Method and apparatus for seismic wavelet estimation

Abstract

A method for source wavelet estimation for seismic surveys includes a number of steps. First, seismic data is acquired using seismic data recording sensors and well logging data is acquired using well logging tools in a well site within a survey area. The seismic data and well logging data are stored and processed in a computer system. The acquired and processed time-shifted seismic data is the observed data. The well logging data is processed to obtain one or more formation models representing one or more formation properties; reflectivity modeling is performed to obtain reflectivity, a bandpass filter, and time-shifted reflectivity to generate band-limited reflectivity; the band-limited reflectivity is cross-correlated with the observed data to obtain weights; and inversion is performed based on the weights, the reflectivity, and the observed data to obtain a source wavelet.

Description

Technical Field

[0001] This disclosure relates to seismic exploration methods and apparatus for constructing high-resolution geological models, and in particular to source wavelet estimation. Background Technology

[0002] 1. Overview In oil and gas exploration, high-resolution subsurface property models, such as seismic wave propagation velocity, anisotropy, absorptivity, porosity, and reflectivity, are generated by acquiring and processing seismic data. Integrating these geophysical properties effectively reveals subsurface structures. Seismic data processing typically involves seismic inversion to construct propagation velocity models for long- to medium-wavelengths, followed by seismic migration to obtain short-wavelength seismic reflectivity images. These images are used to determine the location and size of natural resources (e.g., hydrocarbons in oil and gas), thus informing exploration / drilling plans. Obtaining high-resolution seismic reflectivity images requires high-magnification acquisition systems to acquire sufficient seismic data, along with a high-quality seismic velocity model with accurate dynamic information before seismic migration.

[0003] Existing methods for seismic velocity inversion include ray-based seismic tomography and full waveform inversion (FWI). Ray-based tomography is efficient and can produce smooth models, which is sufficient for relatively simple geological targets, such as shallow sedimentary environments. However, for complex geological environments, such as salt domes, subbasal structures, thrust zones, and piedmont areas, ray-based tomography is not effective, and FWI has become an essential tool for constructing velocity models.

[0004] FWI directly solves the seismic wave equations and matches them to seismic data, enabling the generation of more accurate seismic wave propagation velocity models for complex subsurface structures, such as those associated with salt bodies. These propagation velocity models can be used to generate accurate and high-resolution seismic reflectivity images through seismic migration, facilitating time-series monitoring of oil and gas reservoirs, and can even be directly converted into high-resolution seismic image volumes as FWI images.

[0005] 2. Seismic wave equation Seismic waves propagating below the Earth's surface are simulated by solving the seismic wave equations. These equations employ different physical models to describe the geological formations, assuming they are isotropic or anisotropic, elastic or acoustic, and that attenuation is present. In most FWI studies, seismic waves are assumed to be purely acoustic because the acoustic wave equations are relatively simple and can be solved efficiently. Regardless of the assumptions used, all wave equations can be mathematically expressed as:

[0006] Where, vector A model of the Earth is a description of the Earth's subsurface properties, such as the distribution of seismic wave velocity, density, and other physical properties. For spatial location, For time, It is the corresponding (forward) simulation operator, and For the source wavelet The forward wave field generated by excitation at a specific location.

[0007] Numerical solution of equation (1) with respect to spatial variables and time variables All are discretized so that the solution Discretized into ,in Represents grid points, totaling [number] N Grid points, Indicates the time step, totaling The time step is specified. Commonly used numerical methods include the finite difference method, the finite element method, and the spectral element method. As shown in this disclosure, the methods described herein are independent of the form of the wave equation (1) and the numerical methods used to solve it.

[0008] 3. Full Waveform Inversion (FWI) FWI is a data-driven tool that automatically constructs subsurface parameters by iteratively minimizing the difference between recorded data and simulated synthetic data. (e.g., velocity and / or density). An initial estimate of the given subsurface velocity. By solving the wavelet with the seismic source The forward modeling seismic wave equation (1) is used to predict synthetic data. The residual between the synthetic and synthetic data is used as the source to solve the adjoint equation of the forward modeling seismic wave equation (1), thereby obtaining the solution to the adjoint wave equation. The gradient is obtained by cross-correlation between the solutions of the forward modeling and adjoint wave equations. This gradient is then used to update the model along a specific direction to reduce the mismatch between the simulated synthetic data and the observed field data. This process is repeated iteratively until the data mismatch is sufficiently small.

