6987results about "Water resource assessment" patented technology

The invention provides an adaptive multi-modal feature fusion well logging interpretation method based on multi-task joint learning, and the model comprehensively employs a graph convolution network, a multi-head attention mechanism, a gating mechanism and an adaptive loss weight adjustment algorithm, and effectively integrates multi-well group well logging data and geological structure information. And the complex spatial relationship and geological similarity between the wells are fully captured. The model can process multi-source data in a unified manner, extracts high-level feature representation containing spatio-temporal correlation, avoids repeated feature learning, reduces calculation complexity, and ensures the consistency and complementarity of prediction task features at the same time. The self-adaptive loss weight adjustment mechanism dynamically optimizes weight distribution according to the loss change of each task, realizes balance optimization among different tasks, and comprehensively improves the prediction precision of each task. The physical constraint regularization module ensures that the prediction result not only has statistical accuracy, but also conforms to the actual geological physical law, so that the reasonability and reliability of the prediction result are improved.

The invention relates to the technical field of energy efficiency management, in particular to a water conservancy sensing system based on edge computing. The system comprises the following modules: a riverway edge sensing module, a water conservancy characteristic analysis module, a sediment migration simulation module and a deposition scouring early warning module. Constructing a river channel water conservancy sensing network; performing real-time water conservancy monitoring data acquisition according to the river channel water conservancy sensing network to obtain river channel water conservancy sensing data; performing three-dimensional flow field model processing on the river channel water conservancy sensing data to obtain a three-dimensional sediment flow field network model; carrying out particle flow field simulation on the three-dimensional sediment flow field network model to generate sediment migration space-time distribution data; and according to the sediment migration space-time distribution data, carrying out siltation and scour area identification, and uploading the siltation and scour area data to a cloud platform through an edge computing node to carry out abnormal event alarm so as to obtain river section monitoring data. According to the invention, real-time, accurate and intelligent monitoring and early warning of the sediment deposition and scouring process of the river channel are realized.

The invention discloses a sonar image underwater detection method and system based on multi-scale feature fusion and a college up-sampling algorithm, and the method comprises the steps: collecting the foresight sonar data of an underwater typical target, and making a detection training and testing data set of a sonar image; the method comprises the following steps: constructing a feature fusion network comprising an efficient upsampling module, a multi-scale feature fusion module and a small target detection branch, constructing a target detection algorithm model based on YOLOv11, taking the feature fusion network as a neck network of YOLOv11, and removing a deep feature map detection branch of YOLOv11; and training a target detection algorithm model by using the manufactured data set, and inputting a foresight sonar image into the trained model for reasoning to obtain a detection result of the underwater typical target. According to the invention, the accuracy and robustness of an underwater target detection algorithm are improved.

The invention relates to the technical field of grouting simulation tests, in particular to a sandy gravel stratum catastrophe grouting prevention and control virtual-real blending collaborative test method and system, and the method comprises the steps: carrying out a grouting test on a sandy gravel stratum simulation model, and obtaining grouting parameters and monitoring parameters in the grouting process; based on the resistivity and the radar data, further obtaining a slurry diffusion path and a diffusion range of the area with the higher surrounding rock grade; based on the resistivity and the optical fiber sensing data, the slurry diffusion path and the diffusion range of the area with the low surrounding rock grade are further obtained; inputting the initial grouting parameters into the trained neural network model to obtain simulation data; carrying out preprocessing and alignment operation on the previous data, and carrying out fusion analysis; and according to the fusion analysis result, grouting equipment parameters and a grouting scheme are pre-controlled and adjusted. And real-time regulation and control of a grouting scheme and equipment are realized.

The invention discloses a virtual water gauge water level identification method based on a multi-mask matching segmentation network. The method comprises the steps that S1, a real-time image of a target water body area is acquired through a preset position camera; S2, a virtual water gauge geometric model is constructed; s3, extracting a region with high confidence as a candidate region of the segmentation model; s4, designing a new training data structure optimization method to help the model better understand and utilize information of different levels; s5, respectively carrying out maximum matching on the rows and the columns, finding out an optimized matching path with the minimum total cost, and obtaining a finer segmentation result; s6, identifying a water body boundary in the high-precision water body segmentation map through a Canny edge detection algorithm, and extracting a waterline position; and S7, calculating an actual water level value, and for the water body with dynamic movement, smoothing the water level reading by adopting an interval time sampling moving average method. The defects of a traditional physical water gauge and an existing image processing technology are overcome, and an innovative solution is provided for water level monitoring.

