Method, device, equipment, medium and program product for geothermal micromotion detection

Abstract

The present invention relates to the field of geophysical exploration technology and discloses a method, apparatus, equipment, medium, and program product for geothermal microtremor detection. The present invention extracts an actual surface wave dispersion curve based on collected earth background noise; performs a point sampling operation on the actual surface wave dispersion curve according to a preset sampling method, and uses the obtained sampling points as prior data; and uses the prior data in combination with the Bayesian theorem for inversion to obtain stratum velocity structure data, wherein the stratum velocity structure data includes the thickness of each stratum within a preset detection depth range and the shear wave velocity of each stratum. The present invention introduces the Bayesian theorem and provides a probability distribution for inversion, thereby better reflecting the uncertainty in parameter estimation, effectively analyzing interlayers in the geothermal microtremor process, and thus ensuring the accuracy of geological structure results during geothermal microtremor detection.

Description

Technical Field

[0001] The present invention relates to the field of geophysical detection technology, and in particular to a method, device, equipment, medium and program product for geothermal micro-motion detection. Background Art

[0002] Geophysical exploration is a key technical tool in the exploration and development of geothermal resources. High-precision geophysical exploration information is essential for both early-stage resource assessment, favorable target area selection, identification of heat channels and reservoirs, and later-stage operational development monitoring.

[0003] Traditional geophysical inversion methods are typically based on a given initial model. However, when no prior information is available, the initial model often adopts a normal geological model with increasing velocity. This model assumes that formation velocities increase with depth. However, in actual geological environments, the formation structure does not always exist in a velocity-increasing manner. For example, there may be low-velocity soft interlayers or high-velocity hard interlayers (i.e., formation structural singularities). Therefore, using a formation model with increasing velocity for inversion often fails to capture the true geological structure. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, equipment, medium and program product for geothermal micro-tremor detection to solve the problem of how to improve the accuracy of geological structure results during geothermal micro-tremor detection.

[0005] In a first aspect, the present invention provides a method for detecting geothermal micromotion, the method comprising:

[0006] Extracting actual surface wave dispersion curves based on the collected earth background noise;

[0007] Performing a point sampling operation on the actual surface wave dispersion curve according to a preset sampling method, and using the obtained sampling points as prior data;

[0008] The prior data is used in combination with Bayesian theorem to perform inversion to obtain formation velocity structure data, wherein the formation velocity structure data includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

[0009] This embodiment extracts the actual surface wave dispersion curve by collecting the earth's background noise, fully utilizes the underground medium information carried in the earth's background noise, improves the inversion accuracy, selects sampling points from the surface wave dispersion curve, makes the extracted data more representative, and uses it as prior data. Compared with the traditional inversion method that only provides a certain shear wave velocity increment model, this embodiment introduces Bayes' theorem to provide a probability distribution for inversion. The inversion result includes not only the shear wave velocity of each layer, but also the thickness of each stratum, providing more comprehensive stratum velocity structure information. Since the inversion with the introduction of Bayes' theorem can better reflect the uncertainty in parameter estimation, whether non-velocity-increasing interlayers appear in the actual stratum can be accurately analyzed, thereby ensuring the accuracy of the geological structure results during geothermal microtremor detection.

[0010] In an optional embodiment, the inversion using the prior data in combination with Bayesian theorem to obtain formation velocity structure data specifically includes:

[0011] Obtaining a preset shear wave velocity model within a preset detection depth range, and a proportional factor describing the variance corresponding to the preset shear wave velocity model;

[0012] determining a theoretical surface wave phase velocity based on the preset shear wave velocity model;

[0013] determining an actually observed surface wave phase velocity based on the prior data;

[0014] The posterior probability of the inversion result within the preset detection depth range is calculated using an inversion algorithm based on Bayes' theorem based on the preset shear wave velocity model, a proportional factor describing the variance corresponding to the preset shear wave velocity model, a theoretical surface wave phase velocity, and the actually observed surface wave phase velocity;

[0015] The distribution of the posterior probability under the inversion result within the preset detection depth range is analyzed to determine the thickness of each stratum within the preset detection depth range and the shear wave velocity of each stratum.

