GEOSCAN Search Results: Fastlink

GEOSCAN Menu


TitlePredicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: motivations, algorithms and application
AuthorParadis, D; Lefebvre, R; Gloaguen, E; Rivera, A
SourceWater Resources Research vol. 51, 2015 p. 481-505, https://doi.org/10.1002/2014WR015452
Year2015
Alt SeriesEarth Sciences Sector, Contribution Series 20130456
PublisherWiley
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
ProvinceQuebec
NTS21L/11
AreaSt. Lambert
Lat/Long WENS -71.5000 -71.0000 46.7500 46.5000
Subjectshydrogeology; geophysics; geochemistry; hydraulic conductivity; groundwater flow; penetrometers; electrical resistivity; aquifers; water wells; soil moisture; pore pressures
Illustrationsgeological sketch maps; graphs; tables; histograms; diagrams; plots
AbstractThe spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.
Summary(Plain Language Summary, not published)
The heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. This paper proposes a new methodology to image heterogeneous spatial distribution of K through indirect geophysical measurements using learning machine. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient and it appears as a generally applicable approach for K heterogeneity characterization for unconsolidated aquifers.
GEOSCAN ID293615