# Earthquake recurrence and survival analysis: How long should we wait for an overdue earthquake?

#### Richard Styron

Tags: tectonics, research, python, active faults, earthquake forecast, San Andreas, statistics, data science

Earlier this spring, Dr. Tom Jordan of USC and the Southern California Earthquake Center caused a bit of a stir by claiming that the southern San Andreas fault was 'locked, loaded and ready to roll'. This was interpreted by many as meaning that an earthquake on the fault is 'overdue', although it's not clear what 'overdue' means in this scientific context.

In a general sense, 'overdue' means that an event was *expected* to have
occurred by now, and has not yet; this requires a due date for
the event. A due date for an earthquake is an earthquake prediction, which
can be made with precision proportional to B.S. content. And of course
a precise prediction for an earthquake date in the past that didn't happen is
pretty stuffed with it.

The root of the issue is that large, surface-breaking earthquakes happen
on the southern San Andreas about every 100 years, and it's been
159 years (as of 2016) since the last event, the *M* 7.9 Fort Tejon
earthquake in 1857. So, if earthquakes occur very regularly (periodically
in the mathematical sense), an earthquake is overdue. This either means
that the system is fundamentally changed in some sense (like the fault
stopped moving) or that disaster is imminent. But geology ain't math,
and earthquakes are never perfectly periodic. On the other hand, if
earthquake reoccurrence is highly irregular, while we might have been
waiting a little bit longer than average for The Big One, it's often
late and this may not mean much.

What we really need to do, then, is to nail down our expectations of when the next earthquake should occur, by looking at the recurrence intervals (the time in between earthquakes) in the past. Then, we can adjust our expectations of how much longer we might wait for the next earthquake based on the fault's history.

I'm going to look at this quantitatively, using a branch of statistics called 'survival analysis' that deals with the time to occurrence of events. Survival analysis is probably most familiar in the form of life expectancies (i.e. a person born in 2015 can expect to live for 71.4 years), but it's pretty common in other fields. Engineers use the same tools and concepts for the lifecycles of products, in what is often referred to as failure or reliability analysis. It is quite applicable in studying earthquake recurrence, as well, although I am not sure how much of the earthquake research community really uses the tools; I have found some uses of the vocabulary and concepts in the hazard modeling literature, which has a strong engineering influence, but I haven't seen a whole lot in the paleoseismology or neotectonics literature. I'm guessing this is because paleoseismologists and neotectonicists (myself included) are trained as geologists and don't receive a lot of formal statistical training, but are smart and figure things out on their own as needed (lots of wheel reinventing here, but most of the time the wheels are round-ish and sort of roll.)

The data I'll be using is from the Wrightwood and Pallet Creek paleoseismic
sites on the southern San Andreas Fault. (Note that Dr. Jordan may have been
referring to the *more* southern San Andreas section by the Salton Sea,
which has not had an earthquake since the late 1680s; but the best data is
from the San Andreas in between Parkfield and Palm Springs, which contains
the Wrightwood and Pallet Creek sites, and is often called the Southern
San Andreas, and is the big dangerous one because of proximity to L.A.) I've
collected the data from Scharer et al., 2010 and [Biasi et al., 2002]
bs, and formatted it into a CSV file. The analysis will be done right
here in this blog post, as a Jupyter Notebook, in Python. If you want to download
and execute it, fetch it here.

```
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
```

```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.interpolate import interp1d
from scipy.integrate import trapz
```

## Wrightwood earthquake data¶

```
wrightwood_eqs = pd.read_csv('wrightwood_eq_times.csv')
wrightwood_eqs.head()
```

eq | age_mean | age_5 | age_95 | |
---|---|---|---|---|

0 | W350 | -2915 | -2974 | -2883 |

1 | W380 | -2746 | -2807 | -2695 |

2 | W390 | -2657 | -2742 | -2601 |

3 | W402 | -2610 | -2670 | -2569 |

4 | W410 | -2503 | -2561 | -2450 |

These are the years that earthquakes occurred at Wrightwood and Pallet
Creek, in Years AD. The `age_mean`

column is the mean of the age PDFs from
the geochronology, and `age_5`

and `age_95`

columns are the 5th and 95th
percentiles.

