  We describe an algorithm for simulating ultrasound propagation in random
one-dimensional media, mimicking different microstructures by choosing physical
properties such as domain sizes and mass densities from probability
distributions. By combining a detrended fluctuation analysis (DFA) of the
simulated ultrasound signals with tools from the pattern-recognition
literature, we build a Gaussian classifier which is able to associate each
ultrasound signal with its corresponding microstructure with a very high
success rate. Furthermore, we also show that DFA data can be used to train a
multilayer perceptron which estimates numerical values of physical properties
associated with distinct microstructures.
