A noise-reduction method with sliding called the local f-x Cadzow noise-reduction method, windows in the frequency-space （f-x） domain, is presented in this paper. This method is based on the assumption that the sign...A noise-reduction method with sliding called the local f-x Cadzow noise-reduction method, windows in the frequency-space （f-x） domain, is presented in this paper. This method is based on the assumption that the signal in each window is linearly predictable in the spatial direction while the random noise is not. For each Toeplitz matrix constructed by constant frequency slice, a singular value decomposition （SVD） is applied to separate signal from noise. To avoid edge artifacts caused by zero percent overlap between windows and to remove more noise, an appropriate overlap is adopted. Besides flat and dipping events, this method can enhance curved and conflicting events. However, it is not suitable for seismic data that contains big spikes or null traces. It is also compared with the SVD, f-x deconvolution, and Cadzow method without windows. The comparison results show that the local Cadzow method performs well in removing random noise and preserving signal. In addition, a real data example proves that it is a potential noise-reduction technique for seismic data obtained in areas of complex formations.展开更多
The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in ...The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.展开更多
目的分析峰度在非稳态噪声暴露所致汽车制造业作业人员高频听力损失中的作用,为补充和修订我国现有噪声评估标准提供依据。方法选取汽车制造业916名噪声作业人员为观察组,同时选择208名接触稳态噪声的作业人员为对照组,记录噪声波形,计...目的分析峰度在非稳态噪声暴露所致汽车制造业作业人员高频听力损失中的作用,为补充和修订我国现有噪声评估标准提供依据。方法选取汽车制造业916名噪声作业人员为观察组,同时选择208名接触稳态噪声的作业人员为对照组,记录噪声波形,计算峰度及能量指标8 h等效声级(equivalent weighted sound level,L_(EX,8h))和累积噪声暴露量(cumulative noise exposure,CNE);测试双耳纯音气导听阈,计算3、4、6 kHz高频永久性听阈位移(Noise-induced permanent threshold shift at frequencise of 3,4,6 kHz,NIPTS_(346))及高频听力损失检出率(the prevalence of high frequency noise-induced hearing loss,HFNIHL%)。结果调查对象平均年龄为(35.1±8.7)岁,平均工龄为(9.4±8.1)年,男性占73.49%,接触的噪声L_(EX,8h)平均水平为(89.31±5.0)dB(A),CNE为(97.15±6.80)dB(A)·年,峰度为13.7(7.60,26.40),总体HFNIHL%为24.82%。经趋势χ^(2)检验,随着年龄(χ^(2)=28.032,P<0.05)、工龄(χ^(2)=52.697,P<0.05)、峰度(χ^(2)=22.341,P<0.05)、L_(EX,8h)(χ^(2)=7.206,P<0.05)的增加,高频听力损失检出率逐渐增加;经logistics回归分析,工龄、峰度和L_(EX,8h)是影响HFNIHL的主要因素;多元线性回归分析,工龄(b=1.14)峰度(b=2.66)和L_(EX,8h)(b=3.27)均对NIPTS346产生影响。应用峰度调整CNE后(CNE-K),非稳态噪声组的CNE-K和HFNIHL患病率的线性方程与稳态噪声组的方程几乎重合,两组之间的HFNIHL%平均差值从10.82%降至1.74%。结论除噪声能量外,噪声峰度也是评估非稳态噪声所致听力损失的一个重要参量。展开更多
目的观察13个典型工种的噪声暴露特征分布情况,评估ISO-1999对我国制造业工人永久性听阈位移(Noise-induced permanent threshold shift,NIPTS)的预测效果,为诊断和预防复杂噪声引起的职业性听力损失提供依据。方法通过横断面调查,选择1...目的观察13个典型工种的噪声暴露特征分布情况,评估ISO-1999对我国制造业工人永久性听阈位移(Noise-induced permanent threshold shift,NIPTS)的预测效果,为诊断和预防复杂噪声引起的职业性听力损失提供依据。方法通过横断面调查,选择1404名制造业工人为研究对象,研究听力损失和噪声暴露特征及流行病学分布,分析ISO-1999预测值与NIPTS实测值之间的差异,探究峰度与该差异之间的关系。结果本次调查的4个行业13个工种的1404名制造业工人中,平均噪声暴露水平为(88.6±6.7)dB(A),平均峰度为(68.9±110.9),噪声性高频听力损失患病率(The prevalence of high frequency noise-induced hearing loss,HFNIHL%)为35.6%,平均高频噪声性永久性听阈位移(Noise-induced permanent threshold shift at frequencies of 3,4,and 6 kHz,NIPTS_(346))为(24.2±13.0)dB HL。除织造工和纺纱工,其余各工种ISO-1999预测值与NIPTS实测值差异均有统计学意义(P<0.05);对峰度进行分层分析,NIPTS实测值和预测值之间均存在统计学差异(t_(1)=4.801,t_(2)=11.468,t_(3)=13.247,t_(4)=9.498,P<0.05);不同峰度的低估值之间存在统计学差异(F=5.082,P<0.05),随峰度的升高而增加。结论制造业接噪工种分布广泛,高噪声水平、长期暴露和噪声复杂的时域结构是NIHL的风险因素;不同工序噪声源产生独特的随时间变化而变化的噪声时域波形;ISO-1999低估了工人实际的听力损失,低估值随峰度的升高而增加。展开更多
基金support from the National Key Basic Research Development Program(Grant No.2007CB209600)National Major Science and Technology Program(Grant No.2008ZX05010-002)
文摘A noise-reduction method with sliding called the local f-x Cadzow noise-reduction method, windows in the frequency-space （f-x） domain, is presented in this paper. This method is based on the assumption that the signal in each window is linearly predictable in the spatial direction while the random noise is not. For each Toeplitz matrix constructed by constant frequency slice, a singular value decomposition （SVD） is applied to separate signal from noise. To avoid edge artifacts caused by zero percent overlap between windows and to remove more noise, an appropriate overlap is adopted. Besides flat and dipping events, this method can enhance curved and conflicting events. However, it is not suitable for seismic data that contains big spikes or null traces. It is also compared with the SVD, f-x deconvolution, and Cadzow method without windows. The comparison results show that the local Cadzow method performs well in removing random noise and preserving signal. In addition, a real data example proves that it is a potential noise-reduction technique for seismic data obtained in areas of complex formations.
