Improved inversion of SPAC data

Abstract

The goal of our project was to develop an evolutionary programming approach to more efficiently characterise site conditions using the SPAC (Spatial Auto Correlation) method. SPAC or Spatial Auto Correlation is a non-invasive seismic method of determining shallow shear wave velocity profiles at sites of interest. This characterisation is important in earthquake-resistant design and liquefaction assessment.

Previously GNS Science has carried out SPAC analysis using a Simplex inversion algorithm which requires a considerable amount of manual intervention. However, work under this project has established proof-of-concept that an evolutionary programming approach (ESPAC) has the potential to efficiently extract velocity information with far less manual operation. This work has been published as a peer-reviewed conference paper (Scoble et al, 2013) presented at the June 2013 Congress on Evolutionary Computation, Mexico. 

The funding from the EQC grant has enabled us to produce a functional proof of concept to use as a basis for further research and funding applications to improve the SPAC technique for site investigation.