1.College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130026, China 2.Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, Jilin University, Changchun 130026, China
Fund Project:Project supported by the Excellent Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 41722405), the National Natural Science Foundation of China (Grant Nos. 41874209, 41974208), the Key Research Projects of Jilin Provincial Science and Technology Department, China (Grant No. 20180201017GX), the Science and Technology Development Projects of Jilin Province, China (Grant No. 20180520183JH), and the Interdisciplinary Program for Doctoral Students of Jilin University, China (Grant No. 101832020DJX066)
Received Date:02 December 2020
Accepted Date:07 April 2021
Available Online:07 June 2021
Published Online:20 August 2021
Abstract:Magnetic resonance sounding (MRS) has the advantage of detecting groundwater content directly without drilling, but the signal-to-noise ratio (SNR) is extremely low which limits the application of the method. Most of the current researches focus on eliminating spikes and powerline harmonic noise in the MRS signal, whereas the influence of random noise cannot be ignored even though it is difficult to suppress due to the irregularity. The common method to eliminate MRS random noise is stacking which requires extensive measurement repetition at the cost of detection efficiency, and it is insufficient when employed in a high-level noise surrounding. To solve this problem, we propose a modified short-time Fourier transform(MSTFT) method, in which used is the short-time Fourier transform on the analytical signal instead of the real-valued signal to obtain the high-precision time-frequency distribution of MRS signal, followed by extracting the time-frequency domain peak amplitude and peak phase to reconstruct the signal and suppress the random noise. The performance of the proposed method is tested on synthetic envelope signals and field data. The using of the MSTFT method to handle a single recording can suppress the random noise and extract MRS signals when SNR is more than –17.21 dB. Compared with the stacking method, the MSTFT achieves an 27.88dB increase of SNR and more accurate parameter estimation. The findings of this study lay a good foundation for obtaining exact groundwater distribution by utilizing magnetic resonance sounding. Keywords:MRS/ random noise/ modified short-time Fourier transform/ data processing
$\begin{split} {E_{{\rm{noise}}}}\left( k \right) =\;& {N_{\left( {0,1} \right)}}\left( k \right) \cdot x\left( k \right) \\ &\times \sqrt {{{\left( {\frac{{{V_{{\rm{bac}}}}\left( k \right)}}{{x\left( k \right)}}} \right)}^2} + V_{{\rm{uni}}}^2\left( k \right)} ,\end{split}$
其中, k为离散时间样本, ${N_{\left( {0, 1} \right)}}\left( k \right)$表示均值为0标准差为1 nV的高斯噪声, ${V_{{\rm{bac}}}}\left( m \right)$表示背景噪声, 考虑到结构噪声等非特定噪声的存在, ${V_{{\rm{uni}}}}\left( m \right)$为加入的均匀噪声. 图4给出了不同噪声条件下MSTFT方法对随机噪声的消除效果. 信号1中背景噪声${V_{{\rm{bac}}}}\left( m \right)$是均值为0标准差为50 nV的高斯噪声, 均匀噪声${V_{{\rm{uni}}}}\left( m \right)$是1%的理想信号值, 加入噪声后信噪比为0.68 dB. 然后应用MSTFT算法处理噪声, 图4(a)为含噪信号的高精度时频幅度谱, 噪声在时频域内随机分布, 但是对信号峰值影响较弱, 峰值出现在0 Hz附近, 幅度在300 nV以内. 