the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# Extracting microphysical fault friction parameters from laboratory and field injection experiments

### Martijn P. A. Ende

### Marco M. Scuderi

### Frédéric Cappa

### Jean-Paul Ampuero

Human subsurface activities induce significant hazard by (re-)activating slip on faults, which are ubiquitous in geological reservoirs. Laboratory and field (decametric-scale) fluid injection experiments provide insights into the response of faults subjected to fluid pressure perturbations, but assessing the long-term stability of fault slip remains challenging. Numerical models offer means to investigate a range of fluid injection scenarios and fault zone complexities and require frictional parameters (and their uncertainties) constrained by experiments as an input. In this contribution, we propose a robust approach to extract relevant microphysical parameters that govern the deformation behaviour of laboratory samples. We apply this Bayesian approach to the fluid injection experiment of Cappa et al. (2019) and examine the uncertainties and trade-offs between parameters. We then continue to analyse the field injection experiment reported by Cappa et al. (2019), from which we conclude that the fault-normal displacement is much larger than expected from the adopted microphysical model (the Chen–Niemeijer–Spiers model), indicating that fault structure and poro-elastic effects dominate the observed signal. This demonstrates the importance of using a microphysical model with physically meaningful constitutive parameters, as it clearly delineates scenarios where additional mechanisms need to be considered.

Induced seismicity is of primary concern in human subsurface activities, including geothermal energy
production, wastewater and CO_{2} injection, and hydrocarbon extraction
(Ellsworth, 2013). Seismicity triggered around injection sites is generally attributed to
elevated pore fluid pressures, which lower the clamping stress that keeps the fault locked
(Elsworth et al., 2016). Additionally, recent field injection tests at a decametric scale reveal the
importance of aseismic creep in driving seismicity (Duboeuf et al., 2017), and long-range poro-elastic
effects and earthquake interactions have been inferred to trigger seismicity well beyond the extent
of the stimulated region (Catalli et al., 2016; Goebel and Brodsky, 2018; Schoenball and Ellsworth, 2017). To better assess the
earthquake hazard associated with the injection and extraction of geo-fluids, potential mechanisms
underlying the nucleation of induced seismic events need to be identified.

Laboratory experiments provide the means to investigate the mechanisms for (unstable) fault slip at high resolution under well-controlled conditions (e.g. Kaproth et al., 2016; Scuderi et al., 2016, 2017; Tenthorey et al., 2003). Many laboratory studies report their results in terms of rate-and-state friction (RSF; Dieterich, 1979; Ruina, 1983) parameters, which may serve as input for numerical modelling studies (Cubas et al., 2015; Kroll et al., 2017; McClure and Horne, 2011; Noda et al., 2017). Unfortunately, it is well-established that the RSF parameters depend on a plethora of thermodynamic conditions (Blanpied et al., 1998; Boulton et al., 2019; Chester, 1994; He et al., 2016; Hunfeld et al., 2017), including fluid pressure (Cappa et al., 2019; Sawai et al., 2016; Scuderi et al., 2016), which needs to be accounted for when attempting to extrapolate laboratory measurements to nature through RSF-based numerical models. The relationships between RSF parameters and observable quantities (such as porosity, grain size, or fluid chemistry) are not well understood, and so great care must be taken when generalising laboratory results to natural systems.

As an alternative approach, decametric-scale fluid injection tests allow one to probe the response of a tectonic fault to fluid pressure perturbations under in situ conditions (Derode et al., 2015; Duboeuf et al., 2017; Guglielmi et al., 2015; Rivet et al., 2016). While these tests provide more direct insights into the (potentially seismic) behaviour of the fault, they are also more complicated to interpret owing to the complexity inherent to natural faults. Generalisation of the results and extrapolation to other fault or reservoir conditions is therefore challenging. Moreover, fluid injection rates and volumes are limited by regulatory restrictions, which inhibits a comparison with systems characterised by larger injection volumes and rates. Numerical models remain essential to investigate faults in this context (e.g. Dempsey and Riffault, 2019; Rutqvist et al., 2007; Wynants-Morel et al., 2020), which in turn rely on constraints offered by laboratory experiments.