[0016] This embodiment obtains a preset shear wave velocity model within a preset detection depth range, along with a corresponding variance-determining scaling factor. This provides an initial preset shear wave velocity model as a reference, providing a reasonable starting point for subsequent calculations. The variance scaling factor helps quantify model uncertainty. The theoretical surface wave phase velocity is determined based on the preset shear wave velocity model, and the observed surface wave phase velocity is determined based on the prior data, making the inversion results more realistic and reliable. Using an inversion algorithm based on Bayes' theorem, the posterior probability of the inversion results within the preset detection depth range is calculated, quantifying the model uncertainty and effectively determining the structure of the specific layer.

[0017] In an optional embodiment, the inversion algorithm is expressed by the following formula:

[0018]

[0019] Among them, p(d|x;σ 2 ) represents the posterior probability under the inversion result; x represents the shear wave velocity model, σ 2 represents the scale factor describing the variance; d represents the actual observed surface wave phase velocity, and the dimension corresponding to the actual observed surface wave phase velocity is N d ; E represents the diagonal covariance matrix; f(x) represents the theoretical surface wave phase velocity.

[0020] In this embodiment, the inversion algorithm not only relies on the observation data, that is, the actual observed surface wave phase velocity, but also combines prior information, that is, the theoretical surface wave phase velocity and the shear wave velocity model, which can make the inversion result more accurate. By calculating the posterior probability distribution, the uncertainty of the inversion result can be quantitatively evaluated. Regardless of the formation situation, it can be displayed through the distribution of the posterior probability.

[0021] In an optional embodiment, analyzing the distribution of the posterior probability under the inversion result within the preset detection depth range to determine the thickness of each stratum within the preset detection depth range and the shear wave velocity of each stratum specifically includes:

[0022] Dividing different strata within the preset detection depth range according to peak intervals in the distribution of the posterior probability;

[0023] Based on the peak value in the distribution of the posterior probability, the shear wave velocity corresponding to each stratum is determined from a preset shear wave velocity model.

[0024] By analyzing the peak intervals in the posterior probability distribution, the present invention accurately delineates different strata. The peak intervals in the posterior probability distribution typically correspond to the locations of stratum interfaces, enabling accurate determination of the thickness of each stratum. This allows accurate analysis of complex geological structures.

[0025] In an optional implementation, performing a point extraction operation on the actual surface wave dispersion curve and using the extracted points as prior data specifically includes:

[0026] The sampling points obtained by sampling the actual surface wave dispersion curve in a preset interval jump manner are used as prior data.

[0027] This embodiment performs jump sampling at preset intervals, which can reduce the amount of data that needs to be processed and reduce data complexity. Since the jump sampling is performed at preset intervals, the main characteristics and trends of the surface wave dispersion curve are maintained, important structural information is not lost, and the signal-to-noise ratio of the data is improved, providing reliable initial conditions for subsequent modeling, inversion, etc.

[0028] In an optional embodiment, before performing a point sampling operation on the actual surface wave dispersion curve according to a preset sampling method and using the obtained sampling points as prior data, the method further includes:

[0029] Based on the earth's background noise, a preset surface wave simulation algorithm is used to extract the simulated surface wave dispersion curve;

[0030] The actual surface wave dispersion curve is optimized based on the simulated surface wave dispersion curve to obtain an optimized actual surface wave dispersion curve.

[0031] In this embodiment, before the point picking operation, a preset surface roll simulation algorithm is used to extract a simulated surface roll dispersion curve, and the simulated surface roll dispersion curve is used to optimize the actual surface roll dispersion curve, thereby reducing errors caused by the actual measurement environment and improving the reliability of subsequent prior data.

