中文摘要：

低频阴影现象为油气识别的一个重要标志.然而对于薄储层,低频阴影现象仍然较弱,因此有必要采用更高时频分辨率的时频分析方法进行低频阴影的识别.将信号的短时傅立叶变换谱与窗函数Wigner-Ville分布进行二维反褶积可得到信号Wigner-Ville分布,该二维反褶积方法即为反褶积短时傅立叶变换.该方法不仅提高了时间和频率分辨率而且减少了交叉项.文中对两种理论信号进行多种时频分析方法的计算机仿真效果对比,结果证明,反褶积短时傅立叶变换与传统的时频分析方法相比更具优势.文中采用反褶积短时傅立叶变换的方法对信号进行时频分析,首次将该方法用于单频剖面的提取.由低频阴影现象的数值模拟结果可知,该方法比广义S变换在薄储层预测中取得了更好的效果.在实际资料的应用中证实了此方法检测含油气储层的可行性.

英文摘要：

The low frequency shadow is important symbol of Oil and Gas Identification. For thin reservoir,the low frequency shadow is not obvious,so we need to recognize low frequency shadow by time-frequency representation methods with higher time-frequency resolution.A 2-D deconvolution operation on the STFT spectrogram of the signal and the WVD of the window function is the WVD of the signal,which is called deconvolutive short-time Fourier transform.The method not only improves the time-frequency resolution but also reduces the cross-terms.In the letter,computer simulations of two theory signals in the use of several time-frequency analysis methods show that this method achieves better results compared with some traditional time-frequency representation methods.Based on the theory of low-frequency shadows,we use the deconvolutive Short-Time Fourier Transform（DSTFT）spectrogram method to do the spectral decomposition and detect oil-gas potential of the reservoirs.Real data processing proves the feasibility of the method to detect oil and gas.