基金supported financially by the National Natural Science Foundation(No.41174117)the Major National Science and Technology Projects(No.2011ZX05031–001)
文摘The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.
文摘目的分析峰度在非稳态噪声暴露所致汽车制造业作业人员高频听力损失中的作用,为补充和修订我国现有噪声评估标准提供依据。方法选取汽车制造业916名噪声作业人员为观察组,同时选择208名接触稳态噪声的作业人员为对照组,记录噪声波形,计算峰度及能量指标8 h等效声级(equivalent weighted sound level,L_(EX,8h))和累积噪声暴露量(cumulative noise exposure,CNE);测试双耳纯音气导听阈,计算3、4、6 kHz高频永久性听阈位移(Noise-induced permanent threshold shift at frequencise of 3,4,6 kHz,NIPTS_(346))及高频听力损失检出率(the prevalence of high frequency noise-induced hearing loss,HFNIHL%)。结果调查对象平均年龄为(35.1±8.7)岁,平均工龄为(9.4±8.1)年,男性占73.49%,接触的噪声L_(EX,8h)平均水平为(89.31±5.0)dB(A),CNE为(97.15±6.80)dB(A)·年,峰度为13.7(7.60,26.40),总体HFNIHL%为24.82%。经趋势χ^(2)检验,随着年龄(χ^(2)=28.032,P<0.05)、工龄(χ^(2)=52.697,P<0.05)、峰度(χ^(2)=22.341,P<0.05)、L_(EX,8h)(χ^(2)=7.206,P<0.05)的增加,高频听力损失检出率逐渐增加;经logistics回归分析,工龄、峰度和L_(EX,8h)是影响HFNIHL的主要因素;多元线性回归分析,工龄(b=1.14)峰度(b=2.66)和L_(EX,8h)(b=3.27)均对NIPTS346产生影响。应用峰度调整CNE后(CNE-K),非稳态噪声组的CNE-K和HFNIHL患病率的线性方程与稳态噪声组的方程几乎重合,两组之间的HFNIHL%平均差值从10.82%降至1.74%。结论除噪声能量外,噪声峰度也是评估非稳态噪声所致听力损失的一个重要参量。
文摘目的观察13个典型工种的噪声暴露特征分布情况,评估ISO-1999对我国制造业工人永久性听阈位移(Noise-induced permanent threshold shift,NIPTS)的预测效果,为诊断和预防复杂噪声引起的职业性听力损失提供依据。方法通过横断面调查,选择1404名制造业工人为研究对象,研究听力损失和噪声暴露特征及流行病学分布,分析ISO-1999预测值与NIPTS实测值之间的差异,探究峰度与该差异之间的关系。结果本次调查的4个行业13个工种的1404名制造业工人中,平均噪声暴露水平为(88.6±6.7)dB(A),平均峰度为(68.9±110.9),噪声性高频听力损失患病率(The prevalence of high frequency noise-induced hearing loss,HFNIHL%)为35.6%,平均高频噪声性永久性听阈位移(Noise-induced permanent threshold shift at frequencies of 3,4,and 6 kHz,NIPTS_(346))为(24.2±13.0)dB HL。除织造工和纺纱工,其余各工种ISO-1999预测值与NIPTS实测值差异均有统计学意义(P<0.05);对峰度进行分层分析,NIPTS实测值和预测值之间均存在统计学差异(t_(1)=4.801,t_(2)=11.468,t_(3)=13.247,t_(4)=9.498,P<0.05);不同峰度的低估值之间存在统计学差异(F=5.082,P<0.05),随峰度的升高而增加。结论制造业接噪工种分布广泛,高噪声水平、长期暴露和噪声复杂的时域结构是NIHL的风险因素;不同工序噪声源产生独特的随时间变化而变化的噪声时域波形;ISO-1999低估了工人实际的听力损失,低估值随峰度的升高而增加。
