Methods for predicting radioactive anomalies in the target strata of the area to be surveyed

Abstract

The embodiments of this application relate to the field of exploration or detection using nuclear radiation, specifically to a method for predicting radioactive anomalies in a stratum within a target area. This method includes: collecting seismic pure wave data, resistivity data, gamma logging data, and spectral uranium logging data from the target area; determining logarithmic gamma data; determining resistivity curves, logarithmic gamma logging curves, and wave impedance curves; determining lithology curves for the target area; determining the probability density function, variogram function, and longitudinal and transverse simulated distances of the variogram function for the target stratum in the target area; determining a three-dimensional wave impedance data model for the target stratum; determining a three-dimensional logarithmic gamma data model for the target stratum; determining a three-dimensional spectral uranium data model for the target stratum; and predicting radioactive anomalies in the target stratum based on the spectral uranium data model. The prediction method provided by the embodiments of this application can effectively predict radioactive anomalies in deep strata, reducing the difficulty of predicting radioactive anomalies in deep strata.

Description

Technical Field

[0001] The embodiments of this application relate to the field of exploration or detection using nuclear radiation, specifically to a method for predicting radioactive anomalies in a target stratum in an area to be explored. Background Technology

[0002] The statements herein are provided merely as background information in connection with this application and do not necessarily constitute prior art.

[0003] With the continuous depletion of sandstone-type uranium resources, surface sandstone-type uranium deposits and shallow strata sandstone-type uranium deposits are becoming increasingly scarce. In order to ensure the normal supply of uranium resources, it is necessary to predict sandstone-type uranium deposits in deep strata in order to find sandstone-type uranium deposits in deep strata.

[0004] Since sandstone-type uranium deposits are closely related to the radioactive anomalies of the strata, it is necessary to predict radioactive anomalies in deep strata. However, current methods for predicting radioactive anomalies in deep strata still have many limitations. Summary of the Invention

[0005] A brief overview of this application is provided below to offer a basic understanding of certain aspects thereof. It should be understood that this overview is not an exhaustive summary of the application. It is not intended to identify key or essential parts of the application, nor is it intended to limit its scope. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0006] This application provides a method for predicting radioactive anomalies in a target formation in an area to be explored, comprising the following steps: S00, collecting seismic pure wave data, resistivity data, gamma logging data, and energy spectrum uranium logging data of the area to be explored, and determining logarithmic gamma data based on the gamma logging data; S10, determining resistivity curves and logarithmic gamma logging curves based on the resistivity data and logarithmic gamma data, and determining wave impedance data based on the resistivity data, and further determining wave impedance curves based on the wave impedance data; S20, determining the lithology curve of the area to be explored based on the resistivity curve and logarithmic gamma logging curve; S30, determining the probability density function and variability function of the target formation in the area to be explored based on the lithology curve, resistivity curve, and logarithmic gamma logging curve, and determining the longitudinal and transverse simulated distances of the variability function;

[0007] S40. Based on the seismic pure wave data, lithology curves, wave impedance curves, logarithmic gamma logging curves, probability density functions, and longitudinal and transverse simulated distances of the variogram, determine the three-dimensional wave impedance data model of the target formation in the area to be explored; S50. Determine the relationship between wave impedance data and logarithmic gamma logging data; S60. Based on the relationship determined in step S50 and the three-dimensional wave impedance data model determined in step S40, determine the logarithmic gamma three-dimensional data model of the target formation in the area to be explored; S70. Determine the relationship between logarithmic gamma logging data and spectral uranium logging data; S80. Based on the relationship determined in step S70 and the logarithmic gamma three-dimensional data model determined in step S60, determine the spectral uranium three-dimensional data model of the target formation in the area to be explored; S90. Based on the spectral uranium three-dimensional data model of the target formation in the area to be explored, predict the radioactive anomalies of the target formation in the area to be explored.

[0008] The prediction method provided in this application, based on the relationship between acoustic impedance data and logarithmic gamma logging data, the relationship between logarithmic gamma logging data and spectral uranium logging data, and the acoustic impedance three-dimensional data model of the target formation in the area to be explored, obtains a spectral uranium three-dimensional data model. Based on the spectral uranium three-dimensional data model, the radioactive anomaly of the target formation in the area to be explored can be determined, thereby determining the radioactive anomaly of the deep formation in the area to be explored, achieving effective prediction of the radioactive anomaly of the deep formation in the area to be explored, and reducing the difficulty of predicting the radioactive anomaly of the deep formation. Attached Figure Description

[0009] Other objects and advantages of this application will become apparent from the following description of embodiments of this application with reference to the accompanying drawings, and will help to provide a comprehensive understanding of this application.

