SBIR-STTR Award

In regions with relatively few seismic stations and small-to-intermediate-magnitude events, traditional location methods frequently fail to provide event depth estimates with the accuracy required for nuclear test monitoring. Therefore, improved algorithms for precise depth estimation from regional seismic array data are needed. This project will develop an innovative three-step procedure to estimate event depth from regional seismic array data. The method will be statistically evaluated with a set of well-located events. Phase I will create a small database of well-located events. A wavelet filtering technique will be used to extract depth phases from the waveform. Phase II will automate the algorithm and prepare it for implementation into an operational testbed. Commercial Applications and Other Benefits as described by awardee: Improved seismic event depth estimates not only should improve the detection and identification of nuclear tests in foreign countries, but also would provide earthquake seismologists the necessary tools to more accurately delineate earthquake faults, thereby, contributing to the mitigation of earthquake hazards. The technique also could be used by gas and oil exploration firms to improve their ability to process seismic data for the discovery of new oil reserves.

In regions with relatively few seismic stations and small-to-intermediate-magnitude events, traditional location methods frequently fail to provide event depth estimates with the accuracy required for nuclear test monitoring. Therefore, improved algorithms for precise depth estimation from regional seismic array data are needed. This project will develop a synergistic tool that combines three different technologies for estimating depth from regional seismic waveforms: (1) improved depth phase detection in the complex Pn coda of regional seismograms, (2) depth estimates from sparse-network locations with Monte Carlo confidence regions, and (3) focal depths resulting from surface-wave spectral inversions. Phase I demonstrated that wavelet de-noising is a successful method of isolating depth phases on complex regional seismograms, thereby proving the feasibility of the first technique. In addition, it was determined that optimal Wiener filtering offered increased depth phase detection over standard Fourier filtering. Phase II will continue the development of the depth phase detection algorithm by incorporating improved wavelet de-noising and optimal Wiener filtering. The technique will be applied to regional array data from regions of high nuclear monitoring interest. Commercial Applications and Other Benefits as described by awardee: Improved seismic event depth estimates should improve the detection and identification of nuclear tests in foreign countries. In addition, the technology could be used by gas and oil exploration firms to remove unwanted multiples or ghosts (e.g., ocean bottom/surface reflections) from seismic reflection surveys, thereby improving their ability to process seismic data for the discovery of new oil reserves