[0032] In a second aspect, the present invention provides a device for detecting geothermal micro-motion, comprising:

[0033] Dispersion curve acquisition module, used to extract the actual surface wave dispersion curve through the earth's background noise;

[0034] A priori data analysis module is used to extract points from the actual surface wave dispersion curve and use the extracted points as priori data;

[0035] The inversion calculation module is used to perform inversion based on the prior data combined with the Bayesian principle to obtain a formation velocity structure, wherein the formation velocity structure includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

[0036] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for geothermal micromotion detection according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for geothermal micromotion detection according to the first aspect or any corresponding embodiment thereof.

[0038] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for geothermal micromotion detection according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 is a flow chart of a method for geothermal micromotion detection according to an embodiment of the present invention;

[0041] Figure 2 is a flow chart of another method for detecting geothermal micromotion according to an embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of posterior probability distribution according to an embodiment of the present invention;

[0043] Figure 4 is a schematic diagram of sampling points according to an embodiment of the present invention;

[0044] Figure 5 is a flow chart of another method for detecting geothermal micromotion according to an embodiment of the present invention;

[0045] Figure 6 is a structural block diagram of a device for detecting geothermal micro-motion according to an embodiment of the present invention;

[0046] Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0048] The velocity-increasing model used in traditional inversion methods cannot accurately reflect the actual geological conditions in the presence of structural singularities. For example, in a geothermal resource exploration area, the actual geological structure contains a low-velocity soft interlayer, with higher velocities above and below it. If the velocity-increasing model is used for inversion, the initial model assumes that velocity increases with depth, resulting in large errors in the inversion results at the location of the low-velocity soft interlayer. This is because the inversion algorithm attempts to force the assumption of increasing velocity, failing to accurately reflect the presence of the low-velocity interlayer.

[0049] The same applies to high-velocity interlayers. If high-velocity interlayers exist in the stratum, the velocity-increasing model will also fail to correctly identify them. This seriously affects the accurate evaluation of geothermal resources and the formulation of geological development strategies in the subsequent process.

[0050] According to an embodiment of the present invention, a method embodiment of geothermal micro-motion detection is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] In this embodiment, a method for detecting geothermal micro-motion is provided, which can be used in the above-mentioned computer. Figure 1 FIG. 1 is a flow chart of a method for detecting geothermal micromotion according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0052] Step S101: extracting an actual surface wave dispersion curve based on the collected earth background noise.

[0053] It should be noted that Earth background noise refers to the naturally occurring tiny vibrations on the Earth's surface. The surface wave dispersion curve, which plots the velocity of surface waves as a function of frequency, can characterize the velocity structure of the subsurface medium.

[0054] Specifically, seismographs and other equipment are used to collect Earth's background noise. The collected noise data is then filtered and processed through time-frequency analysis to extract the dispersion information of surface waves. Dispersion curve extraction algorithms, such as automatic dispersion curve extraction methods (e.g., frequency-time domain analysis), are then used to obtain the actual surface wave dispersion curve. Because surface waves in Earth's background noise carry information about the velocity structure of the subsurface medium, analyzing the dispersion characteristics of surface waves can reveal the subsurface velocity structure, providing accurate observational data for the inversion process.

[0055] Step S102 : performing a point sampling operation on the actual surface wave dispersion curve according to a preset sampling method, and using the obtained sampling points as prior data.

[0056] It should be noted that the preset sampling method refers to selecting data points from the surface wave dispersion curve according to pre-set rules. The prior data are the selected data points and are used to constrain the inversion process in the subsequent process.

[0057] The sampling method can be uniform sampling, random sampling, stratified sampling, etc., and sampling points can be selected as needed. By sampling the dispersion curve, representative data points are extracted as prior information in the inversion process, thereby improving the accuracy of the inversion.

[0058] Step S103 , performing inversion using the prior data in combination with Bayesian theorem to obtain formation velocity structure data, wherein the formation velocity structure data includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

[0059] It should be noted that inversion refers to the process of inferring underground structures or physical parameters through observation data. Bayes' theorem is an important theorem in probability theory, which is used to calculate the conditional probability of an event.