[0010] Figure 1 This is a flowchart illustrating a method for predicting radioactive anomalies in a target stratum in an area to be surveyed, provided in an embodiment of this application.

[0011] Figure 2 This is a schematic diagram showing the distribution of boreholes in the Qiharigtu area.

[0012] Figure 3 yes Figure 2 The diagram shows the resistivity curve and logarithmic gamma logging curve of one of the radioactive anomalous boreholes.

[0013] Figure 4 This is a schematic diagram of the log-gamma three-dimensional data model of the K1s2 stratigraphic framework in the Qiharigtu region.

[0014] Figure 5 This is a schematic diagram of the cross-analysis curve of the logarithmic gamma value determined from logarithmic gamma logging data and the uranium content determined from energy spectrum uranium logging data in the Qiharigtu area.

[0015] Figure 6 This is a schematic diagram of the three-dimensional energy spectrum uranium data model of the K1s2 stratigraphic framework in the Qiharigtu area.

[0016] Explanation of reference numerals in the attached figures:

[0017] 21. Anomalous radioactive well; 22. Normal radioactive well;

[0018] 31. Resistivity curve; 32. Log-gamma logging curve;

[0019] 51. Intersection analysis curve.

[0020] It should be noted that the accompanying drawings are not necessarily drawn to scale, but are shown only in a schematic manner without affecting the reader's understanding. Detailed Implementation

[0021] Exemplary embodiments of this application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of actual implementations are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the development of any such actual embodiment to achieve the developer's specific goals, such as complying with constraints related to the system and business, and these constraints may vary depending on the implementation. Furthermore, it should be understood that while development work can be very complex and time-consuming, such development work is merely a routine task for those skilled in the art who benefit from the content of this application.

[0022] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the equipment structure and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0023] Among related technologies, airborne gamma spectroscopy, ground gamma spectroscopy, measurement of radon concentration in soil, and drilling techniques are used to predict radioactive anomalies in deep strata of the area to be explored. However, these technologies can only predict radioactive anomalies in shallow strata at depths of tens of meters, and are difficult to predict in deep strata at depths of hundreds or even thousands of meters.

[0024] To address the aforementioned issues, embodiments of this application provide a method for predicting radioactive anomalies in target strata within a survey area.

[0025] See Figure 1 , Figure 1This is a flowchart illustrating a method for predicting radioactive anomalies in a target stratum in an area to be surveyed, provided in an embodiment of this application. The prediction method includes the following steps: S00, collecting seismic pure wave data, resistivity data, gamma logging data, and energy spectrum uranium logging data of the area to be surveyed, and determining logarithmic gamma data based on the gamma logging data; S10, determining resistivity curves and logarithmic gamma logging curves based on the resistivity data and logarithmic gamma data, and determining wave impedance data based on the resistivity data, and further determining wave impedance curves based on the wave impedance data; S20, determining the lithology curve of the area to be surveyed based on the resistivity curve and logarithmic gamma logging curve; S30, determining the probability density function and variability function of the target stratum in the area to be surveyed based on the lithology curve, resistivity curve, and logarithmic gamma logging curve, and determining the longitudinal and transverse simulation distances of the variability function; S40, based on... Based on seismic pure wave data, lithology curves, wave impedance curves, logarithmic gamma logging curves, probability density functions, and longitudinal and transverse simulated distances of the variogram function, determine the three-dimensional wave impedance data model of the target formation in the area to be explored; S50, determine the relationship between wave impedance data and logarithmic gamma logging data; S60, based on the relationship determined in step S50 and the three-dimensional wave impedance data model determined in step S40, determine the logarithmic gamma three-dimensional data model of the target formation in the area to be explored; S70, determine the relationship between logarithmic gamma logging data and spectral uranium logging data; S80, based on the relationship determined in step S70 and the logarithmic gamma three-dimensional data model determined in step S60, determine the spectral uranium three-dimensional data model of the target formation in the area to be explored; S90, based on the spectral uranium three-dimensional data model of the target formation in the area to be explored, predict the radioactive anomalies of the target formation in the area to be explored.

