The computation of a dynamic displacement field between pairs of seismic traces has numerous geophysical applications including the estimation of displacement vectors in time lapse seismic, Pto S-wave image registration and residual moveout correction. In this paper, I present a cross-correlation based methodology for the estimation of small time displacements between signals followed by stretc...

S U M M A R Y It has previously been shown that the Green’s function between two receivers can be retrieved by cross-correlating time series of noise recorded at the two receivers. This property has been derived assuming that the energy in normal modes is uncorrelated and perfectly equipartitioned, or that the distribution of noise sources is uniform in space and the waves measured satisfy a hi...

the mixture of natural and artificial seismic sources with random distributions cause diffuse wave field with random amplitudes and phases called noise. when noise is analyzed in a long-term process, it contains surface waves which are spread in all directions. thus, ambient noise contains data relevant to the surface waves. in recent years, as broadband seismic networks have been distributed v...

Repeated cross-correlations of ambient seismic noise indicate a long-term seismic velocity change associated with the 2006 M7.5 slow-slip event (SSE) in the Guerrero region, Mexico. Because the SSE does not radiate seismic waves, the measured velocity change cannot be associated with the response of superficial soil layers to strong shaking as observed for regular earthquakes. The perturbation ...

Two-dimensional and three-dimensional seismic surveys were conducted across the Oquirrh fault, Utah with the purpose of imaging the fault structure to a depth of about 15 m. Results show that the 3-D tomogram clearly delineates the fault zone and a colluvial wedge, both of which correlate extremely well with the geologic cross-section interpreted from an adjacent trench. The thickness of the co...

This paper proposes a classiication scheme based on the integration of multiple Ensembles of ANNs. It is demonstrated on a classiication problem, in which Seismic recordings of Natural Earthquakes must be distinguished from the recordings of Artiicial Explosions. A Redundant Classiication Environment consists of several Ensembles of Neural Networks is created and trained on Bootstrap Sample Set...