[0009] 4. Source wavelet estimation As can be seen from the above discussion, estimating the source wavelet... This is a crucial step in seismic data processing, which includes wave equation-based AVO inversion, classical AVO and / or attribute inversion, and FWI. One method for estimating the source wavelet is to match observed data with synthetic data, assuming the model used to generate the data is known. For FWI of ocean data, the arrival of the first wave propagating in the water is typically used to estimate the source wavelet because identifying the first wave in the observed data is relatively easy, and the water wave velocity is known. For wavelet estimation in wave equation-based AVO inversion and classical AVO inversion, the source wavelet is estimated through well-seismic correlation, which matches well synthetic data (i.e., synthetic data) with seismic data measured at the well location.

[0010] For field data, observational data may not match the composite well data for various reasons. For example, in a specific event, the travel time in the observational data may not match the travel time in the composite data. An event present in the observational data may not exist in the composite data, and vice versa. This mismatch may be due to the observational data being derived from a 3D geometric model, while the composite data is based on a 1.5D model. Furthermore, well logging may not be vertical; both logging and observational data may contain errors; and the well location may not be exactly the same as the common midpoint (CMP) location.

[0011] Since only matched events contribute positively to the estimated source wavelet, it is necessary to select and enhance only the matched events to estimate the source wavelet more accurately. Summary of the Invention

[0012] This disclosure provides a method and system for estimating source wavelets in seismic data processing. One application of the estimated source wavelet is in AVO inversion based on the wave equation.

[0013] In one embodiment, a source estimation wavelet is first generated from the time-shifted gather. Second, a time shift is applied to the synthetic data within a time window calibrated to well depth markers associated with the stratigraphic horizon, maximizing the cross-correlation between the observed and synthetic data. The association between the depth horizon and the well markers ensures consistency in the time and depth windows used. Next, the cross-correlation between the observed and synthetic data is used as a weight in the objective function, thus increasing the contribution of events with higher matching degrees to the estimated source wavelet.

[0014] A method for estimating a source wavelet in seismic exploration includes several steps. First, seismic data is acquired at a well site within the exploration area using seismic data recording sensors, and well logging data is acquired using well logging tools. The seismic data and the well logging data are stored and processed in a computer system. The time-shifted seismic data acquired and processed in this way is the observation data. The well logging data is processed to obtain one or more formation models representing one or more formation attributes; reflectivity modeling is performed to obtain reflectivity, a bandpass filter, and time-shifted reflectivity, thereby generating a band-limited reflectivity; the band-limited reflectivity is cross-correlated with the observation data to obtain weights; and an inversion is performed based on the weights, the reflectivity, and the observation data to obtain the source wavelet.

[0015] According to one embodiment, the reflectivity modeling is performed based on Kennett modeling.

[0016] According to another embodiment, the formation model is a P-wave velocity model, an S-wave velocity model, or a density model of the formation. Attached Figure Description

[0017] The teachings of the present invention can be readily understood by taking into account the accompanying drawings and the following description.

[0018] Figure 1 This is a schematic top view of a survey area containing incident points of various seismic sources according to an embodiment of the present disclosure.

[0019] Figure 2 It is a schematic diagram showing a cross-sectional view of an environment according to one embodiment, including the incident point of the seismic source, seismic data recording sensors, well location, wellbore, various transmission rays, and various incident angles.

[0020] Figure 3 This is a schematic cross-sectional view illustrating an environment including a wellbore and a logging tool according to one embodiment, the logging tool including one or more acoustic generators and one or more logging data recording sensors.

[0021] Figure 4 This is a schematic diagram illustrating a high-performance computing system according to one embodiment.

[0022] Figure 5 This is a flowchart illustrating the method for estimating source wavelets in this disclosure.

