Advances in nonlinear geoscientific experimental and survey design
Seminar Room 1, Newton Institute
AbstractGeoscience is replete with inverse problems that must be solved routinely. Many such problems such as using satellite remote-sensing data to estimate properties of the Earth's surface, or solving Geophysical imaging and monitoring problems for potentially dynamic properties of the Earth's subsurface, involve large datasets that cost millions of dollars to collect. Optimising the information content of such data is therefore crucial. While linearised experimental design methods have been deployed within the Geosciences, most Geophysical problems are significantly nonlinear. This renders linearised design criteria invalid as they can significantly over- or under-estimate the information content of any dataset. Over the past few years we have therefore focussed on developing new nonlinear design methods that can be applied to practical data types and geometries for surveys of increasing size. We will summarise three advances in practical nonlinear design, one using a new design criterion applied in the data space, one using a new 'bi-focal' model space criterion, and one using a fast Monte Carlo refinement procedure that significantly speeds up nonlinear design calculations. Applications of the first two techniques are to design subsurface (micro-)seismic energy-source location problems, application of the third is to design so-called industrial seismic amplitude-versus-offset data sets to derive (an)elastic properties of subsurface geological strata. Using the first of these we managed to design an industrially practical Geophysical survey design using fully non-linearised methods.
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