Accurate arrival time picking of microseismic events is crucial for understanding subsurface processes and assessing associated risks. However, the low signal-to-noise ratio (SNR) inherent in field microseismic data poses significant challenges. To address the above issues, this article proposes a novel methodology based on sequence self-similarity and multicenter fuzzy clustering (MFC). Initially, the similarity among subsequences at different time points is leveraged to extract the self-similarity feature from the raw data, attenuating the impact of noise and enhancing the data quality. Subsequently, the fuzzy category is introduced between the microseismic waveform and the noise categories to avoid misclassification caused by the traditional binary picking methods. Finally, temporal information and neighboring information are incorporated to reclassify the fuzzy category, thus improving waveform integrity and the accuracy of first arrival picking. We conducted tests using synthetic and field microseismic data to validate the reliability of the proposed method. The results indicate that the proposed method both outperforms traditional statistical methods in picking accuracy and provides automatic high-quality labels for deep learning model training. This work offers a promising tool for real-time monitoring and early warning systems in various subsurface applications.

In microseismic monitoring, the collected data is distorted by noise, bringing difficulties and challenges to the subsequent processing of event identification, localization, and others. Wavelet packet transform (WPT) is one of the most commonly used noise reduction methods, but the usage of threshold can significantly lead to the informative content loss of target signals, especially at high frequency or low signal-to-noise ratio (SNR). To address above issues, this paper proposes a new noise reduction method based on WPT, supported by Fuzzy C-Means clustering (FCM) and k-nearest neighbors (KNN) algorithms. The experiment results show that the proposed method has less performance fluctuation than WPT at low frequencies, and outperforms WPT and FCM at high frequencies and low SNR.

It is crucial to retain as many signal components as possible, while noise is eliminated for wavelet packet noise reduction algorithms. Conventional thresholding functions take coefficients smaller than a given threshold as the noise component and get them removed in many ways. In this letter, considering the potential probability of being the signal-related component of coefficients whose value is smaller than the given threshold and the nonnegligible fact that microseismic (MS) event is sparse compared with the noise, we proposed a new wavelet packet-based denoising method via fuzzy partition. First, instead of a hard partition from a given threshold, wavelet packet coefficients get reduced by a fuzzy partition to retain more potential signal elements and suppress the noise. Second, we employ fuzzy c-means (FCM) clustering to identify the interval period of the MS event further to remove the residual noise and additional resonance in processed time series. We tested our method on synthetic datasets and real-field data from an MS monitoring experiment in a coal mine in Sichuan Basin, China. We utilize Pearson correlation coefficient and root-mean-square error between ideal signal and denoised data as performance indicators in synthetic tests, while sample entropy and kurtosis of denoised data are involved in the real field dataset. Test results from synthetic datasets and the real field dataset demonstrate that the proposed noise reduction method is superior to traditional hard-, soft-, and garrote-thresholding and is more applicable and effective in MS data processing.

Accurate first arrival picking plays a crucial role in microseismic data processing. However, it is challenging to guarantee satisfactory accuracy with conventional approaches when the signal-to-noise ratio (SNR) of data is low. This article proposes an automatic first arrival time picking method based on fuzzy$C$means clustering (FCM) and Akaike information criterion (AIC). The proposed method consists of three steps: clustering, rough picking, and adjusting. First, we employ FCM to divide each data point into the signal cluster and the noise cluster according to a fuzzy partition. Second, unlike conventional FCM-based picking approaches, we utilize Otsu’s method to determine a data-dependent threshold, instead of an artificially predefined one, to obtain a coarse result of the microseismic event interval from clustering partition. Finally, note that the microseismic event data points are concentrated in amplitude and also correlated in time. Therefore, we employ the AIC of the clustering partition to seek time-varying information to adjust the coarse result. Besides, we investigated several commonly used characteristic factors to introduce a supervised guideline for feature selection in first arrival picking with FCM. At last, we carried out simulations and real field data tests to verify the reliability of the proposed method. The experimental results demonstrate that the proposed method outperforms the short-and long-time average ratio (SLTA) method, the AIC method, and the conventional FCM-based picking method.

Accurate picking of first arrival time plays a critical role in event localization and further data processing in microseismic(MS) monitoring. A large amount of data from receivers make effective automatic time picking method an urgent issue. In this letter, we proposed an adaptive automated time picking approach based on fuzzy c-means (FCM) clustering algorithm. First, by applying FCM, data points are assigned to two clusters with certain membership degrees: signal cluster and noise cluster. Then the vector describing data points to signal cluster center is extracted from the membership degree matrix. Second, considering the shortcoming of a preset threshold, correlation coefficient based adaptive selection algorithm is performed to obtain an optimal threshold for MS event picking. Finally, tests on the synthesis and real data illustrate that our approach outperforms the short-term and long-term average ratio (SLTA) and Akaike information criterion (AIC), and it is more robust than the traditional FCM-based picking method.

Joint monitoring is more potent than conventional micro-seismic(MS) monitoring in understanding underground processes. With simultaneous observations from the surface and downhole, joint monitoring has shown its advantages in disaster early-warning in tunneling and mining, station deployment. However, in Sichuan Basin, China, due to the complex environment, it is intuitively more difficult to transport, install, and maintain stations on the surface. Thus, we have developed a new joint monitoring system for real-time and long-term MS observations based on novel self-developed stations. First, we developed a novel lightweight wireless MS sensor with flexible support of power supply from the battery, solar power, even alternating current for surface monitoring. Second, we also developed a wired station powered by the electricity network in the downhole for underground observation. Finally, a case study in a coal mine located in Sichuan Basin was undertaken. The result illustrates advantages of our joint monitoring system in deployment, real- and long-time joint observation.