[0023] Figure 6 An exemplary synthetic model with a single reflector is shown.

[0024] Figure 7 It was used Figure 6The time-shifted observation data of the synthetic model shown indicates a strong reflection at approximately 300 ms, along with relatively weak noise.

[0025] Figure 8 This demonstrates the use of the Kennett modeling method for... Figure 6 The band-limited reflectance obtained by processing the model shown.

[0026] Figure 9 It is the weight used for source estimation, which is estimated by the local cross-correlation between simulated and observed data.

[0027] Figure 10a shows the estimated wavelet without the use of weights during the estimation process.

[0028] Figure 10b shows the estimated wavelet when using weights according to an embodiment of the present disclosure.

[0029] Figure 10c shows the real wavelet. Detailed Implementation

[0030] Reference will now be made in detail to several embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. It should be noted that similar or identical reference numerals are used in the drawings where possible, and may denote similar or identical functions. The drawings depict embodiments of the present disclosure and are for illustrative purposes only. Those skilled in the art will readily recognize from the following description that alternative embodiments of the structures, systems, and methods shown herein exist without departing from the principles described herein.

[0031] Throughout this specification, the terms "means" and "method" are used interchangeably and have the same meaning. The table below provides the definitions of the symbols used in the various formulas throughout this specification.

[0032] This disclosure relates to constructing high-resolution geological models by performing improved full-waveform seismic inversion using methods, apparatus, and media including one or more source-independent mismatch functions, in order to improve the imagery of complex subsurface structures (strata) within the survey area.

[0033] Figures 1 to 4 Exemplary embodiments of methods, apparatuses, and media for acquiring and storing seismic data are illustrated. The seismic data, after processing, can generate one or more high-resolution geological models for high-resolution imaging of complex subsurface structures in an exploration area, enabling lithological identification, fluid identification, and reservoir description. The exploration area can be a subsurface structure beneath land or beneath the seabed.

[0034] Figure 1 This is a schematic diagram showing a top-down view of a survey area containing incident points of different seismic sources according to one embodiment. More specifically, Figure 1 A seismic survey area (exploration area) 101 is shown, which is a land-based area indicated by reference numeral 102. Reference numeral 102 indicates the top strata 102 of the land-based area. Those skilled in the art will recognize that seismic survey areas can generate detailed images of the local geology to determine the location and size of potential hydrocarbon (oil and gas) reservoirs, thereby establishing well sites 103. In these survey areas, seismic waves are reflected back from the subsurface rock strata when emitted from one or more seismic sources located at different incident points 104. A blast is an example of a seismic source generated by a seismic device. The seismic waves reflected back to the surface are captured by a seismic data recording sensor 105, transmitted from the seismic data recording sensor 105 via one or more data transmission systems (typically wireless), and stored for post-processing and analysis by a high-performance computing system. While this example shows the top strata 102 of a land-based area, it should be understood that this is merely an example, and the method and system can also be applied to survey areas on the ocean floor.

[0035] Figure 2 It is shown according to one embodiment Figure 1 This is a schematic diagram of a cross-sectional view of the seismic survey area 101, including the incident point of the seismic source, seismic data recording sensors (seismographs), well location, well casing, various transmission rays, and various incident angles. More specifically, in Figure 2 In the figures, the cross-sectional view of the subsurface portion above the seismic survey area is indicated by reference numeral 201, showing the different types of strata labeled by reference numerals 102, 203, and 204. Although the seismic survey area in this example is based on land, it should be understood that this is only an example, and the system and method can also be applied to survey areas on the ocean floor. Figure 2 A common-center gather is shown, where seismic data is ordered according to surface geometry to simulate individual reflection points on Earth. Exploration seismic data can also be called traces, gathers, or image gathers. Figure 2 In this example, data from one or more shot points or blast points and detectors can be combined into a single image gather, or used individually depending on the type of analysis to be performed.