Here is what the data look like:

```
plt.figure(figsize=(6,6))
plt.errorbar(wrightwood_eqs.index + 1, wrightwood_eqs.age_mean,
yerr=np.array((wrightwood_eqs.age_mean - wrightwood_eqs.age_5,
wrightwood_eqs.age_95 - wrightwood_eqs.age_mean)),
fmt='.')
plt.ylabel('Calendar Year')
plt.xlabel('Earthquake in sequence')
plt.title('Wrightwood Earthquake Chronology')
plt.show()
```

We can see that there are basically two time series here, with a big gap in between. This is because of a change in the locations of the paleoseismic trenches, which record different time periods; it's not a 2000-year cessation of earthquakes.

The older series looks pretty linear, meaning that earthquakes were pretty 'periodic', or were spaced regularly in time. The younger series is a little less linear, but also has less uncertainty. It's possible that the greater uncertainty in the timing of older earthquakes masks the aperiodicity that is seen in the younger series, or that changes between the paleoseismic sites or the dating methods (and assumptions) leads to the differences. It's also possible that there have been real changes in the periodicity of earthquakes on the San Andreas, captured here. (My bet is on the former.)

I'm going to calculate the recurrence interval distribution on the San Andreas based on this data; it's a good exercise in data science and Monte Carlo techniques, but may not be of interest to those who came to this page solely to learn about earthquake hazards. But for those of you who are interested, head to the bottom third of the blog post. (For those who want to run this post as a Jupyter notebook, that code will have to be run now.) For those who are not, just note that I made a million synthetic earthquake histories drawn from the data, that incorporates the uncertainty in the ages for each earthquake. Then, I calculated the time between each event, and then aggregated the results. The recurrence interval distribution has uncertainty that reflects both the natural variability in the system (aleatoric uncertainty) and the uncertainty involved in the dating methods themselves (epistemic uncertainty).

I split the older and younger time series apart for those calculations, so there wasn't a 2000 year recurrence interval wreaking havoc on the stats.

Here's what the resulting recurrence interval distributions look like:

```
plt.figure(figsize=(8,3))
plt.subplot(121)
plt.hist(old_rec_ints.ravel(), bins=100, normed=True,
alpha=0.5, histtype='stepfilled', label='old')
plt.hist(young_rec_ints.ravel(), bins=100, normed=True,
alpha=0.5, histtype='stepfilled', label='young')
plt.legend()
plt.xlabel('Wrightwood Recurrence intervals')
plt.subplot(122)
plt.hist(old_rec_ints.ravel(), bins=100, normed=True, cumulative=True,
alpha=0.5, histtype='step', label='old')
plt.hist(young_rec_ints.ravel(), bins=100, normed=True, cumulative=True,
alpha=0.5, histtype='step', label='young')
plt.ylim([0,1])
plt.legend()
plt.xlabel('Wrightwood Recurrence intervals')
plt.show()
```

This is pretty interesting. It shows that the young series has a bimodal recurrence interval, while the older one does not. This makes sense given how the older time series looked more linear than the young one. Nonetheless, their cumulative distributions look similar, and match quite impressively through much of the range.

But in general, we see that there is a broad mode at ~100 years, which may be shorter (up to less than a year) and as old as ~350 years. So there is a fair amount of variability in the system.

The spike at just under 50 years has to do with the timing between the historical 1857 and 1812 earthquakes; because these two events are known to the year level with no uncertainty, there is no uncertainty in the time between them, which produces a big spike in the PDF here.