[0060] In an embodiment of the present invention, the preset detection depth range refers to the underground depth interval that needs to be detected and analyzed in advance during the geothermal micro-tremor detection process, which is determined based on specific detection needs and goals, such as the distribution of a specific geothermal micro-tremor stratum or geological structure.

[0061] It can be understood that through Bayesian theorem, the inversion process can combine the prior information of the formation velocity structure (such as the prior distribution of formation thickness and shear wave velocity) with the actually observed surface wave dispersion curve data, so that it can be comprehensively reflected in the posterior distribution, which can effectively reduce the deviation and uncertainty that may be caused by relying solely on observation data (such as the inability to distinguish the presence of low-speed soft interlayers or high-speed interlayers due to the use of only velocity increment models). In other words, the use of Bayesian theorem can naturally quantify uncertainty and represent parameter uncertainty through the posterior distribution. Based on the principle of comprehensive analysis of formation uncertainty, after the introduction of Bayesian theorem, the structure of the formation can be analyzed more accurately, without being affected by the traditional shear wave velocity increment model. Even if low-speed soft interlayers or high-speed interlayers appear, they can be accurately identified.

[0062] This embodiment extracts the actual surface wave dispersion curve by collecting the earth's background noise, fully utilizes the underground medium information carried in the earth's background noise, improves the inversion accuracy, selects sampling points from the surface wave dispersion curve, makes the extracted data more representative, and uses it as prior data. Compared with the traditional inversion method that only provides a certain shear wave velocity increment model, this embodiment introduces Bayes' theorem to provide a probability distribution for inversion. The inversion result includes not only the shear wave velocity of each layer, but also the thickness of each stratum, providing more comprehensive stratum velocity structure information. Since the inversion with the introduction of Bayes' theorem can better reflect the uncertainty in parameter estimation, whether non-velocity-increasing interlayers appear in the actual stratum can be accurately analyzed, thereby ensuring the accuracy of the geological structure results during geothermal microtremor detection.

[0063] In this embodiment, a method for detecting geothermal micro-motion is provided, which can be used in the above-mentioned computer. Figure 2 FIG. 1 is a flow chart of a method for detecting geothermal micromotion according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0064] Step S201: extract the actual surface wave dispersion curve based on the collected earth background noise. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0065] Step S202: Sampling points on the actual surface wave dispersion curve according to a preset sampling method, and using the obtained sampling points as prior data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0066] Step S203 , performing inversion using the prior data in combination with Bayesian theorem to obtain formation velocity structure data, wherein the formation velocity structure data includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

[0067] Specifically, the above step S203 includes:

[0068] Step S2031 : obtaining a preset shear wave velocity model within a preset detection depth range, and a proportional factor describing the variance corresponding to the preset shear wave velocity model.

[0069] It should be noted that the scaling factor describing the variance is intended to help quantify model uncertainty. The pre-defined S-wave velocity model is a hypothetical distribution model of underground S-wave (S-wave) velocities, based on existing geological data and experience, prior to geothermal microtremor detection. This model provides a preliminary prediction of the underground velocity structure, including S-wave velocity values ​​at different depths. It can also be understood as a matrix containing depth and S-wave velocity. Information for each location in the matrix can be found in Table 1:

[0070] Table 1 Comparison of formation depth and shear wave velocity

[0071] Depth (m) Shear wave velocity (Km / s) 0-50 500 50-100 600 100-150 700

[0072] Step S2032: determining a theoretical surface wave phase velocity based on the preset shear wave velocity model.

[0073] Specifically, the theoretical surface wave phase velocity is calculated by using the dispersion equation in conjunction with a pre-set shear wave velocity model. The dispersion equation relates the phase velocity of surface waves to the velocity structure of the subsurface medium. Solving this equation yields the theoretical surface wave phase velocity at various frequencies. Alternatively, numerical simulations can be performed using the pre-set velocity model to simulate the propagation characteristics of surface waves at different frequencies, thereby obtaining the theoretical surface wave phase velocity.

