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SMART Aquifer Characterization and Mapping
with Machine-Learning and Evolutionary
Techniques Data 2 Knowledge
Keynote, Groundwater Session
Australian Earth Sciences Convention
Adelaide, Australia
28 June, 2016
Michael J. Friedel, Ph.D.
[email protected]
Outline
 Background
 Applications
 Future
BACKGROUND
Goal
 Save Money and Reduce Time (SMART) in
characterization and mapping of aquifers
Past - disparate data
 Hydrogeologic surveys
 Physical properties (lithologic composition, Ks or T,
porosity, bulk density, retention, water levels)
 Hydrochemistry (major ions, metals, isotopes, tracers)
 Field parameters (pH, temp, sc)
 Sampling (pump/injection, point/crosswell)
 Geophysical surveys
 Physical properties (density, velocity, resistivity)
 Sampling (point, crosswell, surface, remote)
Today - big data
 Velocity – rate data are generated
 Volume – number of records
 Variety – coupled, disparate, nonlinear, scaledependent, sparse, spatiotemporal, uncertain
The challenge ...
 “We're drowning in data and starving for
knowledge” Rutherford D. Rogers
Objective
 SMART aquifer characterization and mapping with
machine-learning and evolutionary techniques
= Model
Objective
 SMART aquifer characterization and mapping with
machine-learning and evolutionary techniques
Machine Learning
= Model
Objective
 SMART aquifer characterization and mapping with
machine-learning and evolutionary techniques
Traditional
Machine Learning
= Model
Objective
 SMART aquifer characterization and mapping with
machine-learning and evolutionary techniques
Machine Learning
= Model
Evolutionary
Objective
 SMART aquifer characterization and mapping with
machine-learning and evolutionary techniques
Traditional
Machine Learning
= Model
Evolutionary
Reality
Model
Simplification of Reality
Model accuracy
 Dependent on data diversity
 Observations across multiple gradients
 Natural and anthropogenic features/stresses
Data diversity
 Physical - geophysical and hydrogeologic response
to lithology (natural)
 Chemical - agricultural to urban land use, forest to
mining land use (anthropogenic)
Data diversity
 Climate change – time continuum
 Groundwater archive paleotemperatures
 Aquatic biology archive El Niño – La El Niña events
 Sample support – space and time
 Borehole induction resistivity to airborne resistivity
 Slug test conductivity to pump test transmissivity
 Algae to macroinvertebrates to fish
Model algorithms
Supervised
 Machine-learning
 Supervised - maps inputs to outputs
 Unsupervised - models set of patterns
 Evolutionary
 Unsupervised optimization (heredity, fitness)
 Functional evolution (functions, parse trees)
Unsupervised
Model algorithms
 Hybrid
 Traditional plus machine-learning
 Evolutionary plus machine-learning
 ML plus minimum spanning tree
 Workflows
 Multiple ML processes
Selecting algorithms
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Classification or regression
Imputation, estimation, prediction, forecast
Sparse or complete data
Number of attributes, processes, responses
Linearly separable or nonlinear relations
Imputation, estimation, prediction, forecast
APPLICATIONS
Machine learning - supervised
 Perceptron
 Mapping presence/absence till (Gunnik et al. 2012)
 Back-propagation (BP)
 Mapping 3-layer resistivity (Zhu et al. 2012)
 Naïve Bayes/BP/Support Vector Machine
 Precipitation effects on groundwater recharge (Unpublished)
Predict precipitation effects on GW recharge (unpublished)
Recharge, cm
Recharge, cm
ANN – supervised artificial neural network
Minnesota,
USA
Year
Precipitation, cm
Predict precipitation effects on GW recharge (unpublished)
Recharge, cm
Recharge, cm
ANN – supervised artificial neural network
Minnesota,
USA
Year
Precipitation, cm
Data-driven challenge – predicting extremes
Solution - train ANN with set correlated random variables
Machine-learning - unsupervised
 Modified-Self-Organizing Map (SOM)