The invention provides a manifold constraint-based sonar imaging reverberation elimination method and system, and is applied to the field of imaging data processing. According to the method, the time domain waveform of the echo signal is collected in real time, whether the irregular reflection peak value is detected or not is judged, potential interference and reflection in the underwater environment can be rapidly recognized, and particularly, in the complex underwater environment, non-target objects (such as seabed obstacles or sundries) possibly cause irregular reflection signals, so that the interference and reflection in the underwater environment can be rapidly recognized. Meanwhile, the underwater propagation characteristics of sound waves can be accurately modeled by collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal. And accurate background data can be provided for the propagation modeling of the reverberation signal through the pre-recorded information of submarine topography, geological characteristics, sound wave propagation characteristics and the like.

The invention relates to an underwater environment three-dimensional reconstruction method. The method comprises the following steps: projecting a multi-band structured light pattern in an underwater environment, and capturing an image by using a multispectral camera; fusing images of different wavebands through a spectrum matching algorithm, and extracting depth information to generate three-dimensional reconstruction basic data; a depth error caused by underwater scattering and absorption is corrected by using a depth compensation technology; generating an environment characteristic model by using real-time monitoring environment parameters including illumination, turbidity and temperature of an underwater sensor; adaptively adjusting image acquisition and processing parameters including exposure, contrast and color balance according to the environment characteristic model; detecting a distorted area caused by environment change in the image through an intelligent correction algorithm, and executing local correction and overall optimization; generating a multi-scale image pyramid, and decomposing the structure information of the three-dimensional image layer by layer from coarse to fine; and through an adaptive detail enhancement technology, carrying out refinement processing on the key area to generate an underwater three-dimensional image.

In the present invention, a location joint monitoring solution based on the state of a distributed optical fiber and based on an ultra-short baseline is used, and the solution consists of a distributed optical fiber monitoring device, an ultra-short baseline positioning device and a health monitoring host computer. The distributed optical fiber monitoring device consists of a distributed optical fiber cable (300), a distributed optical fiber demodulator (421), etc. The ultra-short baseline positioning device consists of an ultra-short baseline array (200), an underwater beacon (100), a signal processing device (411), etc. The health monitoring host computer (431) can perform signal conditioning, digital signal processing and parameter estimation by means of the signal processing device and the distributed optical fiber demodulator and by using acquired data such as key point locations, deformation and postures, and complete space curve reconstruction of an underwater riser by means of a space curve reconstruction algorithm and an existing finite element analysis software platform, thereby realizing visual outputs of an underwater state of a marine riser.

The invention relates to the technical field of water level surveying devices, and discloses a hydrogeological multi-layer underground water level surveying device and a surveying method.The hydrogeological multi-layer underground water level surveying device comprises a support and a water sampling barrel, the support is provided with a winding mechanism, and the winding mechanism is used for adjusting the sampling height of the water sampling barrel; the upper part of one side of the water sample sampling barrel is communicated with a liquid inlet pipe, and the bottom of the water sample sampling barrel is fixedly connected with a balancing weight. In the sampling process, large-particle impurities in water are blocked through the filter screen, the impact force of water flow can be converted into the rotating force of the blades, the rotating shaft and the scraping rod, the function of automatically cleaning the impurities in the sampling process is achieved, and the rope winding condition caused by uncontrollable winding position is prevented through the adjusting mechanism; the dustproof mechanism prevents dust and stones from falling into the hydrological hole to affect sampling of a water sample in the surveying process, and the highest water level in the hydrological hole can be accurately known through the water level monitoring mechanism.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention discloses a water surface target detection method based on a visual language large model, which mainly solves the problem of inaccurate water surface target detection in the prior art, and the implementation scheme comprises the following steps: constructing a water surface target detection data set; extracting multi-scale features of the image by using the backbone network; constructing a dynamic feature aggregation network to fuse the multi-scale features of the image; extracting language features of the target category text; constructing a cross-modal cross attention fusion network, and fusing the visual dynamic aggregation features and the language features; utilizing a dynamic target detection head to obtain a prediction target category and a target frame; forming a water surface target detection model based on visual language multi-modal fusion by using the backbone network, the dynamic feature aggregation network, the text encoder, the cross-modal cross attention fusion network and the dynamic target detection head, and training the water surface target detection model; and obtaining a water surface target detection result by using the trained detection model. The method can effectively detect the water surface target, is high in accuracy and robustness, and can be used for intelligent ship driving.

