Abstract
The sensitivity of energy ratio method varies with the size of time window. We propose a multi-time window energy boundary detection method which improves the picking accuracy for data with a low-to-medium signal to noise ratio (SNR). The multi-time window algorithm effectively improves the system reliability of the energy ratio method. Through the distribution of characteristic values in different time windows, the picking results of low signal-to-noise ratio data can be effectively deleted. Then, a small-step fitting algorithm is applied to the remaining first-arrival characteristic values to obtain the final characteristic value evaluation. Based on the retained first-arrival characteristic values, the missing values were assigned by interpolation, then map the result on the original record and finally, first-arrival picking was completed by using a small time window. We test the performance of each module to prove the validity of the proposed methods. Finally, we design and develop auto-picking system, and the application shows that the accuracy of picking up can meet the needs of production.
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