Linear(-ized) Inverse Problems Linear inverse problems - Formulation - Some Linear Algebra - Matrix calculation – Revision - Illustration under(over)determined, unique case - Examples Linearized inverse problems Partial derivatives Scope: Formulate linear inverse problems as a system of equations in matrix form. Find the conditions under which solutions exist. Understand how to linearize a non-linear system to be able to find solutions.

Literature Stein and Wysession: Introduction to seismology, Chapter 7 Aki and Richards: Theoretical Seismology (1s edition) Chapter 12.3 Shearer: Introduction to seismology, Chapter 5 Menke, Discrete Inverse Problems http://www.ldeo.columbia.edu/users/menke/gdadit/index.htm Full ppt files and matlab routines

Formulation Linear(-ized) inverse problems can be formulated in the following way: (summation convention applies) i=1,2,...,N number of data j=1,2,...,M number of model parameters Gij known (mxn) We observe: - The inverse problem has a unique solution if N=M and det(G)≠0, i.e. the data are linearly independent the problem is overdetermined if N>M the problem is underdetermined if M>N

Illustration – Unique Case In this case N=M, and det(G) ≠0. Let us consider an example Let us check the determinant of this system: det(G)=10

Illustration – Overdetermined Case In this case N>M, there are more data than model parameters. Let us consider examples with M=2, an overdetermined system would exist if N=3. A physical experiment which could result in these data: Individual Weight measurement of two masses m1 and m2 leading to the data d1 and d2 and weighing both together leads to d3. In matrix form:

Illustration – Overdetermined Case Let us consider this problem graphically A common way to solve this problem is to minimize the difference between data vector d and the predicted data for some model m such that is minimal.

Illustration – Overdetermined Case Using the L2-norm leads us to the least-squares formulation of the problem. The solution to the minimization (and thus the inverse problem) is given as: best model In our example the resulting (best) model estimation is: and is the model with the minimal distance to all three lines in the plot.

Illustration – Underdetermined Case Let us assume we made one measurement of the combined weight of two masses: Clearly there are infinitely many solutions to this problem. A model estimate can be defined by choosing a model that fits the data exactly Am=d and has the smallest l2 norm ||m||. Using Lagrange multipliers one can show that the minimum norm solution is given by

Examples – Inversion of Gravity Data Let’s go back to the problem of gravity, in 2-D the Bouguer anomaly at point x0 with arbitrary topography is given by (e.g. Telford et al., 1990) z x,x0 d(x0) To bring this into the form d=Gm we discretize the space r(x,z) h xj h j=1 2 3 4 5 6 7 8 9 ... 20 zj mj

Master-Event-Method Let s assume we have have previously located an earthquake (x0,y0,z0) at time t0 and we recorded a new event at stations 1, ..., N 1 2 Dti 3 Event 1 x Li Event 2 gi ui This is a system of linear equations for 4 unknowns: y z

Master-Event-Method Let us put this system into the common form d=Gm x z y Event 2 Event 1 Li ui gi Let us put this system into the common form d=Gm

Vertical Seismic Profile Let us consider a string of receivers in a borehole v1 We assume straigt rays The ground is discretized with M layers of equal thickness dz with velocities vi The seismometers (N) are located at depths zj Formulate the forward problem in matrix form d=Gm! Is the problem linear? What would happen if the rays are not modelled as straight lines? v2 seismometers vM

Linearized Inversion Let us formalize the situation where we are able to linearize a otherwise nonlinear problem around some model m0. In this case the forward problem is given by this m-dimensional function is developed around some model m0=(m01, m02, ..., m0M) where we neglect higher-order terms: d0 Gij Dmj d0 synthetic data of starting model (known) Ddi=di-d0 data difference vector (residuals, misfit, cost ...) Dmj=mj-m0 model difference vector (gradient)

Linearized Inversion: Hypocenter location Above a homogeneous half space we measure P wave travel times from an earthquake that happens at time t at (x,y,z) at i receiver locations (xi, yi ,zi). So our model vector is m=(t,x,y,z)T. The arrival times are given by this is a nonlinear problem! Now let us assume we have a rough idea about the time of the earthquake and its location. This is our starting model m0= (t0, x0, y0, z0)T. To linearize the problem we now have to find the partial derivatives of F with respect to all model parameters at m0.

Hypocenter location – partial derivatives ... we obtain : Ri0

Hypocenter location – partial derivatives ... let us now define a vector ui=1/Ri0(xi-x0, yi-y0,-z0) which is a vector pointing from the initial source location to receiver i. We obtain: Ddi Dm1 Dm2 Dm3 Dm4 which is exactly the form we obtained for the Master-Event Method, what is the difference, however? This approach is an iterative algorithm

Linearized Travel-Time Inversion We learned in seismology that for a given ray parameter p the delay time t(p) is given by the difference of the travel time T and the distance X(p) the ray ermerges times p Graphically this can be interpreted as: p=dT/dX X pX T t(p)

Linearized Travel-Time Inversion p=dT/dX X pX T t(p) … the important property of t(p) is the fact that it decreases monotonically with increasing p so it is a function easier to handle than the travel-times (which may contain triplications). t(p) is nonlinearly related to the velocity model c(z). So in order to invert for it we would have to linearize. We obtain Now the perturbation in t(p) (the data residual) is linearly related to the perturbation in the velocity model c(z). This integral can easily be brought into the form d=Gm by subdividing the Earth into layers (e.g. of equal thickness).

Partial Derivatives Let us take a closer look at the matrix Gij for linearized problems. What useful information is contained in this matrix (operator)? When d=g(m), then the linearization leads to And the matrix Gij contains the partial derivatives The actual (relative) values of Gij determine how the model parameters influence the data (or data difference). Example: Gik are small for all i. This implies that the model Parameter mk has almost no influence on the data. It can be varied Without changing them. Therefore, its resolution is poor.

Resolution – Hypocenter Location Example: Earthquake hypocenter location Ddi Dm1 Dm2 Dm3 Dm4 Remember the elements of Gij where the components of the unit vector which points from the original (known) hypocenter to the receiver. Small uiz with respect to the other ones means bad resolution in depth: The depth resolution of shallow earthquakes Far away is poor.

Linear Dependence When two columns of Gij are linearly dependent then for all i What are the consequences for a model perturbation in parameters k and l? Linear dependence implies In words: Parameters mk and ml cannot be independently determined as they compensate each other. This is called a trade-off.

Trade-Off Fault Zone Waves

Calculating Partial Derivatives (1) Generally we need to calculate the partial derivatives … depending on the formulation of the forward problem … 1. For explicit functions Fi, for example: Gravity problem Master-Event Method … we can directly calculate the partial derivatives.

Calculating Partial Derivatives (2) 2. The data di are given implicitly through we differentiate with respect to mj The arguments being the model and data parameter of the starting model m0. Often the data d0 are obtained by finding the roots of the topmost equation.

Calculating Partial Derivatives (3) 3. In more complicated cases the partial derivatives have to be obtained by numerical differentiation. Note that for the evaluation of each element of Gij a solution of the forward problem is necessary! In cases where the number of model parameters is large or where the forward problem is very involved this is impractical. But at least this method always works (approximately).

Summary Most inverse problems can be formulated as discrete linear problems either as … or – if the problem is linearized - … In which case the Gij contains the partial derivatives of the problem. The elements of Gij contain useful information on the resolution of the model parameters and linear dependence may indicate trade-offs between model parameters.