oa Processing Methods for Extracting Subsurface Information from Ambient Seismic Noise

The ambient seismic wave field carries information about the subsurface in the vicinity of the recording<br>site. It also carries a lot of less useful information about anthropogenic activities nearby. Potentially<br>interesting variations in the naturally occurring seismic background wave field happen at extremely low<br>power spectral density levels (typically between -120 to -180 dB [w.r.t. 1 m/s]), and is oftentimes<br>masked by noise of anthropogenic origin. In addition, the spatial, temporal and frequency-domain<br>variability of cultural noise often exceeds the variations of the natural background wave field. It is<br>therefore critical to remove any influence of cultural noise from the records before an attempt can be<br>made at analyzing the ambient seismic noise level with respect to any meaningful signatures of<br>subsurface variations. Here, we present methods to characterize the ambient seismic wave field<br>recorded with broadband seismometers and propose methods for extracting subsurface-related<br>information. The analysis includes the characterization of spectral signatures of different types of<br>sources (both anthropogenic and natural origin), as well as spectral signatures that are indicative of<br>the subsurface underneath the recording site. The characterization also drives the selection of certain<br>noise-removal techniques. Due to the typically small amplitude of these subsurface-related variations,<br>it is critical to remove as much of the surface energy as possible, and to correct for near surface effects<br>in the recorded data. Analysis of the cleaned data then allows looking for special attributes that may<br>carry subsurface information. A possibly important tool for achieving these goals is the ratio between<br>vertical and horizontal components (V/H) in the frequency domain. In this domain, the receiver terms<br>contain information about the shallow subsurface, but sometimes also information about fluid content<br>in the deeper subsurface. Both pieces of information can be of use in practical applications. For<br>illustration, we present examples from low-frequency passive seismic (LFPS) surveys with particularly<br>strong anthropogenic noise contaminations. We show that, despite remnant contamination of the<br>records by anthropogenic noise, we observe statistically significant variations of spectral attributes that<br>can be used for subsurface characterization.