Bayesian mechanistic imaging of two-dimensional heterogeneous elastic media from seismic geophysical observations Conference Paper uri icon

abstract

  • © 2015 Taylor & Francis Group, London. This paper proposes a methodology to deduce the spatial variation of the elastic characteristics of a two-dimensional earth model from seismic data, given the media’s response to interrogating SH waves. A reduced dimension, self regularized treatment of the inverse problem using partition modeling is introduced, where the SH wave velocity field is discretized by Voronoi tessellations, and the number and geometry of these tessellations dynamically alter during the inversion to adapt the form of geologic units and geomorphological features (e.g. transitions between soil layers, faults, concentration of materials, etc.). The subsurface material characteristics is treated as a random field where the measure of uncertainty associated with the deduced subsurface image is casted into a form of a probability density function at each point in space. This allows for a) propagating full probabilistic descriptions of material properties obtained from geophysical data to full probabilistic descriptions of geotechnical properties; b) borrowing a stratigraphic earth random model to populate any other mechanical property, after the probabilistic identification of the location of boundaries between materials is obtained (i.e. spatial probabilistic definition of the geomorphological features); and c) making available the probabilistic identification of geomorphological features to be merged with other geomorphological features, retrieved from probabilistic inversions related to different geophysical technologies. Consequently, it is anticipated that the resulting probabilistic descriptions of the earth model will contribute to improve the confidence in the identification of subsurface hazards such as over-pressured water or gas hydrate occurrences, to improve the decision-making on the definition of the scope of the geotechnical surveying, and to improve the reliability assessment of geotechnical structures, among others, including the improvement on the risk assessment of offshore drilling and field structural developments.

author list (cited authors)

  • Esmailzadeh, S., Medina-Cetina, Z., Kang, J. W., & Kallivokas, L. F.

publication date

  • January 2015