An imaging method based on full waveform inversion
By optimizing the velocity and reflectivity models using full waveform inversion (FWI) technology, the problems of high computational requirements and time-consuming preprocessing in traditional seismic imaging are solved, enabling more efficient and accurate imaging of underground structures.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-12-13
- Publication Date
- 2026-06-16
AI Technical Summary
Traditional seismic imaging techniques suffer from high computational requirements, time-consuming preprocessing, and limited imaging quality when processing full-wavelength data. In particular, they struggle to generate accurate velocity models under complex geological structures, resulting in insufficient imaging accuracy.
By employing full waveform inversion (FWI) technology and iteratively optimizing the velocity and reflectivity models, subsurface images can be directly output using full-wavelength data, reducing preprocessing steps and improving imaging accuracy and efficiency.
It enables clearer images of underground structures under complex geological conditions, improves imaging accuracy and signal-to-noise ratio, and eliminates time-consuming preprocessing steps in traditional methods.
Smart Images
Figure CN122218804A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present invention relate to the field of seismic inversion imaging technology, and in particular to an imaging method based on full waveform inversion. Background Technology
[0002] Seismic imaging is one of the key technologies in geophysics for exploring subsurface structures. Traditional seismic imaging techniques mainly rely on wavefield migration methods, which extrapolate the wavefields of the seismic source and receiver to the subsurface using specific velocity models and apply imaging conditions to estimate the reflectivity model for each grid point in the subsurface. Since Claerbout proposed the basic principles of wavefield migration in 1971, this technology has been central to seismic research and plays a crucial role in seismic imaging.
[0003] However, traditional wavefield migration methods have some limitations. First, these methods typically only handle single-scattering energy, thus requiring preprocessing of the input data to meet this requirement. Preprocessing steps include denoising, demultiplexing, and ghost wave removal, which are not only time-consuming but also difficult to completely remove unwanted "noise" without compromising the main signal. Second, traditional migration algorithms often struggle to generate accurate velocity models when dealing with complex geological structures, such as salt domes, leading to a decline in image quality.
[0004] Furthermore, traditional seismic imaging methods face challenges in processing full-wavefield data. Full-wavefield data includes first arrivals, transmitted waves, and their multiples, and these wave patterns carry rich information about subsurface structures. However, due to limitations in computational power and the lack of stable full waveform inversion (FWI) algorithms, the application of these data types in seismic imaging is restricted.
[0005] Over the past few decades, although FWI technology is theoretically capable of imaging using full-wavelength data, it has failed to routinely produce directly interpretable images of Earth's subsurface due to two main obstacles:
[0006] There is a lack of a consistent and effective FWI scheme applicable to different types of data and geological environments. The computational requirements are high, especially when large apertures (for large migrations in deep subsurface wave penetration) and high frequencies (for high resolution in seismic interpretation) are needed.
[0007] With the development of high-performance computing technology and the progress of FWI algorithms in solving problems such as loop skipping and amplitude mismatch, the application scope of FWI technology has expanded to include different types of data, such as seabed node (OBN) or submarine cable data, wide azimuth and narrow azimuth towed streamline data (WATS and NATS), and different geological environments, from deep water to shallow water, from sea to land.
[0008] Despite significant progress in simplifying the velocity model building (VMB) process, FWI has so far been primarily used to provide velocity models for migration purposes, and seismic imaging remains limited by time-consuming preprocessing and migration steps. Furthermore, the accuracy of FWI imaging largely depends on the accuracy of the velocity model, which is particularly challenging in areas with complex geology and insufficient data constraints.
[0009] To overcome these challenges, improved imaging techniques have been explored, including using multiples for imaging to improve underground illumination and using techniques such as least-squares migration (LSM) to reduce crosstalk noise caused by multiples. However, these methods still rely on the Born approximation and require good separation of the first and multiples. Summary of the Invention
[0010] To address the aforementioned technical problems, at least one embodiment of the present invention provides an imaging method based on full waveform inversion.
[0011] In some optional embodiments, the method includes the following steps:
[0012] S10, acquire the initial velocity model and observed seismic data;
[0013] S20 uses the current velocity model to simulate the propagation of seismic waves and generates simulated seismic data;
[0014] S30, compare the differences between the observed seismic data and the simulated seismic data, and update the velocity model with the goal of minimizing the differences;
[0015] S40, Based on the updated velocity model, determine the reflectivity model used to describe the reflection characteristics of the subsurface interface;
[0016] S50, determine whether the updated velocity model and the reflectivity model meet the preset requirements;
[0017] If the updated velocity model and the reflectivity model do not meet the preset requirements, steps S20 to S50 are repeated until the last updated velocity model and the reflectivity model meet the preset requirements.
[0018] S60, when the updated velocity model and the reflectivity model meet the preset requirements, output the full-wavefield seismic imaging results.
[0019] In some alternative embodiments, the seismic waves include first arrival waves, transmitted waves, and multiple waves.