[0026] The prediction method provided in this application, based on the relationship between acoustic impedance data and logarithmic gamma logging data, the relationship between logarithmic gamma logging data and spectral uranium logging data, and the acoustic impedance three-dimensional data model of the target formation in the area to be explored, obtains a spectral uranium three-dimensional data model. Based on the spectral uranium three-dimensional data model, the radioactive anomaly of the target formation in the area to be explored can be determined, thereby determining the radioactive anomaly of the deep formation in the area to be explored, achieving effective prediction of the radioactive anomaly of the deep formation in the area to be explored, and reducing the difficulty of predicting the radioactive anomaly of the deep formation.

[0027] In some embodiments, the target formation may be the K1s2 stratigraphic framework. In some embodiments, the radioactive anomaly of the target formation may be the gamma anomaly of the target formation.

[0028] In some embodiments, in step S00, resistivity data, gamma logging data, and spectral uranium logging data of the area to be explored can be collected by collecting resistivity data, gamma logging data, and spectral uranium logging data of each borehole in the area to be explored.

[0029] In some embodiments, the seismic pure wave data of the area to be surveyed in step S00 can be three-dimensional seismic pure wave data.

[0030] In some embodiments, during step S00, the logarithmic gamma data of the area to be explored can be determined by taking the ln logarithm of the gamma logging data. In such embodiments, this setting can narrow the value range of the gamma logging data, facilitating the subsequent determination of the probability density function and variation function of the target formation in the area to be explored.

[0031] In some embodiments, in step S10, the resistivity curve can be the resistivity curve of each borehole in the area to be explored, the logarithmic gamma logging curve can be the logarithmic gamma logging curve of each borehole in the area to be explored, and the wave impedance curve can be the wave impedance curve of each borehole in the area to be explored.

[0032] In some embodiments, in step S20, the lithology curve of the area to be surveyed can be the lithology curve of each borehole in the area to be surveyed.

[0033] In some embodiments, in step S20, the lithological curve of the area to be surveyed may include a radioactive anomalous lithological curve and a radioactive normal lithological curve, such as a gamma-anomalous sand body curve and a gamma-normal lithological body curve.

[0034] In some embodiments, step S20 includes the following steps: S21, determining the resistivity and log-gamma values ​​corresponding to the formation with radioactive anomalies; S22, obtaining the radioactive anomaly lithology curve and the radioactive normal lithology curve based on the resistivity and log-gamma values, as well as the resistivity and log-gamma logging curves determined in step S21. In such embodiments, the above setup helps to improve the accuracy of the determined lithology curves.

[0035] In some embodiments, the radioactive anomalous lithology curve can be the radioactive anomalous sand body curve, and the radioactive normal lithology curve can be the radioactive normal lithology body.

[0036] In some embodiments, in step S22, if the resistivity and log-gamma values ​​corresponding to the lithology of the area to be surveyed are greater than the resistivity and log-gamma values ​​determined in step S21, then the radioactivity anomaly of the lithology is determined. In such embodiments, the above-mentioned setup facilitates the rapid and accurate determination of radioactivity anomalies in the lithology.

[0037] In some embodiments, step S30 includes the following steps: S321, determining the proportion of various lithologies in the target strata of the area to be explored based on the lithology curve; S322, determining the parameters of the probability density function based on the proportion of various lithologies in the area to be explored, and thus determining the probability density function. In such embodiments, the above-mentioned setup helps to improve the accuracy of the determined probability density function.

[0038] In some embodiments, the probability density function can be the probability density function of a discrete lithology curve.

[0039] In some embodiments, the proportion of various lithologies in the target strata of the area to be explored can be the proportion of gamma-ray anomalous sand bodies to gamma-ray normal lithologies. In some embodiments, in step S321, the proportion of gamma-ray anomalous sand bodies to gamma-ray normal lithologies can be determined based on the lithology curve of the borehole in the target strata in the vertical direction.

