Improved modified energy ratio method using a multi-window approach for accurate arrival picking

- We developed an microseismic event detection algorithm through the use of a multi-window approach.
- We conducted numerical tests using synthetic and real-field microseismic datasets.
- We also conducted various numerical tests to show hypocenter location results for synthetic and real microseismic datasets.

To identify accurately the location of microseismic events generated during hydraulic fracture stimulation, it is necessary to detect the first break of the P- and S-wave arrival times recorded at multiple receivers. These microseismic data often contain high-amplitude noise, which makes it difficult to identify the P- and S-wave arrival times. The short-term-average to long-term-average (STA/LTA) and modified energy ratio (MER) methods are based on the differences in the energy densities of the noise and signal, and are widely used to identify the P-wave arrival times. The MER method yields more consistent results than the STA/LTA method for data with a low signal-to-noise (S/N) ratio. However, although the MER method shows good results regardless of the delay of the signal wavelet for signals with a high S/N ratio, it may yield poor results if the signal is contaminated by high-amplitude noise and does not have the minimum delay. Here we describe an improved MER (IMER) method, whereby we apply a multiple-windowing approach to overcome the limitations of the MER method. The IMER method contains calculations of an additional MER value using a third window (in addition to the original MER window), as well as the application of a moving average filter to each MER data point to eliminate high-frequency fluctuations in the original MER distributions. The resulting distribution makes it easier to apply thresholding. The proposed IMER method was applied to synthetic and real datasets with various S/N ratios and mixed-delay wavelets. The results show that the IMER method yields a high accuracy rate of around 80% within five sample errors for the synthetic datasets. Likewise, in the case of real datasets, 94.56% of the P-wave picking results obtained by the IMER method had a deviation of less than 0.5 ms (corresponding to 2 samples) from the manual picks.