A new approach for high fidelity seismic data recovery by fractal interpolation

Recovering accurate data is important for both earthquake and exploration seismology studies, when data are sparsely sampled or partially missing. We present a method that allows for precise and accurate recovery of seismic data using a localized fractal recovery method. This method requires that the data are self-similar on local and global spatial scales. We present examples that show that the intrinsic structure associated with seismic data can be easily and accurately recovered by using this approach. This result, in turn, indicates that seismic data are indeed self-similar on local and global scales. This method is applicable not only for seismic studies, but also for any field studies that require accurate recovery of data from sparsely sampled datasets with partially missing data. Our ability to recover the missing data with high fidelity and accuracy will qualitatively improve the images of seismic tomography.