Application to geophysics: - PowerPoint Presentation

Slide1

Application to geophysics:Challenges and some solutions

Andrew Binley

Email: a.binley@lancaster.ac.ukSlide2

Hydrogeophysics – the drivers

Characterising groundwater systems is challenging because of the (physical and chemical) complexity of the shallow subsurface and the difficulty in observing the

structure of the system …

Hartman et al. (2007)

… and the complex response due to external loading.

Robin

Nimmer

, Moscow, IdahoSlide3

Hydrogeophysics – the drivers

Resistivity profile and hydrogeological section, Penitencia, CA (after Zohdy, 1964).

Geophysics has been widely used to support groundwater investigations for many years. However, many of the earlier approaches concentrated on using geophysics to define lithological boundaries and other subsurface structures.Slide4

Hydrogeophysics – the drivers

Tiedeman

& Hsieh (2004)

During the 1990s there was a rapid growth in the use of geophysics to provide quantitative

information about hydrological properties and processes.

Much of this was driven by:

- the recognition of the importance of heterogeneity of subsurface properties that influence mass transport in groundwater systems.

- the need to gain information of direct value to hydrological models, particularly given the developments of ‘data hungry’ stochastic hydrology tools.Slide5

Hydrogeophysical approach

structure

(e.g. permeability maps)

process

(e.g. transport of solute)

Kemna (2003)

Dynamic imaging

Static imaging

Rock physics

model(s)

Rock physics

model(s)

Improved

hydrogeological model

Kowalsky et al. (2006)Slide6

Hydrogeophysical approach

Micro-

structure

Well logs

Cross-borehole

imaging

Surface imaging

Airborne

Core

imaging

Survey scale

ResolutionSlide7

Commonly used approach – static imaging

A1

C2

C5

C3C4

2.3

3.2

l

og

10

(resistivity, in

W

m)

Boise, Idaho , USA

14m

Keery

, Binley, Slater, Barrash and Cardiff (in prep)Slide8

16-Mar-03

Depth (m)

Distance (m)

Distance (m)

15-Mar-03

Depth (m)

Distance (m)

Distance (m)

21-Mar-03

Depth (m)

Distance (m)

Distance (m)

Depth (m)

Distance (m)

Distance (m)

24-Mar-03

Depth (m)

Distance (m)

Distance (m)

27-Mar-03

Depth (m)

Distance (m)

Distance (m)

02-Apr-03

Winship, Binley and Gomez (2006)

Hatfield, UK

Monitoring changes in resistivity due to tracer injection.

Ultimately to understand pathways of solutes from ground surface to the aquifer.

Commonly used approach – dynamic imaging

H

-

E2

H

-

R2

H

-

R1

H

-

E1

H

-

E3

H

-

E4

H

-

I2

Tracer injected

at H-I2Slide9

But many of the hydrological challenges are at a larger scale

Challenge 1: Larger

scale applicationSlide10

Larger scale

example

Elevation (m above sea level)

Objective: determine potential connectivity between land surface and regional sandstone aquiferSlide11

Electromagnetic (EM) conductivity surveys reveal variation over top 6m

Larger scale

exampleSlide12

Larger scale

example

Current is injected between C+ and C-

The voltage difference between P+ and P- is measured

The voltage difference is a function of the current

injected and the resistivity beneath the electrode array

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

C+

C-

P+

P-

Electrical resistivity tomography (ERT) provides an assessment of vertical structureSlide13

log10 (resistivity, in

W

m

)

Conductivity (

mS

/m)

stream

Clayey drift

Sandstone

Window in the clay?

Larger scale

exampleSlide14

Local sampling and geology

Resistivity & Induced Polarisation

Borehole

logs

Ground Conductivity

GPR

How do we bring all these data together to form

one

consistent, improved model of the system?

Challenge 2: Data fusionSlide15

Challenge 2: Data fusion

Can we use other information to help constrain the inversion of geophysical data?

For example, we may be able to estimate spatial covariance

structure based on well log data?

Linde

, Binley,

Tryggvason

, Pedersen and

Revil

(2006)Slide16

Challenge 2: Data fusion

In areas where the gradients are in the same or opposite direction (or where one of the gradients is zero)

t

will be zero (and the pixels structurally similar)

We could jointly invert the two (or more) data using a structural similarity, e.g. by minimising the

cross-gradients

operator

Gallardo (2006)Slide17

Challenge 2: Data fusion

structure

(e.g. permeability maps)

Static imaging

Rock physics

model(s)

We cannot use geophysical imaging alone – we need to use geophysics to support other data (not replace it)

Well log

data

Measurements of hydrological statesSlide18

At times there is a need to

assess information content in data (this has been significantly overlooked to date)

£X

drilling

£X

geophysics

Understanding the value of different information will permit appropriate resource allocation

to the

project and help with survey design.

This is becoming more and more relevant as large hydrological

projects

invest in hydrogeophysical surveys.

Challenge 3: Assessing information contentSlide19

Data fusion

ERT

Parameter resolution

Spatial resolution

Quantified

information

EM

GPR

Other methods

Geophysical method

Inversion

(

McMC

)

Output

Prior information

Uncertainties

MappingSlide20

Data fusion

Site represented as series of 1D models

Permits practical application of

Markov chain Monte Carlo (

McMC

)

Misfit

Likelihood

MH sampling

(accept/reject)

Prior

Posterior

Bayes’ theorem

Joint likelihood functionSlide21

Data fusion

(e.g.,

Maurer et al., 2010)

Shannon’s

Entropy (Shannon

, 1948)

Information

(Shannon’s Entropy)

Increase in information as uncertainty in property reducesSlide22

Data fusion

Bayesian

Maximum

Entropy (BME)

Serre

&

Christakos (1999)

Expected knowledge

Maximization

(

Lagrange multipliers method

)

G

: general knowledge

S

: site-specific knowledge

K

: total knowledge

Predicted pdfBMELIB (http://www.unc.edu/depts/case/BMELIB/)Christakos (2000)

   Prediction

Hard data

(Information >2)

Soft data

(Information <

2

)Slide23

Data fusion

JafarGandomi & Binley (in review)

1D synthetic example showing how different data provides constraint to resistivity structureSlide24

Distance (m)

Example data fusion on quasi 2D

profile from Trecate, Italy

Data fusionSlide25

Coupled hydrogeophysical inversion

Hydrological model,

e.g. permeability structure

Geophysical surveys

?

Hydrological model

Inversion

(assumed

known)

Rock physics

model(s)

And, if so, then we should use this in our inversion

Surely we know something about the hydrology? Slide26

Scholer, Irving, Binley and Holliger

(2011)

Coupled hydrogeophysical inversion

Do we need to invert geophysical data?

We have been exploring

the potential of using

geophysical

data (not images) as a means of constraining hydrological

models in an

McMC

framework

.Slide27

Scholer, Irving, Binley and Holliger

(2011)

Coupled hydrogeophysical inversion

Prior distribution for the 4 hydrological model parameters

Posterior distribution for the 4 hydrological model parameters for each of the 4 layersSlide28

Summary

Deterministic inversion of 3D geophysical data is now relatively common, although the assessment of uncertainty is lacking.

We need to develop ways of combining multiple data (multiple scales).

Attempts have been made to use geophysical data within a hydrological model inversion. So far these have been limited to relatively low dimensional models.

These fusion approaches must allow some assessment of information value, particularly as we look at new survey designs (for future data).

Attempts have been made to jointly invert geophysical data, although most of these have been done in 2D.