 Forecast climate change on groundwater recharge
(unpublished)
 Climate-change reconstruction (Friedel, 2012)
Forecast climate change on GW recharge (unpublished)
ANN – supervised artificial neural network with correlated random variables
Wisconsin,
USA
Use for water-resource management
Climate-change reconstruction (Friedel, 2012)
MEDIEVAL LITTLE ICE
WARMING PERIOD AGE
MODERN WARMING
PERIOD
Northern and Southern
Hemisphere
Climate-change reconstruction (unpublished)
Hybrid Modeling – SOM plus others …
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GW recharge scaling equations (Friedel, unpublished)
Spatial continuity for GW models (Friedel et al., 2013)
Climate-change forecasting (Esfahani and Friedel, 2014)
Toward real-time aquifer mapping (Friedel et al., 2015)
Estimation and scaling hydrostratigraphy (Friedel, 2016)
Real-time satellite mapping landscape features (in review)
Groundwater-recharge scaling equations (unpublished)
Self-Organizing Map
Symbolic Regression
Scaling Equations
Precipitation
Recharge
Recharge
Recharge
Use to adjust groundwater recharge based on scale of model
Spatial continuity for GW models (Friedel, 2013)
 …
Various scales,
Brazil
Use to conceptualize and inform groundwater model calibration
Climate-change forecasting (Esfahani and Friedel, 2014)
Precipitation trend for California, USA: 2012 to 2020
DROUGHT  WILDFIRES
California, USA
Use to evaluate climate change effects on groundwater modeling
Toward real-time aquifer mapping (Friedel, 2015)
Numerical Inversion
Machine-Learning Estimates
0
5km
Aquifer
Confining
Unit
Aquifer
Confining
Unit
Use to conceptualize groundwater system
Estimation & scaling hydrostratigraphy (Friedel, 2016)
Borehole Hydrostratigraphic Units
Continuous Hydrostratigraphic Units
SOM Scaling Network
HSU(lithology, hydraulic properties, water chemistry, geophysical properties)
Use to conceptualize and inform groundwater modeling process
Real-time satellite landscape mapping (in review)
Subpixel Soil and Vegetation Fractions
Nonphotosynthetically Active Vegetation (NPAV)
Brazil
Soil
Use for downscale GW recharge estimates
Photosynthetically Active Vegetation (PAV)
FUTURE
Feature selection
 Spatial autocorrelation
 Linear PCA on SOM estimates
 Clustering ACM on minimum spanning tree
 Genetic doping
Feature Selection – Spatial Autocorrelation
Self-Organizing Map 1
Otago Region, NZ
Layer Resistivities
Electromagnetic Measurements
Distance of Resistivity Sounding to Borehole
0 10 20 30 40 50 60
F41-0389
Correlation
C2017
Correlation
Correlation
Autocorrelation @ Boreholes
0 10 20 30 40 50 60
g41-0308
0 10 20 30 40 50 60
Distance from borehole, m
Relate borehole lithology to AEM soundings <10 m
Feature Selection – Spatial Autocorrelation
Self-Organizing Map 2
Predict Hydraulic Conductivity
Lithology + Hydraulic properties
+ Chemistry + Soundings < 10 m
Predict Lithology
Predict Total Dissolved Solids
Feature Selection – Genetic Doping
Southland Region, NZ
Model (53 Variables)
Linear PCA on SOM estimates
Feature Selection – Genetic Doping
Feature Selection
Data Worth
6 clusters
Genetic Doping
Conceptualize Model
3
2
1
3 clusters
Workflows – Murray-Darling, AU
Modified self-organizing map
 Parallelization – processing big data (speed)
 Regularization – elastic net estimation (add Lasso, ...)
 Open source – flexibility with model-independent
software (Python, R) for customizable workflows
Modified self-organizing map
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Drill-log uncertainty - random correlated variables
Generalize cross-validation to n-fold
Ensemble - boosting with SOM as base learner
Quantile regression – mcmc training resistivity models
ML proposal distributions - mcmc resistivity inversions
Conclusion
 Data 2 knowledge paradigm provides a SMART
approach to characterizing and mapping aquifers
 SMART = Save Money And Reduce Time
Thank you!
Answers ?
Questions ?
Mike Friedel
[email protected]
[email protected]