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principles and applications of microearthquake networks

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116 5. Inversion <strong>and</strong> Optimization<br />

Because <strong>of</strong> uncertainties in our model <strong>and</strong> data, an exact solution to Eq.<br />

(5.44) may never be obtainable. Furthermore, there may be many sohtions<br />

satisfying Ax = b. By Eq. (5.45), our estimate 5i is related to the true<br />

solution by the product HA. Let us call this product the resolution matrix<br />

R, i.e.,<br />

(5.47) R = HA<br />

<strong>and</strong> hence Eq. (5.45) becomes<br />

(5.48) x = Rx<br />

This equation tells us that the matrix R maps the entire set <strong>of</strong> solutions x<br />

into a single vector x. Any component <strong>of</strong> vector x, say xk, is the product <strong>of</strong><br />

the kth row <strong>of</strong> matrix R with any vector x that satisfies Ax = b. Therefore,<br />

R is a matrix whose rows are “windows” through which we may view the<br />

general solution x <strong>and</strong> obtain a definite result. If R is an identity matrix,<br />

each component <strong>of</strong> vector x is perfectly resolved <strong>and</strong> our solution ii is<br />

unique. If R is a near diagonal matrix, each component, say &, is a<br />

weighted sum <strong>of</strong> components <strong>of</strong> x with subscripts near k. Hence, the<br />

degree to which R approximates the identity matrix is a measure <strong>of</strong> the<br />

resolution obtainable from the data.<br />

In a similar manner, we may call the product AH the information density<br />

matrix D (Wiggins, 1972), i.e.,<br />

(5.49) D = AH<br />

It is a measure <strong>of</strong> the independence <strong>of</strong> the data. The theoretical datab that<br />

satisfies exactly our solution X is given by<br />

n<br />

(5.50) b 3 AX = AHb = Db<br />

In actual <strong>applications</strong>, the criteria (l), (2), <strong>and</strong> (3) mentioned previously<br />

are not equally important <strong>and</strong> may be weighted for a specific problem. For<br />

example, there is a trade-<strong>of</strong>f between resolution <strong>and</strong> variance.<br />

5.3. Computational Aspects <strong>of</strong> Solving Inverse Problems<br />

There are many ways to solve the problem Ax = b. For example, one<br />

learns Cramer’s rule <strong>and</strong> Gaussian elimination in high school. We also<br />

know that by applying the least squares method, we can reduce any overor<br />

underdetermined system <strong>of</strong> equations to an even-determined one. With<br />

digital computers generally available, one wonders why the machines<br />

cannot simply grind out the answers <strong>and</strong> let the scientists write up the<br />

results. Unfortunately, this point <strong>of</strong> view has led many scientists astray.

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