In the present study, we reinterpret the laboratory and decametric-scale fluid injection experiments reported by Cappa et al. (2019) in the framework of the Chen–Niemeijer–Spiers (CNS) microphysical model (Chen and Spiers, 2016; Niemeijer and Spiers, 2007). To this end, we propose a robust approach for the extraction of the CNS microphysical parameters from laboratory or field observations based on the relation between fault dilatancy and shear slip and the temporal evolution of the slip rate. In this Bayesian approach, we examine the uncertainties associated with each parameter, and the trade-offs between parameters, which are both important for choosing suitable parameter ranges for numerical modelling efforts. Lastly, we discuss the limitations of and perspectives offered by the adopted microphysical model in the context of induced seismicity modelling.

## 2.1 The Chen–Niemeijer–Spiers model

To describe the observed laboratory observations of Cappa et al. (2019) in terms of micro-physical quantities, we adopt the Chen–Niemeijer–Spiers (CNS) model proposed by Niemeijer and Spiers (2007) and extended by Chen and Spiers (2016). In the following section, we briefly summarise the basic mechanics of this microphysical model and the numerical implementation adopted in this study. For a detailed derivation and discussion of this model, we refer to the original works of Niemeijer and Spiers (2007) and Chen and Spiers (2016) (see also Verberne et al., 2020, this special issue).

Firstly, the CNS model considers a representative elementary volume of fault gouge of thickness *L*
and porosity *ϕ*, which is subjected to an effective normal stress *σ*_{e} (i.e. total normal
stress minus the fluid pressure) and shear stress *τ*. In response to this state of stress, the
gouge deforms internally through parallel operation of dilatant granular flow and one or more
non-dilatant creep mechanisms. The timescales considered in the present study are too short (of the
order of seconds to minutes) to justify a detailed consideration of the non-dilatant creep
component, and hence we focus purely on the granular flow component. As will be shown later, this
simplification is well-warranted by the laboratory observations. In line with this assumption, the
shear and volumetric deformation of the fault gouge can be described as

Here, *V* denotes the rate of slip on the fault *δ* and ${\dot{\mathit{\gamma}}}_{\text{gr}}$ and
${\dot{\mathit{\epsilon}}}_{\text{gr}}$ are the shear and volumetric strain rate of granular flow, respectively
(compression defined positive). We consider only fault-normal volumetric strains (i.e. no
fault-parallel expansion/contraction). The amount of volumetric deformation associated with an
increment of shear strain is described by the dilatancy angle tan *ψ*, i.e.
$\mathrm{d}{\mathit{\epsilon}}_{\text{gr}}=-\mathrm{tan}\mathit{\psi}\mathrm{d}{\mathit{\gamma}}_{\text{gr}}$, and is given by
(Niemeijer and Spiers, 2007)

where *H* is a geometric constant of order 1 and *ϕ*_{c} is referred to as the
“critical-state” porosity, i.e. the maximum attainable porosity of the gouge. The parameter *H*
represents how much dilatancy is involved when grains are sliding past one another and is likely
affected by grain shape, angularity, and size distribution. Based on a first-order geometric
analysis, Niemeijer and Spiers (2007) estimated that the maximum dilatancy angle at zero porosity is
$\mathrm{tan}\mathit{\psi}=\sqrt{\mathrm{3}}$, which puts an upper bound on $H<\sqrt{\mathrm{3}}/\mathrm{2}{\mathit{\varphi}}_{\mathrm{c}}$. Likewise,
the critical-state porosity *ϕ*_{c} is likely not a universal constant. Nonetheless, in
the absence of tight theoretical constraints on *H* and *ϕ*_{c}, we treat these
quantities as constant parameters.

The rate of granular flow is itself a function of stress and porosity and can be written as (Chen and Spiers, 2016)

The reference grain boundary friction coefficient ${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$ corresponds with a shear strain rate ${\dot{\mathit{\gamma}}}_{\text{gr}}^{*}$, and $\stackrel{\mathrm{\u0303}}{a}$ is a proportionality constant for the logarithmic velocity dependence of the grain boundary friction $\stackrel{\mathrm{\u0303}}{\mathit{\mu}}$, given by

We highlight that ${\dot{\mathit{\gamma}}}_{\text{gr}}$ is exponentially sensitive to the fluid pressure *p*
through the effective stress ${\mathit{\sigma}}_{\mathrm{e}}=\mathit{\sigma}-p$, and so the CNS model predicts an
acceleration of *V* upon an increase in the fluid pressure. Moreover, the experiments analysed in
this study are conducted at constant shear stress, so that a force balance (which typically takes
the place of Eq. 1a) is not required.

