forked from randlab/hytool
-
Notifications
You must be signed in to change notification settings - Fork 0
/
jlq_dmo.m
45 lines (36 loc) · 1.35 KB
/
jlq_dmo.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
%% Constant head test
% This demonstrates the interpretation of discharge rate in the test well
% with the Jacob and Lohman (1952) solution
%
% MIT License
% Copyright (c) 2017 Philippe Renard - University of Neuchâtel (CHYN)
%%
% The data set for this example comes from the following reference:
% Lohman (1965) Geology and artesian water supply of the Grand
% Junction area, Colorado: U.S. Geol. Survey Prof. Paper 451, 149p.
% Tables 6 and 7, well 28
%
% Let us first load the data and plot them.
[t,q]=ldf('jlq_ds1.dat');
%%
% Once the data have been loaded , we can interpret the data. We do that
% as usually in two steps. First the parameters p of the model are
% estimated with the function jlq_gss. Then we find an optimum fit.
p=jlq_gss(t,q);
p=fit('jlq',p,t,q);
%%
% We can then display the result of the interpretation.
% Hytool find that the folowing values fort the transmissivity and
% storativity:
%
% T = 1.3 e-5 m2/s and S = 1.6 e-5
%
jlq_rpt(p,t,q,[28.142,0.084],'Glover (1978) example - automatic fit')
%%
% The results are in reasonable agreement with the values found by this
% differents authors:
% Swamee et al (2000) : S = 3.88 e-5 and T = 1.16 e-5 m2/s
% Glover (1978) : S = 4.14 e-5 and T = 1.18 e-5 m2/s
% Batu (1998) : S = 1.58 e-4 and T = 9.3 e-6 m2/s
% Nota Bene: If required, one can use trial and error
% trial('jlq',p,t,q)