Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the reflection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold filtering(CTF)is performed by using the difference of the curvelet coefficient of the reflection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coefficient.Under the constraints of seismic wavelet and local weighting coefficient,the L1 norm of the reflection coefficient is minimized in the curvelet domain to recover the damaged reflection wave and attenuate the residual noise.The local weighting coefficient in this paper is obtained by calculating the local correlation coefficient between the high-pass filtering result and the CFT result.We applied the TFWS method to simulate and field data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the reflection events.