Weak signal characteristics and its identificationin high-density single sensor data

The detection and identification of weak signal is a wellknown technical issue in todayrsquo;s geophysical industry. For high-density single sensor data, there is little information on how weak the signal will be called weak signal and how to detect and identify it in existing academic literatures. Based on theoretical study and combined with analyzing LJ high density data from Shengli Oilfield these questions were touched with and discussed in this paper.We draw the following conclusions: ①In terms of visual resolution, the weak signal is more easily identified when signal to noise ratio S/N2, it may be wrongly identified when S/N=1,and it is basically impossible by visual recognition and interpretation when S/N0.5. 20= 170= 210= for= thin= n= is= the= lower= limit= estimating= its= background= noise= will= significantly= affect= weak= signals= in= deep= part= and= death= value= of= high-density= data= signal= just= amplitude= environmental= noise.= a= single= shares= less= frequency= spectrum.= random= mainly= affects= high= low= spectrum= response= remarkably= altered= even= if= comes= up= to= 5.= has= wide= band= hz.= target= layer= faster= high-frequency= attenuation= at= above= hz= shows= similar= variation= with= difficult= be= horizontal= co-phase= mixed=1) can still be effectively detected after processed with singular value decomposition (SVD), and the S/N=0.5 is the cut-off point determining whether SVD can be used to process the common midpoint (CMP) data after normal moveout (NMO) or not. Even if N/S reaches to 3, it can still be restored by curvelet transform. This gives us an enlightenment that, for high-density single-point data, there is still large potential of identifying more weak signals as long as we use a proper processing technique.