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Extra resources for 25.Electromagnetic Subsurface Remote Sensing

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It represents what would be recorded if the source waveform were purely an impulse function δ(t) (a spike). Recall that the reflectivity sequence r(t) contains informa- Geophysical Signal and Image Processing 7 Figure 13. An example of a stacked seismic section. Note the folded and thrust-faulted structure (9). tion about the subsurface characteristics of the earth. The source waveform w(t) is therefore a blurring (or smearing) function that makes it difficult to recognize the reflectivity sequence by directly observing the trace s(t).

The ML classifier often performs very well in cases where the minimum-distanceto-means classifier or the parallelepiped classifier perform poorly. This is because the ML classifier not only accounts for differences in variance between classes but also accounts for differences in between-band correlations. An even more general classification approach is a neural network classifier. The flexibility of the neural network classifier comes from its ability to generate totally arbitrary feature space partitions.

Because, in a practical remote sensing problem, the prior probabilities p(Ͷi) are not known, it is common practice to assume equal priors. Other factors that are significant in the analysis process are the matter of how the class probability density functions are modeled and, related to this, how many training samples are available by which to train the classifier. Parametric models, assuming that each class is modeled by one or a combination of Gaussian distributions, are very common and powerful.

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25.Electromagnetic Subsurface Remote Sensing by John G. Webster (Editor)

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