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Seismic event location: nonlinear inversion using a neighbourhood algorithm

Sambridge, Malcolm; Kennett, Brian

Description

A recently developed direct search method for inversion, known as a neighbourhood algorithm (NA), is applied to the hypocentre location problem. Like some previous methods the algorithm uses randomised, or stochastic, sampling of a four-dimensional hypocentral parameter space, to search for solutions with acceptable data fit. Considerable flexibility is allowed in the choice of misfit measure. At each stage the hypocentral parameter space is partitioned into a series of convex polygons called...[Show more]

dc.contributor.authorSambridge, Malcolm
dc.contributor.authorKennett, Brian
dc.date.accessioned2015-12-13T23:16:32Z
dc.date.available2015-12-13T23:16:32Z
dc.identifier.issn0033-4553
dc.identifier.urihttp://hdl.handle.net/1885/89457
dc.description.abstractA recently developed direct search method for inversion, known as a neighbourhood algorithm (NA), is applied to the hypocentre location problem. Like some previous methods the algorithm uses randomised, or stochastic, sampling of a four-dimensional hypocentral parameter space, to search for solutions with acceptable data fit. Considerable flexibility is allowed in the choice of misfit measure. At each stage the hypocentral parameter space is partitioned into a series of convex polygons called Voronoi cells. Each cell surrounds a previously generated hypocentre for which the fit to the data has been determined. As the algorithm proceeds new hypocentres are randomly generated in the neighbourhood of those hypocentres with smaller data misfit. In this way all previous hypocentres guide the search, and the more promising regions of parameter space are preferentially sampled. The NA procedure makes use of just two tuning parameters. It is possible to choose their values so that the behaviour of the algorithm is similar to that of a contracting irregular grid in 4-D. This is the feature of the algorithm that we exploit for hypocentre location. In experiments with different events and data sources, the NA approach is able to achieve comparable or better levels of data fit than a range of alternative methods; linearised least-squares, genetic algorithms, simulated annealing and a contracting grid scheme. Moreover, convergence was achieved with a substantially reduced number of travel-time/slowness calculations compared with other nonlinear inversion techniques. Even when initial parameter bounds are very loose, the NA procedure produced robust convergence with acceptable levels of data fit.
dc.publisherBirkhauser Verlag
dc.sourcePure and Applied Geophysics
dc.subjectKeywords: nuclear weapons testing; seismic discrimination; source parameters Event location; Hypocentre; Neighbourhood algorithm
dc.titleSeismic event location: nonlinear inversion using a neighbourhood algorithm
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume158
dc.date.issued2001
local.identifier.absfor040407 - Seismology and Seismic Exploration
local.identifier.ariespublicationMigratedxPub19491
local.type.statusPublished Version
local.contributor.affiliationSambridge, Malcolm, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationKennett, Brian, College of Physical and Mathematical Sciences, ANU
local.bibliographicCitation.startpage241
local.bibliographicCitation.lastpage257
dc.date.updated2015-12-12T08:48:35Z
local.identifier.scopusID2-s2.0-0035091360
CollectionsANU Research Publications

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