[0040] In some embodiments, step S30 further includes the following steps: S323, determining the vertical simulation distance of the variogram based on the lithology curve and resistivity curve; S324, determining the horizontal simulation distance of the variogram based on the lithology curve and log-gamma logging curve; S325, determining the fitting function of the variogram; S326, determining the variogram based on the vertical simulation distance, the horizontal simulation distance, and the fitting function. In such embodiments, the above-described configuration helps improve the accuracy of the determined variogram and its vertical and horizontal simulation distances.

[0041] In some embodiments, the fitting function for the variation function can be a Gaussian function. Since the variation of a Gaussian function is stable, the above setting helps to improve the accuracy of the determined variation function.

[0042] In some embodiments, in step S323, the longitudinal simulation distance of the variation function can be determined based on the lithology curves and resistivity curves of each borehole in the area to be surveyed.

[0043] Specifically, the lithology curves, resistivity curves, and longitudinal simulated distances of the variation function of each borehole in the area to be surveyed satisfy the following relationship:

[0044] H = 2 * ∑H i / (4*V), i=1, 2, 3,..., n;

[0045] Where H represents the longitudinal simulation distance of the variogram, H i V represents the thickness of the lithology with radioactive anomalies in each borehole in the area to be explored, V represents the average velocity of the target formation determined based on the resistivity curves of each borehole in the area to be explored, and n represents the number of boreholes in the area to be explored.

[0046] In some embodiments, the lateral simulation distance of the variogram can be divided into a laterally distributed principal direction simulation distance and a laterally distributed connecting direction simulation distance. Step S324 may include the following steps: determining the principal direction simulation distance and the connecting direction simulation distance; and determining the lateral simulation distance of the variogram based on the principal direction simulation distance and the connecting direction simulation distance. The principal direction is, for example, 45°, and the connecting direction is, for example, 135°.

[0047] In some embodiments, the simulated distance in the main direction and the simulated distance in the connecting direction can be determined based on the variation of the uranium mineralization geological environment and sedimentary facies.

[0048] Specifically, the simulated distance W1 in the main direction satisfies the following relationship:

[0049] W1 = (a / 3) * lg(a) - 150,

[0050] 'a' represents the length of the predetermined rock mass in the area to be surveyed. In the embodiments of this application, it may be, for example, the length of a single set of braided river mid-shoal microfacies sand bodies.

[0051] The simulated distance W2 in the communication direction satisfies the following relationship:

[0052] W2 = (b / 3) * lg(b) + 100

[0053] b represents the extension width in the connection direction of the predetermined rock mass in the area to be surveyed. In the embodiments of this application, it may be, for example, the extension width in the connection direction of a braided river sand body.

[0054] In this embodiment, the above method provides a way to determine the simulated distance in the main direction and the simulated distance in the connecting direction. That is, the simulated distance in the main direction and the simulated distance in the connecting direction are determined according to the spread length and the extension width, respectively. The simulated distance in the main direction and the simulated distance in the connecting direction determined by the above method can effectively improve the accuracy of the three-dimensional data model of uranium energy spectrum, thereby further improving the accuracy of predicting the radioactive anomalies of the target strata in the area to be surveyed.

[0055] In some embodiments, in step S40, seismic inversion software can be used to perform geostatistical inversion calculations on the longitudinal and transverse simulated distances of seismic pure wave data, lithology curves, wave impedance curves, logarithmic gamma logging curves, probability density functions, and variogram functions to obtain a three-dimensional wave impedance data model of the target strata in the area to be surveyed.

[0056] Seismic inversion software, such as Geoeast or Jason, can be used.

[0057] In some embodiments, in step S50, an artificial neural network can be used to determine the relationship between wave impedance data and log-gamma logging data through training.

[0058] In some embodiments, step S70 includes steps S71 and S72.

[0059] S71. Determine that the logarithmic gamma logging data and the spectral uranium logging data conform to the following relationship:

[0060] U1=a+bG2+cG2 2 +dG2 3 ,

[0061] Where a, b, c, and d are parameters, U1 represents the uranium content determined based on spectral uranium logging data, and G2 represents the logarithmic gamma logging value determined based on logarithmic gamma logging data.

[0062] S72. Using cross-plot analysis, determine a, b, c, and d. In this embodiment, the above setup facilitates the rapid and accurate determination of the relationship between logarithmic gamma logging data and spectral uranium logging data.

