Complex trace analysis provides seismic interpreters with a view to identify the nature of challenging subsurface geologic features. However, the conventional procedure based on the Hilbert transform (HT) is highly sensitive to random noise and sudden frequency variations in seismic data. Generally, conventional filtering methods reduce the spectral bandwidth while stabilizing complex trace analysis, whereas obtaining high-resolution images of multiple thin-bed layers requires wideband data. It is thus a challenging problem to reconcile the conflict between the two purposes, and a powerful signal processing device is required. To overcome the issue, I first introduced the fast sparse S-transform (ST) as a powerful time-frequency decomposition method to improve the windowed Hilbert transform (WHT). Then, in addition to the mixed-norm higher resolution provided by the fast sparse ST, I have developed a novel sparsity-based optimization for window parameters. The process adaptively regularizes sudden...

High-resolution seismic complex trace analysis by adaptive fast sparse S-transform

Velocity analysis with local event slopes related probability density function

To better retain useful weak low-frequency magnetotelluric (MT) signals with strong interference during MT data processing, we propose a SVM-CEEMDWT based MT data signal-noise separation method, which extracts the weak MT signal affected by strong interference. First, the approximate entropy, fuzzy entropy, sample entropy, and Lempel-Ziv (LZ) complexity are extracted from the magnetotelluric data. Then, four robust parameters are used as the inputs to the support vector machine (SVM) to train the sample library and build a model based on the different complexity of signals. Based on this model, we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold (WT) for noise suppression. Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression. Compared with the CEEMDWT, the proposed SVM-CEEMDWT method retains more low-frequency low-variability information, and the apparent resistivity curve is smoother and more continuous. Moreover, the results better reflect the deep electrical structure in the field.

Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

Objective: Ultrasound is an effective tool for rapid noninvasive assessment of cardiac structure and function. Determining the cardiorespiratory phases of each frame in the ultrasound video and capturing the cardiac function at a much higher temporal resolution are essential in many applications. Fulfilling these requirements is particularly challenging in preclinical studies involving small animals with high cardiorespiratory rates, requiring cumbersome and expensive specialized hardware. Methods: We present a novel method for the retrospective estimation of cardiorespiratory phases directly from the ultrasound videos. It transforms the videos into a univariate time series preserving the evidence of periodic cardiorespiratory motion, decouples the signatures of cardiorespiratory motion with a trend extraction technique, and estimates the cardiorespiratory phases using a Hilbert transform approach. We also present a robust nonparametric regression technique for respiratory gating and a novel kernel-regression model for reconstructing images at any cardiac phase facilitating temporal superresolution. Results: We validated our methods using two-dimensional echocardiography videos and electrocardiogram (ECG) recordings of six mice. Our cardiac phase estimation method provides accurate phase estimates with a mean-phase-error range of 3%–6% against ECG derived phase and outperforms three previously published methods in locating ECGs R-wave peak frames with a mean-frame-error range of 0.73–1.36. Our kernel-regression model accurately reconstructs images at any cardiac phase with a mean-normalized-correlation range of 0.81–0.85 over 50 leave-one-out-cross-validation rounds. Conclusion and significance: Our methods can enable tracking of cardiorespiratory phases without additional hardware and reconstruction of respiration-free single cardiac-cycle videos at a much higher temporal resolution.

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Image-Based Methods for Phase Estimation, Gating, and Temporal Superresolution of Cardiac Ultrasound

Seismic attributes calculated by conventional methods are susceptible to noise. Conventional filtering reduces the noise in the cost of losing the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition while is stabilized for random noise. The procedure initiates by using Sparsity-based adaptive S-transform to regularize abrupt variations in frequency content of the nonstationary signals. Then, considering the fact that a higher amplitude of a frequency component results in a higher signal to noise ratio, an adaptive filter is applied to the time-frequency spectrum which is sparcified previously. The proposed zero adaptive filter enhances the high amplitude frequency components while suppresses the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in estimation of instantaneous attributes by applying on synthetic and real data sets. Seismic attributes estimated by the proposed method is superior to the conventional ones in terms of its robustness and high resolution image. The proposed approach has a vast application in interpretation and identification of geological structures.

Robust and high-resolution seismic complex trace analysis

The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.

Robust estimation of instantaneous phase using a time-frequency adaptive filter

Due to the limited space in the tunnel environment, multiple-input multiple-output (MIMO) systems with double-port fed leaky coaxial cables (LCXs) can not only reduce the number of LCXs, but also improve the channel capacity. Based on the geometry based on single bonce (GBSB) and electromagnetic field radiation theory of LCX, a MIMO channel model with double–port fed LCX in a tunnel scenario is proposed in this paper. The channel impulse response (CIR) is derived, and verified with measurement results in terms of channel capacity. The distribution of channel capacity of single double-port fed LCX under different LCX lengths in the tunnel scenarios has also been analyzed in this work, and the distribution of channel capacity for the LCX–MIMO system with long LCX is predicted. The results show that the single double-port fed LCX–MIMO system outperforms the dipole antenna MIMO system with respect to channel capacity in the considered tunnel scenarios.

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Channel Model and Performance Analysis for MIMO Systems with Single Leaky Coaxial Cable in Tunnel Scenarios

Sub-terahertz (sub-THz) communications have promising prospects for future beyond-5G or 6G applications due to the sufficient spectrum resources. However, the bit-error-rate (BER) performance of sub-THz is seriously deteriorated by phase noise because of the defective high-frequency oscillators. In this paper, a novel modulation and demodulation method with low peak-to-average power ratio (PAPR) is proposed to resist phase noise, where the signal waveform is generated in the form of Gray-mapping amplitude and differential phase shift keying (Gray-ADPSK). As phase noise varies slowly in time, the amplitude and differential phase between adjacent symbols almost remain after transmission with Gray-ADPSK, and hence the BER performance is barely affected by phase noise. In order to support decoding effectively, a soft-decision demodulation method with low complexity is also put forward. Numerical simulations demonstrate that the proposed Gray-ADPSK can resist phase noise significantly, and the high-spectral-efficiency transmission even in the order of 4096 is feasible under high-intensity phase noise.

Chang-Kai Zhang

Papers

Robust estimation of instantaneous phase using a time-frequency adaptive filter

Channel Model and Performance Analysis for MIMO Systems with Single Leaky Coaxial Cable in Tunnel Scenarios

Phase-Noise-Resisting Modulation and Demodulation for Sub-Terahertz Communications