In the present study, we treat the laboratory sample as a single degree-of-freedom (spring block)
system, with uniform porosity and an internal state of stress. This implies that the fluid pressure is
considered to be uniform and constant throughout the sample, with no coupling between volumetric
deformation and fluid pressure. This assumption is valid for samples with sufficiently high
permeability, such that the characteristic timescale of fluid diffusion is smaller than the timescale of deformation. In other words, the sample is assumed to be in equilibrium with the externally
applied fluid pressure (“drained”) at all times. In the laboratory experiments of
Cappa et al. (2019), the gouge permeability was estimated to be above the intrinsic permeability of
the apparatus (10^{−14} m^{2}), so the sample can be considered to be drained. For
low-permeability gouges, such as shales (Scuderi and Collettini, 2018), coupling between volumetric deformation
and fluid pressure needs to be considered (e.g. Segall and Rice, 1995).

## 2.2 Microphysical parameter inversion procedure

In the simplified CNS framework laid out above, the dynamics of the system are fully governed by
*L*, *H*, *ϕ*_{c}, $\stackrel{\mathrm{\u0303}}{a}$, and ${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$ (which simultaneously constrains
${\dot{\mathit{\gamma}}}_{\text{gr}}^{*}$), for a given state of stress and initial porosity. In principle, the
forward model given by Eqs. (1a) and (1b) can be solved iteratively and used to invert
laboratory measurements for these constitutive parameters. However, owing to the exponential
sensitivity of *V* to *ϕ* through ${\dot{\mathit{\gamma}}}_{\text{gr}}$, such inversion procedure is
unstable and ill-posed. As an alternative, we propose a two-step inversion procedure that robustly
constrains the constitutive parameters. Firstly, we rewrite Eq. (1b) as

where d*δ*=*V*d*t* is an increment of slip across the fault. While we
recognise that *L* varies with *ϕ*, integration of Eq. (5) does not yield an
analytical solution when taking *L*=*f*(*ϕ*). Fortunately, as will be shown later, we find that the
inferred variations in *L* are of the order of 10 %–20 % of the absolute value of *L*,
warranting a first-order approximation of a constant value of *L*. By integrating the above relation
from the initial porosity *ϕ*_{0} up to *ϕ* (cf. van den Ende et al., 2018) and recognising that
$\mathrm{\Delta}L/L=\left(\mathit{\varphi}-{\mathit{\varphi}}_{\mathrm{0}}\right)/\left(\mathrm{1}-\mathit{\varphi}\right)$ (for *L*≈*L*_{0}), we
obtain an expression for the dilatancy Δ*L* as a function of slip *δ*:

This expression already provides sufficient means to constrain the constitutive parameters *L*, *H*,
*ϕ*_{c}, and the initial condition *ϕ*_{0} without numerically solving the full forward
model given by Eqs. (1a) and (1b). The second step of the inversion involves constraining the
remaining parameters $\stackrel{\mathrm{\u0303}}{a}$ and ${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$ by comparing Eq. (1a) with the
laboratory measured slip rate. Since the slip rate can span orders of magnitude, we perform the
inversion in terms of $\mathrm{ln}\left({\dot{\mathit{\gamma}}}_{\text{gr}}\right)$ (and correspondingly
ln (V) measured during the experiment), which renders a more stable inversion task.

Since the proposed inversion protocol does not involve numerically solving a forward model, a single
evaluation of either Eqs. (6) or (3) yields a sample of the posterior
distribution, hence permitting extensive random sampling. To inspect the trade-offs between
parameter values and their uncertainties, we cast the protocol above in a Bayesian inversion
procedure, in which we estimate the posterior distributions
$P\left(m=\left\{L,H,{\mathit{\varphi}}_{\mathrm{0}},{\mathit{\varphi}}_{\mathrm{c}}\right\}|\mathit{\delta},\mathrm{\Delta}L\right)$ and
$P\left(\stackrel{\mathrm{\u0303}}{a},{\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}|\mathit{\delta},V,{\mathit{\sigma}}_{\mathrm{e}},\mathit{\tau},m\right)$
separately. We assume a uniform prior distribution over a bounded range of admissible parameter
values, and a Gaussian likelihood with an unknown data variance *ν*^{2} that is simply treated as a
nuisance parameter and co-inverted. The posterior distributions are sampled using an affine invariant Markov chain Monte Carlo ensemble sampler as implemented in the Python emcee
package (Foreman-Mackey et al., 2013). While it is also possible to estimate the posterior
distributions from numerically solving the forward problem, each forward model evaluation from
*t*=0 up to the point where *V*>1 mm s^{−1} takes several tens of seconds on a single
CPU. The practical reason for this is that the fault is critically stressed and hence requires
small time step evaluations to ensure sufficient numerical accuracy and stability.