[0063] In some embodiments, step S72 may include the following steps: using cross-plot analysis to determine the cross-plot analysis curve of logarithmic gamma logging data and energy spectrum uranium logging data; and determining a, b, c, and d based on the cross-plot analysis curve.

[0064] In some embodiments, step S70 may further include the following steps: S73, determining the correlation coefficient based on a, b, c, and d determined in step S72; S74, determining the relationship between logarithmic gamma-ray logging data and spectral uranium logging data based on a, b, c, d, and the correlation coefficient determined in step S72. In such embodiments, the above-mentioned arrangement helps to improve the accuracy of the determined relationship between logarithmic gamma-ray logging data and spectral uranium logging data.

[0065] In some embodiments, step S74 may further include the following step: when the correlation coefficient is greater than a preset value, determining the relationship between logarithmic gamma-ray logging data and spectral uranium logging data based on a, b, c, and d determined in step S72. Since the logarithmic gamma-ray logging data and spectral uranium logging data have a good fit when the correlation coefficient is greater than the preset value, and the determined relationship between the logarithmic gamma-ray logging data and spectral uranium logging data is accurate, in such embodiments, the above setting is beneficial for obtaining an accurate relationship between logarithmic gamma-ray logging data and spectral uranium logging data.

[0066] In some embodiments, the preset value of the correlation coefficient can be 0.9. In some embodiments, when the correlation coefficient is greater than 0.9, spectral uranium logging data can be obtained from log-gamma logging data.

[0067] In some embodiments, step S74 may further include the following steps: when the correlation coefficient is less than a preset value, redetermine a, b, c, and d; and determine the relationship between logarithmic gamma-ray logging data and spectral uranium logging data based on the redetermined a, b, c, and d. Since the correlation coefficient is less than the preset value, the fit between logarithmic gamma-ray logging data and spectral uranium logging data is low. Therefore, in such embodiments, a, b, c, and d are redetermined to ensure an accurate relationship between the logarithmic gamma-ray logging data and the spectral uranium logging data can be obtained.

[0068] In some embodiments, step S90 may include the following steps: S91, determining the spectral uranium logging values ​​of the formation with radioactive anomalies; S92, predicting the radioactive anomalies of the target formation in the area to be explored based on the spectral uranium logging values ​​determined in step S91 and the spectral uranium three-dimensional data model. In such embodiments, the above setup facilitates the rapid determination of radioactive anomalies in the target formation in the area to be explored.

[0069] The following describes the process of predicting the radioactive anomalies of the target strata in the area to be surveyed using the prediction method provided in the embodiments of this application. The area to be surveyed is the Qiharigtu region, and the target strata are the K1s2 stratigraphic framework.

[0070] First, one set of 3D seismic pure wave data was collected in the Qiharigtu area, and borehole logging data was also collected in the study area, for example... Figure 2 , Figure 2 This is a schematic diagram of the borehole distribution in the Qiharigtu area. In this embodiment, resistivity data, gamma logging data, and spectral uranium logging data of four boreholes were collected. The four boreholes are divided into radioactive anomaly borehole 21 and radioactive normal borehole 22.

[0071] Subsequently, based on the resistivity data of the radioactive anomalous aperture 21 and the radioactive normal aperture 22, the wave impedance data of the radioactive anomalous aperture 21 and the radioactive normal aperture 22 are determined, and then the wave impedance curve is determined.

[0072] Subsequently, the gamma logging data of radioactive anomaly well 21 and radioactive normal well 22 were processed by taking the ln logarithm to obtain the logarithmic gamma logging data of radioactive anomaly well 21 and radioactive normal well 22. Based on the resistivity data and logarithmic gamma logging data of radioactive anomaly well 21 and radioactive normal well 22, respectively, the resistivity curves and logarithmic gamma logging curves of radioactive anomaly well 21 and radioactive normal well 22 were obtained. The resistivity value and logarithmic gamma value corresponding to the formation with radioactive anomaly were determined, and based on the above resistivity value and logarithmic gamma value, the resistivity curves and logarithmic gamma logging curves of radioactive anomaly well 21 and radioactive normal well 22 were used to obtain the radioactive anomaly lithology curve and the radioactive normal lithology curve.

