""" ========================================== EM, DC, and Hydro parameters computing ========================================== Real-world examples for showing the computation of EM tensor, DC parameters and how to implement the mixture learning strategy (MXS) from the naive aquifer group (NGA) for the permeability coefficient k prediction. """ # Author: L.Kouadio # Licence: BSD-3-clause #%% # Import required modules import numpy as np import matplotlib.pyplot as plt plt_style ='classic' # Load real data collected during the drinking water supply campaign # occured in 2012-2014 in Cote d'Ivoire. Read more in the dataset # module ( :mod:`~watex.datasets`) from watex.datasets import ( load_edis, load_gbalo, load_semien, load_tankesse, load_boundiali ) # Real logging data collected in Hongliu coal mine, in China, Hunan province from watex.datasets import load_hlogs from watex.methods import( DCProfiling, DCSounding, ResistivityProfiling, VerticalSounding,EMAP , Logging, MXS ) #%% # EM :mod:`~watex.methods.em` # ============================ # The EM module is related to a few meter exploration in the case of groundwater # exploration. The module provides some basic EMAP steps for EMAP data filtering # and removing noises. Commonly the method mostly used in groundwater # exploration is the audio-magnetotelluric because of the shortest frequency # and rapid executions. Furthermore, we can also list some other advantages # such as: # # * is useful for imaging both deep geologic structure and near-surface geology and can provide significant details. # * includes a backpack portable system that allows for use in difficult terrain. # * the technique requires no high-voltage electrodes, and logistics are # relatively easy to support in the field. Stations can be acquired almost # anywhere and can be placed any distance apart. This allows for large-scale # regional reconnaissance exploration or detailed surveys of local geology # and has no environmental impact # # :note: For deep implementation or exploring a large scale of EM/AMT data # EMAP, it is recommended to use the package `pycsamt `_. # Create EM object as a collection of EDI-file. # Collect edi-files and create an EM object. It sets he properties from # audio-magnetotelluric. The two(2) components XY and YX will be set and # calculated.Can read MT data instead, however, the full handling transfer # function like Tipper and Spectra is not completed. Use other MT software for a long period's data. from watex.methods.em import EM edi_data = load_edis (return_data =True, samples =7) # object from Edi_collection emObjs = EM().fit(edi_data) ref=emObjs.getfullfrequency () ref emObjs.freqs_ # # however full frequency can just be fetched using the attribute `freqs_` # get the reference frequency rfreq = emObjs.getreferencefrequency () rfreq # Fast process EMAP and AMT data. Tools are used for data sanitizing, # removing noises and filtering. p = EMAP().fit(edi_data) p.window_size =2 p.component ='yx' rc= p.tma() # get the resistivity value of the third frequency at all stations # >>> p.res2d_[3, :] # get the resistivity value corrected at the third frequency # >>> rc [3, :] #%% # Plot 1D raw and corrected tensor plt.semilogy (np.arange (p.res2d_.shape[1] ), p.res2d_[3, :], '--', np.arange (p.res2d_.shape[1] ), rc[3, :], 'ok--') #%% # Compute the skew: The conventional asymmetry parameter based on the Z magnitude. # p = EMAP().fit(edi_data) # sk,_ = p.skew() # sk[0:, ] # restore tensor pObjs= EMAP().fit(edi_data) # One can specify the frequency buffer like the example below, however # it is not necessary at least there is a specific reason to fix the frequencies # buffer = [1.45000e+04,1.11500e+01] zobjs_b = pObjs.zrestore( # buffer = buffer ) zobjs_b # control the quality of the EM data # pobj = EMAP().fit(edi_data) # f = pobj.getfullfrequency () # # len(f) # # ... 55 # 55 frequencies # c, = pobj.qc ( tol = .6 ) # mean 60% to consider the data as # # representatives # c # the representative rate in the whole EDI- collection # # ... 0.95 # the whole data at all stations is safe to 95%. # # now check the interpolated frequency # c, freq_new, = pobj.qc ( tol=.6 , return_freq =True) # # len(freq_new) # # ... 