14 results about "Geophysical inversion" patented technology

The invention provides a method for constructing a geoelectric structure prediction model based on time-frequency electromagnetism, and belongs to the field of oil exploration. The method comprises the following steps: firstly, establishing an initial reservoir model based on a model-driven geophysical inversion process; then geophysical data and a corresponding reservoir model data set are collected, classified and standardized, and construction of the data set is completed; establishing a reservoir geoelectric structure prediction model based on the deep learning model; and finally training the reservoir geoelectric structure prediction model by using the data set to obtain an optimized reservoir geoelectric structure prediction model. Wherein the reservoir comprises an ultra-deep carbonate rock reservoir; the invention further provides a method for constructing the model for predicting the geoelectric structure of the ultra-deep carbonate reservoir. Compared with the prior art, according to the geoelectric structure prediction model constructed by the method, when observation data is input, an inversion result can be obtained without executing an iteration process, and the reservoir oil-water relationship can be further analyzed according to the inversion result.

The invention belongs to the technical field of geophysical inversion, and relates to a gravity anomaly three-dimensional inversion method based on multi-scale feature fusion, which comprises the following steps: randomly generating density anomaly bodies with different numbers, densities, sizes and shapes to obtain an underground medium three-dimensional density model; carrying out forward modeling on the three-dimensional density model of the underground medium to obtain two-dimensional gravity abnormal data of the earth surface; a multi-scale feature fusion model is trained with the underground medium three-dimensional density model as output and the two-dimensional gravity anomaly data as input, and the multi-scale feature fusion model comprises a two-dimensional encoder, a three-dimensional decoder, a dimension converter and a multi-scale feature fusion device, wherein the dimension converter and the multi-scale feature fusion device are located between the two-dimensional encoder and the three-dimensional decoder; and inputting actually measured two-dimensional gravity anomaly data into the trained multi-scale feature fusion model, and performing inversion to obtain a three-dimensional density body constrained by a physical range. According to the method, the expression capability of the multi-scale features of the underground structure is enhanced, and the multi-solution problem and the deep resolution reduction problem of inversion can be relieved.

The application belongs to the technical field of geophysical inversion, and relates to a gravity anomaly three-dimensional inversion method based on multi-scale feature fusion, comprising the following steps: randomly generating density anomaly bodies with different number, density, size and shape to obtain a three-dimensional density model of underground medium; forward modeling the three-dimensional density model of underground medium to obtain two-dimensional gravity anomaly data of the ground surface; training a multi-scale feature fusion model with the three-dimensional density model of underground medium as output and the two-dimensional gravity anomaly data as input, wherein the multi-scale feature fusion model comprises a two-dimensional encoder and a three-dimensional decoder, and a dimension converter and a multi-scale feature fusion device located between the two-dimensional encoder and the three-dimensional decoder; inputting the measured two-dimensional gravity anomaly data into the trained multi-scale feature fusion model to obtain a three-dimensional density body constrained by a physical range through inversion. The application enhances the expression capability of multi-scale features of underground structures, and can relieve the multi-solution problem of inversion and the problem of resolution decline in deep parts.

The application discloses a regional lithology interpretation method, system, device and medium based on multiple physical characteristics, and the method comprises the following steps: acquiring regional logging data and geophysical data; preprocessing the regional logging data to obtain a target regional logging data set; establishing multiple classification models associated with the input of multiple physical characteristics; training and verifying the multiple classification models by using the target regional logging data set to determine the best classification model; performing multidimensional inversion on the geophysical data to obtain an inversion result; and inputting the inversion result into the best classification model for processing to obtain a lithology interpretation result. Therefore, by establishing multiple classification models associated with the input of multiple physical characteristics, and determining the model with the optimal classification effect, i.e. the best classification model, from the multiple classification models, the best classification model can be applied to the lithology interpretation of the geophysical inversion result, so that the lithology interpretation result with relatively better accuracy, efficiency and intelligence can be obtained.

The invention provides a goaf positioning method and system for different mining degrees, and belongs to the technical field of geophysical inversion, and the method comprises the steps: obtaining multi-temporal SAR image data and on-site mining degree data of a target coal mine area; constructing an integrated probability model suitable for different mining degrees; and setting a search range of goaf characteristic parameters and model parameters of the integrated probability model according to the field mining degree data, and optimizing the goaf characteristic parameters by using a nonlinear inversion algorithm to obtain the spatial-temporal position of the goaf. Technical support is provided for coal mining monitoring, illegal mining supervision and historical mining working face information in a large area.

The application provides a kind of side slope fissure passive source seismic wave imaging method, the patent adopts multi-wave multi-component seismograph, simultaneously gathers multiple seismic wave signals on three components, including surface wave, body wave and the like.This design significantly improves the richness of data, so that in data processing, a variety of seismic wave signals can be comprehensively utilized for inversion analysis.By joint processing and constraint of different wave types, the multi-solution problem in geophysical inversion can be effectively reduced, and the stability and reliability of the results are improved.In addition, the acquisition of multi-component data makes the imaging more comprehensive, especially in the characterization of fissure directionality and geometric features, which has obvious advantages.

This invention discloses a natural electric field source inversion method based on a genetic algorithm, belonging to the field of geophysical inversion technology. It solves the technical problems of low inversion accuracy and large source location errors in natural electric field inversion caused by optimization algorithms. The key points of the technical solution are: first, expanding the input electrical resistivity data using head-to-tail cross-interpolation; second, using the extreme values ​​and dispersion of the electrical resistivity data as the center and radius of the population distribution area; third, forming a population by describing individuals based on the number and location of source sources; and finally, using a genetic algorithm based on double-threshold cross-iteration to achieve natural electric field source inversion with smaller source fitting errors. This application can effectively utilize a small amount of input electrical resistivity data, reasonably expand and extract extreme value features, and largely solve the problems of large inversion fluctuations caused by large spatial areas, small data volumes, and the inherent multiple solutions in geophysical inversion, while achieving smaller source fitting errors.