[0074] Step S2033: determining the actually observed surface wave phase velocity based on the priori data.

[0075] It can be understood that since the prior data are data sampled from the actual surface wave dispersion curve, which describes the relationship between the surface wave phase velocity and frequency, the actually observed surface wave phase velocity is the surface wave phase velocity at different frequencies in the prior data.

[0076] Step S2034, based on the preset shear wave velocity model, the proportional factor describing the variance size corresponding to the preset shear wave velocity model, the theoretical surface wave phase velocity, and the actually observed surface wave phase velocity, an inversion algorithm based on the Bayesian theorem is used to calculate the posterior probability of the inversion result within the preset detection depth range.

[0077] It should be noted that the initial Bayesian formula is as follows:

[0078]

[0079] Where m represents the model parameter, p(m) represents the prior probability distribution of model m, p(d|m) represents the conditional probability of obtaining observation data d given model m, which is the likelihood function (which is similar to the objective function in the deterministic inversion method), p(d) is the probability of the occurrence of observation data d, which can be regarded as a normalization constant, so that the total integral of the probability value in the entire model interval is 1, and p(m|d) is the conditional probability of model m given the observation data d, called the posterior probability, which represents the probability value of the model parameter m under the joint constraints of prior information and observation data. According to the principle of the above Bayesian formula, the specific calculation method of each probability is brought into the initial Bayesian formula, and the deterministic inversion algorithm is expressed by the following formula:

[0080]

[0081] Among them, p(d|x;σ 2 ) represents the posterior probability under the inversion result; x represents the shear wave velocity model, σ 2 represents the scale factor describing the variance; d represents the actual observed surface wave phase velocity, and the dimension corresponding to the actual observed surface wave phase velocity is N d ; E represents the diagonal covariance matrix; f(x) represents the theoretical surface wave phase velocity.

[0082] In this embodiment, the inversion algorithm not only relies on the observation data, that is, the actual observed surface wave phase velocity, but also combines prior information, that is, the theoretical surface wave phase velocity and the shear wave velocity model, which can make the inversion result more accurate. By calculating the posterior probability distribution, the uncertainty of the inversion result can be quantitatively evaluated. Regardless of the formation situation, it can be displayed through the distribution of the posterior probability.

[0083] Step S2035 , analyzing the distribution of the posterior probability under the inversion result within the preset detection depth range, and determining the thickness of each stratum within the preset detection depth range, and the shear wave velocity of each stratum.

[0084] Specifically, step S2035 includes:

[0085] Step A1: Divide different strata within the preset detection depth range according to the peak interval in the distribution of the posterior probability.

[0086] Step A2: determining the shear wave velocity corresponding to each stratum from a preset shear wave velocity model based on the peak value in the distribution of the posterior probability.

[0087] It is understandable that the peak intervals of the posterior probability distribution correspond to the positions of the stratum interfaces, because the model parameters change significantly at these positions. By identifying these peak intervals, different strata can be divided. Within each stratum, the peak of the posterior probability distribution corresponds to the most likely shear wave velocity value of the stratum. By selecting the peak, the shear wave velocity of each stratum can be determined from the preset shear wave velocity model. The advantage of using Bayesian inversion is that it does not limit specific parameters, thus avoiding the limitations of the incremental model. During the iterative process of inversion, Bayes' theorem can continuously search for the optimal solution, especially in the presence of stratum structural singularities (such as high-speed interlayers or low-velocity layers), and can obtain results consistent with the real stratum model.

