lecture 1 describing inverse problems

Lecture 1

Describing Inverse Problems

SyllabusLecture 01 Describing Inverse ProblemsLecture 02 Probability and Measurement Error, Part 1Lecture 03 Probability and Measurement Error, Part 2 Lecture 04 The L2 Norm and Simple Least SquaresLecture 05 A Priori Information and Weighted Least SquaredLecture 06 Resolution and Generalized InversesLecture 07 Backus-Gilbert Inverse and the Trade Off of Resolution and VarianceLecture 08 The Principle of Maximum LikelihoodLecture 09 Inexact TheoriesLecture 10 Nonuniqueness and Localized AveragesLecture 11 Vector Spaces and Singular Value DecompositionLecture 12 Equality and Inequality ConstraintsLecture 13 L1 , L∞ Norm Problems and Linear ProgrammingLecture 14 Nonlinear Problems: Grid and Monte Carlo Searches Lecture 15 Nonlinear Problems: Newton’s Method Lecture 16 Nonlinear Problems: Simulated Annealing and Bootstrap Confidence Intervals Lecture 17 Factor AnalysisLecture 18 Varimax Factors, Empircal Orthogonal FunctionsLecture 19 Backus-Gilbert Theory for Continuous Problems; Radon’s ProblemLecture 20 Linear Operators and Their AdjointsLecture 21 Fréchet DerivativesLecture 22 Exemplary Inverse Problems, incl. Filter DesignLecture 23 Exemplary Inverse Problems, incl. Earthquake LocationLecture 24 Exemplary Inverse Problems, incl. Vibrational Problems

Purpose of the Lecture

distinguish forward and inverse problems

categorize inverse problems

examine a few examples

enumerate different kinds of solutions to inverse problems

Part 1

Lingo for discussing the relationship between observations and the things

that we want to learn from them

three important definitions

things that are measured in an experiment or observed in nature…

data, d = [d1, d2, … dN]T

things you want to know about the world …

model parameters, m = [m1, m2, … mM]T relationship between data and model parameters

quantitative model (or theory)

gravitational accelerationstravel time of seismic waves

data, d = [d1, d2, … dN]T model parameters, m = [m1, m2, … mM]T quantitative model (or theory)

densityseismic velocity

Newton’s law of gravityseismic wave equation

Quantitative Model

Quantitative Model

mest dpre

mest dobs

Forward Theory

Inverse Theory

estimates predictions

observationsestimates

Quantitative Model

Quantitative Model

mtrue dpre

dobs≠

mest

due to observational

error

Quantitative Model

Quantitative Model

mtrue dpre

dobs≠

mest

due to observational

error≠ due to error

propagation

Understanding the effects ofobservational error

is central to Inverse Theory

Part 2

types of quantitative models(or theories)

A. Implicit Theory

L relationships between the data and the model are known

Example

mass = density ⨉ length ⨉ width ⨉ height

MH

M = ρ ⨉ L ⨉ W ⨉ HLρ

weight = density ⨉ volumemeasure

mass, d1size, d2, d3, d4,

want to knowdensity, m1 d1 = m1 d2 d3 d4 or d1 - m1 d2 d3 d4 = 0

d=[d1, d2, d3, d4]T and N=4m=[m1]T and M=1f1(d,m)=0 and L=1

note

No guarantee thatf(d,m)=0contains enough information

for unique estimate m

determining whether or not there is enoughis part of the inverse problem

B. Explicit Theory

the equation can be arranged so that d is a function of m

L = N one equation per datum

d = g(m) or d - g(m) = 0

Example

Circumference = 2 ⨉ length + 2 ⨉ heightL

rectangle H

Area = length ⨉ height

C = 2L+2Hmeasure

C=d1A=d2

want to knowL=m1 H=m2

d=[d1, d2]T and N=2m=[m1, m2]T and M=2

Circumference = 2 ⨉ length + 2 ⨉ heightArea = length ⨉ heightA=LH

d1 = 2m1 + 2m2d2 m1m2 d=g(m)

C. Linear Explicit Theory

the function g(m) is a matrix G times m

G has N rows and M columns

d = Gm

C. Linear Explicit Theory

the function g(m) is a matrix G times m

G has N rows and M columns

d = Gm“data kernel”

