基于自适应短时傅里叶变换的品质因子Q值估算方法

品质因子Q是描述地下介质对地震波吸收衰减强弱程度的参数, 同时也是地层含油气性的重要标志。在地震资料Q估算中, 常用的方法是短时傅里叶变换方法, 当窗函数被选定以后, 其时频分辨率就固定了。针对该问题, 提出一种自适应窗短时傅里叶变换的方法, 以获得更准确的瞬时中心频率, 并利用峰值频移法来估算品质因子Q。首先, 利用固定窗长的短时傅里叶变换来提取信号的瞬时中心频率作为初始频率; 然后, 根据初始频率自适应计算不同频率的窗长, 并利用自适应窗长短时傅里叶变换来求取瞬时中心频率; 最后, 结合峰值频移法得到高分辨率的品质因子Q值。利用合成数据和实际数据进行了测试, 结果表明, 相比于固定时窗短时傅里叶变换方法, 自适应短时傅里叶变换方法具有更好的时间和频率分辨率, 可以获得更高分辨率的品质因子Q值。该结果可以为地下介质的研究提供更准确、可靠的工具, 有助于更好地了解地下结构和油气资源分布情况。

The quality factor Q characterizes the absorption and attenuation of seismic waves as they spread through underground media.It is an important indicator of hydrocarbon accumulation.Estimating Q based on time-frequency analysis has emerged as one of the most common and effective methods in seismic data analysis.The short-time Fourier transform (STFT) is a popular time-frequency localization technique with a predetermined size and geometry of the time window.However, the fixed window function limits its adaptability.In order to accurately capture both low and high-frequency components of the signal, it is necessary to adjust the window width based on the signal's characteristics.A wider time window is needed to represent low-frequency components, whereas a narrower time window is required for high-frequency components.We propose an adaptive window length method for the short-time Fourier transform to calculate instantaneous frequency and determine the Q value using the peak-frequency shift method.The core idea is to employ a large window for the short-time Fourier transform to extract the instantaneous center frequency as the initial frequency, which is then used to dynamically select the optimal window length for subsequent calculation.By adapting the window length based on the initial frequency, a better balance between frequency resolution and time resolution is achieved.Both synthetic data tests and field data application demonstrate that the short-time Fourier transform with adaptive window length outperforms the traditional fixed-window short-time Fourier transform in Q estimation with higher vertical resolution and lateral consistency.Our approach enhances the accuracy and reliability of Q-value estimation, which may lead to comprehensive understanding of underground media and potential oil and gas resources.