一种可靠的强噪声三分量微地震数据初至拾取方法
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摘要
精确的初至拾取是微地震数据处理中至关重要的环节。主流的长短时窗比法(STA/LTA)和基于自回归模型的赤池信息准则(AR-AIC)方法,对强噪声数据的拾取效果并不理想。为了更为精确的估计强噪声数据初至,提出了一种基于小波多尺度分析(WMA)和AIC算法的联合拾取方法。首先使用WMA对强噪声三分量(3C)微地震数据进行分解,并重构其近似数据作为实际计算数据,同时计算其绝对值的最大值点,来约束AIC计算数据段,最终选取AIC序列的全局最小值点作为其初至点。文中采用合成数据和实测数据对该改进算法进行了验证,拾取结果表明该算法能有效适用于强噪声微地震数据初至拾取,并明显提高其拾取精度(误差在0.25～0.5 ms之间)。
Accurate first-arrival picking plays a significant role in microseismic data processing.The dominate methods,i.e.,short-term average to long-term average ratio( STA/LTA) and the autoregressive( AR) model using Akaike Information Criterion( AIC) algorithm,are not optimal for picking up high-noise data.In order to optimize the first-arrival point estimation of high-noise data,the authors propose an improved picking method based on wavelet multi-scale analysis( WMA) and AIC algorithm.In this algorithm,WMA is used to decompose the high-noise three-component( 3 C) microseismic data,and the actual calculation data are reconstructed based on the approximation data.Then the maximum value of the absolute value is calculated to constrain the AIC calculation data segment; on such a basis,the global minimum value of the AIC sequence is selected as its first arrival point.The improved algorithm is verified by both synthetic data and field data in this paper.The results show that the improved first-arrival picking algorithm can be effectively applied to high-noise three component microseismic data processing and greatly enhance its accuracy( the error range is 0.25 ～ 0.5 ms).
Accurate first-arrival picking plays a significant role in microseismic data processing.The dominate methods,i.e.,short-term average to long-term average ratio( STA/LTA) and the autoregressive( AR) model using Akaike Information Criterion( AIC) algorithm,are not optimal for picking up high-noise data.In order to optimize the first-arrival point estimation of high-noise data,the authors propose an improved picking method based on wavelet multi-scale analysis( WMA) and AIC algorithm.In this algorithm,WMA is used to decompose the high-noise three-component( 3 C) microseismic data,and the actual calculation data are reconstructed based on the approximation data.Then the maximum value of the absolute value is calculated to constrain the AIC calculation data segment; on such a basis,the global minimum value of the AIC sequence is selected as its first arrival point.The improved algorithm is verified by both synthetic data and field data in this paper.The results show that the improved first-arrival picking algorithm can be effectively applied to high-noise three component microseismic data processing and greatly enhance its accuracy( the error range is 0.25 ～ 0.5 ms).
引文
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[12]宋维琪,吕世超.基于小波分解与Akaike信息准则的微地震初至拾取方法[J].石油物探,2011,50(1):14-21.Song W Q,Lv S C. Automatic detection method of microseismic event based on wavelet decomposition and Akaike information criteria[J]. Geophysical Prospecting for Petroleum,2011,50(1):14-21.
[13] Mousavi S M,Langston C A.Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding,Bulletin of the Seismological Society of America 106,2016:1380-1393.
[14]牟力,陈召曦.重力资料多尺度分析最优小波基的选择[J].物探与化探,2015,39(5):1013-1019.Mou L,Chen Z X. The optimal choice of wavelet bases in gravity data multi-scale analysis[J].Geophysical and Geochemical Exploration,2015,39(5):1013-1019.
[15] Führ H,Tousi R R.Simplified vanishing moment criteria for wavelets over general dilation groups,with applications to abelian and shearlet dilation groups[J]. Applied and Computational Harmonic Analysis 43,2017:449-481.
[16] Aguilera E,Nannini M,Reigber A. Wavelet-based compressed sensing for SAR tomography of forested areas[J]. IEEE Transactions on Geoscience and Remote Sensing 51,2013:5283-5295.
