hydro-learn: An intelligent solver for hydrogeology engineering issues

Overview

Hydro-learn is a Python-based package for solving hydro-geology engineering issues. From methodologies based on Machine Learning, It brings novel approaches for reducing numerous losses during the hydrogeological
exploration projects. It allows to:

• reduce the cost of hydraulic conductivity (K) data collection during the engineering projects,
• guide drillers for to locating the drilling operations,
• predict the water content in the well such as the level of water inrush, ...

Licence

hydro-learn is under BSD-3-Clause License.

Installation

The system requires preferably Python 3.10+.

Demo

Predict hydraulic conductivity K from logging dataset using MXS approach

MXS stands for mixture learning strategy. It uses upstream unsupervised learning for K -aquifer similarity label prediction and the supervising learning for final K-value prediction. For our toy example, we use two boreholes data stored in the software and merge them to compose a unique dataset. In addition, we dropped the remark observation which is subjective data not useful for K prediction as:

import hlearn
h= hlearn.fetch_data("hlogs", key='h502 h2601', drop_observations =True ) # returns log data object.
h.feature_names
Out[3]: Index(['hole_id', 'depth_top', 'depth_bottom', 'strata_name', 'rock_name',
           'layer_thickness', 'resistivity', 'gamma_gamma', 'natural_gamma', 'sp',
           'short_distance_gamma', 'well_diameter'],
          dtype='object')
hdata = h.frame 

K is collected as continue values (m/darcies) and should be categorized for the naive group of aquifer prediction (NGA). The latter is used to predict upstream the MXS target ymxs. Here, we used the default categorization provided by the software and we assume that in the area, there are at least 2 groups of the aquifer. The code is given as:

from hlearn.api import MXS
mxs = MXS (kname ='k', n_groups =2).fit(hdata) 
ymxs=mxs.predictNGA().makeyMXS(categorize_k=True, default_func=True)
mxs.yNGA_ [62:74]
Out[4]: array([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2])
ymxs[62:74]
Out[5]: array([ 0,  0,  0,  0, 12, 12, 12, 12, 12, 12, 12, 12])

Once the MXS target is predicted, we call the make_naive_pipe function to impute, scale, and transform the predictor X at once into a compressed sparse matrix ready for final prediction using the support vector machines and random forest as examples. Here we go:

X= hdata [h.feature_names]
from hlearn.utils.mlutils import make_naive_pipe
Xtransf = make_naive_pipe (X, transform=True) 
Xtransf 
Out[6]: 
<218x46 sparse matrix of type '<class 'numpy.float64'>'
	with 2616 stored elements in Compressed Sparse Row format> 
Xtrain, Xtest, ytrain, ytest = hlearn.sklearn.train_test_split (Xtransf, ymxs ) 
ypred_k_svc= hlearn.sklearn.SVC().fit(Xtrain, ytrain).predict(Xtest)
ypred_k_rf = hlearn.sklearn.RandomForestClassifier ().fit(Xtrain, ytrain).predict(Xtest)

We can now check the K prediction scores using accuracy_score function as:

hlearn.sklearn.accuracy_score (ytest, ypred_k_svc)
Out[7]: 0.9272727272727272
hlearn.sklearn.accuracy_score (ytest, ypred_k_rf)
Out[8]: 0.9636363636363636

As we can see, the results of K prediction are quite satisfactory for our toy example using only two boreholes data. Note that things can become more interesting when using many boreholes data.

Contributions

1. Department of Geophysics, School of Geosciences & Info-physics, Central South University, China.
2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration Changsha, Hunan, China
3. Laboratoire de Geologie Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Mini�res, Universite Felix Houphouet-Boigny, Cote d'Ivoire.

Developer: L. Kouadio <[email protected]>