[0088] For example, refer to Figure 3 , Figure 3 The middle vertical axis represents the depth of the stratum, the ground plane depth is 0, assuming the preset detection depth is 60, and in fact different strata correspond to different velocities. In the field of geothermal micro-tremors, the S wave velocity in the same stratum is the same, so Figure 3 As can be seen, there are seven different velocities in the range from the ground plane depth of 0 to the preset detection depth of 60. The method provided in this embodiment calculates the distribution of the posterior probability. When the probability is at a peak, different stratum boundaries can be determined. That is, the depth range of the same stratum can be confirmed based on the adjacent posterior probability peak intervals. Once the depth range is determined, the shear wave velocity corresponding to each stratum is determined from the preset shear wave velocity model.

[0089] also, Figure 3 It can be clearly seen that the shear wave velocity of the third stratum starting from the ground plane depth of 0 is neither higher than the shear wave velocity of the second stratum starting from the ground plane depth of 0, nor higher than the shear wave velocity of the fourth stratum starting from the ground plane depth of 0. It can be seen that the method provided in this embodiment can accurately analyze the stratum structure singularities, such as low-velocity soft interlayers, which cannot be analyzed by traditional inversion methods. Traditional inversion methods can only obtain stratum models with successively increasing shear wave velocity structures.

[0090] By analyzing the peak intervals in the posterior probability distribution, the present invention accurately delineates different strata. The peak intervals in the posterior probability distribution typically correspond to the locations of stratum interfaces, enabling accurate determination of the thickness of each stratum. This allows accurate analysis of complex geological structures.

[0091] This embodiment obtains a preset shear wave velocity model within a preset detection depth range, along with a corresponding variance-determining scaling factor. This provides an initial preset shear wave velocity model as a reference, providing a reasonable starting point for subsequent calculations. The variance scaling factor helps quantify model uncertainty. The theoretical surface wave phase velocity is determined based on the preset shear wave velocity model, and the observed surface wave phase velocity is determined based on the prior data, making the inversion results more realistic and reliable. Using an inversion algorithm based on Bayes' theorem, the posterior probability of the inversion results within the preset detection depth range is calculated, quantifying the model uncertainty and effectively determining the structure of the specific layer.

[0092] In an optional embodiment, performing a point extraction operation on the actual surface roll dispersion curve and using the extracted points as prior data specifically includes: using the points obtained by skip sampling the actual surface roll dispersion curve at preset intervals as prior data.

[0093] It is understandable that skip sampling at preset intervals can suppress the impact of random noise to a certain extent. Since noise is usually high-frequency, proper sampling can filter out some of the high-frequency noise and improve the signal-to-noise ratio of the data.

[0094] For example, refer to Figure 4 The horizontal axis represents frequency, and the vertical axis represents surface wave phase velocity. This embodiment uses skip sampling at preset intervals to extract valid surface wave phase velocity data by selecting appropriate intervals. When the intervals are appropriately selected, the key features and trends of the surface wave dispersion curve are preserved without losing important structural information. As can be seen, the resulting sample data volume is small while still being able to well reflect the key characteristics of the actual surface wave dispersion curve.

[0095] This embodiment performs jump sampling at preset intervals, which can reduce the amount of data that needs to be processed and reduce data complexity. Since the jump sampling is performed at preset intervals, the main characteristics and trends of the surface wave dispersion curve are maintained, important structural information is not lost, and the signal-to-noise ratio of the data is improved, providing reliable initial conditions for subsequent modeling, inversion, etc.

[0096] In this embodiment, a method for detecting geothermal micro-motion is provided, which can be used in the above-mentioned computer. Figure 5 FIG. 1 is a flow chart of a method for detecting geothermal micromotion according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0097] Step S501: extract the actual surface wave dispersion curve based on the collected earth background noise. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0098] Step S502 : extracting a simulated surface wave dispersion curve based on the earth background noise using a preset surface wave simulation algorithm.