A transient electromagnetic scanning detection system and method. An all-terrain vehicle carrying an instrument tows a transmitting coil and a receiving coil having an offset distance of 6-8 meters therebetween, wherein the transmitting coil arbitrarily rotates within an XZ plane, the receiving coil moves in an X direction and a Z direction, and GPSs are carried on the coils. During working, the vehicle stays for a short time every few meters, the transmitting coil performs rotary scanning until 180 degrees, and the receiving coil completes scanning detection of one point; and the process is repeated when the vehicle travels to the next detection point. The inversion is implemented by means of a deep learning model, a large number of resistivity models are randomly generated, and on the basis of an observation system, numerical simulation is performed on an induced electromotive force corresponding to each resistivity model. On the basis of induced electromotive force data and the resistivity models, a deep learning framework-based TEM prediction model is established, wherein the induced electromotive force data is used as an input, and a two-dimensional resistivity structure below the coils is used as an output. The vehicle collects TEM data and imports same into a deep learning network to obtain an underground resistivity structure below the coils in real time to perform transient electromagnetic scanning detection.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention relates to the technical field of coal mine water disaster prevention and control, and particularly discloses a hidden structure detection system for mine water disaster intelligent monitoring, early warning and prevention, which is used for solving the problems of low mine hidden structure detection precision, mine water disaster early warning lag and large seismic wave propagation path simulation error. Comprising a multi-source data comprehensive detection module, a real-time imaging module, a micro-seismic multi-scale intelligent analysis module, a seismic wave signal identification module, a regional difference analysis module and an intelligent data processing module. According to the invention, through fusion of multi-source data, real-time imaging, micro-seismic analysis, seismic wave identification and intelligent data processing technologies, precise detection of a mine hidden structure, high-precision prediction of mine earthquakes and water disasters and real-time risk early warning are realized, and timeliness and accuracy of mine water disaster prevention and control are improved.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention discloses a seismic data constrained direct current method three-dimensional inversion method, which relates to the field of geophysical exploration and comprises the steps of processing seismic data and performing inversion processing on resistivity data. The seismic wave layer velocity information is used for constraining high-density electrical method inversion, so that a supplement effect to a certain degree can be achieved, three-dimensional seismic image information can be used for supplementing the resolution ratio of resistivity inversion, the boundary of an underground electrical anomalous body is reflected, and meanwhile, through the comprehensive geological-geophysical information, the electrical anomalous body can be accurately determined. And boundary features of the anomalous body can be further depicted. Seismic data constraints are applied to three-dimensional resistivity inversion, numerical simulation and inversion processing are performed on electrical method data under rugged topography according to finite volume method numerical simulation and a Gaussian-Newton inversion method, and meanwhile, seismic anisotropic velocity tensor constraints are introduced to perform inversion, so that the problem of multiplicity of solutions of inversion of a single method is reduced, and the inversion accuracy is improved. A more accurate three-dimensional underground electrical structure is obtained, and the resolution of inversion to a longitudinal boundary is improved.