## 3.1 Laboratory experiment

We apply the above procedure to the laboratory fluid injection experiment performed by
Cappa et al. (2019) – see Fig. 1. In this experiment, a carbonate gouge sample was
subjected to a constant shear stress of *τ*=1.2 MPa and a total normal stress of
*σ*=5 MPa. The fluid pressure was increased step-wise every 150 s with steps of
0.5 MPa, until the sample “failed” macroscopically at a fluid pressure of
*p*=3.5 MPa. Prior to the final stage of pressurisation, only negligible amounts of slip
were measured, and hence we focus our inversion efforts on the final stage of the experiment in
which the sample measurably accelerated. Additionally, through the stage of fluid injection, no
gouge compaction was measured, supporting our assumption made prior to Eqs. (1a) and (1b) that
the time-dependent creep rate is negligible compared to the rate of granular flow.

We first fit Eq. (6) to the measured dilatancy as a function of slip. Since the data
are sampled uniformly in time but not in slip (as the sample deformation is accelerating), we
interpolate the slip data to assign uniform weight to each measurement during the inversion. The
bounds on the prior distribution are given by $\mathrm{10}<L<\mathrm{100}$ µm, $\mathrm{0.1}<H<\mathrm{1}$,
$\mathrm{0.1}<{\mathit{\varphi}}_{\mathrm{0}}<{\mathit{\varphi}}_{\mathrm{c}}$, and $\mathrm{0.2}<{\mathit{\varphi}}_{\mathrm{c}}<\mathrm{0.4}$. The resulting posterior
distributions are presented in a corner plot (Fig. 2),
showing appreciable trade-offs between *L* and *H* and between *ϕ*_{0} and
*ϕ*_{c}. Nonetheless, the parameters *L* and *H* are reasonably well resolved as
$L=\mathrm{64.3}\pm \mathrm{8.9}$ µm and $H=\mathrm{0.45}\pm \mathrm{0.06}$ (median ± 1 SD). And while
*ϕ*_{0} and *ϕ*_{c} trade off almost perfectly and hence span a near-uniform
distribution over the permitted parameter range, their difference is well resolved as
${\mathit{\varphi}}_{\mathrm{c}}-{\mathit{\varphi}}_{\mathrm{0}}=\mathrm{0.09}\pm \mathrm{0.01}$. These parameter values are perfectly consistent with
previous studies (e.g. Chen and Spiers, 2016; van den Ende et al., 2018). Although the inferred layer thickness *L*
is much less than the total thickness of the sample (initially around 5 mm), one should keep in mind
that deformation localises in a much narrower zone, so that the effective thickness of the actively
deforming region of the gouge is much less than the total sample thickness. In similar experiments
conducted by Scuderi et al. (2017), the localised region was observed to have a thickness of
10–20 µm, which was inevitably affected by post-experiment compaction. Moreover, the experiment
was performed in a double-direct shear configuration, so that the total thickness inferred here
represents the thickness of localised regions on both sides of the central forcing block. In other
words, the total layer thickness inferred from the post-mortem microstructural observations would be
at least 20–40 µm. Hence, our inferred estimate of 64 µm seems appropriate for an actively
deforming localised gouge layer. Niemeijer and Spiers (2007) derived a theoretical lower bound on
$H\ge \sqrt{\mathrm{3}}/\mathrm{2}{\mathit{\varphi}}_{\mathrm{c}}\approx \mathrm{0.96}$ for a dense 2D packing of hexagonal
grains. Since the third dimension plays an important role in strain accommodation within granular
media (Frye and Marone, 2002; Hazzard and Mair, 2003), we expect this lower bound to be lower in a 3D system with
more degrees of freedom. Upon inspection of Eq. (6), we can formulate the mapping
between layer thickness and slip as $\mathrm{\Delta}L=A\left[\mathrm{1}-\mathrm{exp}\left(-\mathrm{2}B\mathit{\delta}\right)\right]$ and infer $A=L\left({\mathit{\varphi}}_{\mathrm{c}}-{\mathit{\varphi}}_{\mathrm{0}}\right)/\left(\mathrm{1}-{\mathit{\varphi}}_{\mathrm{c}}\right)$
and $B=H\left(\mathrm{1}-{\mathit{\varphi}}_{\mathrm{c}}\right)/L$ as lumped parameters (upper triangle of
Fig. 2). Since *A* and *B* are the only parameters directly
constrained by the data, the original four parameters depend on them and show strong trade-offs.

