21 results about "Seismic noise" patented technology

A metal automatic processing machine tool operation state prediction system of the present invention belongs to the technical field of automatic processing, and comprises a data acquisition module used for acquiring multi-scale physical parameters representing the machine tool operation state in real time, the multi-scale physical parameters comprise a structure thermal gradient tensor, a kurtosis change rate of a tool interface signal, a relative fluctuation value of a cooling liquid dielectric constant and an environment low-frequency seismic noise spectrum; the dynamic instability risk coupling module is used for determining a harmonic locking risk index based on the multi-scale physical parameters acquired by the data acquisition module; and the risk level evaluation module is used for determining a current system operation risk level based on the harmonic locking risk index determined by the dynamic instability risk coupling module and generating a normalized risk evaluation index.

The invention discloses a U-Net seismic data denoising method based on multi-scale fusion boundary feature enhancement, and the method specifically comprises the steps: 1, preliminarily constructing a seismic data training data set A which is not subjected to noise enhancement, and carrying out the preprocessing of the preliminarily obtained seismic data training data set A which is not subjected to noise enhancement, obtaining a seismic data training data set B which is finally subjected to noise enhancement; step 2, building a U-Net network framework with enhanced multi-scale fusion boundary features; step 3, performing parameter configuration and optimization setting on the U-Net network with enhanced multi-scale fusion boundary features constructed in the step 2; and step 4, training and optimizing the U-Net network enhanced by multi-scale fusion boundary features. The method can retain more weak signals and detail information while suppressing seismic noise.

The DAS seismic noise suppression method based on the conditional latent diffusion model belongs to the fields of machine learning and seismic data processing. This invention utilizes a latent signal representation module encoder to compress high-dimensional seismic data into a low-dimensional latent space, improving computational efficiency while preserving signal characteristics. In the inference phase, noise components are estimated through a noise prediction network, and clean latent variable estimation and state transitions are iteratively performed according to a deterministic sampling strategy. The decoder then reconstructs the denoised seismic data. In the training phase, self-supervised pre-training of the latent signal representation module, supervised training of the noise prediction network, and joint fine-tuning of all network parameters are performed sequentially, using KL divergence loss, reconstruction loss, and mean square error loss to simultaneously optimize network weights. This invention significantly reduces the computational overhead of the diffusion model while suppressing various complex noises.

The invention belongs to the technical field of seismic signal processing, and provides a seismic noise de-noising processing method and system based on transfer learning, and the method comprises the steps: optimizing an initial de-noising convolutional neural network, and obtaining an optimized de-noising convolutional neural network; inputting target noisy seismic data to be subjected to intelligent denoising into the optimized denoising convolutional neural network for segmentation processing, splicing processing and data edge filling processing in sequence, and predicting corresponding target denoising seismic data; and based on the plurality of denoising effect evaluation indexes, evaluating the denoising effect of the target denoised seismic data from an image angle and a numerical angle, and generating a corresponding evaluation result. Through the intelligent denoising processing method, the data edge can be filled, the overweight splicing illusion can be effectively avoided, and the precision of the intelligent denoising result is finally improved.

The invention discloses a method and system for reconstructing and purifying a noise cross-correlation function of seismic background noise, and the method comprises the following steps: collecting an original noise signal, carrying out the non-uniform downsampling of the original noise signal through employing the sparse characteristic of the noise cross-correlation function of the seismic background noise in an effective frequency band, and combining with a compressed sensing theory, and obtaining a non-uniform downsampling signal; obtaining under-sampling data; reconstructing a full-band noise cross-correlation function from the undersampled data with high precision by adopting a fast iterative shrinkage threshold algorithm to obtain a reconstructed noise cross-correlation function; performing frequency-wave number transformation on the reconstructed noise cross-correlation function, and realizing three-component separation by using an FK filtering mask to obtain a separated FK domain effective signal; and carrying out inverse transformation on the separated FK domain effective signal, and finally outputting a purified noise cross-correlation function.

The invention discloses a seismic data noise identification method and device based on deep learning, and relates to the technical field of seismic data processing, and the method comprises the steps: collecting repeated seismic event records of different seismic magnitudes and different propagation paths in multiple regions in the world, extracting seismic waveform pairs corresponding to the same station, marking multi-type noise labels, and carrying out the recognition of the seismic data noise. Constructing a pure real training data set without synthetic data; building a multi-scale-global collaborative deep learning network, training through a pure real training data set and a multi-target coupling loss function, and iteratively optimizing network parameters by using a gradient descent algorithm until a preset convergence condition is met, so as to obtain a trained seismic noise recognition model; and inputting the standardized seismic data into the trained seismic noise recognition model, and reasoning noise type, noise intensity and noise distribution position information. According to the invention, accurate recognition of multiple types of complex noise can be realized, the fidelity of effective seismic signals is ensured, and the automation and real-time performance of noise recognition are improved.

