scholarly journals Expression of Concern: Least-squares decomposition with time–space constraint for denoising microseismic data

2020 ◽  
Vol 221 (3) ◽  
pp. 2055-2055
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Yufeng Wang ◽  
Min Bai
Keyword(s):  
Least Squares ◽  
Space Constraint ◽  
Microseismic Data ◽  
Time Space

scholarly journals Least-squares decomposition with time–space constraint for denoising microseismic data

2019 ◽  
Vol 218 (3) ◽  
pp. 1702-1718 ◽  
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Yufeng Wang ◽  
Min Bai
Keyword(s):  
Least Squares ◽  
Space Constraint ◽  
Microseismic Data ◽  
Time Space

Least-squares decomposition with time–space constraint for denoising microseismic data

2020 ◽  
Vol 222 (3) ◽  
pp. 1864-1880
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Yufeng Wang ◽  
Min Bai
Keyword(s):  
Least Squares ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Microseismic Data ◽  
Smoothness Constraint ◽  
Time Space ◽  
Wide Range ◽  
The Stability ◽  
And Inversion

SUMMARY Microseismic data are usually of low signal-to-noise ratio (SNR), which makes it difficult to utilize the microseismic waveforms for imaging and inversion. We develop a useful denoising algorithm based on a non-stationary least-squares decomposition model to enhance the quality of microseismic signals. The microseismic signals are assumed to be represented by a superposition of several smoothly variable components. We construct a least-squares inverse problem to solve for the the smooth components. We constrain the least-squares inversion via both time and space constraints. The temporal smoothness constraint is applied to ensure the stability when calculating the non-stationary autoregression coefficients. The space-smoothness constraint is applied to extract the spatial correlation among multichannel microseismic traces. The new algorithm is validated via several synthetic and real microseismic data and are proved to be effective. Comparison with the state-of-the-art algorithms demonstrates that the proposed method is more powerful in suppressing random noise of a wide range of levels than its competing methods.

Energy Field Filling of NEIC Broadband Radiated Energy Catalogue Based on Method of Least Squares

2013 ◽  
Vol 760-762 ◽  
pp. 1339-1342 ◽  
Author(s):  
Mao Fa Wang ◽  
Qing Jie Liu ◽  
Ji Lin Feng ◽  
Zhen Zhang
Keyword(s):  
Least Squares ◽  
Earthquake Prediction ◽  
Seismic Energy ◽  
Method Of Least Squares ◽  
Energy Field ◽  
Radiated Energy ◽  
Time Space ◽  
Modern Natural ◽  
Modern Natural Science ◽  
Data Source

Earthquake prediction is one of the most difficult problems in modern natural science. Undoubtedly, various seismic parameters included in broadband radiated energy catalogue of NEIC is very important data source of investigate correlation between differernt earthquake within specific time space scope and earthquake prediction. In the paper, we a linear fitting mode of seismic magnitude based on method of least squares and seismic energy is established, using which we can fill the absent energy filed in the broadband radiated energy catalogue.

Seismic data interpolation and denoising in the frequency-wavenumber domain

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V71-V80 ◽  
Author(s):  
Mostafa Naghizadeh
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Real Data ◽  
Sampled Data ◽  
Frequency Range ◽  
Unified Approach ◽  
Low Frequencies ◽  
Time Space ◽  
Least Squares Fit ◽  
Mask Function

I introduce a unified approach for denoising and interpolation of seismic data in the frequency-wavenumber ([Formula: see text]) domain. First, an angular search in the [Formula: see text] domain is carried out to identify a sparse number of dominant dips, not only using low frequencies but over the whole frequency range. Then, an angular mask function is designed based on the identified dominant dips. The mask function is utilized with the least-squares fitting principle for optimal denoising or interpolation of data. The least-squares fit is directly applied in the time-space domain. The proposed method can be used to interpolate regularly sampled data as well as randomly sampled data on a regular grid. Synthetic and real data examples are provided to examine the performance of the proposed method.

Numerical Solution of Coupled Cahn-Hilliard and Navier-Stokes System Using the Least-Squares Spectral Element Method

2016 ◽  
Author(s):  
Keunsoo Park ◽  
Carlos A. Dorao ◽  
Maria Fernandino
Keyword(s):  
Least Squares ◽  
Phase Field ◽  
Spectral Element Method ◽  
Stokes System ◽  
Phase Field Model ◽  
Spectral Element ◽  
Navier Stokes ◽  
Two Phase ◽  
Time Space ◽  
Element Method

We consider the least-squares spectral element method to solve the phase field model for two immiscible, incompressible and density-matched fluids. The coupled Cahn-Hilliard and Navier-Stokes system is selected as the numerical model, which was introduced by Hohenberg et al. [1]. The least-squares spectral element scheme is combined with a time-space formulation where both time and space domains are discretized by the same finite element approach to cope with time dependent multidimensional problems in an efficient way. C1 Hermite basis functions are applied for approximating the coupled system. An element-by-element conjugated gradient method is used to facilitate parallelization of the solver. The convergence analysis is conducted to verify our solver, and two numerical experiments are addressed to show applicability of the solver in general situations. Energy dissipation with conserved phase field at equilibrium state is confirmed through the bubble coalescence case, and the influence of the interface mobility is studied with the two-phase lid-driven cavity flow example.

scholarly journals Efficient 2D multiple attenuation using SRME with curvelet-domain subtraction

2022 ◽  
Vol 43 (1) ◽  
Author(s):  
Szu-Ying Lai ◽  
Yunung Nina Lin ◽  
Ho-Han Hsu
Keyword(s):  
Finite Number ◽  
Least Squares ◽  
Seismic Data ◽  
Parameter Determination ◽  
Prediction Errors ◽  
Geological Condition ◽  
Multiple Model ◽  
Multiple Attenuation ◽  
Time Space ◽  
Multiple Elimination

AbstractSurface Related Multiple Elimination (SRME) usually suffers the issue of either over-attenuation that damages the primaries or under-attenuation that leaves strong residual multiples. This dilemma happens commonly when SRME is combined with least-squares subtraction. Here we introduce a more sophisticated subtraction approach that facilitates better separation of multiples from primaries. Curvelet-domain subtraction transforms both the data and the multiple model into the curvelet domain, where different frequency bands (scales) and event directions (orientations) are represented by a finite number of curvelet coefficients. When combined with adaptive subtraction in the time–space domain, this method can handle model prediction errors to achieve effective subtraction. We demonstrate this method on two 2D surveys from the TAiwan Integrated GEodynamics Research (TAIGER) project. With a careful parameter determination flow, our result shows curvelet-domain subtraction outperforms least-squares subtraction in all geological settings. We also present one failed case where specific geological condition hinders proper multiple subtraction. We further demonstrate that even for data acquired with short cables, curvelet-domain subtraction can still provide better results than least-squares subtraction. We recommend this method as the standard processing flow for multi-channel seismic data.

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