1�� College of Geo- Exploration Science and Technology��Jilin University��Changchun 130026��China; 2�� Wantai Microseismic Lab of School of Earth and Space Sciences��University Science and Technology of China��Hefei 230026�� China ; 3�� Laboratory of Seismology and Physics of Earth's Interior��University of Science and Technology of China��Hefei 230026��China

When using borehole sensors and microseimic events to image��spatial aliasing often occurred be- cause of the lack of sensors underground and the distance between the sensors which were too large�� To solve the aliasing problem��data reconstruction is often needed�� Curvelet transform sparsity constrained inversion was widely used in the seismic data reconstruction field for its anisotropic��multiscale and local basis�� However��for the downhole case��because the number of sampling point is much larger than the number of the sensors��the advantage of the curvelet basis can't perform very well�� To mitigate the problem��the method that joints spline and curvlet- based compressive sensing was proposed�� First��we applied the spline interpolation to the first arri- vals that to be interpolated�� And the events are moved to a certain direction��such as horizontal��which can be represented by the curvelet basis sparsely�� Under the spasity condition��curvelet- based compressive sensing was applied for the data��and directional filter was also used to mute the near vertical noises�� After that��the events were shifted to the spline line to finish the interpolation workflow�� The method was applied to a synthetic mod- el��and better result was presented than using curvelet transform interpolation directly�� We applied the method to a real dataset��a microseismic downhole observation field data in Nanyang��using Kirchhoff migration method to image the microseimic event�� Compared with the origin data��artifacts were suppressed on a certain degree��