The invention provides a dynamic monitoring method for a soil mass seepage field of a power transmission tower slope. According to the method, related data of the slope are acquired through multi-source data acquisition and technologies of pressure sensing and static pressure measurement, displacement monitoring, multi-sensor fusion, Beidou / GPS positioning, resistivity method exploration, soil moisture content monitoring and the like; fusing the collected multi-source data, analyzing the dynamic change of the seepage field by using a risk assessment model and a machine learning algorithm, and predicting the evolution trend of the seepage field; a lightweight model is deployed at a monitoring terminal to realize local rapid processing of data, alarm information is immediately sent out once an abnormal state is monitored, and scientific and reasonable decision support is provided for protection and treatment of a transmission tower slope according to early warning information in combination with a pre-made decision scheme; according to the invention, the accuracy and timeliness of transmission tower slope soil seepage field monitoring can be effectively improved, and the threat of landslide and other disasters to a transmission line is reduced.

The invention relates to the technical field of intelligent identification, and discloses an intelligent identification method for a geological stress concentration area. The method comprises the steps that a plurality of vector electromagnetic induction coils are arranged in a monitoring area in a latticed mode, and original electromagnetic pulse signals containing components in the X direction, the Y direction and the Z direction are collected; performing denoising and time-frequency analysis on the original electromagnetic pulse signal to obtain a multi-dimensional data cube; processing the multi-dimensional data cube by using an integer linear programming model and an adaptive fuzzy correction algorithm to obtain a corrected electromagnetic pulse signal data set; performing decomposition and mapping relation construction on the corrected electromagnetic pulse signal data set to obtain a geological stress distribution estimation value; and constructing a geological stress concentration area three-dimensional model according to the geological stress distribution estimation value. According to the method, the stress field boundary identification precision is enhanced, and the blind area is reduced, so that high-precision stress field reconstruction under complex geological conditions is realized.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention discloses a deep water-rich weak-impact coal seam surrounding rock control method based on rapid tunneling, and relates to the technical field of surrounding rock control. The method comprises the steps that S1, a high-risk area is identified through address detection, a waterproof curtain is constructed, and the tunneling disturbance risk is reduced; s2, rapid tunneling and instant supporting are conducted, the surrounding rock exposure time is shortened, tunneling and supporting are completed synchronously, and deformation development is restrained; s3, sensing a surrounding rock-support response in real time, optimizing parameters and early warning risks, and realizing dynamic monitoring and feedback regulation and control; s4, later-stage continuous reinforcement maintenance is conducted, so that the roadway adapts to the rheological characteristics of deep surrounding rock, and the service cycle of the roadway is prolonged; according to the method, a high-risk area is identified through various detection technologies, and instant supporting is achieved through cutting process optimization and digging and supporting collaborative operation; real-time monitoring and dynamic feedback are achieved, and automatic optimization of supporting parameters is achieved; and later reinforcement and periodic maintenance are adopted to ensure that the surrounding rock supporting system can adapt to the rheological characteristics of deep surrounding rock for a long time.

The invention discloses an integrated measurement method and system for hydrological elements in a high-salinity and high-sand environment, and relates to the technical field of hydrological monitoring, and the method comprises the steps: correcting the theoretical signal receiving strength of each sonar through an established sound attenuation model, and then calculating an effective signal-to-noise ratio, thereby achieving the measurement of the underwater topography in the high-salinity and high-sand environment. According to the method, a dynamic weight fusion algorithm is adopted to perform data fusion on the water depth data of each sonar to obtain fused water depth data, and an inversion method based on residual error minimization is adopted to perform iterative optimization on model parameters, so that the accuracy is relatively high. When the water velocity and the flow rate are measured in the high-salt and high-sand environment, iterative decoupling is carried out based on the sediment particle motion comprehensive model to obtain the sediment particle velocity, then the water velocity is updated based on the measured apparent velocity and the difference value, and after the updated water velocity is converged, the flow rate is calculated. And the accurate water flow rate can be calculated by combining the flow cross section area. The measurement precision of the unmanned ship on the hydrological elements in the saline-alkali area can be improved.