[0020] In some optional embodiments, comparing the differences between the observed seismic data and the simulated seismic data, and updating the velocity model with the objective of minimizing the differences, includes:
[0021] Set an objective function, which is the difference between the observed seismic data and the simulated seismic data;
[0022] The velocity model is adjusted with the goal of minimizing the objective function.
[0023] In some alternative embodiments, the velocity model is adjusted using gradient descent or Newton's method to minimize the objective function.
[0024] In some alternative embodiments, the velocity model is adjusted to minimize the objective function according to the following formula:
[0025]
[0026] Where v represents the velocity model, It is observed earthquake data. These are simulated earthquake data, where i and j represent the data points and the number of simulation iterations, respectively.
[0027] In some optional embodiments, a reflectivity model for describing the reflection characteristics of the subsurface interface is determined based on the updated velocity model, using the following formula:
[0028]
[0029] Where I is reflectivity, ρ is density, v is velocity model, z0 is initial impedance, and θ and β are the dip angle and azimuth angle of the underground interface normal, respectively.
[0030] In some optional embodiments, determining whether the updated velocity model and the reflectivity model meet preset requirements includes: determining whether the updated velocity model and the reflectivity model reach a predetermined convergence criterion.
[0031] At least one embodiment of the present invention also provides an electronic device, characterized in that it comprises:
[0032] At least one processor; and,
[0033] A memory communicatively connected to the at least one processor; wherein,
[0034] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the imaging method based on full waveform inversion as described above.
[0035] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the imaging method based on full waveform inversion as described above.
[0036] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the imaging method based on full waveform inversion as described above.
[0037] Compared with existing technologies, embodiments of the present invention provide an imaging method based on full waveform inversion (FWI). This method can directly output subsurface images or reflectance using FWI technology, improving imaging accuracy and providing clearer images of subsurface structures, especially under complex geological conditions. This method utilizes full-wavelength data and optimizes the velocity and reflectance models through a least-squares data fitting process, resulting in a higher signal-to-noise ratio. Furthermore, this method can directly output subsurface images or reflectance using FWI, potentially eliminating time-consuming preprocessing and velocity model construction steps in traditional seismic imaging, thus improving imaging efficiency. Attached Figure Description
[0038] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.
[0039] Figure 1 This is a flowchart of the imaging method based on full waveform inversion used in Embodiment 1 of the present invention;
[0040] Figure 2 This is a schematic diagram of the initial velocity model and RTM imaging results provided in Embodiment 2 of the present invention;
[0041] Figure 3 This is a schematic diagram of the full waveform inversion velocity model and the full waveform inversion imaging results provided in Embodiment 2 of the present invention. Detailed Implementation
[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.
[0043] As mentioned earlier, the core of the full-wavelength seismic imaging method lies in using full-wavelength inversion (FWI) technology to process seismic data to obtain high-resolution images of subsurface structures. FWI technology can simultaneously simulate all modes of seismic waves, including transmitted waves, reflected waves, and multiples, thus providing more comprehensive subsurface information. Compared to traditional wavelength imaging methods, FWI minimizes the discrepancy between observed and simulated seismic data by iteratively optimizing velocity and reflectivity models.
[0044] The principles of FWI technology include the following key points:
[0045] 1. Wavefield Simulation: The propagation of seismic waves is simulated using a velocity model, including first arrival waves, transmitted waves, and multiple waves.
[0046] 2. Data Fitting: The velocity model is updated by minimizing the difference between observed and simulated data.
[0047] 3. Reflectivity Modeling: A reflectivity model is calculated based on the updated velocity model, which describes the reflectivity characteristics of the subsurface interface.
[0048] 4. Iterative optimization: Repeat the above process until the velocity model and reflectivity model converge to a stable state.
[0049] The mathematical expression for FWI can be summarized as follows:
[0050]
[0051] Where v represents the velocity model, It is observed earthquake data. These are simulated earthquake data, where i and j represent the data points and the number of simulation iterations, respectively.
[0052] The reflectivity model is calculated based on the following formula:
[0053]
[0054] Where I is reflectivity, ρ is density, v is velocity model, z0 is initial impedance, and θ and β are the dip angle and azimuth angle of the underground interface normal, respectively.
[0055] The implementation details of the above method are described in detail below through examples. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.
[0056] Example 1:
[0057] like Figure 1 As shown, this embodiment provides an imaging method based on full waveform inversion, which mainly includes the following steps:
[0058] 1. Initialization steps: Select an initial velocity model and acquire the observed seismic dataset, including all recorded seismic signals.
[0059] 2. Wavefield Simulation Steps: Simulate seismic wave propagation using the current velocity model to generate a simulated seismic dataset.
[0060] 3. Data fitting steps: Calculate the observed seismic data according to the right-hand side of formula (1). With simulated earthquake data The differences between them are identified, and the corresponding objective function is constructed.
