherein, The method of iteratively solving the joint objective function of the joint inversion model to obtain three-dimensional distribution data of P-wave velocity and three-dimensional distribution data of resistivity of the target exploration area comprises: iterating the joint inversion model by using a nonlinear conjugate gradient method to obtain an iteration result; In a case where the iteration result meets a preset ending condition or a preset number of iterations meets a preset number threshold, the iteration result is determined as an optimal result. Based on the optimal result, three-dimensional distribution data of P-wave velocity and resistivity of a target exploration area is obtained.

7. The method according to any one of claims 1 to 6, wherein, The constructing a joint inversion model based on a cross-gradient constraint according to the seismic sampling data and the electromagnetic sampling data comprises: determining a seismic sampling data fitting term of the joint inversion model according to a difference between the seismic sampling data and a result of acoustic wave equation forward modeling; determining an electromagnetic sampling data fitting term of the joint inversion model according to a difference between the electromagnetic sampling data and a result of Maxwell equation forward modeling; determining a function relationship between a spatial gradient of P-wave velocity and a spatial gradient of resistivity based on a cross-gradient constraint, to obtain a cross-gradient constraint term of the joint inversion model; constructing the joint inversion model according to the seismic sampling data fitting term, the electromagnetic sampling data fitting term and the cross-gradient constraint term.

8. A marine resource exploration apparatus, characterized by comprising: The apparatus comprises: a sending module configured to send an excitation instruction to a seismic source and a seabed electromagnetic transmitter, so that the seismic source excites a seismic wavelet and the seabed electromagnetic transmitter emits a current signal; the seismic source and the seabed electromagnetic transmitter are pre-deployed in a target exploration area, and the seismic wavelet and the current signal are cross-correlated and orthogonal to each other; a collecting module configured to collect signals of the seismic wavelet and the current signal by using a seabed seismic detector and a seabed electromagnetic receiver respectively, to obtain corresponding seismic sampling data and electromagnetic sampling data; a constructing module configured to construct a joint inversion model based on a cross-gradient constraint according to the seismic sampling data and the electromagnetic sampling data; a solving module configured to iteratively solve a joint objective function of the joint inversion model, to determine an exploration result.

9. An ocean resource exploration apparatus, characterized by comprising: The device comprises a processor and a memory having computer program instructions stored therein; the processor reads and executes the computer program instructions to implement the marine resource exploration method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium has computer program instructions stored thereon, and the computer program instructions are executed by a processor to implement the marine resource exploration method according to any one of claims 1-7.

11. A computer program product, characterised in that, The instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method of claim 1 7. The method of any one of the preceding claims.

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