However different the old and young recurrence intervals are, we're going to just add them together and not worry here about what causes the differences.

```
wr_rec_ints = np.hstack([old_rec_ints.ravel(),
young_rec_ints.ravel()])
```

```
plt.hist(wr_rec_ints, bins=100, normed=True,
histtype='stepfilled')
plt.xlabel('Southern San Andreas Recurrence Intervals')
plt.show()
```

Next, we need to extract the values from the histogram so that we can make this into a function:

```
wr_int_probs, wr_int_vals = np.histogram(wr_rec_ints,
bins=100, normed=True)
wr_int_probs = np.append(0., wr_int_probs)
```

```
plt.figure(figsize=(8,3))
plt.subplot(121)
plt.plot(wr_int_vals, wr_int_probs)
plt.xlabel('Southern San Andreas Recurrence Intervals')
plt.subplot(122)
plt.plot(wr_int_vals, np.cumsum(wr_int_probs)/ np.sum(wr_int_probs))
plt.gca().axvline(2016-1857, color='grey')
plt.gca().set_ylim([0,1])
plt.show()
```

We now have a good, quantitative idea of how frequently earthquakes occur on the southern San Andreas. The mean recurrence interval is 108 years; the maximum is almost 400 years. The current 159-year long duration is well beyond the mean and the median (both about 100 years) but is about the 80th percentile, meaning that one recurrence interval in five is longer.

This is consistent with the 5 in 26 recurrence intervals in between the mean earthquake ages above being greater than 159 years. The real difference is in the tails: The PDF we've made, incorporating the uncertainty in the dates, allows for earthquakes very closely spaced in time, or up to 375 years apart. These are not high-probability intervals, but they're possible nonetheless.

## Time-dependent earthquake forecasting on the southern San Andreas¶

The motivation for this little study is the question of *What does
it mean when an earthquake is overdue?* We can answer this question to some
degree by using survival analysis, which will give us quantitative
expectations of how long we might keep waiting for the tardy event, given that
we've waited 159 years already.

This is a very similar situation to life expectancy. If a population has a life expectancy at birth of (say) 65 years, but it also has a high infant mortality rate, then the life expectancy of those who made it through infancy alive will be considerably longer than 65 years: That 65 years is an average of both very short lifetimes and longer ones, and the average of the longer lifetimes will be higher than the average of the whole group. So if we want to know the life expectancy of someone who is 5 years old, we have to throw out the lifetime data from those who died in infancy. And if we want to know how long a 66-year old woman will live, we'll ignore the data from those that didn't make it past 65.

Survival analysis provides the tools to do this in an effective way. First, we will define some simple functions that formalize the intuitive concepts involved:

The first two are very basic, and we've already seen them:

$pdf(t)$ := the probability distribution function of $t$, the time (in years) between earthquakes.

$cdf(t)$ := the cumulative distribution function of $t$.

The next is called the *survival function* (or *reliability function*), and it
is the probability that a given lifetime (or recurrence interval) is longer
than $t$. It's also the compliment of the $cdf$. We'll call it $S(t)$.

$S(t) = 1 - cdf(t)$

The survival function is interesting and useful in its own right, but we won't
dwell on it. Instead, we'll use it for calculating a few other, more topical
quantities. The first is the *hazard function* ($\lambda (t)$), which is the
rate of occurrence of events (earthquakes) at time $t$, given that an earthquake
has not occurred in $t$ years.

$\lambda(t) = pdf(t) \, / \,S(t)$

The second important use of $S(t)$ is in calculating the *mean value* of $t$, which
is the mean of $pdf(t)$, but also conveniently $\int_0^\infty S(t) dt$. The mean
value is often called the 'expected lifetime' and in our situation, is the average
recurrence interval.

