In correlative least-squares reverse time migration (CLSRTM), the estimation of the optimal step size is usually determined by fitting a parabola and finding its minimum; it involves at least two times extra reading of all seismic records, which significantly lowers the efficiency of the algorithm. To improve the efficiency of the CLSRTM algorithm, we have deduced an analytical step-length (ASL) formula based on the linear property of the demigration operator. Numerical examples performed with the data synthetized by the Marmousi and Sigsbee2A models were used to test its validity. In complex models with imperfect migration velocity, such as the Sigabee2A model, our formula makes the value of the objective function converges to a much smaller minimum. Additional numerical tests performed with the data either acquired irregularly or contaminated by different noise levels verify the robustness of the ASL formula. Compared with the commonly used parabolic search method, the ASL formula is much more efficient because it is free from an extra estimation of the value of the objective function.

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