We continue by fitting the (logarithm of) measured slip rate based on Eq. (3), using the
parameter values inferred in the previous step to compute the time evolution of tan *ψ*. Without
loss of generality, we define ${\dot{\mathit{\gamma}}}_{\text{gr}}^{*}=\mathrm{1}$ µm s^{−1} *L*^{−1}, so that
${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$ represents the grain boundary friction coefficient at a slip rate of
$V=\mathrm{1}\phantom{\rule{0.125em}{0ex}}\mathrm{\mu}\mathrm{m}\phantom{\rule{0.125em}{0ex}}{\mathrm{s}}^{-\mathrm{1}}$. Since *ϕ*_{0} and *ϕ*_{c} individually are ambiguous, we take
*ϕ*_{0}=0.25 and increment this value by the inverted *ϕ*_{c}−*ϕ*_{0} to obtain
*ϕ*_{c}=0.34. The slip rate parameters are extremely well resolved (see
Fig. 3), and found to be
$\stackrel{\mathrm{\u0303}}{a}=\left(\mathrm{10.26}\pm \mathrm{0.15}\right)\times {\mathrm{10}}^{-\mathrm{3}}$ and
${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}=\mathrm{0.6852}\pm \mathrm{0.00028}$, with minimal trade-off between the two parameters. With
these parameters, the fit to the slip rate data is excellent (Fig. 4b).

Finally, for verification, we numerically solve the forward model given by Eqs. (1a) and (1b)
with the parameters obtained in the inversion procedure (Fig. 4c and
d). While we obtain an excellent fit with the observed time evolution of slip and dilatancy, we also
find that the forward model is extremely sensitive to the initial condition *ϕ*_{0}. While the
overall features of the simulated sample response are similar, the exponential sensitivity to
porosity leads to critical behaviour and strong variations in the timing of the sample
failure. Since the rate of increase in porosity is proportional to the shear strain rate, which in
turn is an exponential function of porosity (refer to Eqs. 1b and 3),
the positive feedback loop leads to an extremely rapidly diverging state. This is highlighted in
Fig. 4c and d, where we vary the initial porosity between 0.2491 and 0.2514
(−1.5 % and +1 % around the reference value of 0.25). The initial condition that gives the
best match in terms of the onset of accelerated slip is close to the initially chosen value of
*ϕ*_{0}=0.25, although we assign no significance to tiny deviations in the initial porosity. In a
laboratory setting, the sensitivity of the modelled slip rate falls well within the measurement
resolution of the sample porosity (typically of the order of several percent of units of porosity),
so verification of this sensitivity would be challenging.

## 3.2 Field experiment

Encouraged by the results of the proposed inversion method for the laboratory experiment, we
continue to apply the same procedure to the field injection test of Cappa et al. (2019). Like in the
laboratory experiment, the in situ pressurisation of a tectonic fault triggered
accelerating slip and, associated with it, fault opening (dilatancy) – see
Fig. 5a. While the acceleration of shear and normal displacement on
the fault was more gradual than in the laboratory experiment, a phase of rapidly accelerating slip
at *t*>800 s can be clearly seen. The amount of dilatancy measured as a function of slip
(Fig. 5b) was proportionally more than in the laboratory experiment by
at least 1 order of magnitude, so we expect a priori that the frictional parameters inferred from
the laboratory cannot immediately describe the behaviour of the fault in situ. Indeed, when
we perform the inversion of the dilatancy-slip data from the field experiment, we find median values
of $L=\mathrm{4.9}\pm \mathrm{1.0}\phantom{\rule{0.125em}{0ex}}\mathrm{mm}$, $H=\mathrm{13.0}\pm \mathrm{2.5}$, and ${\mathit{\varphi}}_{\mathrm{c}}-{\mathit{\varphi}}_{\mathrm{0}}=\mathrm{0.082}\pm \mathrm{0.016}$
(Fig. 6).

