提出了基于二进小波变换快速Ｋａｌｍａｎ滤波自适应反褶积方法．该方法对时变非平稳信号分时分频精细地进行快速Ｋａｌｍａｎ滤波自适应反褶积，比时域内的预测反褶积和时域内的快速Ｋａｌｍａｎ滤波自适应反褶积分辨率高．它抛弃了预测反褶积对信号平稳性的假设，具有明显的抗噪能力，比传统的自适应Ｋａｌｍａｎ滤波反褶积运算速度大大提高。经大量的模型及实际资料处理表明该方法具有明显的效果．该算法除了适合于处理地震信号外，也可以借鉴应用到其它类似信号的处理． A fast Kalman filter adaptive deconvolution method based on binary wavelet transform is proposed. The proposed method performs fast Kalman filtering adaptive deconvolution on the time-varying and time-varying non-stationary signals, which has a higher resolution than the fast de-convolution in the time domain and the fast Kalman filtering in the time domain. It abandons the hypothesis of predicting de-convolution for signal smoothness and has obvious anti-noise ability, which is much faster than the traditional adaptive Kalman filter deconvolution algorithm. After a large number of models and actual data processing shows that the method has obvious effect. In addition to being suitable for processing seismic signals, the algorithm can also be used for processing applied to other similar signals.