The invention discloses a salt cavern hydrogen storage reservoir group geologic model construction method based on multi-means technology fusion. The method comprises the steps that a preliminary model is obtained through electromagnetic and earthquake independent inversion; performing primary joint inversion based on cross gradient constraint, and constructing a macroscopically consistent model pair; constructing a multi-physics field database through drilling sampling; and finally, by using drilling data as strong prior information, constructing a well-seismic-electric constraint term containing a rock physical statistical mapping relation and a formation interface geometric trend, carrying out constrained secondary inversion, and outputting a final three-dimensional geologic model. According to the technical scheme, through combination of two-stage inversion and strong geometric constraint, the problem of multiplicity of solutions of geophysical inversion is solved, one-dimensional well hole actual measurement data is expanded into three-dimensional space constraint by using a strong constraint term, the reliability of a geologic model in a well-free area is improved, and the recognition capability of a hydrogen storage bank group stratum boundary is enhanced.

This invention relates to the field of geophysical inversion technology and provides a high-precision joint gravity and magnetic property vector inversion method based on data transformation. The method includes the following steps: Step S1, dividing the subsurface space into N block units in spherical coordinates; Step S2, establishing a kernel function matrix A; Step S3, establishing a regularized inversion objective function; Step S4, constructing Gram constraint terms; Step S5, establishing the objective function for Gram joint property vector inversion; Step S6, iteratively calculating the density and magnetic vector results using the objective function. This invention enhances the correlation between different parameters of the Gram constraint based on data transformation, and utilizes the strengthened constraint relationship for joint gravity and magnetic property vector inversion, thus improving the resolution of the inversion results.

The application discloses a slice quantitative analysis method for positioning error of a target body in geophysical inversion results, and comprises the following steps: a parameter model is established and sampling initialization is performed; position parameters of the target body and model parameters are jointly sampled by combining with geological prior information. An error evaluation mechanism is introduced in the sampling process to guide the sampling results to concentrate in the area with smaller error. The collected multiple position samples are used to construct a three-dimensional uncertainty boundary structure, and a series of error cross-section images are generated by performing geometric slicing in multiple profile directions. Further, the images are weighted and fused according to the importance of different profiles, and finally a three-dimensional positioning error heat map is generated to reflect the uncertainty spatial distribution of the underground target position. The application can express the spatial deviation degree in the inversion results in an intuitive form, and provides a quantitative basis for geological interpretation and resource assessment.

The invention provides a cross-band rock physical parameter prediction method under a pressure condition. According to the method, unified and accurate prediction of key parameters such as longitudinal wave velocity, transverse wave velocity, Young modulus, Poisson's ratio and the like under a full-band and multi-pressure condition from a static state, namely a low frequency, to a dynamic state, namely a high frequency is realized by utilizing strong nonlinear mapping and feature correlation capturing capability of a deep neural network and a Transform architecture. The method effectively overcomes the limitation of traditional experiments and models, and provides complete and reliable rock physical parameter support for geophysical inversion and engineering design.

The present application is suitable for the field of geophysical inversion technology, and provides a non-structure triangular grid gravity inversion method based on equivalent coplanar-function fitting, comprising the following steps: step S1, obtaining the Bouguer gravity anomaly; step S2, carrying out tetrahedron dissection on the underground space according to the terrain data; step S3, calculating the kernel function according to the equivalent principle; step S4, carrying out iterative inversion according to the function fitting method to obtain the density distribution of the underground space. The present application accelerates the kernel function calculation and iterative calculation process by combining the equivalent principle and the function fitting method, effectively improves the calculation efficiency, saves the calculation time, effectively solves the high-efficiency calculation problem of gravity inversion under the non-structure grid dissection of large data in the undulating terrain area, and is suitable for mineral exploration with high resolution in the undulating terrain area and scientific research on large-area deep complex structure.

The invention belongs to the field of geophysical inversion, and particularly discloses a shear wave velocity two-dimensional profile probability inversion method based on multi-source data fusion, and the method comprises the steps: introducing transverse constraint and drilling information, and achieving the joint probability inversion of a multi-measurement-point shear wave velocity profile; under a Bayesian framework, uniformly regarding multi-measurement-point frequency dispersion curve data, transverse constraint and drilling data as observation data items, establishing posterior probability distribution of multi-measurement-point shear wave velocity profile model parameters under three types of data constraints, and introducing various types of observation information step by step by adopting a sequential Bayesian updating strategy; and finally solving the Bayesian equation of each stage to obtain the posterior distribution of the shear wave velocity profile of the multiple measuring points. The method of the invention can effectively quantify the identification uncertainty of the shear wave velocity profile of multiple measuring points, enhance the continuity and physical consistency of adjacent profiles in the transverse direction, and improve the identifiability of the shear wave velocity profile.

The application discloses a geophysical exploration inversion method based on ensemble learning and belongs to the technical field of engineering geophysical exploration inversion. In order to solve the problems that multi-source geophysical exploration inversion is strong in non-uniqueness, is dependent on experience for interpretation and is difficult to be uniformly constrained with in-situ drilling test, the application fuses features through a deep neural network, carries out posterior sampling after condition regularization flow and applies interface monotone order and parameter boundedness and drilling anchoring constraint, combines neural operator proxy forward and consistent screening iteration, and outputs interfaces, parameters and uncertainty by means of multi-member adaptive weighted ensemble, so that the technical effects of stable inversion and multi-solution credible screening are realized.