While these other values seem entirely reasonable, the inferred value of *H* is well above the
estimated upper bound of $\sqrt{\mathrm{3}}/\mathrm{2}{\mathit{\varphi}}_{\mathrm{c}}\approx \mathrm{2.9}$. This suggests that the CNS model is
unable to explain the relationship between fault slip and fault opening in this experiment. In the
CNS model, dilatancy is envisioned to originate from grain sliding and rolling, neighbour swapping,
and “jostling”, which requires a volume increase of the gouge to accommodate. However, the model
fault itself is mathematically planar, and so no dilatancy occurs due to geometric constraints. In
the case of a macroscopically non-planar fault geometry (as is inevitable for tectonic
faults; Candela et al., 2012), additional dilatancy (with associated permeability changes) at the onset of
slip is necessary. Moreover, poro-elastic effects (elastic fault opening) due to fluid pressure
changes are not considered here. The inability of the CNS model to describe the fault opening with a
reasonable choice of parameters is therefore not a shortcoming of the CNS model (which describes the
mechanics of a small representative volume element), but is rather due to an incomplete coupling
with processes that transcend the scale envisioned by the CNS model. For simple, spatially uniform
relationships between geometric fault opening and fault slip, this first-order contribution to the
fault dilatation may be incorporated into Eq. (1b). However, for more realistic
(i.e. spatially heterogeneous) fault opening, a multi-scale numerical extension of the adopted model
is required.

Since the CNS model fault strength (and therefore the fault slip rate) is directly controlled by the
dilatancy parameter *H*, it is unwarranted to attempt to infer $\stackrel{\mathrm{\u0303}}{a}$ and ${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$ based
on the parameters inferred from the dilatancy. While this may seem like a severe limitation of the
CNS model, it actually serves as an important indication of the applicability of the model, and the
validity of its parameters, when attempting to extrapolate to nature. Moreover, the basic mechanics
of the CNS model are still expected to govern the strength and slip rate of the fault, even though
part of the model predictions (the dilatancy) cannot be constrained by independent measurements. By
numerically solving the forward model, the fault slip as a function of time and fluid pressure may
be reproduced within a reasonable range of parameter values, for which the predicted fault opening
would likely be much less than measured by Cappa et al. (2019).

## 4.1 Comparison with rate-and-state friction

Traditionally, laboratory experiments are interpreted within the framework of rate-and-state friction (RSF), commonly presented as (Dieterich, 1979; Ruina, 1983)

where *μ*(*V*,*θ*) is the macroscopic friction coefficient at slip rate *V* and “state”
*θ*, *μ*^{*} is a reference friction coefficient at slip rate *V*^{*}, and *a*, *b*, and *D*_{c}
are empirical constants. As has been shown by Chen et al. (2017), the CNS model is asymptotically
identical to RSF for small departures from steady state, for which the CNS equivalents of the RSF
parameters *a*, *b*, and *D*_{c} can be treated as constants. For large departures from steady state,
the behaviours predicted by CNS and RSF diverge, as the aforementioned parameters can no longer be
considered to be constant (van den Ende et al., 2018). Nonetheless, because of their similarity, the
limitations of the CNS model also apply to rate-and-state friction. One advantage of using the CNS
microphysical model over traditional RSF, is that the governing parameters have a more physically
meaningful interpretation. Even though numerous studies have attempted to elucidate the physical
origin of RSF (Aharonov and Scholz, 2018; Brechet and Estrin, 1994; Ikari et al., 2016; Putelat et al., 2011), in practice these
theoretical constraints are not considered. Instead, it is more convenient to constrain the RSF
parameters empirically through laboratory velocity-step experiments (Blanpied et al., 1998; Carpenter et al., 2016; Chester, 1994; Hunfeld et al., 2017; Reinen and Weeks, 1993). With these laboratory measurements of the
RSF parameters, fault slip observed during decametric-scale fluid injection tests can be accurately
modelled (Cappa et al., 2019), although the same behaviour can be obtained for a wide range of
parameter values: in the study of Cappa et al. (2019) a similar fit to the data was obtained for
velocity-weakening ($\left(a-b\right)<\mathrm{0}$) and velocity-strengthening
($\left(a-b\right)>\mathrm{0}$) friction, even though seismic slip can only be produced in the former
case of velocity-weakening friction. Hence, more observational constraints are required to
distinguish between the different types of behaviour.

