Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion

Hinnell, A. C.; Ferré, T. P. A.; Vrugt, Jasper A.; Huisman, Johan Alexander; Moysey, S. M.; Rings, J.; Kowalsky, Michael B.

doi:10.1029/2008wr007060

Cited by 213 publications

(218 citation statements)

References 81 publications

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“…Whether the geophysical data are used to define hydrostratigraphic units or subregions or to parameterize the model, the data are often inverted. The way in which hydrologic and geophysical data are inverted and integrated can impact the extraction of information from geophysical data (Dam and Christensen, 2003;Day-Lewis, 2005;Moysey et al, 2005;Linde et al, 2006;Singha and Gorelick, 2006;Singha and Moysey, 2006;Hinnell et al, 2010).…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…In the first step, the geophysical inversions are typically stabilized by using regularization and smoothing constraints that do not reflect real physical conditions (Day-Lewis, 2005;Linde et al, 2006;Singha and Gorelick, 2006;Singha and Moysey, 2006). Therefore one must be cautious when using such geophysical property estimates to infer hydraulic zones or property estimates to be used in the second step of the SHI (Day-Lewis, 2005;Slater, 2007;Hinnell et al, 2010). Furthermore, with the SHI approach used in the following, the geophysical models cannot be easily updated to conform to the hydrologic observations.…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…Two inversion alternatives to SHI are coupled hydrogeophysical inversion (CHI) and joint hydrogeophysical inversion (JHI) (Hinnell et al, 2010). For both alternatives, the hydrologic and geophysical data sets are inverted simultaneously.…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

See 2 more Smart Citations

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Christensen¹,

Christensen²,

Ferré³

2016

Hydrol. Earth Syst. Sci.

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Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization.For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

See 1 more Smart Citation

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Christensen¹,

Christensen²,

Ferré³

2016

Hydrol. Earth Syst. Sci.

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“…Hydraulic conductivity distributions in alluvial fans can be assigned according to the various hydrofacies simulated by conditional indicator geostatistical methods (Eggleston and Rojstaczer, 1998;Fogg et al, 1998;Weissmann et al, 2002a, b;Ritzi et al, 2004Ritzi et al, , 2006Proce et al, 2004;Dai et al, 2005;Harp et al, 2008;Hinnell et al, 2010;Maghrebi et al, 2015;Soltanian et al, 2015;Zhu et al, 2016a). However, the geostatistical methods require the stationary assumption; i.e., the distribution of the volumetric proportions and correlation lengths of hydrofacies converge to their mean values in the simulation do-main.…”

Section: Introductionmentioning

confidence: 99%

Modeling 3-D permeability distribution in alluvial fans using facies architecture and geophysical acquisitions

Zhu

Gong

Dai

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Alluvial fans are highly heterogeneous in hydraulic properties due to complex depositional processes, which make it difficult to characterize the spatial distribution of the hydraulic conductivity (K). An original methodology is developed to identify the spatial statistical parameters (mean, variance, correlation range) of the hydraulic conductivity in a three-dimensional (3-D) setting by using geological and geophysical data. More specifically, a large number of inexpensive vertical electric soundings are integrated with a facies model developed from borehole lithologic data to simulate the log 10 (K) continuous distributions in multiplezone heterogeneous alluvial megafans. The Chaobai River alluvial fan in the Beijing Plain, China, is used as an example to test the proposed approach. Due to the non-stationary property of the K distribution in the alluvial fan, a multiplezone parameterization approach is applied to analyze the conductivity statistical properties of different hydrofacies in the various zones. The composite variance in each zone is computed to describe the evolution of the conductivity along the flow direction. Consistently with the scales of the sedimentary transport energy, the results show that conductivity variances of fine sand, medium-coarse sand, and gravel decrease from the upper (zone 1) to the lower (zone 3) portion along the flow direction. In zone 1, sediments were moved by higher-energy flooding, which induces poor sorting and larger conductivity variances. The composite variance confirms this feature with statistically different facies from zone 1 to zone 3. The results of this study provide insights to improve our understanding on conductivity heterogeneity and a method for characterizing the spatial distribution of K in alluvial fans.

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“…By comparing the results of the numerical simulations with observed geophysical and hydraulic data from the site, inferences about the likely hydrogeologic model can be updated in order to better match observations. Future efforts will focus on a more rigorous integrated hydrogeophysical interpretation, where geophysical data are more directly incorporated into the dynamic estimation of hydrogeologic parameters (Bauer-Gottwein et al, 2010;Ferré et al, 2009;Hinnell et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Hydrogeophysical Investigations at Hidden Dam, Raymond, California

Minsley

Burton

Ikard

et al. 2011

JEEG

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Self-potential and direct current resistivity surveys are carried out at the Hidden Dam site in Raymond, California to assess present-day seepage patterns and better understand the hydrogeologic mechanisms that likely influence seepage. Numerical modeling is utilized in conjunction with the geophysical measurements to predict variably-saturated flow through typical two-dimensional dam cross-sections as a function of reservoir elevation. Several different flow scenarios are investigated based on the known hydrogeology, as well as information about typical subsurface structures gained from the resistivity survey. The flow models are also used to simulate the bulk electrical resistivity in the subsurface under varying saturation conditions, as well as the self-potential response using petrophysical relationships and electrokinetic coupling equations.The self-potential survey consists of 512 measurements on the downstream area of the dam, and corroborates known seepage areas on the northwest side of the dam. Two directcurrent resistivity profiles, each approximately 2,500 ft (762 m) long, indicate a broad sediment channel under the northwest side of the dam, which may be a significant seepage pathway through the foundation. A focusing of seepage in low-topography areas downstream of the dam is confirmed from the numerical flow simulations, which is also consistent with past observations. Little evidence of seepage is identified from the self-potential data on the southeast side of the dam, also consistent with historical records, though one possible area of focused seepage is identified near the outlet works. Integration of the geophysical surveys, numerical modeling, and observation well data provides a framework for better understanding seepage at the site through a combined hydrogeophysical approach.

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Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion

Cited by 213 publications

References 81 publications

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Modeling 3-D permeability distribution in alluvial fans using facies architecture and geophysical acquisitions

Hydrogeophysical Investigations at Hidden Dam, Raymond, California

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