Let's code these up and do some analysis:

```
def pdf(t):
pdf_ = interp1d(wr_int_vals, wr_int_probs, kind='cubic',
bounds_error=False, fill_value=0.)
return pdf_(t)
def cdf(t):
cdf_ = interp1d(wr_int_vals,
np.cumsum(wr_int_probs) / np.sum(wr_int_probs),
kind='cubic', bounds_error=False, fill_value=1.)
return cdf_(t)
def S(t):
return 1 - cdf(t)
def λ(t):
return pdf(t) / S(t)
def mean_rec_interval(t):
return trapz(S(t), t)
```

### Earthquake hazards each year¶

The earthquake hazard for the year 2016, i.e. the probability that an earthquake will occur on the southern San Andreas this year, is:

```
λ(2016-1857)
```

0.020610501157809748

i.e. a 2% chance. This probability will increase basically every year, unless an earthquake happens:

```
ts = np.arange(1,370) # vector of years since last event (starting from 0)
```

```
plt.plot(ts + 1857, λ(ts))
plt.gca().set_yscale('log')
plt.gca().axvline(2016, color='grey')
plt.gca().axhline(λ(2016-1857), color='grey', linestyle='--')
plt.title('Earthquake hazard on the southern San Andreas,\n'+
'given no earthquake since 1857')
plt.xlabel('Year')
plt.ylabel('annual earthquake probability')
plt.ylim([1e-3, 1])
plt.show()
```

We are, as of this writing, at the grey line.

It's pretty obvious from this figure that the hazard on the southern San Andreas will keep increasing until an earthquake happens. This isn't the case for all faults or fault systems; some faults show evidence for earthquake clusters, where there is a high hazard in the years to decades following a significant event, which drops off after that. These clusters may be caused by triggering of subsequent earthquakes due to stress changes induced by the first one. Volumes of crust with many faults may have an essentially uniform or time-invariant hazard, which is indicative of an exponential recurrence interval distribution.

The slight oscillations at the far end of the time series are caused by the histogram binning of very sparse samples at the long tail of the $pdf(t)$; in reality this should increase smoothly. Similarly, the little oscillations near the 50 year spike are from the cubic spline interpolation.

### Expected time until the next earthquake¶

*It's been 159 years since the last earthquake; when will the next one happen?*

This is the big question. We're not going to try to issue a hyper-precise *prediction*,
but we can issue a probabilistic *forecast* (for a more in-depth discussion of the difference, go here.)

We can use a function based on the survival function $S(t)$, called the *conditional survival
function* (or the 'remaining lifetime' function in demographic studies) to help with the
forecasting. The conditional survival function describes the probability of a lifetime $t$
given that $t > t_e$, where $t_e$ is the elapsed time (the time since the last earthquake).

The conditional survival function $S_{cond}(t, t_e)$ is derived from $S(t)$:

$S_{cond}(t, t_e) = S(t) \, / \, S(t_e)$ (where $t > t_e$).

For our purposes, the conditional survivor function is again most useful in providing us with the conditional recurrence interval PDF, i.e. the PDF of recurrence intervals longer than the $t_e$. We can get this by working backwards:

$pdf_{cond}(t, t_e) = -d\, S_{cond}(t, t_e) \,/ \, dt $

We can also take the mean recurrence interval calculation (the integral of $S(t)$),
and substitute $S_{cond}$ for $S$, giving us the *mean time remaining* (this is often
called the 'mean remaining lifetime' in demographics). It is, simply, the mean number
of years left until the next earthquake.

Mean time remaining = $\int_{t_e}^\infty S_{cond}(t, t_e) dt$

OK, let's code this up so we can see the results.

```
def mas(t,t_e):
return t[t>t_e]
def menos(t,t_e):
return t[t<=t_e]
def S_cond(t, t_e, return_past=True):
t_ = mas(t, t_e)
_t = menos(t, t_e)
S_conds = S(t_) / S(t_e)
if return_past == True:
return np.hstack([np.ones(len(_t)), S_conds])
else:
return S_conds
def pdf_cond(t, t_e):
diffs = np.diff( 1 - S_cond(t, t_e))
return np.append(0., diffs)
def mean_time_remaining(t,t_e):
t_ = mas(t, t_e)
return trapz(S_cond(t_, t_e, return_past=False), t_)
```

```
plt.plot(ts + 1857, pdf_cond(ts, 159))
plt.xlim([2015, 2250])
plt.ylim([0, 0.02])
plt.xlabel('Year')
plt.ylabel('probability')
plt.title('Probability of next southern San Andreas earthquake by year')
plt.show()
```

Ouch! The highest probability year for the next earthquake is 2016! (Note again that this probability is ~0.02, as we calculated above).