Aside from the fault-parallel slip, the fault opening potentially provides a second prominent
constraint. In the classical RSF framework, volumetric deformation is not explicitly accounted
for. Traditionally, the state parameter *θ* has been interpreted as encoding the average lifetime of asperity contacts (at steady state) or the relative area of asperity contacts
(Dieterich, 1994; Scholz, 2019), both of which do not entail volumetric deformation of the fault
gouge. Empirical relations between the state parameter *θ* and porosity have also been proposed
(Segall and Rice, 1995; Sleep, 2005) and used in hydro-mechanical modelling (Jeanne et al., 2018), but these
relations are typically not employed as additional constraints of the RSF constitutive
parameters. Moreover, relations between the steady-state coefficient of friction (and its
velocity dependence) have been established based on energy balance considerations (Beeler et al., 1996; Marone et al., 1990). Since these relations pertain to the steady-state coefficient of friction, they do
not apply to non-steady-state conditions (for which $\mathrm{d}V/\mathrm{d}t\ne \mathrm{0}$ and
$\mathrm{d}\mathit{\varphi}/\mathrm{d}t\ne \mathrm{0}$) and do not offer additional insight on the relationship
between *θ* and *ϕ*. On the other hand, volumetric deformation is an integral part of the
CNS model, hence allowing (and requiring) us to incorporate these measurements to arrive at a better
constrained set of parameters.

## 4.2 Relationships between experiments and nature

While the CNS microphysical parameters can be directly estimated from laboratory experiments, their
incorporation into numerical models of tectonic faults may be subject to moderation based on
geological or physical considerations. In laboratory experiments conducted at room ambient
conditions and comparatively high deformation rates (of the order of micrometres per second up to millimetres per second), the gouge
porosity remains close to the critical-state porosity. Likewise, in the laboratory experiment of
Cappa et al. (2019), the initial porosity was estimated to be less than 0.1 units of porosity below
the critical-state porosity. Given longer timescales and higher temperatures, compaction induced by
one or more time-dependent creep mechanisms (such as pressure solution creep or subcritical crack
growth) would gradually reduce the porosity of the gouge, thereby increasing its strength and
critical fluid pressure at which the fault slip rates become appreciable. In numerical simulations
of fault slip, the initial state of a tectonic fault is likely not the same as for the laboratory
fault. Fortunately, this initial state could be estimated from microstructural analyses of drill
cores. Moreover, the choice of the initial state of the fault does not affect any of the other
frictional parameters of the CNS model. This is in contrast to rate-and-state friction, where the
initial value of the state parameter (*θ* at *t*=0) should also affect the magnitude of *b*,
which has been found to increase with decreasing porosity (or increasing
*θ*; Chen et al., 2015, 2017).

The property that *b* (or more precisely: *b*∕*D*_{c}) is sensitive to the gouge porosity
can also be derived from stability analysis of the CNS model. Consider the general criterion for
unstable slip of a spring block:

where *K* is the shear stiffness of the fault. For an instantaneous step change in velocity,
$\mathrm{d}V/\mathrm{d}t=\mathrm{0}$ for *t*>0. Assuming that unstable slip is governed by the onset
of granular flow, the shear strength is given by the CNS model as (Chen and Spiers, 2016)

Hence, using Eq. (1b), the stability criterion can be expressed in terms of microstructural quantities as (van den Ende et al., 2018)

In the vicinity of steady state, the above statement should be identical to the stability criterion derived from rate-and-state friction, i.e. (Rubin and Ampuero, 2005)

Here, *K*_{b} is a critical stiffness value that facilitates acceleration of slip (seismic or
aseismic). From the comparison of the two inequalities, it can be concluded that the *K*_{b} therefore
must increase with decreasing porosity. This was also observed in the discrete element model simulations of van den Ende and Niemeijer (2018), which were conducted completely independently of the
assumptions and limitations of the CNS model. We note that the comparison between
Eqs. (10) and (11)
only holds in the vicinity of steady state. Nonetheless,
Eq. (10) can be used to describe the stability of fault
slip far from steady state, circumventing the issue of the velocity and state dependence of *a*,
*b*, and *D*_{c} (as observed by Cappa et al., 2019; den Hartog and Spiers, 2013; Reinen et al., 1992; Takahashi et al., 2017, and many others). For a more detailed analysis of the frictional stability of a model
fault governed by the CNS model, we refer to Chen and Niemeijer (2017).

