seismic source estimation is an inverse geophysical problem which is done by different methods and algorithms. in this study, compressed sensing method has been used. by making new samples, the original signal can be reconstructed and the volume of computation is reduced. Examples are linear combinations of signal data and weighting factors in these combinations are random numbers which do not depend on the signal. in this method, the recovery of the source location using the seismic moment tensor and the estimation of the signal under a sparse dictionary that are green's functions is performed. sampling is followed by network search method based on dictionary functions and then it is retrieved on the basis of compressed parameters. it is done using a network of geophones and seismometers is important in various fields of geophysical, such as seismology, hydraulic cracks and the study of volcanoes .simulation, artificial data production and modeling of green functions are written from the method (Bouchon,1989 ) and codes required under matlab software.