[0073] For example Figure 3 , Figure 3 yes Figure 2 The diagram shows a resistivity curve 31 and a logarithmic gamma logging curve 32 for one of the radioactive anomaly boreholes 21. In this embodiment, the resistivity value of the formation corresponding to the radioactive anomaly is 11 ohm*m, and the logarithmic gamma value is 5.8 API. Based on the above resistivity and logarithmic gamma values... Figure 3 The resistivity curve 31 and the logarithmic gamma logging curve 32 in the data are divided to obtain the radioactive anomalous lithology curve and the radioactive normal lithology curve of the radioactive anomalous borehole 21. Figure 3 The circled part is the radioactive anomalous sand body curve of the radioactive anomalous well.

[0074] Subsequently, the lithological proportion of the gamma-ray anomalous sand body was determined to be 8%, and the lithological proportion of the gamma-ray normal lithological body was determined to be 92%. Based on these proportions, the parameters of the probability density function were determined, and thus the probability density function was determined. The number of boreholes was determined to be 4, and the average velocity of the K1s2 stratigraphic framework was determined to be 2000 m / s. Based on the lithological curves of the 4 boreholes and the average velocity of the K1s2 stratigraphic framework at 2000 m / s, the longitudinal simulation distance H = 7 ms of the variogram was obtained. The main direction of the lateral distribution of the sand body was determined to be 45°, the connecting direction to be 135°, the simulation distance of the main direction to be 1000 m, and the simulation distance of the connecting direction to be 300 m. The Gaussian function was selected as the fitting function for the variogram.

[0075] Subsequently, an artificial neural network was used to determine the relationship between wave impedance data and log-gamma logging data. Based on this relationship and the three-dimensional wave impedance data model, a three-dimensional log-gamma data model was obtained, for example... Figure 4 , Figure 4 This is a schematic diagram of the log-gamma three-dimensional data model of the K1s2 stratigraphic framework in the Qiharigtu area, with radioactive anomaly well 21 located in an area with high log-gamma values.

[0076] Subsequently, cross-plot analysis was used to determine the cross-plot curves of logarithmic gamma logging data and energy-spectral uranium logging data, for example... Figure 5 , Figure 5 This is a schematic diagram of the cross-analysis curve of the logarithmic gamma value determined from logarithmic gamma logging data and the uranium content determined from energy spectrum uranium logging data in the Qiharigtu area. Figure 5 The expression corresponding to the intersection analysis curve 51 is y = -94.6376 + 99.8869x - 37.4282X. 2 +5.2692X 3 Based on the expression, we determine a = -94.6376, b = 99.8869, c = -37.4282, and d = 5.2692, thus obtaining a correlation coefficient of 0.975 > 0.9.

[0077] Subsequently, based on the expression corresponding to cross-plot curve 51 and the log-gamma three-dimensional data model, the energy spectrum uranium three-dimensional data model of the K1s2 stratigraphic framework in the Qiharigtu area was determined, for example... Figure 6 , Figure 6 This is a schematic diagram of the three-dimensional energy spectrum uranium data model of the K1s2 stratigraphic framework in the Qiharigtu area. Radioactive anomaly well 21 is located in the region with high energy spectrum uranium values, while radioactive normal well 22 is located in the region with low energy spectrum uranium values. Based on the schematic diagram of the three-dimensional energy spectrum uranium data model of the K1s2 stratigraphic framework in the Qiharigtu area, the radioactive anomalies of the K1s2 stratigraphic framework in the Qiharigtu area are determined.

[0078] Regarding the embodiments of this application, it should also be noted that, without conflict, the embodiments of this application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0079] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for predicting radioactive anomalies in a target stratum in an area to be surveyed, characterized in that, It includes the following steps: S00. Collect seismic pure wave data, resistivity data, gamma logging data and energy spectrum uranium logging data of the area to be surveyed, and determine logarithmic gamma logging data based on the gamma logging data. S10. Based on the resistivity data and the logarithmic gamma logging data, determine the resistivity curve and the logarithmic gamma logging curve, and based on the resistivity data, determine the wave impedance data, and then based on the wave impedance data, determine the wave impedance curve. S20: Determine the lithology curve of the area to be explored based on the resistivity curve and the logarithmic gamma logging curve; S30: Based on the lithology curve, the resistivity curve, and the log-gamma logging curve, determine the probability density function and variation function of the target formation in the area to be explored, and determine the longitudinal and transverse simulation distances of the variation function; S40: Based on the seismic pure wave data, the lithology curve, the wave impedance curve, the log-gamma logging curve, the probability density function, and the longitudinal and transverse simulated distances of the variation function, determine the wave impedance three-dimensional data model of the target strata in the area to be explored; S50. Determine the relationship between the wave impedance data and the logarithmic gamma logging data; S60. Based on the relationship determined in step S50 and the three-dimensional wave impedance data model determined in step S40, determine the logarithmic gamma three-dimensional data model of the target strata in the area to be surveyed. S70. Determine the relationship between the logarithmic gamma logging data and the energy spectrum uranium logging data; S80. Based on the relationship determined in step S70 and the log-gamma three-dimensional data model determined in step S60, determine the energy spectrum uranium three-dimensional data model of the target strata in the area to be surveyed. S90. Based on the three-dimensional uranium energy spectrum data model of the target stratum in the area to be surveyed, predict the radioactive anomalies of the target stratum in the area to be surveyed.

