20 results about "Nonlinear inversion" patented technology

The present application relates to a kind of crack morphology parameter's multiple initial value subinterval nonlinear inversion method, it is applied to petroleum technical field, the multiple initial value subinterval nonlinear inversion method for explaining crack morphology parameter based on crack front fiber strain includes: based on three-dimensional displacement discontinuity method, crack front fiber strain calculation model is established;According to crack parameter and the crack front fiber strain calculation model, predicted strain is calculated;According to the strain residual of measured fiber strain and the predicted strain, nonlinear least square inversion model with parameter boundary constraint is established;The crack parameter includes crack half crack length, crack height, crack net pressure in crack, crack vertical offset;Under coarse grid division, multiple initial values are set in parameter feasible region and are carried out parallel optimization, and first candidate solution is obtained;Again with the crack height of the first candidate solution as center, crack height feasible region is recursively divided into multiple subintervals.

The application provides a method and device for determining a comprehensive friction coefficient of a friction torque, and a method for early warning, wherein the method for determining comprises the following steps: acquiring a basic data set corresponding to each position in the well depth direction of a current well; segmenting the current well along the well depth direction based on the basic data set corresponding to each position, and determining a basic data set corresponding to each segment in the current well; determining a hyperparameter corresponding to each segment according to the basic data set corresponding to each segment in the current well; setting a multi-parameter nonlinear inversion algorithm corresponding to each segment according to the hyperparameter corresponding to each segment; and calculating a predicted value of the comprehensive friction coefficient corresponding to each segment by using the multi-parameter nonlinear inversion algorithm corresponding to each segment according to the basic data set corresponding to each segment in the current well. The application can improve the calculation accuracy of the comprehensive friction coefficient of the friction torque, and the friction torque can be further calculated by using the predicted value of the comprehensive friction coefficient, thereby improving the calculation accuracy of the friction torque.

The invention discloses a Young impedance and longitudinal and transverse wave velocity ratio nonlinear direct seismic inversion method based on an accurate YIRD-Zoeppritz equation. The Young impedance and longitudinal and transverse wave velocity ratio nonlinear direct seismic inversion method comprises the steps that the YIRD-Zoeppritz equation is obtained, and the Young impedance YI, the longitudinal and transverse wave velocity ratio PSR and the rock density are inversed through an ADA-MCMC-LpSC nonlinear inversion algorithm providing sparse prior information based on the Lp norm. Based on an accurate YIRD-Zoeppritz equation, the accurate prediction method for the Young impedance and the longitudinal and transverse wave velocity ratio under the ADA-MCMC-LpSC nonlinear optimization inversion algorithm is achieved, and the key geophysical problem that the accurate Zoeppritz equation cannot be directly used for predicting the Young impedance and the longitudinal and transverse wave velocity ratio parameters at the same time at present is solved.

The invention discloses an anisotropy parameter prediction method based on a coal rock anisotropy rock physical model. The method comprises the following steps: S1, constructing an initial anisotropy rock physical model for a target coal rock; s2, inputting the current hard pore aspect ratio and soft pore aspect ratio into the anisotropic rock physical model to obtain a predicted longitudinal wave speed; s3, acquiring an actually measured longitudinal wave speed, and calculating an error value between the actually measured longitudinal wave speed and the predicted longitudinal wave speed; s4, judging whether the error value is smaller than a preset threshold value or not; s5, if not, adjusting the aspect ratio of the hard pores and the aspect ratio of the soft pores, and returning to the step S2; and S6, if yes, based on the parameter state of the current anisotropic rock physical model, outputting anisotropic parameters of the target coal rock. According to the method, a variable pore aspect ratio method is introduced, hard pores and soft pores are taken as key control variables in inversion, actually measured longitudinal wave speed is taken as a constraint, and a nonlinear inversion technology is combined to realize accurate prediction of coal rock anisotropy parameters.

A multi-voltage phase recovery device and method based on a photorefractive effect are characterized in that the photorefractive effect is utilized to replace a non-linear effect under a strong light condition, diffraction images after an optical system under different non-linear effect degrees are collected through a photoelectric detector, a reconstruction algorithm based on non-linear inversion is used, and a multi-voltage phase recovery model is obtained. Reconstructing amplitude and phase information of the sample to be measured; compared with a traditional linear multi-plane phase recovery method, the method has the advantages that the multiple diffraction images can be obtained only by changing the voltage without mechanical displacement, the axial mechanical displacement error and the transverse alignment error of the multiple images can be avoided, and therefore the precision is higher, and the convergence speed is higher.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

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 invention discloses a prefabricated segment beam damage identification method based on acoustic emission and deep learning. Multi-channel acoustic emission sensors are arranged in the midspan of a prefabricated segment beam and in a segment joint area to collect acoustic emission signals generated in the normal operation traffic load and environmental action process of a bridge; acoustic signal first arrival time is extracted by adopting AR-AIC and a generalized cross-correlation method, a sound source space coordinate is determined by utilizing nonlinear inversion, and crack position identification is realized; an acoustic emission one-dimensional signal is converted into a two-dimensional image through GAF, MTF or time-frequency transformation, the two-dimensional image is input into a depth residual network, and classification of damage types such as crack stretching, shear slippage, segment joint friction and prestress loss is achieved; and meanwhile, a damage three-dimensional spatio-temporal evolution model is constructed based on a sound source localization result, and damage stage division and instability early warning are realized in combination with event energy accumulation and density. The method is suitable for long-term online monitoring and early warning of in-service bridges, and provides technical support for health monitoring and early warning of prefabricated assembly type segmental beam structures.