Combining now the observations made in Sect. 3 with the discussion above, we
propose that the seismogenic potential of faults subjected to fluid pressure perturbations is best
described in terms of the dilatant behaviour of the fault and its initial degree of compaction. One
can infer the microphysical parameters *H*, *ϕ*_{c}, $\stackrel{\mathrm{\u0303}}{a}$, and ${\stackrel{\mathrm{\u0303}}{\mathit{\mu}}}^{*}$
from laboratory experiments, and assume reasonable in situ values of *ϕ* and *L* for the
tectonic fault to simulate its response to a changing stress field (fluid pressure). If permitted by
the numerical method, fault non-planarity, permeability changes, and elastic moduli reduction may be
introduced to add further complexity, as anticipated based on the results of
Sect. 3.2. In this way, the evident pressure and velocity dependence of the
rate-and-state friction parameters can directly be accounted for in a self-consistent and
transparent manner and the model outcomes interpreted in terms of physical observables.

In this work, we analysed the fluid injection experiments conducted by Cappa et al. (2019) in the
laboratory and in situ, in terms of the Chen–Niemeijer–Spiers (CNS) microphysical
model. We proposed a Bayesian inversion approach to extract the governing parameters without the
need for numerically solving the forward problem, while elucidating the uncertainties and trade-offs
between the model parameters. We showed that while the localised gouge layer thickness *L* and the
dilatancy parameter *H* can be well resolved, the initial and critical-state porosities trade off
perfectly, so that only their difference *ϕ*_{c}−*ϕ*_{0} can be resolved in the
experiments. When numerically solving the forward model with the inferred parameter values, we
obtained almost perfect agreement with the measurements, indicating that the CNS model accurately
describes fault deformation in response to a fluid pressure perturbation. When the same inversion
approach was applied to a decametric-scale field injection experiment, we found that the inferred
parameters fell outside of the feasible range of values, highlighting the relevance of other
mechanisms, such as fault structure and poro-elastic effects, in this scenario.

The excellent agreement between the CNS model and the laboratory data allows us to interpret the
dynamics of the fault in terms of volumetric deformation (porosity changes). By doing so, we
circumvent the velocity dependence of the rate-and-state friction parameters *a*, *b*, and
*D*_{c}, which increases the predictive power of numerical models of natural
faults. Adopting the CNS model expedites the extrapolation of laboratory results to nature and
permits better assessment of the applicability of the model and accuracy of the parameter values.

A Python script that reproduces the results and figures in this paper, along with the laboratory and field injection data, is available at https://doi.org/10.6084/m9.figshare.12613007 (van den Ende et al., 2020).

MPAvdE conceptualised the study and performed the analyses. MMS and FC provided the laboratory and field data. JPA supervised MPAvdE. All authors discussed and prepared the contents of the paper.

The authors declare that they have no conflict of interest.

This article is part of the special issue “Thermo–hydro–mechanical–chemical (THMC) processes in natural and induced seismicity”. It is a result of the 7th International Conference on Coupled THMC Processes, Utrecht, Netherlands, 3–5 July 2019.

The authors thank the topical editor André Niemeijer and two anonymous reviewers for their
encouraging and constructive comments on the paper. Martijn P. A. van den Ende, Frédéric Cappa, and
Jean-Paul Ampuero are supported by French government through the UCA^{JEDI} Investments in
the Future project managed by the National Research Agency (ANR) with the reference number
ANR-15-IDEX-01.

This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-15-IDEX-01).

This paper was edited by André R. Niemeijer and reviewed by two anonymous referees.

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_{2}) into the subsurface could cause earthquakes when existing geological faults inside the reservoir are (re-)activated. To assess the hazard associated with this, previous studies have conducted experiments in which fluids have been injected into centimetre- and decimetre-scale faults. In this work, we analyse and model these experiments. To this end, we propose a new approach through which we extract the model parameters that govern slip on faults.

_{2}) into the subsurface could cause earthquakes...