Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes

Caractérisation d'un milieu poreux colmaté par la méthode du potentiel spontané

Laboratory experiments are performed on the physical clogging of a porous medium by injecting polydispersive suspended particles ranged from 1.7 to 40 μm in a column filled with sand (d50=715 µm). Three suspensions have been injected (2g/l, 3g/l and 5g/l) at constant flow velocity in a saturated porous medium. Hydrodynamic effects on particles deposition and porous medium damage were investigated. Particles retentions are significant at the inlet and decreases with depth which induces the drop of permeability and porosity. The coupling between electric and hydraulic flows is electrokinetic in nature, the second objective of this work is to test the sensitivity of the method of self-potential (SP) and to show the usefulness of this method to understand the controlling parameters of the electrical field associated with the filtration of suspended particles through a porous medium. Electrodes have been used to record the SP signal which detect and quantify the clay particles in the porous medium during injection tests. The variations of the SP signals are linearly correlated to the piezometric level changes. SP signals are directly sensitive to deposition particles distribution, and it is high at the inlet of the column where the deposit is large and decreases with depth. For the test injection with 2g/l the SP signal varies between +16 mv and +13 mv for the test with 3g/l it varies between +12 mv and +8mv and that with 5g/l it varies between - 22mv to-19mv. When the concentration of clay injected in the porous medium increases, the SP signal decreases and changes from a positive sign to a negative sign. SP method confirmed the behavior of the porous medium clogging obtained by the hydraulic measurements. The obtained results show that SP signal is sensitive to clay particles, their immediate passage to the inlet of the column and the amount deposited particles in each section of the porous medium

Geophysical Signitures From Hydrocarbon Contaminated Aquifers

International audienc

The Self-Potential Method: Theory and Applications in Environmental Geosciences

International audienc

Stochastic joint inversion of temperature and self-potential data to determine the permeability of geological units in sedimentary basins

International audienc

USE OF ELECTRICAL METHODS TO CHARACTERIZE PREFERENTIAL GROUND WATER FLOW IN ENBANKMENT DAMS

International audienc

The self-potential method: Did the Ugly Duckling of Environmental Geophysics Turn into a Beautiful Swan

International audienc

Seismoelectric couplings in a poroelastic material containing two immiscible fluid phases

International audienceA new approach of seismoelectric imaging has been recently proposed to detect saturation fronts in which seismic waves are focused in the subsurface to scan its heterogeneous nature and determine saturation fronts. Such type of imaging requires however a complete modelling of the seismoelectric properties of porous media saturated by two immiscible fluid phases, one being usually electrically insulating (for instance water and oil). We combine an extension of Biot dynamic theory, valid for porous media containing two immiscible Newtonian fluids, with an extension of the electrokinetic theory based on the notion of effective volumetric charge densities dragged by the flow of each fluid phase. These effective charge densities can be related directly to the permeability and saturation of each fluid phase. The coupled partial differential equations are solved with the finite element method. We also derive analytically the transfer function connecting the macroscopic electrical field to the acceleration of the fast P wave (coseismic electrical field) and we study the influence of the water content on this coupling. We observe that the amplitude of the co-seismic electrical disturbance is very sensitive to the water content with an increase in amplitude with water saturation. We also investigate the seismoelectric conversions (interface effect) occurring at the water table. We show that the conversion response at the water table can be identifiable only when the saturation contrasts between the vadose and saturated zones are sharp enough. A relatively dry vadose zone represents the best condition to identify the water table through seismoelectric measurements. Indeed, in this case, the coseismic electrical disturbances are vanishingly small compared to the seismoelectric interface response

Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT

(IF 3.35; Q1)In general, the inverse problem of electrical resistivity tomography (ERT) is treated using a deterministic algorithm to find a model of subsurface resistivity that can numerically match the apparent resistivity data acquired at the ground surface and has a smooth distribution that has been introduced as prior information. In this paper, we propose a new deep learning algorithm for processing the 3-D reconstruction of ERT. This approach relies on the approximation of the inverse operator considered as a nonlinear function linking the section of apparent resistivity as input and the underground distribution of electrical resistivity as output. This approximation is performed with a large amount of known data to obtain an accurate generalization of the inverse operator by identifying during the learning process a set of parameters assigned to the neural networks. To train the network, the subsurface resistivity models are theoretically generated by a geostatistical anisotropic Gaussian generator, and their corresponding apparent resistivity by solving numerically 3-D Poisson's equation. These data are formed in a way to have the same size and trained on the convolutional neural networks with SegNet architecture containing a three-level encoder and decoder network ending with a regression layer. The encoders including the convolutional, max-pooling and nonlinear activation operations are sequentially performed to extract the main features of input data in lower resolution maps. On the other side, the decoders are dedicated to upsampling operations in concatenating with feature maps transferred from encoders to compensate the loss of resolution. The tool has been successfully validated on different synthetic cases and with particular attention to how data quality in terms of resolution and noise affects the effectiveness of the approach

Stochastic joint inversion of temperature and self-potential data to determine the permeability of geological units in sedimentary basins

International audienc