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Stochastic Seismic Inversion Using Both Waveform And Traveltime Data And Its Application to Time-lapse Monitoring

A stochastic approach to seismic inversion using the ensemble Kalman filter (EnKF) is proposed. Seismic depth and time image data are used as the input for EnKF stochastic seismic inversion. The sonic log is used to estimate source wavelet and create initial models for the inversion, which provides an efficient integration of sonic log data and seismic data. We use both travel time and waveform data for the inversion and obtain the absolute seismic velocity instead of the relative impedance. EnKF can continuously update the model using time-lapse data. A synthetic example is used to demonstrate the possible application to seismic monitoring.