This article utilizes Savitzky–Golay (SG) filter to eliminate seismic random noise. is a novel method for noise reduction in which SG adopts piecewise weighted polynomial via leastsquares estimation. Therefore, effective smoothing achieved extracting the original signal from environment while retaining shape of as close possible one. Although there are lots classical methods such Wiener filtering and wavelet denoising applied noise, outperforms them approximating true signal. will obtain...

Time-frequency peak filtering (TFPF) is a classical method in time-frequency domain. It applies Wigner-Ville distribution to estimate the instantaneous frequency of an analytical signal. There pair contradiction this method, i.e., selecting short window length may lead good preservation for signal amplitude but bad random noise reduction whereas long serious attenuation effective reduction. In order make tradeoff between valid and reduction, we adopt empirical mode decomposition (EMD)...

With the widespread use of sector gates in tidal river locks, there is a growing interest operating force during gate operation. This study investigates characteristics forces hydraulic cylinder system using prototype testing and focuses on effects different heads flow velocities under forces. The results reveal significant differences opening processes between normal reverse heads, with head differentials direction playing crucial roles determining flowing conditions. In still water, both...

Abstract The design of sector gates is contingent upon the operating capacity, which represents a critical focus in gate design. Analyzing characteristics force during operation essential for evaluating capacity gate. study focuses on hydrodynamic and dynamic water closure process. Through comparative analysis 1:20 scale physical model three-dimensional fluid-structure interaction numerical model, development dissipation vortices are revealed. results show that closely related to hydraulic...

Time-frequency peak filtering (TFPF) with fixed window length effectively attenuates random noise in seismic data, but performs well only for slow-varying signals. To surmount the limitation, we propose adaptive time-frequency signal-dependent related to belongings of data signal, signal nonlinearity and local variance. The fuzzy c-means clustering algorithm is used obtain feature space, which incorporates spatial information including similarity feature, mean median immediate neighborhood....

The convolutional neural network (CNN) has achieved excellent performance in many fields, which attracted much attention. CNN is a kind of feedforward with convolution computation and depth structure. In this letter, aiming at the intense interference seismic exploration noise desert China, reduction system based on deep residual encoder-decoder proposed. order to extract characteristics variation law noise, set containing large number utilized for training so that forms end-to-end mapping...

Seismic signals are nonlinear, and the seismic state-space model can be described as a nonlinear system. The particle filter (PF) method, an effective method for estimating state of system, applied to deal with random noise attenuation. However, PF suffers from sample impoverishment caused by resampling, which results in serious loss valid information leads inaccurate representation reflected signal. To address issue further improve quality, we propose novel suppress noise-the adaptive...

Time-frequency peak filtering (TFPF) is an effective seismic random noise attenuation method at low signal-to-noise ratio (SNR). However, the conventional TFPF biased for signals with high frequency. We propose a spatial-trace (ST-TFPF) algorithm reducing in data and simultaneously bias of TFPF. The proposed takes into consideration lateral coherence between neighboring traces as constraint To reduce bias, this along events. first stage preliminarily identifies position reflection events...

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, errors are not always satisfactory when applying TFPF to fast-varying seismic signals. We begin with an error analysis for by using spread factor of phase cumulants noise. This shows that nonlinear signal component non-Gaussian lead deviation pseudo-Wigner-Ville distribution (PWVD) peaks from instantaneous frequency. The introduces distortion oscillations in result...

The time–frequency peak filtering (TFPF) algorithm is an effective method for seismic random noise attenuation. conventional TFPF filters data only along the channel direction, ignoring spatial characteristics of reflection events, which results in loss directional information. In order to improve performance TFPF, we adopt a Radon transform implement spatiotemporal 2-D doing domain. Since this takes correlation events into account, it could extract better than TFPF. As may produce smearing...

Time picking is of great concern in the processing microseismic data. However, traditional method based on time/frequency domain cannot pick first arrival time accurately low signal-to-noise ratio. Besides, methods which clustering are sensitive to selecting initial centers and easy converge local optimal value. To solve above problems, we propose a for data locally linear embedding (LLE) improved particle swarm optimization (PSO) algorithm. First, LLE algorithm can obtain inherent...

Desert seismic data are often characterized by low signal-to-noise ratio (SNR) due to the fickle surface conditions and desert random noise with nonstationarity, nonlinearity, spatial directivity, low-frequency characteristics. This SNR is likely affect following inversion interpretation. Therefore, robust attenuation crucial improve of data. We propose a novel method alternating direction multipliers-based denoising convolutional neural network (ADMM-CNN) combining low-rank decomposition...

In recent years, the denoising of low-frequency desert noise has been significant and difficult point in processing seismic data. Traditional random suppression methods could not get a good result data areas. Moreover, convolutional neural network (CNN) made notable achievements many fields recently. order to denoise areas improve signal-to-noise ratio (SNR), CNN is introduced process According characteristics data, we designed new suitable for training denoising, which named DnResNeXt....

In this paper, a new efficient method is proposed to obtain the transient response of linear or piecewise dynamic systems with time delay and periodic coefficients under arbitrary control excitations via Chebyshev polynomial expansion. Since domain can be divided into intervals length equal period, at each such interval fundamental solution matrix for corresponding ordinary differential equation (without delay) constructed in terms shifted polynomials by using previous technique that reduces...

Abstract The use of Chebyshev polynomials in solving finite horizon optimal control problems associated with general linear time‐varying systems constant delay is well known the literature. technique modified present paper for dynamical time periodic coefficients and delay. governing differential equations motion are converted into an algebraic recursive relationship terms system matrices, delayed state vectors, input vector. Three different approaches considered. first approach computes...

Curvelet transform can be effective in eliminating seismic noise by properly setting a threshold to the curvelet coefficients. However, when signal-to-noise ratio (SNR) of data is low, it difficult select suitable remove noise, because coefficients are similar between signals and noise. In this paper, we propose incorporate kurtosis statistic representing non-Gaussian characteristics into an adaptive threshold-setting scheme. decomposes noisy curvelets with different scales directions. The...

A novel method based on nonnegative matrix factorization (NMF) spectral unmixing is proposed for land seismic additive random noise attenuation. In the method, noisy signal first decomposed into a collection of intrinsic mode functions (IMFs) instead being directly processed. These IMFs can be considered as new set observations. short-time Fourier transform (STFT) spectrum each IMF, degree mixing from effective and considerably reduced. Then, sparse NMF used to unmix STFT IMF. We get...

Distributed acoustic sensing (DAS) has been gradually applied to vertical seismic profiling (VSP), where the generated DAS VSP data contains types of complex noise. Therefore, denoising plays an important role in collecting high-quality geological information. Generative adversarial network(GAN) widely used exploration these years, but problems such as insufficient optimization objectives, poor signal retention continuity, and accuracy still remains when processing data. To address problems,...

Single-crystal diamond tools occupy an important position in the field of optical processing as basis and key to advanced manufacturing technology, such grating mirror-turning processing. have become cornerstone development related industries. This paper takes a single-crystal arc tool research object. Sound signal analysis technology vibration are comprehensively applied online orientation identification indexing grinding process. The method is explored, sound taken characteristic signals,...

As the demand for nonrenewable oil and gas resources has increased, distributed acoustic sensing systems (DAS) have been widely used in acquisition of vertical seismic profiles. Complicated subsurface conditions produce many types noise with strong energy. The effective reflection refraction signals are absorbed by inelastic medium during transmission process, resulting significant energy loss aliased frequency characteristics. However, satisfactory processing results quality cannot be...