Abstract
The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.
Highlights
Microseismic monitoring is capable of offering the real-time characteristics of geostructures by capturing the elastic waves released from rock fractures and realizing the localization of the microseismic events
Due to the fact that microseismic data consists of effective signals and noise, the mission of the first arrival picking of the microseismic signals is similar to the classification of the effective signals and noise from the data
It was apparent in this study that the classical short-term average long-term average rations (STA/LTA) method was too sensitive to noise for first arrival picking times, and it had difficulty meeting the situation requirements when the micro-seismic signals had low signal-to-noise ratios (SNR)
Summary
Microseismic monitoring is capable of offering the real-time characteristics of geostructures by capturing the elastic waves released from rock fractures and realizing the localization of the microseismic events. Cano [44] et al proposed a conditional fuzzy c-means clustering to identify the time intervals of possible wave arrivals, and used the Akaike Information Criterion (AIC) picker to pick up the arrival time These progressive techniques have proven that, with properly selected features, clustering methods can be robust to noise and accurately integrate characteristics of different features in order to pick first arrival times. In order to tackle the aforementioned issues, a first arrival picking method for microseismic data based on a clustering method with automatic feature selection was introduced in this study. This was a two-step method which combined feature selection with clustering for first arrival time determinations. The localizations of the microseismic source using the first arrival times obtained using different methods were compared in order to verify the superiority of the proposed method
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