This is consistent with the general observation that the PDF for recurrence intervals drops monotonically after ~130 years; it is more likely that the recurrence interval we're living in now is 159 years long, rather than 160, 180, etc.

However, even though the peak of this PDF is at 2016, that peak
is only 2%, and there is still quite a bit left. This is where the
*mean time remaining* calculation comes in: it gives us the
average time left in the earthquake cycle, given that it's been
159 years so far.

```
mean_time_remaining(ts, 159)
```

38.207254921781896

About 40 years, or about 2055 for the average (or 'expected') next earthquake. So it's possible that it will be a few years, but is still within many of our lifetimes. Again, it's important to remember that this is an average, not a single number, and that the probability distribution shown in the last figure is a much better guide to what may happen. So, you know, don't sue me if it's tomorrow, or in 2020.

(This is the end of the post here. For those who want to read or perform the recurrence interval calculations, read on.)

## Appendix: Calculating earthquake recurrence probabilities¶

We want to know how frequently earthquakes occur on the southern San Andreas; this frequency is a variable, the 'earthquake recurrence interval'. Because of both the uncertainty in the timing of individual earthquakes and the real variation in the recurrence, the recurrence interval will be a distribution.

I'll estimate this distribution through a Monte Carlo analysis, in which I generate many synthetic earthquake time series from the data; the variation in these time series represents the uncertainty in the earthquake times. Then, I will calculate the earthquake recurrence distribution from the synthetic data, and use the results to describe the future probabilities of earthquake timing on the southern San Andreas fault.

I'll split the earthquake time series up into 'old' and 'young' series, calculate their recurrence intervals independently, and then add them together in order to proceed with the analyisis.

```
w_young = wrightwood_eqs[wrightwood_eqs.age_mean > 0]
w_old = wrightwood_eqs[wrightwood_eqs.age_mean < 0]
```

Let's see how much the mean (expected) recurrence interval is different between the two series:

```
np.mean(np.diff(w_young.age_mean)), np.mean(np.diff(w_old.age_mean)),
```

(101.76923076923077, 108.61538461538461)

OK, that's not so different, which is reassuring.

### Making earthquake timing probabilities¶

The next step is to take the data (given as 5%, mean, and 95% values) and make them into probability distributions so that we can make probability distributions of the recurrence intervals, which are the durations in between each earthquake.

The PDFs for the earthquake times themselves are 'arbitrary', or not Gaussian, uniform, etc. They're the result of a combination of several types of constraints. Some analytical radiocarbon dates will be present for certain layers, producing Gaussian ages, then there will be logical relationships based on those: For example, an earthquake may be older than 500 +/- 30 years, because sediment of that age is not disturbed, but younger than 635 +/- 20 years because sediment of that age is cut by the faults (I'm making up numbers here but this is the gist of it).

However, I don't have those PDFs, I just have the 5th and 95th percentiles and the mean, because that's what's reported in the data tables. So I'm just going to simplify things by saying that the 5 and 95 percentiles are the maximum and minimum values. I'll then use some basic equations for triangular distributions to calculate the peak of the triangular distribution, and then define the distribution in order to sample from it (the equations can be found here).

