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In this study, we have developed and implemented a new technology capable of probabilistically selecting phase arrival times and determining the initial polarity of seismic waveforms without the requirement of prior information. In this new method, the arrival time is determined through an eigen-equation associated with the probability distribution of the noise level, which is then used to calculate the probability of the polarity. We have tested this method using synthetic waveforms as well as records from well-established databases. The results demonstrate a high degree of concurrence with manually picked arrival times and polarities (98% accuracy) in the local seismic catalog. This suggests that the proposed method can provide consistent and unified judgments in phase picking tasks. In comparison, this method has shown comparable reliability to existing neural-network-based AI methods while maintaining greater portability due to its lack of dependence on training data. Graphical abstract

Acoustic emission (AE) monitoring technology has been widely used in rock engineering. In addition, the accurate picking of P-wave arrival times is the key to in-depth rock mechanics and AE research. Autoregressive (AR) model-based algorithms such as AR-Akaike information criterion (AR-AIC) and AR-Bayesian information criterion (AR-BIC) are efficient methods currently adopted for P-wave arrival time picking; however, their picking results sometimes have large errors, owing to the complexity of amplitude distribution in the time series of AE waveform data. To minimize these errors, this study improved the adopted algorithms by leveraging the surge phenomenon in AR model variance. Specifically, a single-segment AR-BIC algorithm was developed by pre-processing the time series of AE waveform data. It was verified that the improved AR-BIC algorithm achieved a 50% higher efficiency than the conventional algorithm, including a picking accuracy above 98.5%.

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.

Time picking is a crucial step in microseismic data processing. The picking results have a great influence on the orientation of hypocenter location. Especially when the signal-to-noise ratio (SNR) of data is low, it is difficult to obtain arrival times accurately with conventional approaches. To solve the question above, this paper proposes a new time-picking approach based on the Akaike Information Criterion (AIC) and shearlet transform named the shearlet-AIC, which can accurately pick the arrival times. With the proposed method, the downhole microseismic data can be divided into several scales according to the different statistical characteristic between signals and noise and obtain the feature of a different frequency domain. We can acquire the minimum values that represent the picking results in every scale of the frequency domain by using the shearlet-AIC. To verify the reliability of the proposed method, we conduct it on both synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that our method can precisely pick the arrival times of P-waves even when the SNR of data is as low as − 8 dB and the accuracy is superior to the other methods mentioned in our paper.

The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer (OBS) refraction data. Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors, especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval. In this paper, a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented. First, the distribution law of theoretical error is examined; adjacent traces exhibit variation characteristics in their waveforms. Second, a label cross-correlation superposition method for extracting high-frequency signals is presented to enhance the first-arrival picking precision. Results from synthetic and field data verify that the proposed approach is robust, successfully overcomes the limitations of low sampling frequency, and achieves precise outcomes that are comparable with those of high-sampling-frequency data.

In this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Although the two algorithms lead to similar results in the simulated scenarios, the proposed algorithm results in greater flexibility and automation capacity, as shown in the real data analysis. Indeed, our proposed algorithm does not require testing and optimization phases, resulting potentially very useful in earthquakes routine analysis for novel seismic networks or in regions whose earthquakes characteristics are unknown.

From the seismologists’ point of view, it is extremely important to accurately detect the first P wave arrival time. The P wave arrivals have considerable information about events such as location, magnitude, mechanism, and source parameters. In the classic methods, P wave pickings have been accomplished manually in a visual way. But in the era of information and communication technology, it can be done by computer programs. Seismologists have developed many methods for the picking of the first arrival time of P wave. The wavelet transform is one of the methods to analyze the arrival times and useful for picking up the singularities of any function. Decomposing signals by wavelet transform is a master key to the study of time-frequency varying signals such as earthquake seismograms. This paper presents P phase picking without any prior information using undecimated wavelet transform. For undertaking this study, a simple envelope characteristic function is used for P phase picking. The proposed method is tested on 5 earthquakes recorded by the Fnet network in Japan that have varying signal-to-noise ratio levels for calibrating. Then the method is applied on 50 earthquakes. The observed results are compared with manual phase picking and standard STA/LTA method. The wavelet base method shows the higher accuracy of phase picking in event detection and time picking, respect to the standard STA/LTA method, when compared to manual picking.