The present application provides a kind of seismic structure information accurate extraction method, which comprises the following steps: step 1, according to poststack seismic record, generate time difference data dT and channel difference data dA;Step 2, calculate time difference square data dT2, channel difference square data dA2 and time-channel difference square data dTA;Step 3, calculate smooth time difference square data, smooth channel difference square data and smooth time-channel difference square data;Step 4, according to smooth time difference square data, smooth channel difference square data and smooth time-channel difference square data, construct hessian matrix;Step 5, the eigenvalue T1 of hessian matrix, T2 is solved;Step 6, according to eigenvalue, calculate apparent dip angle.The seismic structure information accurate extraction method can better avoid the influence of seismic noise, fault and seismic amplitude variation, obtain accurate and stable seismic dip angle data body, and extract accurate seismic dip angle.

This invention relates to a desert earthquake noise suppression method based on a multi-scale attention interaction network, belonging to the fields of machine learning and seismic image processing. Addressing the problem of low-frequency noise suppression in desert earthquake images, this invention proposes a multi-scale attention interaction network. This network first downsamples the input seismic data, then adaptively learns the complex features of the multi-scale seismic data using dual-branch convolutional layers with different kernel sizes. It then employs multiple attention mechanisms, including permutation attention and coordinate attention, to integrate and interact with multi-scale features across channel, spatial, and coordinate dimensions to obtain continuous seismic signals. This method uses a hybrid loss function of mean squared error and mean cosine similarity, combining the multi-scale strategy with the attention mechanism to improve the denoising effect of desert earthquake images. Compared with single-scale denoising convolutional neural networks, this invention significantly suppresses desert noise and outperforms single-scale denoising convolutional neural networks in restoring the continuity of the phase axis.

Embodiments of a system and methods for utilizing ambient seismic noise to implement a modality based on the geometric phase for sending the effect of environmental conditions on ground properties are disclosed. The geometric phase is a global measure of the geometry of the seismic field which can be reconstructed via seismic interferometry. The change in geometric phase is the defined as the rotation angle of the complex state vector.

The utility model discloses an anti-seismic noise-reduction type casting supporting structure for a numerical control operation platform. The anti-seismic noise-reduction type casting supporting structure comprises a bottom plate, a damping mechanism on the bottom plate and a limiting mechanism used for fixing a casting. Mounting holes are formed in the four corners of the bottom plate, long-strip-shaped holes are formed in the four corners of the inner end of the bottom plate, damping mechanisms are fixedly connected to the four corners of the upper surface of the bottom plate, lifting cylinders are slidably connected to the upper ends of the inner sides of cylinders in the damping mechanisms, and sound insulation plates are fixedly connected to the inner side faces of the lifting cylinders. The upper and lower ends of the springs in the cylinder and the sound insulation plate are fixedly connected to the bottom surface of the first connecting plate and the top surface of the bottom plate respectively; a shock pad is fixedly connected between the first connecting plate and the second connecting plate, and a limiting mechanism is arranged at the top of the second connecting plate. According to the anti-seismic noise reduction type casting supporting structure for the numerical control operation platform, anti-seismic noise reduction can be conducted on castings, and the situation that when the numerical control operation platform operates, the use conditions of the castings are affected is avoided.

This invention discloses a method and system for reconstructing and purifying the noise cross-correlation function of seismic background noise, comprising the following steps: acquiring the original noise signal; utilizing the sparsity characteristics of the noise cross-correlation function of seismic background noise in the effective frequency band, and combining compressed sensing theory to perform non-uniform downsampling on the original noise signal to obtain undersampled data; employing a fast iterative shrinking threshold algorithm to reconstruct the full-band noise cross-correlation function from the undersampled data with high precision, obtaining the reconstructed noise cross-correlation function; performing frequency-wavenumber transformation on the reconstructed noise cross-correlation function, and using an FK filter mask to achieve three-component separation, obtaining the separated effective signal in the FK domain; performing an inverse transformation on the separated effective signal in the FK domain, and finally outputting the purified noise cross-correlation function.