Here are a couple Python functions that I'll use to do that calculation:

```
def make_eq_pdf(age_min, age_mean, age_max):
age_break = get_age_break(age_min, age_max, age_mean)
xs = np.array([age_min, age_break, age_max])
return xs, triangular_pdf(xs, *xs)
def triangular_pdf(x, age_min, age_break, age_max):
x = np.asarray(x, dtype=float)
p = lambda x: np.piecewise(x, [x <= age_min,
(age_min < x) & (x <= age_break),
(age_break < x) & (x <= age_max),
x > age_max],
[0,
lambda x: 2 * (x - age_min) / ((age_max - age_min) * (age_break - age_min)),
lambda x: 2 * (age_max - x) / ((age_max - age_min) * (age_max - age_break)),
0])
return p(x)
def get_age_break(age_min, age_max, age_mean):
return 3 * age_mean - age_min - age_max
```

Let's check it out:

```
plt.plot(*make_eq_pdf(*wrightwood_eqs.ix[16, ['age_5', 'age_mean', 'age_95']]))
plt.xlabel('Calendar Year')
plt.ylabel('Probability')
plt.title('Timing of Earthquake {}'.format(wrightwood_eqs.ix[16, 'eq']))
plt.show()
```

Now, I can make the PDFs for each, and then sample those PDFs using an Inverse Transform Sampling algorithm. This algorithm can sample an arbitrary PDF by computing the Cumulative Distribution Function (CDF) of the PDF, then inverting the CDF, and using uniformly-random samples from [0,1) as arguments to the inverse CDF. The outputs of the function will be samples drawn from the original PDF.

(Inverse transform sampling is one of the most useful techniques I've come across in years of doing statistical geoscience; the ability to sample from any (bounded) PDF is extremely enabling for all sorts of problems, especially in empirical science where we have lots of nonstandard PDFs. You can lose a lot of accuracy by forcing your data into some standard PDF form, and this allows for a Bayesian posterior distribution to be used as the prior for the next problem.)

```
def make_pdf(vals, probs, n_interp=1000):
val_min = np.min(vals)
val_max = np.max(vals)
# if the PDF is just a point (no uncertainty)
if val_min == val_max:
return val_min, 1.
# if not...
else:
pdf_range = np.linspace(val_min, val_max, n_interp)
pmf = interp1d(vals, probs)
pmf_samples = pmf(pdf_range)
pdf_probs = pmf_samples / np.sum(pmf_samples) # normalize
return pdf_range, pdf_probs
def make_cdf(pdf_range, pdf_probs):
return (pdf_range, np.cumsum(pdf_probs))
def inverse_transform_sample(vals, probs, n_samps, n_interp=1000):
pdf_range, pdf_probs = make_pdf(vals, probs, n_interp)
cdf_range, cdf_probs = make_cdf(pdf_range, pdf_probs)
if len(cdf_probs) == 1:
return np.ones(n_samps) * pdf_range
else:
cdf_interp = interp1d(cdf_probs, cdf_range, bounds_error=False,
fill_value=0.)
samps = np.random.rand(n_samps)
return cdf_interp(samps)
def sample_eq_pdf(row, n_samps):
eq_vals, eq_probs = make_eq_pdf(row['age_5'], row['age_mean'],
row['age_95'])
return inverse_transform_sample(eq_vals, eq_probs, n_samps)
```

Lets see how it works by making a PDF and sampling it:

```
plt.figure(figsize=(8,3))
plt.subplot(121)
plt.plot(*make_eq_pdf(*wrightwood_eqs.ix[16, ['age_5', 'age_mean', 'age_95']]))
plt.xlabel('Calendar Year')
plt.ylabel('Probability')
plt.title('Timing of Earthquake {}, PDF'.format(wrightwood_eqs.ix[16, 'eq']))
plt.subplot(122)
plt.hist(sample_eq_pdf(wrightwood_eqs.ix[16], 50000), bins=20, normed=True)
plt.xlabel('Calendar Year')
plt.title('Timing of Earthquake {}, Samples'.format(wrightwood_eqs.ix[16, 'eq']))
plt.show()
```