[0036] like Figure 2As shown, one or more shot points or blast points represent seismic sources located on the Earth's surface, indicated by reference numeral 104, at various incident points or sites where one or more sources are activated. Seismic energy or seismic sources from multiple incident points 104 will be reflected from interfaces between different strata. These reflections will be captured by multiple seismic data recording sensors 105, each placed at a different offset distance 210 and at well site 103. Since all incident points 104 and all seismic data recording sensors 105 are placed at different offset distances 210, reconnaissance seismic data or sets (also referred to in the art as "gathers" or "image gathers") will be recorded at different incident angles 208. Incident points 104 generate downward propagating rays 205, which are captured at the surface by upward propagating reflections from the seismic data recording sensors 105. In this example, well site 103 connected to an existing drilled well 209 is shown, with multiple measurements taken along well 209 using techniques known in the art. The wellbore 209 is used to acquire logging data, which may include P-wave velocity, S-wave velocity, density, etc. (Not in...) Figure 2 Other sensors shown can be placed within the survey area to capture seismic data. The seismic data can be used to examine the dependence of amplitude, signal-to-noise ratio, time difference, frequency content, phase, and other seismic properties on the incident angle 208°, offset 210°, azimuth, and other geometric properties that are crucial for data processing and imaging in the seismic survey area.

[0037] Figure 3 This is a schematic diagram illustrating a cross-sectional view of a seismic exploration area comprising a wellbore and logging tools according to one embodiment, wherein the logging tools include one or more acoustic wave generators and one or more logging data recording sensors. An acoustic wave generator is an example of a device that generates one or more acoustic waves (acoustic waves). An acoustic wave generator can be referred to as a sound source because it generates or produces one or more acoustic waves (acoustic waves), which are also called seismic waves. The one or more logging data recording sensors are examples of one or more seismic data recording sensors (seismic detectors or seismic data recorders), and are the same seismic data recording sensor as seismic data recording sensor 105. In embodiments of the invention, oil and / or gas production is suspended in order to generate seismic waves and record seismic data, including reflections of seismic waves as they move through one or more subsurface strata in the seismic exploration area.

[0038] Figure 3An oil drilling system 300 on land 305 is shown, including a drilling rig 310. The drilling rig 310 supports the insertion of a logging tool 315 into a wellbore 320. The logging tool 315 includes one or more acoustic generators (sound sources) for generating one or more acoustic waves, which are transmitted to one or more formations to generate reflected or reflected waves in the formations. Although this example shows one or more formations in a land-based exploration area, it should be understood that this is only an example, and the method and system can also be applied to exploration areas on the surface or bottom of a body of water, such as the ocean. The logging tool 315 also includes one or more logging data recording sensors. As described above, the one or more logging data recording sensors receive and record logging data, which includes reflected data received by the one or more logging data recording sensors in response to acoustic waves emitted into the one or more formations by the one or more acoustic generators. The logging data is an example of seismic data. The logging data includes compressive wave velocity or P-wave velocity (Vp), S-wave velocity (Vs), and density as an indicator of porosity. This logging process used to record logging data can also be called sonic logging. The logging vehicle 325 can be coupled to the logging tool 315 to assist in its lowering and raising, and to communicate with the logging tool 315 to obtain logging data. Alternatively, in methods and systems targeting exploration areas on the surface or bottom of water bodies (such as oceans), other equipment or systems can be used to assist in the lowering and raising of the logging tool 315 and to communicate with the logging tool 315 to obtain logging data.