The invention relates to the technical field of geological disaster monitoring, and discloses a slope crack depth monitoring method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring environmental seismic noise through a noise sensor preset at a monitored slope; screening the environmental seismic noise based on a preset spectrum peak detection algorithm to obtain a target noise spectrum signal; and determining a target slope crack depth based on the target noise spectrum signal and a preset crack depth inversion model. Environmental seismic noise of a monitored slope is collected, a target noise spectrum signal is obtained in combination with a preset spectrum peak detection algorithm, and then the target slope crack depth is determined based on the target noise spectrum signal and a preset crack depth inversion model. The problems of low efficiency and high safety risk caused by traditional manual contact type measurement adopted in existing slope crack depth monitoring are solved, the efficiency of slope crack depth monitoring is improved, and the monitoring danger coefficient is reduced.

The invention provides a seismic noise suppression method and system based on reversible decoupling deep learning. The method comprises the steps of decomposing seismic data with noise into high-frequency details and low-frequency structural components through wavelet transform; high-frequency details and low-frequency structural components are processed through reversible blocks, noise and low-resolution effective signal information are obtained, and each reversible block is composed of an affine coupling layer and a reversible convolution layer; after seismic data is successfully decoupled, a noise component is discarded, a new variable is randomly sampled from Laplacian distribution to recover an effective signal part in the noise component, and the effective signal part is recombined with a low-resolution structural component; and reversely inputting the combined components into the network, and reconstructing to obtain noiseless seismic data. According to the invention, all effective signals are kept, data can be restored in a lossless manner, and excessive suppression of the signals is avoided; the correlation between seismic data channels is kept; more effective information decoupling is achieved, and noise in seismic data is suppressed under the condition that effective signals are kept.

The utility model provides an anti-seismic noise reduction structure of a transformer in a booster station, which comprises a transformer main body and a support shell, and further comprises a damping structure arranged in the support shell. By rotating a third rotating plate, two clamping plates are driven to be clamped into two first clamping grooves, damping cannot be affected while the transformer is conveniently and well installed and fixed, and by clamping a clamping frame into a second clamping groove, the situation that the third rotating plate rotates, the clamping plates are disengaged from the first clamping grooves, and consequently a transformer body is not firmly fixed is conveniently avoided. Vibration of the transformer body is reduced and buffered through the damping springs and the damping dampers, working noise of the transformer is well reduced due to reduction of the vibration, meanwhile, collision noise can be reduced through the second buffer silencing pad, the third buffer silencing pad and the second buffer silencing pad, the vibration of the transformer can be well reduced, and the service life of the transformer is prolonged. And the noise of the transformer can be well reduced while the transformer can be well damped.

The invention discloses a strike-slip fracture identification method and device for an ultra-deep fault control fracture-vuggy carbonate reservoir, and the method comprises the steps: carrying out the denoising and fracture enhancement processing of a seismic data volume; determining a plurality of seismic attributes of the seismic data volume after fracture enhancement processing; seismic attributes for fracture identification are screened out, and multi-attribute fusion is carried out to obtain fusion attributes; carrying out ant tracking processing on the fusion attributes; and performing convolution filtering processing on the fusion attribute after ant tracking processing, and determining a fracture position. The strike-slip fracture identification result is less affected by earthquake noise, the fracture structure is clear, and longitudinal and transverse development characteristics conform to geological knowledge; the fracture identification result provides favorable conditions for strike-slip fracture reservoir control and reservoir control analysis.

The invention discloses a multi-dimensional seismic background noise imaging evaluation method and system based on big data, and the method comprises the following steps: obtaining seismic background noise imaging data; constructing an imaging evaluation model; and evaluating real-time seismic background noise imaging data. According to the method, the seismic background noise data are obtained by synchronously performing data imaging preprocessing on the obtained seismic background noise original data, then the processing effect of the data imaging preprocessing is evaluated, whether the reference processing effect is achieved or not is judged, and then the corresponding seismic background noise imaging data are obtained according to the seismic background noise data. An imaging evaluation model is constructed according to the seismic noise imaging data, and the real-time seismic background noise imaging data is evaluated in combination with the imaging evaluation model, so that the effect of reducing the influence of the background noise data on the accuracy of the imaging result is achieved, and the problem that the background noise data has great influence on the accuracy of the imaging result in the prior art is solved.

