Automatic First Arrival Time Identification Using Fuzzy <i>C</i>-Means and AIC

Lan, Zhiqiang; Gao, Peng; Peng, Wang; Wang, Yaojun; Liang, Jiajun; Hu, Guangmin

doi:10.1109/tgrs.2021.3121032

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…In addition, updating the membership degree involves a process that highlights the similarity of feature factors. Compared with the method proposed by [ 9 ], we give a suggested window length (as shown in Figure 6) to extract the corresponding features. By incorporating the more robust feature L , the FCC method greatly enhances signal characteristics while reducing the impact of noise.…”

Section: Methodsmentioning

confidence: 99%

“…Over the past few decades, researchers have proposed numerous well-established methods for first-arrival picking, such as the short/long time average ratio (STA/LTA) [ 7 ], Akaike information criterion (AIC) [ 8 ] and various improved approaches [ 9 , 10 ], wavelet denoising and its improved forms [ 11 , 12 ], PAI-S/K based on skewness and kurtosis [ 13 ], STK/LTK based on the long- and short-time window kurtosis ratio [ 14 ], the Markov optimal decision process [ 15 ], waveform similarity-based approaches [ 16 ], and improved multi-channel cross-correlation [ 17 ]. The STA/LTA offers the advantages of simplicity and computational efficiency in first-arrival picking.…”

Section: Introductionmentioning

confidence: 99%

“…Clustering is an effective unsupervised learning tool employed to classify unlabeled seismic data into distinct clusters [ 43 ]. In recent years, numerous effective clustering methods have been developed for first-arrival picking; fuzzy clustering methods such as fuzzy c-means and its variants [ 9 , 44 , 45 ] have been successfully utilized, and Zhu et al [ 46 ] achieved good results by using three time-domain features (power, mean, and variance) from one-component seismic data as conditions for fuzzy clustering, which also provided us with valuable insights. Additionally, k-means and its improved forms [ 22 , 47 ] have shown promising results for phase picking.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Zhao,

Chen,

et al. 2024

Sensors

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Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings’ polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = −5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Zhao,

Chen,

et al. 2024

Sensors

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show abstract

“…To model the fracture depth, we obtain eight sets of echo signals of the fractures with different depth at the same widths. Then we extract the arrival time of the first echo and the second echo using the AIC arrival time extraction algorithm [52,53], as shown in figure 19. Finally, the arrival time difference between the two echoes can be obtain and are shown in table 5.…”

Section: Fracture Depth Characterization Analysis and Identificationmentioning

confidence: 99%

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

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Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a Self-model Emission Detection (SED), we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a Convolutional Neural Network (CNN) model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

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Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

Cai,

Duan,

Yan

et al. 2024

J Petrol Explor Prod Technol

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Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves.

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Automatic First Arrival Time Identification Using Fuzzy C-Means and AIC

Cited by 6 publications

References 33 publications

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

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