Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

scholarly journals Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Grzegorz Żak ◽  
Agnieszka Wyłomańska ◽  
Radosław Zimroz
Keyword(s):  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Low Frequency ◽  
Real Data ◽  
Vibration Signal ◽  
High Energy ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data

The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

P-Wave Onset Detection Based on the Spectrograms of the AE Signals

2011 ◽  
Vol 250-253 ◽  
pp. 3807-3810 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Acoustic Emissions ◽  
P Wave ◽  
Onset Detection ◽  
Arrival Times ◽  
Time Frequency ◽  
Frequency Representation ◽  
Reasonable Result ◽  
Adaptive Thresholds ◽  
Ae Signal ◽  
Ae Signals

Determining the onset of acoustic emissions (AE) signal is very time consuming if the onset is picked manually, so it is important to read the arrival times of P phase automatically. Automatic onset detection and picking algorithm has been proposed by applying the spectro-ratio on time–frequency sub-band. Powers of frequency sub-bands are determined by spectrogram as a time–frequency representation. Adaptive thresholds are calculated for one of these sub-bands to check if there is a P-wave arrival in the segment or not. To verify this check another test is done using the spectro-ratio. The application of this algorithm on the AE signals from the pencil-breaking test shows a reasonable result.

Identification of impact force on a thick plate based on the elastodynamic and higher-order time-frequency analysis

2007 ◽  
Vol 221 (11) ◽  
pp. 1249-1263 ◽  
Author(s):  
S-K Lee ◽  
S Banerjee ◽  
A Mal
Keyword(s):  
Impact Load ◽  
Plate Theory ◽  
Signal To Noise Ratio ◽  
Higher Order ◽  
Accurate Estimation ◽  
Arrival Times ◽  
Time Frequency ◽  
Novel Approach ◽  
Time Histories ◽  
The Impact

The current paper presents a novel approach to precisely locate and characterize an impact load in thick plates. The approach is based on the analysis of the acoustic waveforms measured by a sensor array located on the plate surface in combination with the theoretical Green's function for the plate. The Green's functions are derived based on either the exact elastodynamic theory or an approximate shear deformation plate theory. For accurate estimation of the location of the impact source, the time differences in the arrival times of the waves at the sensors and their propagation velocities are determined first. This is accomplished through the use of a combined higher-order time-frequency (CHOTF) method, which is capable of detecting signals with lower signal to noise ratio compared with other available methods. Since most of the energy in the wave is carried by the flexural waves ( A0 mode), the group velocity of this mode is extracted using the CHOTF technique for estimating the impact source location. The estimates are shown to be in excellent agreement with the actual locations and time histories of the applied impact loads.

Convolutional neural networks for microseismic waveform classification and arrival picking

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA227-WA240 ◽  
Author(s):  
Guoyin Zhang ◽  
Chengyan Lin ◽  
Yangkang Chen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Data Sets ◽  
Data Set ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Microseismic Data ◽  
Frequency Components ◽  
Specific Frequency

Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.

scholarly journals Multifractal Analysis of Acoustic Emissions during Hydraulic Fracturing Experiments under Uniaxial Loading Conditions: A Niutitang Shale Example

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Jingqiang Tan ◽  
Jun Xie ◽  
Lei Li ◽  
Qiao Lyu ◽  
Jianqiang Han ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Multifractal Analysis ◽  
Uniaxial Loading ◽  
Hydraulic Fractures ◽  
Time Frequency ◽  
Fracturing Process ◽  
Fracturing Experiments ◽  
Fracture Scale ◽  
Ae Signals ◽  
Multifractal Method

Fracture characterization is essential for estimating the stimulated reservoir volume and guiding subsequent hydraulic fracturing stimulations in shale reservoirs. Laboratory fracturing experiments can help provide theoretical and technical guidance for field operations. In this study, hydraulic fracturing experiments on the shale samples from Niutitang Formation in Hunan Province (China) under a uniaxial loading condition are conducted. The multifractal method is used to analyze the acoustic emission (AE) signals and characterize fracture initiation and propagation. The hydraulic fracturing process can be divided into three stages based on the characteristics of AE signals: the initial stage, the quite stage, and the fracturing stage. The multifractal analysis results showed that: (1) the value of the spectrum width, Δα, continues to increase as the energy accumulates until the fracturing stage starts; and (2) the difference in the multifractal spectrum values, Δf, reflects the relationship between small and large signal frequencies and can quantify the fracture scale, i.e., the lower the Δf, the larger the fracture scale and vice versa. The results were further verified using a time-frequency analysis of the AE signals and micro-CT scanning of the samples. This study demonstrates that the multifractal method is feasible for quantitatively characterizing hydraulic fractures and can aid field hydraulic fracturing operations.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

An improved method to estimate Q based on the logarithmic spectrum of moving peak points

2019 ◽  
Vol 7 (2) ◽  
pp. T255-T263 ◽  
Author(s):  
Yanli Liu ◽  
Zhenchun Li ◽  
Guoquan Yang ◽  
Qiang Liu
Keyword(s):  
Seismic Waves ◽  
Wave Attenuation ◽  
Real Data ◽  
Peak Frequency ◽  
Seismic Reflection Data ◽  
The Real ◽  
Time Frequency ◽  
Reflection Data ◽  
Text Filtering ◽  
The Impact

The quality factor ([Formula: see text]) is an important parameter for measuring the attenuation of seismic waves. Reliable [Formula: see text] estimation and stable inverse [Formula: see text] filtering are expected to improve the resolution of seismic data and deep-layer energy. Many methods of estimating [Formula: see text] are based on an individual wavelet. However, it is difficult to extract the individual wavelet precisely from seismic reflection data. To avoid this problem, we have developed a method of directly estimating [Formula: see text] from reflection data. The core of the methodology is selecting the peak-frequency points to linear fit their logarithmic spectrum and time-frequency product. Then, we calculated [Formula: see text] according to the relationship between [Formula: see text] and the optimized slope. First, to get the peak frequency points at different times, we use the generalized S transform to produce the 2D high-precision time-frequency spectrum. According to the seismic wave attenuation mechanism, the logarithmic spectrum attenuates linearly with the product of frequency and time. Thus, the second step of the method is transforming a 2D spectrum into 1D by variable substitution. In the process of transformation, we only selected the peak frequency points to participate in the fitting process, which can reduce the impact of the interference on the spectrum. Third, we obtain the optimized slope by least-squares fitting. To demonstrate the reliability of our method, we applied it to a constant [Formula: see text] model and the real data of a work area. For the real data, we calculated the [Formula: see text] curve of the seismic trace near a well and we get the high-resolution section by using stable inverse [Formula: see text] filtering. The model and real data indicate that our method is effective and reliable for estimating the [Formula: see text] value.

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