【摘要】：微地震事件初至拾取是微地震數據處理的關鍵步驟之一.實際微地震監(jiān)測資料中存在大量低信噪比事件,而傳統(tǒng)方法對這些事件的應用效果并不理想.為了克服傳統(tǒng)方法抗噪性弱的缺點,本文通過綜合地震信號與環(huán)境噪聲在振幅、偏振以及統(tǒng)計特征等方面的存在的差異,設計了一種針對低信噪比微地震事件的初至拾取方法——SLPEA算法.為了檢驗本文方法的可行性和有效性,分別對模型數據和實際資料進行了處理,并將處理結果與傳統(tǒng)方法及手工拾取的結果進行了對比.分析表明,利用本文方法得到的初至到時與手工拾取結果的絕對誤差平均值僅為1.33×10~(-3)s,小于3個采樣點;方差為3.21×10~(-6)s~2;初至到時在手工拾取結果±0.005s誤差范圍內的個數占總數的95.8%.這些參數值均優(yōu)于傳統(tǒng)方法的同類參數,證明了本文方法的可靠性.
[Abstract]:Picking up the first arrival of microseismic events is one of the key steps in microseismic data processing. There are a large number of low signal-to-noise ratio (SNR) events in the actual microseismic monitoring data, but the application effect of traditional methods on these events is not ideal. In order to overcome the disadvantage of weak anti-noise of traditional methods, this paper synthesizes the differences between seismic signal and environmental noise in amplitude, polarization and statistical characteristics. A SLPEA algorithm is designed to pick up the first arrival of microseismic events with low signal-to-noise ratio (SNR). In order to test the feasibility and effectiveness of the proposed method, the model data and the actual data are processed respectively, and the processing results are compared with the traditional methods and the results picked up by hand. The analysis shows that the average absolute error between the first arrival and manual picking results obtained by this method is only 1.33 脳 10 ~ (- 3) s, which is less than 3 sampling points, and the variance is 3.21 脳 10 ~ (- 6) s 鈮，

本文編號：2495945