[0099] It should be noted that the preset surface wave simulation algorithm is a mathematical algorithm based on the geophysical model. In this embodiment, the propagation process of seismic waves in the stratum is simulated by inputting the earth's background noise data, thereby extracting a simulated surface wave dispersion curve. It is worth noting that this surface wave dispersion curve is only a surface wave dispersion curve simulated by the preset surface wave simulation algorithm based on the earth's background noise, and is used to optimize the actual surface wave dispersion curve in subsequent steps.

[0100] Step S503 : optimizing the actual surface wave dispersion curve based on the simulated surface wave dispersion curve to obtain an optimized actual surface wave dispersion curve.

[0101] Specifically, an error analysis is performed on the difference between the actual surface roll dispersion curve and the simulated surface roll dispersion curve to determine the error distribution value between the two. A fitting algorithm (such as a linear optimization algorithm) is used to optimize the actual surface roll dispersion curve according to the error distribution, so that the final actual surface roll dispersion curve is closer to the actual situation, thereby preventing errors in the actual surface roll dispersion curve caused by signal errors occurring during the acquisition process.

[0102] Step S504: perform a point sampling operation on the actual surface wave dispersion curve according to a preset sampling method, and use the obtained sampling points as prior data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0103] Step S505: Invert the prior data in combination with Bayesian theorem to obtain formation velocity structure data, wherein the formation velocity structure data includes the thickness of each formation within the preset detection depth range and the shear wave velocity of each formation. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0104] In this embodiment, before the point picking operation, a preset surface roll simulation algorithm is used to extract a simulated surface roll dispersion curve, and the simulated surface roll dispersion curve is used to optimize the actual surface roll dispersion curve, thereby reducing errors caused by the actual measurement environment and improving the reliability of subsequent prior data.

[0105] In this embodiment, a device for detecting geothermal micro-motion is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0106] This embodiment provides a device for detecting geothermal micro-motion, such as Figure 6 Shown, including:

[0107] The dispersion curve acquisition module 601 is used to extract the actual surface wave dispersion curve through the earth background noise;

[0108] A priori data analysis module 602 is used to extract points from the actual surface wave dispersion curve and use the extracted points as priori data;

[0109] The inversion calculation module 603 is used to perform inversion based on the prior data combined with the Bayesian principle to obtain a formation velocity structure, wherein the formation velocity structure includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

[0110] In an optional embodiment, the inversion calculation module 603 specifically includes:

[0111] An acquisition unit for a preset shear wave velocity model is used to acquire a preset shear wave velocity model within a preset detection depth range, and a proportional factor describing a variance corresponding to the preset shear wave velocity model;

[0112] a theoretical surface wave phase velocity calculation unit, configured to determine a theoretical surface wave phase velocity based on the preset shear wave velocity model;

[0113] an actually observed surface wave phase velocity determining unit, configured to determine the actually observed surface wave phase velocity based on the priori data;

[0114] a Bayesian inversion calculation unit, configured to calculate, based on the preset shear wave velocity model, a proportional factor describing the variance corresponding to the preset shear wave velocity model, a theoretical surface wave phase velocity, and the actually observed surface wave phase velocity, a posterior probability of the inversion result within a preset detection depth range using an inversion algorithm based on the Bayesian theorem;

[0115] The posterior probability distribution analysis unit is used to analyze the distribution of the posterior probability under the inversion result within the preset detection depth range, and determine the thickness of each stratum within the preset detection depth range and the shear wave velocity of each stratum.

[0116] In an optional embodiment, the inversion algorithm is expressed by the following formula:

[0117]

[0118] Among them, p(d|x;σ 2 ) represents the posterior probability under the inversion result; x represents the shear wave velocity model, σ 2represents the scale factor describing the variance; d represents the actual observed surface wave phase velocity, and the dimension corresponding to the actual observed surface wave phase velocity is N d ; E represents the diagonal covariance matrix; f(x) represents the theoretical surface wave phase velocity.