沿频率轴提取各个时刻幅度和相位的最大值后重构信号, 信号时域和频域的处理结果分别如图4(b)和图4(c)所示, 其中灰色曲线表示含噪信号, 黑色曲线为采用MSTFT方法处理数据后重构的MRS信号, 红色曲线为仿真信号, 可以看出, 随机噪声成分被消除, 信号得以保留. 消噪之后提取的信号参数和SNR, RMSE如表1首行所列, 参数提取结果E0 = (200.19 ± 5.01) nV, $T_2^* $ = (149.2 ± 2.8)ms, $\Delta f$ = (–0.03 ± 0.01) Hz, 信号SNR提升为32.67 dB, RMSE为(1.03 ± 0.55) nV. 为了测试该方法在较高噪声水平下的有效性, ${V_{{\rm{bac}}}}\left( m \right)$的标准差增大为100 nV, ${V_{{\rm{uni}}}}\left( m \right)$增大为理想信号的3%, 使信号2的信噪比为–5.20 dB. 表1的中间行为MSTFT方法对信号2的消噪结果, 提取参数E0, $T_2^* $ 和$\Delta f$分别为(202.61 ± 7.90) nV, (152.0 ± 11.8) ms和(0.04 ± 0.03) Hz, SNR提高到24.01 dB, RMSE为(3.79 ± 1.89) nV. 最后, 加入干扰程度严重的随机噪声, ${V_{{\rm{bac}}}}\left( m \right)$标准差为200 nV, ${V_{{\rm{uni}}}}\left( m \right)$为5%理想信号, 信噪比降低至–11.22 dB. 从图4(h)的时间序列上看, 在随机噪声干扰较大的情况下, MSTFT方法仍然具有良好的消噪性能, 由图4(g)可以看出, 虽然随机噪声水平增大了, 但是由于其随机分布而不能在时频域产生聚集性, 因此对信号峰值处幅度和相位影响较小, 可以直接提取而不损耗信号信息. 消噪后MRS信号的参数提取结果E0为(204.12 ± 14.07) nV, $T_2^* $ 为(154.4 ± 14.5) ms, $\Delta f$为(0.04 ± 0.06) Hz, 信噪比SNR和均方根误差RMSE分别为20.81 dB和(5.81 ± 2.42) nV. 图 4 3组仿真随机噪声消噪结果图 (a)低噪声强度下仿真数据高精度时频域振幅; (b) 低噪声强度下消噪结果时域图; (c) 低噪声强度下消噪结果频域图; (d) 中噪声强度下仿真数据高精度时频域振幅; (e) 中噪声强度下消噪结果时域图; (f) 中噪声强度下消噪结果频域图; (g) 高噪声强度下仿真数据高精度时频域振幅; (h) 高噪声强度下消噪结果时域图; (i) 高噪声强度下消噪结果频域图 Figure4. The de-noising results of 3 sets of random noise simulation: (a) High-precision time-frequency domain amplitude of simulated data under low noise intensity; (b) time domain results under low noise intensity; (c) frequency domain results under low noise intensity; (d) high-precision time-frequency domain amplitude of simulated data under moderate noise intensity; (e) time domain results under moderate noise intensity; (f) frequency domain results under moderate noise intensity; (g) high-precision time-frequency domain amplitude of simulated data under high noise intensity; (h) time domain results under high noise intensity; (i) frequency domain results under high noise intensity.
E0/nV
$T_2^* $ /ms
$\Delta f$ / Hz
SNR/dB
RMSE/nV
信号1 (SNR = 0.68 dB)
200.19 ± 3.01
149.2 ± 2.8
–0.03 ± 0.01
32.67
1.03 ± 0.55
信号2 (SNR = –5.20 dB)
202.61 ± 4.90
152.0 ± 6.7
0.04 ± 0.03
24.01
3.79 ± 1.89
信号3 (SNR = –11.22 dB)
204.12 ± 5.96
154.4 ± 12.8
0.04 ± 0.06
20.81
5.81 ± 2.42
表13组仿真随机噪声消噪后参数估计情况表 Table1.The parameter estimation after de-noising of 3 sets of simulated random noise.
24.2.实测噪声与仿真信号的消噪示例 -->
4.2.实测噪声与仿真信号的消噪示例
为了进一步验证MSTFT方法的有效性, 本节进行了另一组仿真实验, 由(2)式给出的仿真信号中E0 = 200 nV, $T_2^* $ = 100 ms, $\Delta f$ = 0 Hz, ${\varphi _0}$ = 57°, 信号持续时间为256 ms, 信号长度为596. 加入在两个不同地点进行MRS实验采集的两组实测噪声数据, 由于噪声记录中既包含尖峰噪声和工频谐波噪声, 又包含随机噪声, 因此先采用统计叠加法和自适应陷波法消除尖峰噪声和工频谐波噪声[16], 只保留随机噪声, 并对随机噪声进行重采样, 使其与仿真信号长度相等. 然后采用MSTFT方法分别对两个测点数据中的一次噪声记录进行随机噪声压制, 并将结果与叠加法处理同一脉冲矩数据的结果进行对比. 由于测点2的噪声水平略高于测点1, 因此叠加法处理随机噪声时, 对测点1的每组脉冲矩数据叠加16次, 测点2的每组脉冲矩数据叠加32次. 图5给出了传统叠加法和改进短时傅里叶变换方法压制随机噪声的对比结果, 从第一列图中可以看出, 叠加法处理数据后的结果中仍残留随机噪声成分, 而图5中间列中, MSTFT方法处理单次噪声记录, 随机噪声抑制效果明显, 信号衰减趋势与仿真信号接近, 并且与传统叠加法相比, 该方法在较低信噪比下仍能提取所需信号. 图 5 叠加法和MSTFT方法消除随机噪声效果对比图 (a) 叠加法消除随机噪声时域图; (b) MSTFT方法消除随机噪声时域图; (c) 叠加法和MSTFT方法消除随机噪声频域对比图 Figure5. Comparison of the de-noising results by using stacking and MSTFT methods: (a) Results of random noise elimination by stacking in time domain; (b) results of random noise elimination by MSTFT in time domain; (c) comparison of the de-noising results by using stacking and MSTFT methods in frequency domain.