[0039] Figure 4 This is a schematic diagram illustrating a high-performance computing system according to one embodiment, which receives (typically wirelessly) data from... Figure 1 and Figure 2 Earthquake data recording sensor 105 and / or Figure 3 Earthquake data recording sensors (in) Figure 3 Seismic data of seismic waves (also known as well logging data recording sensors). Figure 4 A high-performance computer system stores the seismic data in at least one memory for post-processing and analysis via a computer-implemented method and apparatus according to one or more embodiments. The analyzed or processed seismic data can be accessed via a personal computer system. More specifically, Figure 4A data transmission system 400 is shown for wirelessly transmitting seismic data from a seismic data recording sensor to a system computer 405 coupled to one or more storage devices 410 for storing the seismic data in a database. The data transmission system can also wirelessly transmit seismic data directly from the seismic data recording sensor 405 to one or more storage devices 410 for storing the seismic data in a database, which can be accessed by the system computer 405. Wireless transmission is indicated by reference numeral 402. The one or more storage devices 410 may also store other computer software instructions or programs to implement the apparatus and methods described in the embodiments. The system computer 405 may be coupled (e.g., wirelessly) to one or more output storage devices 420, which can receive the results of computer-implemented processes or methods executed by the system computer 405. A personal computer system 425 may be coupled (e.g., wirelessly) to one or more output storage devices 420 and / or the system computer 405 so that a user can use the user interface of the personal computer system 425 to input information or obtain the results of computer-implemented processor methods executed by the system computer 405. One or more storage devices 420 may also store other computer software instructions or programs to implement the apparatus and methods described in the embodiments.

[0040] The user interface of the personal computer system 425 may include, for example, one or more of the following: a keyboard, mouse, joystick, button, switch, electronic pen or stylus, gesture recognition sensor (e.g., for recognizing gestures of the user including body part movements), input sound device or voice recognition sensor (e.g., microphone for receiving voice commands), output sound device (e.g., speaker), trackball, remote control, portable (e.g., cellular or smartphone) phone, tablet computer, pedal or foot switch, virtual reality device, etc. The user interface may also include a haptic device to provide haptic feedback to the user. The user interface may also include, for example, a touchscreen. Furthermore, the personal computer system 425 may be a desktop computer, laptop computer, tablet computer, mobile phone, or any other personal computing system.

[0041] The processes, functions, methods, and / or computer software instructions or programs in the apparatus and methods described in the embodiments herein may be recorded, stored, or fixed in one or more non-transitory computer-readable media (computer-readable storage (recording) media) including program instructions (computer-readable instructions) executable by a computer to cause one or more processors to execute (implement or implement) the program instructions. The media may also be included alone or in combination with program instructions, data files, data structures, etc. The media and program instructions may be specially designed and constructed, or may be well-known and usable by those skilled in the art of computer software. Examples of non-transitory computer-readable media include magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured for storing and executing program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, etc. Examples of program instructions include machine code (e.g., generated by a compiler) and files containing higher-level code that can be executed by a computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware device can be configured as one or more software modules that are recorded, stored, or embedded in one or more non-transitory computer-readable media to perform the above-described operations and methods, and vice versa. Furthermore, the non-transitory computer-readable media can be distributed among computer systems connected via a network, and program instructions can be stored and executed in a distributed manner. In addition, the computer-readable media can also be embodied in at least one application-specific integrated circuit (ASIC) or field-programmable logic array (FPGA).

[0042] One or more databases may include a collection of data and supporting data structures that can be stored, for example, in one or more storage devices 410 and 420. For example, one or more storage devices 410 and 420 may be embodied as one or more non-transitory computer-readable storage media, such as non-volatile memory devices, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and flash memory, USB drives, volatile memory devices (e.g., random access memory (RAM)), hard disks, floppy disks, Blu-ray discs, or optical media (e.g., CD-ROMs and DVDs), or combinations thereof. However, examples of storage devices 410 and 420 are not limited to those described above, and the storage can be implemented by a variety of other devices and structures understood by those skilled in the art.

[0043] Figure 5 This is a flowchart illustrating a method for estimating a seismic source wavelet according to an embodiment of the present disclosure. The survey area can be a subsurface structure on land or underwater.

[0044] refer to Figure 5 The process shown, in operation 505, involves input seismic data from field surveys. d obs The data (i.e., the gathers) are stored on and / or processed on a computing device. In operation 505, basic data processing, including denoising, stacking, and shifting, can be performed. The output is time-shifted observation data. d obs . Figure 1 and Figure 2 Earthquake data recording sensor 105 and / or Figure 3 The logging data recording sensor in logging tool 315 can detect seismic data and transmit the seismic data to... Figure 4 The high-performance computing system shown above. Figures 1 to 4 The seismic data detected in the exploration area can be stored in one or more memories (e.g., one or more storage devices 410 and one or more output storage devices 420).