53 # delete two frequencies #%% # DC-method :mod:`~watex.methods.electrical` # ================================================ # A collection of DC-resistivity profiling and sounding classes. # It reads and computes electrical parameters. Each line or site composes a specific # object and gathers all the attributes of :class:`~.ResistivityProfiling` # or :class:`~watex.methods.electrical.VerticalSounding` for easy use. For instance, # the expected drilling location point and its # resistivity value for two survey lines ( line1 and line2) can be fetched #%% # DC -Profiling # ---------------- # * Get DC -resistivity profiling from the individual Resistivity object robj1= ResistivityProfiling(auto=True) # auto detection robj1.utm_zone = '50N' data = load_tankesse () robj1.fit(load_tankesse ()) print(robj1.sves_ ) robj2= ResistivityProfiling(auto=True, utm_zone='40S') robj2.fit(load_gbalo()) print(robj2.sves_ ) # read the both objects dcobjs = DCProfiling() dcobjs.fit(robj1, robj2) print(dcobjs.sves_ ) print(dcobjs.line1.sves_) print(dcobjs.line2.sves_ ) #%% # Plot conductive zone for line 1 robj1.plotAnomaly (style =plt_style) #%% # Plot conductive zones for second lines robj2.plotAnomaly ( style = plt_style ) #%% # * Get parameters robj1.summary(return_table=True ) #%% # DC Sounding # ------------- # * Read single sounding site dsobj = DCSounding () dsobj.search = 30. # start detecting the fracture zone from 30m depth. dsobj.fit(load_gbalo(kind ='ves')) print(dsobj.ohmic_areas_) print(dsobj.site1.fractured_zone_ ) #%% # * read multiple sounding files dsobj.search = 30 #[ 30, 30, 30, 30] # search values for all sites dsobj.fit(load_semien(index_rhoa= 2 ), load_gbalo(kind ='ves', index_rhoa =2 ,), load_boundiali (index_rhoa=1) ) print(dsobj.ohmic_areas_ ) print(dsobj.nareas_ ) print(dsobj.survey_names_) print(dsobj.nsites_ ) print(dsobj.site1.ohmic_area_) print(dsobj.data_ ) # you can use the `isnotvalid_` attributes to check the unread data before # calling the site object like print("dsobj.isnotvalid_", dsobj.isnotvalid_) # return empty list if all values passed are correct. #%% # * Plot ohmic area ves = VerticalSounding ().fit (dsobj.site3.data_) ves.plotOhmicArea (fbtw=True , style =plt_style) #%% # Hydrogeology :mod:`~watex.methods.hydro` # ========================================== # Hydrogeological parameters of the aquifer are the essential and crucial basic data # in the designing and construction progress of geotechnical engineering and # groundwater dewatering, which is directly related to the reliability of these # parameters. # get the logging data h = load_hlogs () print(h.feature_names) #%% # we can collect the valid logging data and fit it log= Logging(kname ='k', zname='depth_top' ).fit(h.frame[h.feature_names]) print( log.feature_names_in_) # categorical features should be discarded. #%% # Plot default log using the predictor :math:`X` ( composed of features only) # As an example, we will plot the first five features log= Logging(kname ='k', zname='depth_top' ).fit(h.frame[log.feature_names_in_[:5]]) log.plot () #%% # Plot log including the target y # log.plot (y = h.frame.k , posiy =0 )# first position #%% # Mixture Learning Strategy (MXS) # ------------------------------- # The use of machine learning for k-parameter prediction seems an alternative # way to reduce the cost of data collection thereby saving money. However, # the borehole data comes with a lot of missing k since the parameter is # strongly tied to the aquifer after the pumping test. In other words, the # k-parameter collection is feasible if the layer in the well is an aquifer. # Unfortunately, predicting some samples of k in a large set of missing data # remains an issue using the classical supervised learning methods. We, # therefore, propose an alternative approach called a mixture of learning # strategy (MXS) to solve these double issues. It entails predicting upstream # a naïve group of aquifers (NGA) combined with the real values k to # counterbalance the missing values and yield an optimal prediction score. # The method, first, implies the K-Means and Hierarchical Agglomerative # Clustering (HAC) algorithms. K-Means and HAC are used for NGA label # predicting necessary for the MXS label merging. hdata = load_hlogs ().frame # drop the 'remark' columns since there is no valid data hdata.drop (columns ='remark', inplace =True) mxs = MXS (kname ='k').fit(hdata) # predict the default NGA mxs.predictNGA() # default prediction with n_groups =3 # make MXS labels using the default 'k' categorization ymxs=mxs.makeyMXS(categorize_k=True, default_func=True) print(mxs.yNGA_ [62:74] ) print(ymxs[62:74]) # array([ 1, 22, 22, 22, 3, 1, 22, 1, 22, 22, 1, 22]) # to get the label similarity , need to provide the # the column name of the aquifer group and fit again like mxs = MXS (kname ='k', aqname ='aquifer_group').fit(hdata) sim = mxs.labelSimilarity() print(sim ) # [(0, 'II')] # group II and label 0 are very similar #%% # Once the MXS label is created, you can use the supervised training # strategy for training models. Refer to the model modules (:mod:`~watex.models`)