The utility model discloses an anti-seismic noise reduction dry-type transformer which comprises a transformer body, one side of the transformer body is fixedly connected with a heat conducting plate, one side of the heat conducting plate is fixedly connected with a heat pipe, the peripheral side surface of the heat pipe is fixedly connected with heat dissipation fins, and the lower end faces of the heat dissipation fins are fixedly connected with a bottom plate of the transformer body. An air cushion vibration isolator is fixedly connected to the lower end face of the transformer body, a fixing plate is fixedly connected to the lower end of the air cushion vibration isolator, fixing blocks are fixedly connected to the four corners of the fixing plate, anti-seismic supporting hangers are arranged on the side faces of the fixing blocks, and the overall length and width of the transformer body are smaller than the distance between the fixing blocks on one side. The anti-seismic supporting hanger comprises a main supporting rod and an inclined supporting structure. According to the utility model, the air cushion vibration isolators are used for buffering and blocking vibration generated during operation of the transformer body; the heat pipes, the heat dissipation holes and the heat dissipation fin plates are used for replacing the heat dissipation effect of a fan, and noise production of the whole device is reduced.

The application discloses a kind of safety protection type anti-seismic noise reduction electromechanical engineering equipment for building construction, including bottom plate, the top of bottom plate is sequentially provided with several support columns, sound absorption assembly and switch plate, the top of support column is provided with mounting top plate, the middle part of mounting top plate is provided with cutting machine, the top middle part of bottom plate is provided with placing table, and placing table corresponds with cutting machine, the top of bottom plate is sequentially provided with first installation slot, sliding slot, support hole and placing slot from outside to inside, sliding slot is matched with switch plate, and installation slot is connected with sliding slot, support hole is matched with support column.The application is provided with bottom plate, uses the cooperation of support column and sound absorption assembly, reduces the influence of noise during use, and avoids the situation that material splashes and injures people during processing, and through the cooperation of cutting machine and placing table, it is convenient to carry out shock absorption through placing table, improve the stability during processing.

A background noise differential accompanying tomography system (1) is provided. The system (1) comprises a seismic wave recording device (101, 102, 103) and a processor (100). The processor (100) designates three of the seismic wave recording devices (101, 102, 103) as a seismic source (101), a first receiver (102), and a second receiver (103), respectively, and the seismic source (101) is arranged in a linear array with the first receiver (102) and the second receiver (103), the first receiver (102) and the second receiver (103) being arranged adjacent to each other. A processor (100) receives seismic noise and obtains noise interferometry, calculates a synthetic dispersion surface wave signal, and generates differential adjoint tomography based on observed dispersion surface waves obtained from the noise interferometry. A processor (100) estimates a shear wave velocity based on differential accompanying tomography, estimates a feature of the region using the estimated tomography results, and provides a graph with the feature of the region through a display or printer.

The application discloses a seismic data denoising model establishing method, a seismic data denoising method and related devices. The seismic data denoising model establishing method comprises the following steps: based on multiple sets of original seismic data, denoised seismic data corresponding to the original seismic data is obtained by combining a denoising method and a synthetic seismic method according to a set method ratio, as label data, and a sample set is obtained; the original seismic data of each sample in the sample set is subjected to wavelet frequency division processing to obtain multi-band data, and a converted sample set is obtained; a selected deep neural network model is trained by using the converted sample set, and a seismic data denoising model is obtained, which is used for denoising processing of input seismic data. Through the established seismic data denoising model, multiple seismic noises can be suppressed, and on the basis of obviously improving the noise suppression efficiency, the noise recognition precision is effectively improved.

The embodiment of the invention relates to a seismic imaging profile resolution improving method and device, and the method comprises the steps: collecting and analyzing a plurality of seismic imaging profiles, and constructing a plurality of seismic velocity models; obtaining a first target seismic imaging profile based on the seismic convolution model and the plurality of seismic velocity models; by randomly setting different migration imaging parameters, carrying out migration imaging on the plurality of seismic velocity models by using a reverse time migration imaging method to obtain a second target seismic imaging profile; expanding a seismic imaging profile with diversity characteristics by using adaptive synthesis sampling, and adding seismic noise to the seismic imaging profile; designing an initial generative adversarial network in combination with the generative adversarial network, and training to obtain an initial resolution improvement model; and carrying out transfer learning on the initial resolution improvement model to obtain a target resolution improvement model. Therefore, the signal-to-noise ratio and the resolution ratio of the offset profile can be improved, the calculation efficiency is high, and no parameter dependence exists.