Application of high-resolution processing in seismic data based on an improved synchrosqueezing transform

Synchrosqueezing transform (SST) is an effective time-frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience.

The denoising for the microseismic signals in the strong background interferences during the microseismic monitoring of hydraulic fracturing is very important for events identification and first-arrival picking. As characteristics of microseismic signals are normally random, non-stationary, and time-varying, a powerful tool for weak signal extraction of microseismic data is still a great challenge. In this study, a denoising method for microseismic signals is proposed based on the synchrosqueezing transform in the time-frequency domain. Firstly, the microseismic signals are denoised by the adaptive threshold method in the time-frequency domain by the synchrosqueezing transform. Then, the frequency center of the effective signal is integrated and extracted in the time-frequency domain. At last, the weak signal is extracted by the reconstruction of synchrosqueezing transform. The results from the synthesized non-stationary signal with different noise intensities and the real microseismic single of a track record show that this method has better anti-noise ability and higher signal extraction accuracy. Moreover, according to the processing and analysis of microseismic data in field wells, it is proved that this denoising method has a good practical value and can extract the effective and high-precision signals in the strong noise background.

The synchronization of neural activity in the human brain has great significance for coordinating its various cognitive functions. It changes throughout time and in response to frequency. The activity is measured in terms of brain signals, like an electroencephalogram (EEG). The time-frequency (TF) synchronization among several EEG channels is measured in this research using an efficient approach. Most frequently, the windowed Fourier transforms-short-time Fourier transform (STFT), as well as wavelet transform (WT), and are used to measure the TF coherence. The information provided by these model-based methods in the TF domain is insufficient. The proposed synchro squeezing transform (SST)-based TF representation is a data-adaptive approach for resolving the problem of the traditional one. It enables more perfect estimation and better tracking of TF components. The SST generates a clearly defined TF depiction because of its data flexibility and frequency reassignment capabilities. Furthermore, a non-identical smoothing operator is used to smooth the TF coherence, which enhances the statistical consistency of neural synchronization. The experiment is run using both simulated and actual EEG data. The outcomes show that the suggested SST-dependent system performs significantly better than the previously mentioned traditional approaches. As a result, the coherences dependent on the suggested approach clearly distinguish between various forms of motor imagery movement. The TF coherence can be used to measure the interdependencies of neural activities.

年 第 47 卷 第 3 期 目前影响高次谐波广泛应用的一个主要瓶颈 是它的光通量很低, 对于常用的 Ti:sapphire 激光 (800 nm) 而言谐波的转换效率通常小于 10 -5 [25][26][27] .若使用波长更长的红外激光可以有效提高光子的最 大能量 -截止位置, 但是转换效率急剧下降 (单原 子 ∼ λ -5 ).从实际应用的角度来看, 高次谐波的产生 需要解决以下几个问题 [28] : 提高转换效率、扩展截 止位置、选择性增强和抑制某阶或某一范围的谐波 (见图 1).实验中观察到的高次谐波通常都是在稠密的气 体中产生, 它不仅与单个原子在激光场中的响应 (诱 导偶极矩) 有关 [29]

Seismic data in desert area generally have low signal-to-noise ratio (SNR) due to special surface conditions. Desert noise is characterized as low-frequency, non-Gaussian and non-stationary noise, which makes the noise suppression in desert area more challenging by conventional methods. Conventional methods are effective for the signal with high SNR, but in desert seismic signal, the SNR is low and the signal can easily be obliterated in desert noise. In this paper, we propose an approach that operates in synchrosqueezing transform (SST) domain and use classification techniques obtained from supervised machine learning to identify the coefficients associated with signal and noise. First of all, we transform the real desert seismic data into time–frequency domain by SST. Secondly, we select features by calculating the SST coefficients of signal and noise. And then, we train them in the Adaboost classifier. Finally, when the training is completed, we can obtain the final classifier that can effectively separate the signal from noise. We perform tests on synthetic and field records, and the results show great advantages in suppressing random noise as well as retaining effective signal amplitude.

Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment. Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST). In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST. The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction. Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal. Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.

