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Statistical stacking and adaptive notch filter to remove high-level electromagnetic noise from MRS measurements

机译:统计堆栈和自适应陷波滤波器可消除MRS测量中的高水平电磁噪声

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Traditionally, one of the major limitations for magnetic resonance sounding (MRS) measurement is that the weak signal generated by subsurface water molecules is prone to be disturbed by highlevel electromagnetic noise. In China, the power grid coverage is 94.6% and spiky noise and power-line harmonic noise are always present when utilizing MRS measurement in suburban areas or towns. In order to improve the performance of the MRS method, two new techniques, statistical stacking and adaptive notch filter, are introduced to remove spiky noise and power-line harmonic noise. Firstly, four stacking procedures are analysed to suppress the natural noise and spiky noise. It could be found that statistical stacking can be utilized in the areas with serious spiky noise and can improve the signal-to-noise ratio by a factor of 4 to 7. Moreover, the stacking number is less than other stacking procedures and the measurement time may decrease by nearly 50% in some suburban areas or towns. Secondly, there are a variety of filtering procedures available to suppress power-line harmonic noise, which are all based on analogue or digital notch filtering. But nearly all of them may cause distortion. An adaptive notch filter is applied here to remove power-line harmonic noise because harmonic frequencies are away from and (or) close to the Larmor frequency, even when the frequency offset between them is zero. From simulation results, it could be noted that the signal can be recovered after adaptive notch filtering because it is not irretrievably distorted but proportionally attenuated. Thus, the amplitude attenuation can accurately be compensated. The effectiveness of the two techniques applied to MRS measurements is demonstrated by field testing with the prototype of the MRS system developed by Jilin University, China. The results show that the statistical stacking and adaptive notch filter are effective methods to remove high-level electro-magnetic noise from MRS measurements.
机译:传统上,磁共振测深(MRS)测量的主要限制之一是地下水分子产生的微弱信号容易受到高水平电磁噪声的干扰。在中国,电网覆盖率为94.6%,在郊区或城镇使用MRS测量时始终会出现尖峰噪声和电力线谐波噪声。为了提高MRS方法的性能,引入了两种新技术,即统计叠加和自适应陷波滤波器,以消除尖峰噪声和电力线谐波噪声。首先,分析了四种叠加程序以抑制自然噪声和尖峰噪声。可以发现,统计叠加可用于尖刺噪声严重的区域,并且可以将信噪比提高4到7倍。此外,叠加数量比其他叠加程序和测量时间少在某些郊区或城镇中可能会减少近50%。其次,有多种滤波程序可用于抑制电力线谐波噪声,它们全部基于模拟或数字陷波滤波。但是几乎所有这些都可能导致失真。由于谐波频率远离和(或)接近拉莫尔频率,即使它们之间的频率偏移为零,此处也应用自适应陷波滤波器来消除电力线谐波噪声。从仿真结果可以看出,信号可以在自适应陷波滤波后恢复,因为它不会发生不可挽回的失真,但会按比例衰减。因此,可以精确地补偿振幅衰减。通过使用吉林大学开发的MRS系统原型进行现场测试,证明了这两种用于MRS测量的技术的有效性。结果表明,统计叠加和自适应陷波滤波器是从MRS测量中消除高水平电磁噪声的有效方法。

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