2. The prediction method according to claim 1, characterized in that, Step S30 includes the following steps: S321. Determine the proportion of various lithologies in the target strata of the area to be surveyed based on the lithology curve. S322. Based on the proportion of various lithologies in the area to be surveyed, determine the parameters of the probability density function, and then determine the probability density function.

3. The prediction method according to claim 1, characterized in that, Step S30 also includes the following steps: S323. Determine the longitudinal simulation distance of the variability function based on the lithology curve and the resistivity curve; S324. Determine the lateral simulation distance of the variogram based on the lithology curve and the logarithmic gamma logging curve; S325. Determine the fitting function for the variation function; S326. Determine the variogram based on the longitudinal simulation distance of the variogram, the lateral simulation distance of the variogram, and the fitting function of the variogram.

4. The prediction method according to claim 1, characterized in that, In step S20, the lithological curves of the area to be surveyed include radioactive anomalous lithological curves and radioactive normal lithological curves.

5. The prediction method according to claim 4, characterized in that, Step S20 includes the following steps: S21. Determine the resistivity and log-gamma values ​​corresponding to the strata with radioactive anomalies. S22. Based on the resistivity value, the logarithmic gamma value, the resistivity curve, and the logarithmic gamma logging curve determined in step S21, obtain the radioactive anomaly lithology curve and the radioactive normal lithology curve.

6. The prediction method according to claim 5, characterized in that, In step S22, if the resistivity value and log-gamma value corresponding to the lithology of the area to be surveyed are greater than the resistivity value and log-gamma value determined in step S21, the radioactivity anomaly of the lithology is determined.

7. The prediction method according to claim 1, characterized in that, Step S70 includes the following steps: S71. Determine that the logarithmic gamma logging data and the energy spectrum uranium logging data conform to the following relationship: U1=a+bG2+cG2 2 +dG2 3 , Where a, b, c, and d are parameters, U1 represents the uranium content determined based on the energy spectrum uranium logging data, and G2 represents the logarithmic gamma logging value determined based on the logarithmic gamma logging data; S72. Using cross intersection analysis, determine a, b, c, and d.

8. The prediction method according to claim 7, characterized in that, Step S70 also includes the following steps: S73. Based on a, b, c, and d determined in step S72, determine the correlation coefficient; S74. Based on a, b, c, d determined in step S72 and the correlation coefficient, determine the relationship between the logarithmic gamma logging data and the energy spectrum uranium logging data.

9. The prediction method according to claim 8, characterized in that, Step S74 also includes the following steps: When the correlation coefficient is greater than the preset value, the relationship between the logarithmic gamma logging data and the energy spectrum uranium logging data is determined according to a, b, c and d determined in step S72.

10. The prediction method according to claim 8, characterized in that, Step S74 also includes the following steps: When the correlation coefficient is less than the preset value, a, b, c, and d are re-determined; Based on the redefined a, b, c, and d, determine the relationship between the logarithmic gamma logging data and the energy spectrum uranium logging data.

11. The prediction method according to any one of claims 1-10, characterized in that, Step S90 includes the following steps: S91. Determine the spectral uranium logging values ​​of the formation with radioactive anomalies; S92. Based on the energy spectrum uranium logging values ​​and the energy spectrum uranium three-dimensional data model determined in step S91, predict the radioactive anomalies of the target strata in the area to be explored.

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