Chatter is a self-excited vibration between the workpiece and tool with negative effects. In this work, a chatter detection method is proposed based on synchrosqueezing transform (SST) of sound signals. Firstly, the SST is used to analyze the sound signals recorded with the microphone and a time-frequency representation is obtained. Then, filtering is conducted to remove the disturbance of tooth passing frequency and its harmonics in time-frequency domain. Next, singular value decomposition (SVD) method is employed to condense the TF matrix and the first-order singular value is calculated as the chatter indicator. Finally, chatter threshold is set based on 3σ criterion for the detection of chatter occurrence. The proposed method is validated with cutting tests, and the results show that the method has great potential to be used for the online chatter detection of high-speed milling process.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

The main factors responsible for the nonstationarity of seismic signals are the nonstationarity of the geologic structural sequences and the complex pore structure. Time-frequency analysis can identify various frequency components of seismic data and reveal their time-variant features. Choosing a proper time-frequency decomposition algorithm is the key to analyze these nonstationarity signals and reveal the geologic information contained in the seismic data. According to the Heisenberg uncertainty principle, we cannot obtain the finest time location and the best frequency resolution at the same time, which results in the trade-off between the time resolution and the frequency resolution. For instance, the most commonly used approach is the short-time Fourier transform, in which the predefined window length limits the flexibility to adjust the temporal and spectral resolution at the same time. The continuous wavelet transform (CWT) produces an “adjustable” resolution of time-frequency map using dilation and translation of a basic wavelet. However, the CWT has limitations in dealing with fast varying instantaneous frequencies. The synchrosqueezing transform (SST) can improve the quality and readability of the time-frequency representation. We have developed a high-resolution and effective time-frequency analysis method to characterize geologic bodies contained in the seismic data. We named this method the SST, and the basic wavelet is the three-parameter wavelet (SST-TPW). The TPW is superior in time-frequency resolution than those of the Morlet and Ricker wavelets. Experiments on synthetic and field data determined its validity and effectiveness, which can be used in assisting in oil/gas reservoir identification.

Synchrosqueezing transform (SST) benefits from an instantaneous frequency (IF) estimator in the time-frequency domain, providing an energy-concentrated time-frequency representation to describe the time-varying frequency of seismic signals. To enhance the concentration performance of SST, this paper theoretically proposes a statistical SST (SSST) by constructing a spectrum-weighted IF estimator in the short-time Fourier transform (STFT) domain. In this SSST, a linear chirp signal is introduced to better capture its chirp rate, and then the quadratic STFT spectra are weighted by a window-related weighting function. On this basis, two equations related to the IF and chirp rate are constructed to derive the spectrum-weighted IF estimator. Finally, the STFT coefficients are squeezed to the estimated IF trajectories by a frequency fixed-point iterative algorithm, thereby providing a more concentrated time-frequency representation for multi-component signals than existing advanced methods, while enabling to retrieve each component. Two synthetic examples and one field seismic data on thin interbeds are utilized to demonstrate the effectiveness of the proposed SSST and show its ability to highlight the time-varying frequency features of seismic signals, which is a promising seismic data analysis tool, such as characterizing thin interbed thickness variations.

Due to the limitation from the uncertainty principle, the window size consistently affects the resolution of time–frequency distributions. Determining the optimal window size is crucial for time–frequency analysis. In this work, we propose the concept of the reliable window width range along time and frequency axes, which simplifies the process of determining the optimal window size and the search range for adaptive methods. Moreover, in existing work, adaptive time–frequency analyses apply either time- or frequency-varying windows. In this study, we utilize a multitaper technique to integrate time- and frequency-varying windows. The proposed concept of the reliable window width range can be applied to any window-based time–frequency analysis method and enhance post-processing methods in the time–frequency plane. Subsequently, together with the reliable window width range, we propose the multitaper adaptive time–frequency windowed synchroextracting transform. It can estimate the optimal window size for time and frequency axes simultaneously and achieve a more energy-concentrated time–frequency analysis result.

Multi-spectra synchrosqueezing transform

One important application of time-frequency analysis (TFA) is seismic spectral decomposition for reservoir characterization. However, traditional seismic TFA techniques are usually limited by diffused TF distribution, which can result in unreliable seismic interpretations. Synchrosqueezing transform (SST) is an effective TFA method that improves the concentration of the TF representation (TFR) of nonstationary signals. However, for the signal with a rapidly varying instantaneous frequency, the SST method suffers from a blurred TFR. In this letter, we propose a novel TFA method called time-synchroextracting transform (TSET) that provides highly concentrated TFR for transient signals where the TF curve is nearly parallel to the frequency axis. We applied the proposed TSET to synthetic signals and field seismic data to verify its validity of time localization and effective delineation of subsurface geological information.

On the use of short-time fourier transform and synchrosqueezing-based demodulation for the retrieval of the modes of multicomponent signals

Time–frequency (TF) analysis is a powerful tool to describe the frequency response of subsurface rocks and hydrocarbon reservoirs. In this letter, the matching demodulation synchrosqueezing S transform (MDSST) that achieves a highly concentrated TF representation was introduced to seismic TF analysis. The MDSST includes two steps: the matching demodulation technique is preprocessing that provides a rough estimation of instantaneous frequency (IF) of the analyzed signal, and the synchrosqueezing transform (SST) is postprocessing that produces an accurate IF. Numerical experiments on synthetic and real signals demonstrate the superiority of the MDSST in seismic TF analysis.