[2] Pugh D J,White R S,Christie P A F.Automatic Bayesian polarity determination[J]. Geophysical Journal International,2016(1):146.
[3]张唤兰,朱光明,王云宏.基于时窗能量比和AIC的两步法微震初至自动拾取[J].物探与化探,2013,37(2):269-273.Zhang H L,Zhu G M,Wang Y H.Automatic microseismic event detection and picking method[J]. Geophysical and Geochemical Exploration,2013,37(2):269-273.
[4] Zhu M,Wang L,Liu X,et al.Accurate identification of P-and Sphase arrivals using the multi-step AIC algorithm[J]. Journal of Applied Geophysics,2018:150.
[5]杨晶惠,庹先国,蒋鑫,等.一种具有抗噪性的P波初至自动拾取方法[J].金属矿山,2015(2):113-116.Yang J H,Tuo X G,Jiang X,et al.A first arrival automatic picking method of P-wave with noise resistence performance[J]. Metal mine,2015(2):113-116.
[6]谭玉阳,于静,冯刚,等.微地震事件初至拾取SLPEA算法[J].地球物理学报,2016,59(1):185-196.Tan Y Y,Yu J,Feng G,et al.Arrival picking of microseismic events using the SLPEA algorithm[J]. Chinese J. Geophys.(in Chinese),2016,59(1):185-196.
[7]秦晅,宋维琪.基于时窗能量比与互信息量的微地震初至拾取方法[J].物探与化探,2016,40(2):374-379.Qin X,Song W Q. Automatic first arrival pickup method of microseismic event based on energy ratio and mutual information[J].Geophysical and Geochemical Exploration,2016,40(2):374-379.
[8]贾瑞生,谭云亮,孙红梅,等.低信噪比微地震P波震相初至自动拾取方法[J].煤炭学报,2015,40(8):1845-1852.Jia R S,Tan Y L,Sun H M,et al.Method of automatic detection on micro-seismic P-arrival time under low signal to noise ratio[J].Journal of China Coal Society,2015,40(8):1845-1852.
[9] Li H,Tuo X,Shen T,et al.A new first break picking for threecomponent VSP data using gesture sensor and polarization analysis[J].Sensors,2017,17(9):2150.
[10] Maeda N.A method for reading and checking phase times in autoprocessing system of seismic data[J].Zisin,1985,38:365-380.
[11]沈统,庹先国,李怀良,等.利用偏振约束对最小信息准则方法自动拾取微地震初至的改进[J].科学技术与工程,2017,17(23):26-30.Shen T,Tuo X G,Li H L,et al.An improved AIC method for automatic micro-seismic data arrival picking with the constraint by polarization[J].Science Technology and Engineering,2017,17(23):26-30.
[12]宋维琪,吕世超.基于小波分解与Akaike信息准则的微地震初至拾取方法[J].石油物探,2011,50(1):14-21.Song W Q,Lv S C. Automatic detection method of microseismic event based on wavelet decomposition and Akaike information criteria[J]. Geophysical Prospecting for Petroleum,2011,50(1):14-21.
[13] Mousavi S M,Langston C A.Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding,Bulletin of the Seismological Society of America 106,2016:1380-1393.
[14]牟力,陈召曦.重力资料多尺度分析最优小波基的选择[J].物探与化探,2015,39(5):1013-1019.Mou L,Chen Z X. The optimal choice of wavelet bases in gravity data multi-scale analysis[J].Geophysical and Geochemical Exploration,2015,39(5):1013-1019.
[15] Führ H,Tousi R R.Simplified vanishing moment criteria for wavelets over general dilation groups,with applications to abelian and shearlet dilation groups[J]. Applied and Computational Harmonic Analysis 43,2017:449-481.
[16] Aguilera E,Nannini M,Reigber A. Wavelet-based compressed sensing for SAR tomography of forested areas[J]. IEEE Transactions on Geoscience and Remote Sensing 51,2013:5283-5295.