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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/569
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dc.contributor.authorallCurtis, A.; 1Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge CB3 0EL, UK. E-mail: curtis@cambridge.oilfield.slb.comUniversity of Edinburgh, Department of Geology and Geophysics, Grant Institute, West Mains Road, Edinburgh, UKen
dc.contributor.authorallMichelini, A.; Instituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Borgo Grotta Gigante 42/c, Sgonico 34010, Trieste, Italyen
dc.contributor.authorallLeslie, D.; 1Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge CB3 0EL, UK. E-mail: curtis@cambridge.oilfield.slb.comen
dc.contributor.authorallLomax, A.; 4Scientific Software, Mouans-Sartoux, France. E-mail: anthony@alomax.net, www.alomax.neten
dc.date.accessioned2005-11-25T10:26:23Zen
dc.date.available2005-11-25T10:26:23Zen
dc.date.issued2004en
dc.identifier.urihttp://hdl.handle.net/2122/569en
dc.description.abstractSUMMARY Most general experimental design algorithms are either: (i) stochastic and hence give different designs each time they are run with finite computing power, or (ii) deterministic but converge to results that depend on an initial or reference design, taking little or no account of the range of all other possible designs. In this paper we introduce an approximation to standard measures of experimental design quality that enables a new algorithm to be used. The algorithm is simple, deterministic and the resulting experimental design is influenced by the full range of possible designs, thus addressing problems (i) and (ii) above. Although the designs produced are not guaranteed to be globally optimal, they significantly increase the magnitude of small eigenvalues in the model–data relationship (without requiring that these eigenvalues be calculated). This reduces the model uncertainties expected post-experiment. We illustrate the method on simple tomographic and microseismic location examples with varying degrees of seismic attenuation.en
dc.format.extent413855 bytesen
dc.format.mimetypeapplication/pdfen
dc.language.isoEnglishen
dc.relation.ispartofGeophys. J. Int.en
dc.relation.ispartofseries/157(2004)en
dc.subjecttomographyen
dc.subjectmicroseismicity,en
dc.titleA deterministic algorithm for experimental design applied to tomographic and microseismic monitoring surveysen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber(595-606)en
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.07. Tomography and anisotropyen
dc.identifier.doidoi: 10.1111/j.1365-246X.2004.02114.xen
dc.relation.referencesREFERENCES Atkinson, A.C. & Donev, A.N., 1992. Optimum experimental designs,. Clarendon Press, Oxford. Curtis, A., 1999a. Optimal experiment design: Cross-borehole tomographic examples, Geophys. J. Int., 136, 637–650. Curtis, A., 1999b. Optimal design of focussed experiments and surveys, Geophys. J. Int., 139, 205–215. Curtis, A., 2000. Optimizing the design of geophysical experiments: Is it worthwhile?, EOS, Trans. Am. geophys. Un., Forum Article, 81(20), 224– 225. Curtis, A. & Snieder, R., 1997. Reconditioning inverse problems using the genetic algorithm and revised parameterization, Geophysics, 62(5), 1524– 1532. Curtis, A. & Spencer, C., 1999. Survey design strategies for linearized, nonlinear inversion, in: Extended Abstracts, 69th Ann. Internat. Mtg. Soc. of Expl. Geophys., pp. 1775–1778. Curtis, A. & Wood, R., 2004. Optimal elicitation of prior information from experts, in Geological Prior Information, eds Curtis, A.&Wood, R., Geol. Soc. London. Special Publication, in press. Kijko, A., 1977. An algorithm for the optimum distribution of a regional seismic network—I, Pageoph, 115, 999–1009. Maurer, H. & Boerner, D.E., 1998. Optimized and robust experimental design: a non-linear application to em sounding, Geophys. J. Int., 132, 458– 468. Maurer, H., Boerner, D.E. & Curtis, A., 2000. Design strategies for electromagnetic geophysical surveys, Inverse Problems, 16(5), 1097–1117. Menke, W., 1989. Geophysical data analysis: Discrete inverse theory, Revised edn),Vol. 45, International Geophysics Series, Academic Press Inc., Harcourt Brace Jovanovich Publishers, San Diego. Mitchell, T.J., 1974. An algorithm for the construction of ‘d-optimal’ experimental designs, Technometrics,16(2), 203–210. Rabinowitz, N. & Steinberg, D.M., 1990. Optimal configuration of a seismographic network: a statistical approach, Bull. seism. Soc. Am., 80(1), 187–196. Sabatier, P.C., 1977. On geophysical inverse problems and constraints, J. Geophys., 43, 115–137. Silvey, S.D., 1980. Optimum design, Chapman and Hall, London. Smith, M.L., Scales, J.A. & Fischer, T.L., 1992. Global search and genetic algorithms, The Leading Edge, 11(1), 22–26. Steinberg, D.M., Rabinowitz, N., Shimshoni, Y. & Mizrachi, D., 1995. Con- figuring a seismographic network for optimal monitoring of fault lines and multiple sources, Bull. seism. Soc. Am., 85(6), 1847–1857. Tarantola, A., 1987. Inverse problem theory, Elsevier Science Publishers B. V., Amsterdam. Tarantola, A. & Valette, B., 1982. Inverse problems = quest for information, J. Geophys., 50, 159–170. van den Berg, J., Curtis, A. & Trampert, J., 2003. Optimal, non-linear, bayesian experimental design with 1-D examples, Geophys. J. Int., 155, 411–421.en
dc.description.fulltextreserveden
dc.contributor.authorCurtis, A.en
dc.contributor.authorMichelini, A.en
dc.contributor.authorLeslie, D.en
dc.contributor.authorLomax, A.en
dc.contributor.department1Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge CB3 0EL, UK. E-mail: curtis@cambridge.oilfield.slb.comUniversity of Edinburgh, Department of Geology and Geophysics, Grant Institute, West Mains Road, Edinburgh, UKen
dc.contributor.departmentInstituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), Borgo Grotta Gigante 42/c, Sgonico 34010, Trieste, Italyen
dc.contributor.department1Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge CB3 0EL, UK. E-mail: curtis@cambridge.oilfield.slb.comen
dc.contributor.department4Scientific Software, Mouans-Sartoux, France. E-mail: anthony@alomax.net, www.alomax.neten
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item.languageiso639-1en-
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crisitem.classification.parent04. Solid Earth-
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