[0119] In an optional implementation, the posterior probability distribution analysis unit specifically includes:

[0120] a stratum analysis subunit, configured to divide strata into different strata within the preset detection depth range according to peak intervals in the distribution of the posterior probability;

[0121] The shear wave velocity analysis subunit is configured to determine the shear wave velocity corresponding to each stratum from a preset shear wave velocity model based on a peak value in the distribution of the posterior probability.

[0122] In an optional implementation, the priori data analysis module 602 uses the sampling points obtained by sampling the actual surface roll dispersion curve in a jump sampling manner at preset intervals as the priori data.

[0123] In an optional embodiment, the above device further includes:

[0124] A dispersion curve simulation module is used to extract a simulated surface wave dispersion curve based on the earth's background noise using a preset surface wave simulation algorithm;

[0125] The dispersion curve optimization module is used to optimize the actual surface wave dispersion curve based on the simulated surface wave dispersion curve to obtain an optimized actual surface wave dispersion curve.

[0126] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0127] The device for geothermal micro-motion detection in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0128] The embodiment of the present invention also provides a computer device having the above Figure 6 The device for detecting geothermal micro-tremors is shown.

[0129] See also Figure 7 , Figure 7 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 7As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0130] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0131] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0132] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0134] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0135] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0136] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0137] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the claims.

Claims

1. A method for detecting geothermal micro-motion, characterized in that: The method comprises: Extracting actual surface wave dispersion curves based on the collected earth background noise; Based on the earth's background noise, a preset surface wave simulation algorithm is used to extract the simulated surface wave dispersion curve; Optimizing the actual surface wave dispersion curve based on the simulated surface wave dispersion curve to obtain an optimized actual surface wave dispersion curve; The optimized actual surface wave dispersion curve is subjected to a point sampling operation according to a preset sampling method, and the obtained sampling points are used as prior data, including: sampling points obtained by skipping the optimized actual surface wave dispersion curve in a preset interval as the prior data; Using the prior data in combination with Bayesian theorem to perform inversion, the formation velocity structure data is obtained, wherein the formation velocity structure data includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation; The inversion using the prior data in combination with Bayesian theorem to obtain formation velocity structure data specifically includes: Obtaining a preset shear wave velocity model within a preset detection depth range, and a proportional factor describing the variance corresponding to the preset shear wave velocity model; determining a theoretical surface wave phase velocity based on the preset shear wave velocity model; determining an actually observed surface wave phase velocity based on the prior data; The posterior probability of the inversion result within the preset detection depth range is calculated using an inversion algorithm based on Bayes' theorem based on the preset shear wave velocity model, a proportional factor describing the variance corresponding to the preset shear wave velocity model, a theoretical surface wave phase velocity, and the actually observed surface wave phase velocity; According to the peak intervals in the distribution of the posterior probability, different strata are divided within a preset detection depth range; based on the peak values ​​in the distribution of the posterior probability, the shear wave velocity corresponding to each stratum is determined from a preset shear wave velocity model; The inversion algorithm is expressed by the following formula: ； in, represents the posterior probability under the inversion result; x represents the shear wave velocity model, represents the scale factor describing the variance; d represents the actually observed surface wave phase velocity, and the dimension corresponding to the actually observed surface wave phase velocity is ; E represents the diagonal covariance matrix; represents the theoretical surface wave phase velocity.

2. A device for detecting geothermal micro-motion, based on the method for detecting geothermal micro-motion according to claim 1, characterized in that: The device comprises: Dispersion curve acquisition module, used to extract the actual surface wave dispersion curve through the earth's background noise; A priori data analysis module is used to extract points from the optimized actual surface wave dispersion curve and use the extracted points as prior data; The inversion calculation module is used to perform inversion based on the prior data combined with the Bayesian principle to obtain a formation velocity structure, wherein the formation velocity structure includes the thickness of each formation within a preset detection depth range and the shear wave velocity of each formation.

3. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for geothermal micro-motion detection according to claim 1 by executing the computer instructions.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for geothermal micro-motion detection according to claim 1.

5. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for detecting geothermal micro-motion according to claim 1 .

Images