Optimization of Ambient Noise Cross-Correlation Imaging Across Large Dense Array

Abstract:

Ambient Noise Tomography is currently one of the most studied topics of seismology. It gives possibility of studying physical properties of rocks from the depths of subsurface to the upper mantle depths using recorded noise sources. A network of new seismic sensors, which are capable of recording continuous seismic noise and doing the processing at the same time on-site, could help to assess possible risk of volcanic activity on a volcano and help to understand the changes in physical properties of a fault before and after an earthquake occurs. This new seismic sensor technology could also be used in oil and gas industry to figure out depletion rate of a reservoir and help to improve velocity models for obtaining better seismic reflection cross-sections. Our recent NSF funded project is bringing seismologists, signal processors, and computer scientists together to develop a new ambient noise seismic imaging system which could record continuous seismic noise and process it on-site and send Green’s functions and/or tomography images to the network. Such an imaging system requires optimum amount of sensors, sensor communication, and processing of the recorded data. In order to solve these problems, we first started working on the problem of optimum amount of sensors and the communication between these sensors by using small aperture dense network called Sweetwater Array, deployed by Nodal Seismic in 2014. We downloaded ~17 day of continuous data from 2268 one-component stations between March 30-April 16 2015 from IRIS DMC and performed cross-correlation to determine the lag times between station pairs. The lag times were then entered in matrix form. Our goal is to selecting random lag time values in the matrix and assuming all other elements of the matrix either missing or unknown and performing matrix completion technique to find out how close the results from matrix completion technique would be close to the real calculated values. This would give us better idea whether these mathematical techniques could help to reduce the number of sensors to an optimum value.