principles and applications of microearthquake networks
principles and applications of microearthquake networks
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.