Denoising is one of the core steps in seismic data processing flow. The seismic gather consists of multiple traces captured at different receivers. A set of receivers observe waves which are reflected from the same reflection point. Those traces need to be grouped together as they contain the same information about the earth subsurface layers. This is done by finding a common mid-point (CMP) between the source and geophones. The time delay between CMP gathered traces are corrected by the normal move out correction method but the individual traces are corrupted by noise. In this paper we, propose a method for denoising individual traces. The set of traces can be modelled as belonging to a low-dimensional subspace of an ambient signal space. This allows for construction of sparse representations of each trace in terms of other traces in the CMP gather. The resulting sparse representations are subsequently utilised to construct approximations of individual traces and thus, noise is suppressed. We constructed, the approximations using orthogonal matching pursuit. We applied proposed method to synthetic and field seismic data, the proposed technique performs better on established benchmarks while capturing the true locations of weak reflections and effectively attenuating the random noise. Nomenclature M number of traces N length of each trace Y seismic trace matrix A noise-free trace matrix G Gaussian noise matrix E impulse noise matrix w source wavelet L length of source wavelet W wavelet dictionary D number of shifted wavelets (atoms) in the wavelet dictionary R reflection matrix K number of non-zero rows in reflection matrix n time index, n ∈ [N] i trace index in the CMP gather, i ∈ [M]

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Accurate estimation of battery internal model parameters and consequently SOC prediction is crucial in any battery power system. Hence, it is a fundamental need in electric vehicles, smart grids, and energy storage systems. The accuracy of parameters identification will affect the battery management system, battery safety, characteristics, and performance, which mainly depends on battery model parameters. So, to estimate the parameters accurately and easily, we require effective, simple, and robust parameters estimation algorithms. In this article, we propose a new method for estimation of parameters using least square method algorithm for Lithium-Ion Batteries (LIBs) for Electric Vehicle (EV) applications. In this, a second-order RC equivalent circuit model is considered for estimation of parameters of NMC battery. The estimation of parameters and relation between OCV-SOC nonlinear is obtained from the experimental data. This proposed method shows that the calculation of parameters is fast and efficient.

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