The invention relates to the technical field of geographic exploration, and discloses a stratified overlying strata time-varying three-dimensional wave velocity dynamic reconstruction model construction method, which comprises the following steps of: firstly, establishing and solving a double-target constraint optimization model by utilizing geological drilling data to obtain an initial stratum wave velocity model and an optimal layered interface structure; secondly, performing nonlinear inversion correction on the initial model by utilizing active source event arrival time data to obtain a corrected stratum wave velocity model; and then establishing an adaptive grid by taking the optimal layered interface structure as a constraint, performing tomography by utilizing passive source event arrival time data to obtain a reconstructed wave velocity model, performing reliability evaluation on the model to generate a reliability distribution diagram, and fusing the reconstructed model and the corrected model according to the reliability distribution diagram to generate a time-varying three-dimensional wave velocity model. According to the method, multi-source data model fusion is guided through reliability evaluation, the constructed model gives consideration to geologic structure fidelity, dynamic timeliness and stability, and the micro-seismic positioning precision is effectively improved.

The invention provides a hydropower station underground cavern group crustal stress field nonlinear inversion analysis method, which comprises the following steps: S1, based on regional geologic structure conditions and actually measured crustal stress analysis, analyzing the geologic structure conditions of an engineering area where a hydropower station plant area is located, and carrying out field crustal stress measurement on a plurality of measurement points in the plant area, the local geological structure condition of the underground cavern group area is obtained; s2, performing crustal stress field nonlinear inversion on the hydropower station plant area based on an artificial neural network, and establishing a three-dimensional numerical model to obtain crustal stress distribution characteristics of the hydropower station plant area; s3, ground stress field characteristic analysis of the cavern area; and S4, performing field verification analysis on the ground stress of the cavern group. Based on the artificial neural network theory and in combination with the crustal stress field inversion method, the nonlinear inversion method of the crustal stress field is provided, the distribution characteristics of the crustal stress field in the underground multi-cavern group environment of the hydropower station can be calculated and analyzed more accurately, and the accuracy of crustal stress field inversion is greatly improved.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

This invention discloses a method and related equipment for detecting geological structures during ocean drilling. The method includes: acquiring preliminary data of a candidate area and collecting seabed background noise in the candidate area using a concentric ring array to obtain raw noise data; preprocessing and segmenting the raw noise data to obtain segmented noise data; performing azimuth-weighted averaging and Bessel function fitting on the segmented noise data based on the principal noise direction of the concentric ring array to obtain a target dispersion curve; using the target dispersion curve as a constraint, performing nonlinear inversion using a genetic algorithm to obtain a shear wave velocity structure model; jointly verifying the shear wave velocity structure model with the preliminary data, and selecting drilling sites based on the joint verification results. This invention provides independent, quantitative, and traceable geophysical evidence for pre-drilling site selection, effectively reducing drilling risks, and can be widely applied in the field of seismic structure detection technology.

The invention discloses a hydraulic fracture geometric parameter nonlinear inversion method, which comprises the following steps: establishing a strain analysis model according to preset mechanical parameters; according to the established strain analysis model and axial strain observation data acquired by a low-frequency DAS system in an adjacent well monitoring scene, establishing a crack parameter nonlinear inversion model; solving global optimal inversion by adopting a nonlinear least square iteration algorithm according to a preset constraint condition and the fracture parameter nonlinear inversion model, and performing inversion to obtain fracture height, fracture height asymmetric offset and net pressure; and calculating the maximum seam width according to the obtained seam height and net pressure.

The application belongs to the technical field of geophysical exploration, and particularly relates to a method for detecting sea ice thickness based on a Cole-Cole model, which comprises the following steps: a central loop transient electromagnetic method exploration device is used; the device is placed on the sea water and the sea ice surface to observe magnetic field data; a Cole-Cole model is introduced on the basis of a pure resistivity model transient electromagnetic method theory to obtain a time-domain transient electromagnetic response of complex resistivity; a theoretical transient electromagnetic response is calculated according to a time-domain transient electromagnetic response of complex resistivity; a measured transient electromagnetic response is obtained; a nonlinear inversion method is used to perform a transient electromagnetic response inversion containing a polarization effect; Cole-Cole model parameters m 水 、τ 水 、c 水 、ρ 水 of sea water are extracted; and the ice layer thickness and the Cole-Cole model parameters of the ice layer are obtained, so that the method is suitable for the exploration of polar sea ice thickness, and the ice thickness is detected by the dielectric difference between ice and water to distinguish the sea ice medium and the sea water medium with similar resistivities, and the method solves the problem of the existing polar ice thickness detection method.

