#### Ophiolite

**Valued Senior Member**

What could possibly lead you to such a bizarre conclusion? If the data density is sufficient to allow meaningful interpolation and if the validity of that interpolation is confirmed by subsequent, more granular, data then a continuous model is wholly possible. Which just happens to be the situation we find ourselves in.The discontinuous nature of the data collection would force us to conclude that the data says the model needs to be discontinuous.

You would only be called a liar by the ignorant, the unskilled, the thoughtless and the foolish. (Granted, that's a prettybig group.)Let me give an example. I make a continuous line of popcorn around my house. The birds come and randomly eat the popcorn. Now the line is discontinuous. Even though I made a continuous line, I would be called a liar, since the data clearly shows the line is not continuous.

The data clearly show that the line is now discontinuous. If sufficient popcorn is left the data also show that the line was likely originally continuous.

Of course it could be different in detail, it is unlikely it would be different in general.If we had all the samples possible (all the popcorn), could our conclusion be different from the conclusion draw which use only partial and discontinuous data?