[0061] 4. Model update steps: Solve formula (1) and adjust the velocity model by optimization algorithm (such as gradient descent or Newton's method) to minimize the objective function.
[0062] 5. Reflectivity calculation steps: Based on the above reflectivity formula (formula (2)), determine the reflectivity model according to the updated velocity model.
[0063] 6. Iterative optimization steps: Repeat steps 2 to 5 until the final velocity model and reflectivity model reach the predetermined convergence criteria.
[0064] 7. Output Steps: Output the final FWI image, which provides a high-resolution view of the subsurface structure.
[0065] 8. Image post-processing steps: Perform necessary post-processing on FWI imaging, such as filtering or enhancement, to improve image quality.
[0066] Through the steps described above, the full-wavelength seismic imaging method can provide more accurate and comprehensive images of subsurface structures than traditional methods, especially when dealing with complex geological structures.
[0067] Example 2
[0068] The technical solution of the present invention and its beneficial effects will be further illustrated below with a specific example.
[0069] Figure 2 'a' represents the initial velocity model in step 1 of this embodiment. Figure 2 b shows the imaging results of RTM migration imaging using this model. The RTM imaging results show that the low-velocity gas cloud area is not axial and the underlying strata are fragmented. This indicates that the initial model is quite different from the real subsurface model, and full waveform inversion (FWI) is needed to optimize the velocity model and obtain a more realistic subsurface velocity model.
[0070] Figure 3 For the full waveform inversion velocity model and full waveform inversion imaging results, from Figure 3(a) It can be seen that the velocity model resolution is significantly higher at this time. Figure 2 The initial model in the model provides a more detailed depiction of low-velocity gas clouds, resulting in corresponding full-waveform inversion imaging results. Figure 3 (b)) It accurately depicts the upper and lower boundaries of the gas cloud layer with stronger continuity; at the same time, the imaging axis of the underlying strata is more continuous and has better consistency.
[0071] Example 3
[0072] Another embodiment of the present invention relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the imaging method based on full waveform inversion in the above embodiments.
[0073] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.
[0074] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.
[0075] Example 4
[0076] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the imaging method based on full waveform inversion described above.
[0077] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0078] Example 5
[0079] Another embodiment of the present invention relates to a computer program product, including a computer program that, when executed by a processor, implements the steps of the imaging method based on full waveform inversion described above.
[0080] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.
Claims
1. An imaging method based on full waveform inversion, characterized in that, include: S10, acquire the initial velocity model and observed seismic data; S20 uses the current velocity model to simulate the propagation of seismic waves and generates simulated seismic data; S30, compare the difference between the observed seismic data and the simulated seismic data, and update the velocity model with the goal of minimizing the difference; S40, Based on the updated velocity model, determine the reflectivity model used to describe the reflection characteristics of the subsurface interface; S50, determine whether the updated velocity model and the reflectivity model meet the preset requirements; If the updated velocity model and the reflectivity model do not meet the preset requirements, steps S20 to S50 are repeated until the last updated velocity model and the reflectivity model meet the preset requirements. S60, when the updated velocity model and the reflectivity model meet the preset requirements, output the full-wavefield seismic imaging results.
2. The imaging method based on full waveform inversion according to claim 1, characterized in that, The seismic waves include first arrival waves, transmitted waves, and multiple waves.
3. The imaging method based on full waveform inversion according to claim 1, characterized in that, The step of comparing the observed seismic data with the simulated seismic data and updating the velocity model with the objective of minimizing the differences includes: Set an objective function, which is the difference between the observed seismic data and the simulated seismic data; The velocity model is adjusted with the goal of minimizing the objective function.
4. The imaging method based on full waveform inversion according to claim 3, characterized in that, The velocity model is adjusted using gradient descent or Newton's method to minimize the objective function.
5. The imaging method based on full waveform inversion according to claim 3, characterized in that, The velocity model is adjusted according to the following formula, with the objective function as the goal: Where v represents the velocity model, It is observed earthquake data. These are simulated earthquake data, where i and j represent the data points and the number of simulation iterations, respectively.
6. The imaging method based on full waveform inversion according to claim 1, characterized in that, Based on the following equation, a reflectivity model for describing the reflection characteristics of the subsurface interface is determined according to the updated velocity model: Where I is reflectivity, ρ is density, v is velocity model, z0 is initial impedance, and θ and β are the dip angle and azimuth angle of the underground interface normal, respectively.
7. The imaging method based on full waveform inversion according to claim 1, characterized in that, The step of determining whether the updated velocity model and the reflectivity model meet the preset requirements includes: Determine whether the updated velocity model and the reflectivity model have reached the predetermined convergence criteria.
8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the imaging method based on full waveform inversion as described in any one of claims 1 to 7.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the imaging method based on full waveform inversion as described in any one of claims 1 to 7.
10. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the imaging method based on full waveform inversion as described in any one of claims 1 to 7.