Example

M = ρg ⨉ V g+ ρq ⨉ V q

gold quartz

total mass = density of gold ⨉ volume of gold+ density of quartz ⨉ volume of quartzV = V g+ V q

total volume = volume of gold + volume of quartz

M = ρg ⨉ Vg+ ρq ⨉ V qV = V g+ V q

measure V = d1M = d2

want to know Vg =m1 Vq =m2assume ρg ρg

d=[d1, d2]T and N=2m=[m1, m2]T and M=2

d = 1 1ρg ρq

mknown

D. Linear Implicit Theory

The L relationships between the data are linear

L rowsN+M columns

in all these examples m is discrete

one could have a continuous m(x) instead

discrete inverse theory

continuous inverse theory

as a discrete vector m

in this course we will usually approximate a continuous m(x)

m = [m(Δx), m(2Δx), m(3Δx) … m(MΔx)]T

but we will spend some time later in the course dealing with the continuous

problem directly

Part 3

Some Examples

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010-0.5

0

0.5

1

calendar year

tem

pera

ture

ano

mal

y, d

eg C

time, t (calendar years)

temperatu

re anomal

y, T i (deg

C)A. Fitting a straight line to data

T = a + bt

each data pointis predicted by a

straight line

matrix formulation

d = G m M=2

B. Fitting a parabola

T = a + bt+ ct2

each data pointis predicted by a

strquadratic curve

matrix formulation

d = G m M=3

straight line

note similarity

parabola

in MatLab

G=[ones(N,1), t, t.^2];

C. Acoustic Tomography

1 2 3 4

5 6 7 8

13 14 15 16

hh

source, S receiver, R

travel time = length ⨉ slowness

collect data along rows and columns

matrix formulation

d = G m M=16N=8

In MatLabG=zeros(N,M);for i = [1:4]for j = [1:4] % measurements over rows k = (i-1)*4 + j; G(i,k)=1; % measurements over columns k = (j-1)*4 + i; G(i+4,k)=1;endend

D. X-ray Imaging

S

R1R2R3 R4 R5enlarged

lymph node

(A) (B)

theory

I = Intensity of x-rays (data)s = distancec = absorption coefficient (model parameters)

Taylor Seriesapproximation

Taylor Seriesapproximationdiscrete pixelapproximation

Taylor Seriesapproximationdiscrete pixelapproximation length of beam i in pixel jd = G m

d = G m

matrix formulation

M≈106N≈106

note that G is huge106⨉106

but it is sparse(mostly zero)

since a beam passes through only a tiny fraction of the total number of

pixels

in MatLab

G = spalloc( N, M, MAXNONZEROELEMENTS);

E. Spectral Curve Fitting

0 2 4 6 8 10 120.65

0.7

0.75

0.8

0.85

0.9

0.95

1

velocity, mm/s

coun

ts

single spectral peak

0 5 100

0.1

0.2

0.3

0.4

0.5

d

p(d)

0 5 100

10

20

30

d

coun

ts

0 5 100

0.1

0.2

0.3

0.4

0.5

d

p(d)

area, Aposition, f

width, czp(z)

q spectral peaks“Lorentzian”

d = g(m)

e1e2e3e4e5

e1e2e3e4e5

s1 s2

ocean

sediment

F. Factor Analysis

d = g(m)

Part 4

What kind of solution are we looking for ?

A: Estimate of model parameters

meaning numerical values

m1 = 10.5m2 = 7.2

But we really need confidence limits, too

m1 = 10.5 ± 0.2m2 = 7.2 ± 0.1 m1 = 10.5 ± 22.3m2 = 7.2 ± 9.1orcompletely different implications!

B: probability density functions

if p(m1) simplenot so different than confidence intervals

0 5 100

0.1

0.2

0.3

0.4

m

p(m

)

0 5 100

0.1

0.2

0.3

0.4

m

p(m

)

0 5 100

0.1

0.2

0.3

0.4

m

p(m

)

0 5 100

0.1

0.2

0.3

0.4

m

p(m

)

0 5 100

0.1

0.2

0.3

0.4

m

p(m

)0 5 10

0

0.1

0.2

0.3

0.4

m

p(m

)

m is about 5plus or minus 1.5m is eitherabout 3plus of minus 1or about 8plus or minus 1but that’s less likely

we don’t really know anything useful about m

C: localized averages

A = 0.2m9 + 0.6m10 + 0.2m11might be better determined than eitherm9 or m10 or m11 individually

Is this useful?

Do we care aboutA = 0.2m9 + 0.6m10 + 0.2m11?

Maybe …

Suppose if m is a discrete approximation of m(x)

m(x)x

m10 m11m9

m(x)x

m10 m11m9

A= 0.2m9 + 0.6m10 + 0.2m11weighted average of m(x)

in the vicinity of x10

x10

m(x)x

m10 m11m9

average “localized’in the vicinity of x10

x10 weightsof weighted

average

Localized average meancan’t determine m(x) at x=10

but can determineaverage value of m(x) near x=10 Such a localized average might very

well be useful