[0045] Another input data source is logging data recorded by logging tool 315 using sensors located within the wellbore in the exploration area. In operation 500, the logging data is processed to generate formation models representing formation properties, such as a P-wave velocity model (Vp), an S-wave velocity model (Vs), or a density model of the formation surrounding the well site. In operation 510, one or more formation models can be used to perform reflectivity modeling. Operation 510 outputs... This is a vector consisting of reflection coefficients, which is transformed to the time domain in operation 520. In operation 530, A time shift is also performed to generate a time-shifted reflectance vector R. Operations 510, 520, and 530 all utilize high-performance computer systems (such as...). Figure 4 The system shown is used to implement this.

[0046] Reflectivity modeling methods calculate the model's response in the frequency-wavenumber domain, incorporating contributions from all rays within the reflection zone. This method is used to model the propagation and attenuation of various wave types for a given model, offering sufficient accuracy and relatively low computational cost. In this embodiment, reflectivity modeling is based on Kennett's full-wave elastic modeling (also known as "Kennett modeling"), proposed by Kennett (Kennett, BLN, 1983, *Seismic Wave Propagation in Layered Media*: Cambridge University Press). The Kennett modeling method generates synthetic data including primary, secondary, and converted waves, and offers high accuracy for one-dimensional models. Other reflectivity methods (including those proposed by Müller (Müller, G., 1985, *Reflectivity Methods: An Introduction*: J. Geophys., 58, 153-174.)) and other variations can also be used.

[0047] In operation 540, the bandpass filter is convolved with the reflectivity vector R to generate bandwidth and observation data. Band-limited reflectivity matching bandwidth In the time domain, the bandpass filter acts as a zero-phase wavelet, therefore the band-limited reflectivity... Equivalent to synthetic data.

[0048] Operation 550 uses band-limited reflectance R and observation data The weight matrix W is calculated by the local zero-hysteresis cross-correlation between the two. This operation is mathematically represented in the form of equation (1): (1) in, The first W matrix i One diagonal element; M is the half-window length of the local cross-correlation; i and j For index value; For the first The corresponding band-limited reflectance coefficients at each time sampling point. i The weight values ​​at each time sampling point are determined by a length of 2. Within the local window of M+1 and The value is calculated from the given value.

[0049] Operation 560 via minimization With synthetic data (in this embodiment, it is) Solving for wavelets by the difference between them s The weighted least squares optimization method is adopted to first minimize the objective function of the L2 norm of the data residuals, and then the source wavelet is inverted. This operation is mathematically represented by equation (2): (2) Where L is the half-length of the source wavelet, and R is the reflectivity after time shift. s It is the source wavelet.

[0050] Equation (2) can be solved in operation 570 to obtain the source wavelet. s Its matrix form is shown in equation (3): (3) Operations of the 540, 550, 560, and 570 all utilize high-performance computer systems (such as...). Figure 4 The system shown is used to execute this.

[0051] Figures 6 to 10C It shows Figure 5 The result of the process shown. Figure 6 A synthetic model with a single reflector at a depth of 300 meters is shown. (As shown) Figure 6 As shown, the P-wave velocity Vp is 2000 m / s, the S-wave velocity Vs is 1400 m / s, and the density ρ is 1000 kg / m³. 3 .

[0052] Figure 7 These are time-shifted observation data, corresponding to operation 505. ,Should This includes a strong reflection from a single reflector at a depth of 300 meters, as well as some weaker noise signals. Figure 8 It is the band-limited reflectance obtained through Kennett modeling and transformed to the time domain, which corresponds to the value in operation 540. . Figure 9 It is the observation data and band-limited reflectance The local cross-correlation between the time variables and the weight W applied in the source estimation according to formula (3).

