How does one determine the optimum offset in a seismic survey?
It depends.

Caveat: my experience is with reflection seismic surveys, but I think many of the principles are similar to MASW.

As you guessed: it's an optimization problem. There are lots of factors at play. It's up to the geophysicist to balance the various needs of the survey:


You need to image the target with useful accuracy (small natural bin size).
You want useful signal:noise levels (high fold and trace density).
You want near offsets for good estimates of P-wave reflectivity.
You want far offsets for good estimates of elastic properties (e.g. density).
You may need far offsets to image certain types of geology.
You want the acquisition crew to have enough receivers for the design.
You want to be able to afford the acquisition and processing!


A while ago, I wrote a blog post on the subject: Fold for sale. It's about balancing fold (how many traces go into the stack, basically, a big driver of signal:noise) with cost.

My colleague Evan Bianco and I also did a series on modelling seismic acquisition. If you're into Python at all, there's some code to play with:


The race for useful offsets
Laying out a seismic survey
It goes in the bin
Seismic survey layout: from theory to practice


The greatest minimum offset in a bin is an important consideration, as you guessed. Also notice the spider plots, which try to visualize both maximum offset in a bin, and the range of azimuths going into that bin (which could be important for stress analysis, for example):



It's hard to go into a lot more detail, other than pointing at more things to read. There are a couple of really good books on this subject:


Cordsen et al. (2000), Planning Land 3D Seismic Surveys. SEG Books.
Vermeer &amp; Beasley (2012). 3D Seismic Survey Design. SEG Books.


For a quick overview, I recommend reading this great paper by Norm Cooper:


Cooper, N (2004). A world of reality—designing land 3D programs for signal, noise, and prestack migration: a 2-part tutorial. The Leading Edge 23, October and December, DOI:10.1190/1.1813357 and DOI:10.1190/leedff.23.1230_1.