The invention relates to the technical field of pipeline detection, in particular to an optimization method for improving cathode protection detection of a complex buried pipe network, which comprises the following steps of: extracting characteristic information in a pipeline area based on potential value and resistivity data in the pipeline area, performing aggregation analysis on corrosion risk through soil characteristic data, and calculating the corrosion risk of the pipeline. And determining the risk membership degree of the pipeline region, and generating a corrosion risk distribution diagram in the region. According to the invention, through calculation of the potential field around the pipeline and the dynamic change of the electric field, accurate identification of the corrosion risk area and optimization of the protection effect are realized. By establishing a three-dimensional space grid and an electric field model, the change trend of an electric field at different time nodes is captured, and formation and evolution of electric field hotspots can be tracked in space and time dimensions. And the response speed to the abnormal change of the local electric field is improved, so that the corrosion problem caused by overhigh local current density is effectively avoided.

The invention discloses an adaptive environment weighted data fusion navigation and positioning method for a manned submersible in a polar region, which belongs to the technical field of ocean navigation and positioning, is used for navigation and positioning of the submersible in the polar region, and comprises the following steps: acquiring data of IMU (Inertial Measurement Unit), acoustics, optics, environment sensors and Doppler sensors, and carrying out de-noising, time synchronization and coordinate conversion; modeling the polar region environment variable, calculating the adaptive weight, and dynamically adjusting the contribution degree of the sensor by adopting an exponential decay function; introducing an environment factor to correct the weight, and performing nonlinear state estimation by adopting an EKF (Extended Kalman Filter); performing error modeling by using historical data, and optimizing a weight adjustment strategy in combination with machine learning; and filtering optimization is performed on the fusion result, and multi-scale correction is performed in combination with prior environment information. Compared with the prior art, the influence of a single sensor error on a final positioning result is reduced, the positioning precision of the submersible in a polar region complex environment is improved, and the stable navigation capability is kept under the condition that sensor data suddenly changes or is lost.

The invention relates to an image processing method for an underwater robot heterogeneous imager array, and the method comprises the steps: S1, collecting an original underwater image, and carrying out the brightness adjustment, and obtaining a first image with balanced brightness; s2, analyzing pixel distribution of the first image, and judging and executing de-scattering processing to obtain a second image; s3, performing multi-scale detail enhancement on the second image to obtain a third image with richer details; s4, evaluating color distribution of the third image, and performing color correction to obtain a fourth image; s5, performing adaptive denoising processing on the noise features of the fourth image to obtain a fifth image; s6, according to the contrast characteristic of the fifth image, contrast adjustment is carried out to ensure that a preset threshold value is met, and a sixth image is obtained; s7, edge enhancement processing is carried out on the sixth image, and a seventh image with the clear edge is obtained.By means of illumination compensation, scattering removal and multi-scale detail enhancement, the influence of complex optical conditions of the underwater environment on the image quality is effectively overcome.

The invention discloses an underwater robot submarine cable inspection method and system based on two-dimensional image sonar, and relates to the field of submarine cable inspection. The existing submarine cable inspection method has the problems of insufficient detection precision, lack of autonomy, dynamic obstacle avoidance defect and the like. The method comprises the steps of submarine cable feature recognition model construction, underwater robot sensing system configuration, image processing, path planning, obstacle avoidance path generation and detouring. According to the technical scheme, the underwater robot carries the electromagnetic induction sensor, the two-dimensional image sonar and the ultra-short baseline positioning device, and an image processing technology and a path planning algorithm are combined, so that efficient inspection of the submarine cable is realized; when the robot encounters an obstacle, the robot can generate an obstacle point cloud through image processing in combination with prior obstacle information, and performs obstacle avoidance in combination with a local path planning algorithm, so that safe and smooth proceeding of an inspection task is ensured; the detection precision is effectively improved, automatic and efficient inspection is realized, and a powerful guarantee is provided for long-term stable operation of the submarine cable.

