The Expt Types

The methods in this package numerically solve some differential equations commonly faced in geophysical inverse problems. The functionality of this package revolves around the mutable Expt types. Julia's multiple dispatch is used to overload Base methods whenever possible. Which means, if one is familiar with the Base methods of Julia, then (almost) no additional syntax is required to use this package. Easy!

While performing a given experiment, firstly, most of the memory necessary is allocated while creating the Expt variables. Then these variables are input to in-place functions (e.g., update!) which as per Julia convention ends with an exclamation mark, to actually perform the experiment task. For example, the current Expt types within the realm of this package include:

  • SeisForwExpt is the seismic (acoustic) forward modeling experiment;
  • SeisInvExpt is the type for seismic inversion experiment, including migration;
  • PoissonExpt is type for the solving the Poisson experiment.

Some of the commonly used (and exported) mutable types to create the Expt variables are:

  • Medium for bundling medium parameters together;
  • AGeom stores acquisition geometry related parameters;
  • SrcWav allocates source signals input to an experiment;
  • Data allocated the output records that are fitted during inversion.

To get started, as an example, simply load a seismic inversion experiment already defined in our package gallery into REPL:

using GeoPhyInv # load package (after installation)
pa=SeisInvExpt(Fdtd(), LS(), :pizza); # "pizza" is the name of the experiment

Then, simply use update! to perform least-squares inversion.



It is necessary to input the evenly-spaced spatial and temporal grids while creating the Expt variables. These grids can be simply created using Base.range in Julia, as shown below.

zgrid=range(0,stop=1000.0,length=201) # create vertical grid from 0 to 1000 m
xgrid=range(0,stop=1000.0,length=201) # create horizontal grid
mgrid=[zgrid, xgrid] # spatial-grid bundle
@info string("spatial sampling intervals (dz,dx)=", step.(mgrid))
tgrid=range(0,stop=1.0,step=0.001) # a temporal grid from 0 to 1.0 s