BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20220812T074335Z
LOCATION:Singapore Room
DTSTART;TZID=Europe/Stockholm:20220628T173000
DTEND;TZID=Europe/Stockholm:20220628T180000
UID:submissions.pasc-conference.org_PASC22_sess149_msa170@linklings.com
SUMMARY:Monte Carlo Methods and Large-Scale Inverse Problems in Geophysics
DESCRIPTION:Minisymposium\n\nMonte Carlo Methods and Large-Scale Inverse P
 roblems in Geophysics\n\nMosegaard\n\nIn inverse problems, physical measur
 ements are used to infer information about unknown structure of physical s
 ystems. A probabilistic formulation of the inverse problem allows us, in p
 rinciple, to answer any probabilistic question about the system if all inf
 ormation about noise, physical relations, and prior information is integra
 ted. If the problem is highly nonlinear, a practical way of implementing p
 robabilistic inversion is to use Monte Carlo methods, but experience has s
 hown that typical implementations of these methods are computationally int
 ensive. This is usually explained by referring to the so-called 'curse of 
 dimensionality', but we shall see that this explanation is insufficient an
 d overlooks key factors of the phenomenon. Furthermore, we will see how Mo
 nte Carlo analysis of even large-scale problems can be carried out if we b
 ase its sampling strategy on simplified physics.\n\nDomain: Climate, Weath
 er and Earth Sciences, Computer Science and Applied Mathematics, Physics
END:VEVENT
END:VCALENDAR