The invention relates to the field of measurement while drilling in drilling operation, in particular to a small-diameter resistivity while drilling measuring device which comprises a drill bit installed at the bottom of a drill rod, a main shaft rotationally arranged in the drill rod, hinged plates symmetrically arranged at the upper end of the drill rod, and an annular cavity formed in the drill rod. Annular frames with U-shaped sections are symmetrically arranged in the annular cavity in a sliding manner, a receiving end and a transmitting end are respectively mounted in the annular frames which correspond to each other up and down, and mounting units for limiting the transmitting end and the receiving end are arranged in the annular frames. The high-efficiency detection of measured objects with different depths and angles in a complex geological environment is realized, the vibration generated in the drilling process of the drill bit is effectively absorbed, the transmitting end and the receiving end are ensured to work in a stable environment, the measurement error caused by the vibration is avoided, and the detection precision and reliability are greatly improved.

The invention relates to an underwater target detection method based on improved YOLOv8, and belongs to the technical field of underwater target detection. According to the method, an OfficientViT module is adopted as a backbone network for improving a YOLOv8 model, and a RepGFPN module is adopted as a check network for improving the model, so that the following steps are executed: obtaining an underwater picture, and inputting the underwater picture into the backbone network for feature extraction to obtain a plurality of feature maps with different scales; transmitting the feature maps of different scales to a check network for feature fusion, and extracting feature information; a mixed local channel attention mechanism is adopted to fuse channel information, spatial information, local channel information and global channel information, and the feature representation capability is enhanced; performing classification and positioning according to the enhanced feature information through a decoupling detection head to obtain an underwater target detection result; and designing an SIOU loss function to train the above process, and using the training for underwater target detection after the training is completed.

The invention discloses an image enhancement method based on multi-modal enhanced grid masking and fast Fourier convolution, and the method comprises the steps: S1, collecting the foresight sonar data of an underwater typical target, and making an initial data set; s2, performing data enhancement in a multi-mode shielding mode on the original foresight sonar image; s3, performing data enhancement of a traditional image transformation mode on the multi-mode shielding frame enhanced image, simulating changes of illumination, noise, visual angles and the like in different environments, and generating diversified training data; s4, constructing an image enhancement network based on fast Fourier convolution, and improving the processing and enhancement capability of the occlusion region by using a global receptive field; and S5, enhancing the image by using the trained model to obtain a clear target occlusion image with rich details. According to the method, through the data enhancement method in combination with the multi-mode shielding mode and the network based on the fast Fourier convolution, the image enhancement quality and efficiency of the severely shielded target of the forward-looking sonar are remarkably improved, and the method has good generalization ability and practical value.

The invention relates to a depth domain time-lapse seismic joint inversion method and device, equipment and a storage medium, and the method comprises the steps: extracting depth domain seismic wavelets according to obtained logging data, and building a depth domain seismic wavelet matrix; based on the depth domain seismic wavelet matrix, well-seismic calibration is carried out on the basic logging data and the monitoring logging data; solving a difference between the corrected basic logging data and the corrected monitoring logging data, determining differential logging data, performing full-band interpolation according to the differential logging data and the corrected basic logging data to construct an initial model, and obtaining a low-frequency model according to the initial model; establishing a time-lapse seismic simultaneous inversion objective function based on the forward operator of the time-lapse seismic simultaneous inversion; and substituting the low-frequency model into the target function for iterative solution, and outputting a time-lapse seismic inversion result meeting an iteration ending condition, thereby overcoming the limitation of the existing time-lapse seismic method while reducing the workload of time-domain time-lapse seismic inversion processing.

The present invention relates to the technical field of geotechnical engineering safety monitoring, and provides a geotechnical engineering safety monitoring method based on muon detection technology. The method comprises: arranging a plurality of muon detection devices around or inside a geological body to be observed to form an array, and measuring in real time the muon observation flux of multi-angle cosmic rays passing through said geological body; converting the muon observation flux into the density length, and obtaining the three-dimensional density length distribution of said geological body; and analyzing the stress, strain, and deformation. In the present invention, muon particles are used to perform geological detection of geotechnical engineering, and the muons are particles having extremely high penetration capability, and can penetrate through the thick layer of the earth, so that detailed information about the interior of the geotechnical engineering is provided. In addition, the present invention has the advantages of being free of vibration and electromagnetic noise interference, and can implement accurate and rapid dynamic monitoring of the geotechnical engineering. The present invention has a wide application prospect in the fields of geological exploration, mineral exploration, underground resource development, and the like.