The application discloses a hydraulic fracture geometric parameter nonlinear inversion method, which comprises the following steps: a strain analytical model is established according to preset mechanical parameters; a fracture parameter nonlinear inversion model is established according to the established strain analytical model and axial strain observation data obtained by a low-frequency DAS system under a neighboring well monitoring scene; a global optimal inversion is solved by using a nonlinear least square iteration algorithm according to preset constraint conditions and the fracture parameter nonlinear inversion model, and a fracture height, a fracture height asymmetric offset and a net pressure are obtained by inversion; and a maximum fracture width is calculated according to the obtained fracture height and net pressure.

The application discloses a shield tunneling noise source surface wave velocity intelligent inversion method and system, comprising: taking each modal dispersion curve as input and corresponding wave velocity curve as output, constructing an inversion network unit corresponding to each modal dispersion curve, weighting and connecting the inversion network units in parallel through setting a weighted mask of each modal dispersion curve to obtain a multi-modal dispersion curve weighted inversion network; after discarding random dispersion points of the input multi-modal dispersion curve, training the multi-modal dispersion curve weighted inversion network; acquiring tunneling noise source surface wave data corresponding to measuring points in a detection area, extracting a multi-modal dispersion curve, and predicting wave velocity distribution of the multi-modal dispersion curve by using the trained multi-modal dispersion curve weighted inversion network. High-robustness dispersion curve nonlinear inversion is realized.

The invention discloses a natural gas hydrate reservoir classification method based on imaging logging and a rock physical model, and the method comprises the steps: determining a hydrate microscopic occurrence type through rock core, well drilling and CT scanning data, determining stratum parameters in combination with logging and rock core data, and fitting Archie formula parameters to calculate the porosity; using an equivalent medium model to simulate the hydrate formation velocity, and establishing a longitudinal wave velocity-saturation rock physical chart by forward modeling data to determine the hydrate occurrence type; a corresponding equivalent medium model is selected, and reservoir saturation is calculated through simulated annealing nonlinear inversion; and finally, segmenting an imaging logging gray level image by using a K mean value in combination with a morphological algorithm, extracting hydrate and contour parameters, objectively weighting the parameters through an entropy weight method to construct a comprehensive evaluation index, and completing reservoir classification according to the size of the index. The method integrates multi-source data, optimizes the model and the inversion process, improves the classification accuracy, and provides reliable technical support for exploration and development of natural gas hydrate resources.

This invention provides a method and apparatus for crop nutrient detection in a semi-open environment under all weather conditions, relating to the fields of agricultural remote sensing and crop physiological detection technology. The method includes: acquiring ambient light intensity and selecting an acquisition mode; substituting the parameters acquired under the current acquisition mode into a pre-constructed dual-modal adaptive correction model to obtain unified feature parameters; and calculating the chlorophyll content of the target crop using a piecewise nonlinear inversion model. The method and apparatus provided by this invention significantly improve the accuracy and reliability of crop nutrient status inversion across all weather conditions and concentration ranges by substituting parameters acquired under different acquisition modes into a dual-modal adaptive correction model to obtain unified feature parameters and combining them with a piecewise nonlinear inversion model to calculate the chlorophyll content of the target crop.

The present application relates to heavy metal content inversion technical field, specifically relates to a kind of soil heavy metal content inversion modeling method and system based on hyperspectral.The spectral reflectance and elevation information are extracted under the unified grid unit by fusing hyperspectral image and digital elevation model;The source emission intensity index is constructed based on the spectral feature determination vegetation cover coefficient, clay adsorption coefficient and mineral abundance coefficient, and combined with topographic height index;Further, the migration resistance coefficient is calculated using topographic slope, vegetation and clay parameters, and the source emission intensity index and migration resistance coefficient are superimposed and attenuated according to the hydrological flow direction along the topological sequence, to generate the deposition accumulation index representing the secondary deposition accumulation trend of heavy metals;Finally, the dynamic accumulation index and static surface medium parameters are input into the regression algorithm to construct a nonlinear inversion model.The method effectively couples the geological background and hydrological migration process, and improves the accuracy of heavy metal inversion in complex terrain area.

The invention relates to the technical field of geophysical exploration, and discloses a seismic data high-resolution processing method and device under reflection coefficient position constraint, equipment and a medium, which can perform sparse inversion on seismic data of an underground rock stratum to obtain initial high-resolution seismic data. And inputting the initial high-resolution seismic data into the trained reflection coefficient position prediction network for reflection coefficient position prediction to obtain a predicted reflection coefficient position. Based on the reflection coefficient position, sparse inversion under position constraint is carried out on the seismic data to obtain final high-resolution seismic data; wherein the resolution of the final high-resolution seismic data is higher than that of the initial high-resolution seismic data. According to the position constraint reflection coefficient amplitude inversion method, more accurate sparse prior knowledge is provided for inversion by fusing position information, so that complex nonlinear inversion is close to simple linear inversion, the inversion process is easier to converge, the inversion accuracy is higher, and the seismic data resolution is effectively improved.