[0053] Figure 10B The estimated wavelet corresponding to operation 570 is shown, which is the result obtained when using weighted least squares optimization to minimize the objective function (as shown in equations (2) and (3)). On the other hand, Figure 10A The wavelet estimated using least-squares optimization without applied weights is shown. Figure 10C It is a real wavelet. It is not difficult to see that... Figure 10A There are side lobes on its upper part, and Figure 10BThis makes the estimate closer to the true wavelet. The comparison shows that after applying weights, the estimated source wavelet no longer exhibits suspicious sidelobes, and the accuracy is significantly improved. Therefore, the cross-correlation between the reflectance in the observed data and the true reflectance is far superior to the cross-correlation between reflectance and noise. Using the cross-correlation between the synthetic data and the observed data as weights can significantly enhance the signal's influence while minimizing the influence of noise.

[0054] Although these embodiments may employ FWI algorithms with full waveform (differential) mismatch functions and / or utilize elastic wave equations for seismic wave propagation, it will be apparent to those skilled in the art that the method can also be alternatively applied to acoustic and pseudo-acoustic equations, whether in isotropic or anisotropic media, without departing from the true scope of the invention as defined by the following claims.

Claims

1. A source wavelet estimation method for seismic surveys, comprising: (a) Seismic data recording sensors are placed at multiple locations within the survey area, and logging tools are placed in wellbores within the survey area, the logging tools including an acoustic generator and one or more logging sensors; (b) A seismic wave is generated by launching a projectile at an incident point within the survey area, which penetrates the underground strata. (c) Using the seismic data recording sensor to observe the seismic waves and record seismic data based on the seismic waves; (d) Perform logging operations using the logging tools and record logging data using the one or more logging recording sensors; (e) Transmitting the seismic data from the seismic data recording sensor and the well logging data from the one or more well logging sensors to a computer system including one or more memories, and storing the seismic data and the well logging data in the one or more memories; (f) The computer system performs time-shifting on the seismic data to obtain observation data; (g) The computer system processes the well logging data to obtain one or more formation models representing one or more formation properties; (h) The computer system performs reflectivity modeling and time shifting to obtain reflectivity; (i) The computer system convolves the bandpass filter with the reflectivity to produce a band-limited reflectivity; (j) The computer system performs a cross-correlation between the band-limited reflectance and the observed data to obtain weights; as well as (k) The computer system performs an inversion based on the weights, the reflectivity, and the observation data to obtain the source wavelet, and performs least squares regression to minimize the difference between the observation data and the band-limited reflectivity.

2. The method according to claim 1, wherein, The reflectivity modeling is based on Kennett modeling.

3. The method according to claim 2, wherein, The one or more formation models are selected from P-wave velocity models, S-wave velocity models, and density models.

4. The method according to claim 1, wherein, Step (k) is represented as: , in, s Let R represent the source wavelet, R represent the time-shifted reflectivity, and W represent the weight. This refers to the observed data.

5. The method according to claim 1, wherein, Step (j) is represented as: , in, The weight matrix is ​​the first i There are 1 diagonal element, and M is the half-window length of the local cross-correlation; i and j For index value; For the first The corresponding band-limited reflectance coefficients at each time sampling point.

6. The method according to claim 5, wherein, The method also includes using a length of 2 Within the local window of M+1 and To calculate the value of the first i The weight values ​​at each time sampling point.

7. A system for estimating the source wavelet of a survey area, the system comprising: Multiple seismic data recording sensors are located at different locations within the survey area; The logging tool located in the wellbore within the exploration area is equipped with an acoustic generator and a logging recording sensor; Blasting equipment located at each incident point within the survey area is used to generate seismic waves that penetrate underground strata. and The well logging sensor and the well logging tool transmit well logging data to a computer system including one or more memories and at least one processor. The one or more memories are used to store seismic data, well logging data, and instructions. The at least one processor is used to execute the instructions stored in the one or more memories to achieve the following: The well logging data is processed to establish one or more formation models, thereby performing reflectivity modeling to obtain reflectivity, and applying a bandpass filter to the reflectivity to generate band-limited reflectivity; The band-limited reflectivity is cross-correlated with the seismic data to obtain weights; The source wavelet is obtained through inversion based on the reflectivity, the weights, and the seismic data; and The difference between the observed data and the band-limited reflectance is minimized using least squares regression.

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