A fast technological progress is providing seismic tomographers with computers of rapidly increasing speed and RAM, that are not always properly taken advantage of. Large computers with both shared-memory and distributed-memory architectures have made it possible to approach the tomographic inverse problem more accurately. For example, resolution can be quantified from the resolution matrix rather than checkerboard tests; the covariance matrix can be calculated to evaluate the propagation of errors front data to model parameters; the L-curve method can be applied to determine a range of acceptable regularization schemes. We show how these exercises can be implemented efficiently on different hardware architectures.
Global seismic tomography and modern parallel computers
Boschi L;
2006
Abstract
A fast technological progress is providing seismic tomographers with computers of rapidly increasing speed and RAM, that are not always properly taken advantage of. Large computers with both shared-memory and distributed-memory architectures have made it possible to approach the tomographic inverse problem more accurately. For example, resolution can be quantified from the resolution matrix rather than checkerboard tests; the covariance matrix can be calculated to evaluate the propagation of errors front data to model parameters; the L-curve method can be applied to determine a range of acceptable regularization schemes. We show how these exercises can be implemented efficiently on different hardware architectures.
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