Random and coherent noise attenuation by empirical mode decomposition

Abstract

We have devised a new filtering technique for random and coherent noise attenuation in seismic data by applying empirical mode decomposition (EMD) on constant-frequency slices in the frequency-offset [Formula: see text] domain and removing the first intrinsic mode function. The motivation behind this development is to overcome the potential low performance of [Formula: see text] deconvolution for signal-to-noise enhancement when processing highly complex geologic sections, data acquired using irregular trace spacing, and/or data contaminated with steeply dipping coherent noise. The resulting [Formula: see text] EMD method is equivalent to an autoadaptive [Formula: see text] filter with a frequency-dependent, high-wavenumber cut filtering property. Removing both random and steeply dipping coherent noise in either prestack or stacked/migrated sections is useful and compares well with other noise-reduction methods, such as [Formula: see text] deconvolution, median filtering, and local singular value decomposition. In its simplest implementation, [Formula: see text] EMD is parameter-free and can be applied to entire data sets without user interaction.

Decomposing Worldwide Complete Spherical Bouguer Gravity Anomaly Using 2-D Empirical Method

“…It is a quasi-orthogonal decomposition in that the cross-correlation coefficients between the different IMFs are always close to zero. This minimizes energy leakage between the IMFs [12]. The residual mixing within regional can be handled using this methodology.…”

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

“…This paper is not going to cover the basic principles of HHT which have been introduced in much of the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. We will focus only on mode mixing problems of the EMD method in HHT.…”

“…Battista et al used the EMD with signals of seismic reflection data [10]. Bekara et al proposed a new filtering technique to deal with the random noise based on EMD [11]. Cai et al applied HHT to the analysis of magneto telluric data and obtained some meaningful geological information [8].…”

A Nonparametric Method for Automatic Denoising of Microseismic Data

“…Bekara and Van der Baan [9] proposed a frequency-offset EMD technique to suppress the seismic random and coherent noise. In order to eliminate mode mixing present in the original EMD algorithm, ensemble empirical mode decomposition (EEMD) method has been recently proposed by Wu and Huang [19].…”

“…Hence, numerous methods have been presented for this in the past few decades that can be classified into four main categories: (1) infinite impulse response (IIR) filters, known as low-pass, high-pass, band-pass, and band-stop filter (e.g., [5]); (2) Fourier-based filters (e.g., [6][7][8]); (3) EMD-based (empirical mode decomposition) filters (e.g., [9][10][11][12][13][14]); (4) wavelet-based filters (e.g., [15][16][17]). …”