In recent years, the number of waveform data
gathered during seismic acquisitions
has increased strongly.
Here you see exemplary recordings from northern
Chile.
They are just a small extract of a much larger
dataset
of about eleven thousand seismic phase arrivals.
Those measurements are used from seismologists
for earthquake localisation and traveltime
tomography. To process and handle even millions
of phase arrivals, automatic routines have
become an important tool.
However, quantity is not everything and especially
the automatic determination of wave onsets
often lacks of accuracy, compared to manual
readings.
To overcome the discrepancy between accurate
onset determinations and fast processing,
machine-learning algorithms are starting to
be utilised.
They have transformed image-processing
and speech-recognition, and they are just
now starting to be applied in seismology.
Necessary for this is the input of large numbers
of correctly determined wave onsets, which
are used to train the artificial intelligence
of the computer algorithms.
Hello and welcome.
In this video you will learn how machine learning
algorithms are working in principle and how
they are used to determine up to millions
of seismic P- and S-phase arrivals at once.
For this purpose Jack Woollam uses convolutional
neural networks that are trained with seismic
waveforms and already known phase arrival
times.
Those network architectures are then applied
to determine new arrivals.
Jack compared his results to conventional
methods and showed that neural networks can
achieve a better accuracy than those routines.
Since the beginning of quantitative seismology
in the early 20th century, the determination
of phase arrivals – the so-called picking
– is a basic task in seismological analysis.
For small datasets this can be done manually. Over recent years, computer processing and
storage capabilities strongly improved, resulting
in exponential increases in the amount of
seismic data collected.
These factors render manual picking obsolete
and require automatic picker routines for processing.
In fact, a very wide range of seismic automatic
pickers exist, and these are typically based
on manual feature extraction.
Those routines
extract specific properties of the trace,
which are strongly correlated to accurately
characterise different seismic phases.
These could be properties such as the amplitude,
frequency content, or some statistical property.
One such manual feature is the so-called STA/LTA.
STA/LTA is the ratio of the short term average
over the long term average of a seismogram
and is typically an accurate, robust indication
for a phase arrival.
Practically, the seismic energy of the seismic
signal over a short-term window is compared
to that of a long-term window.
Any values of the STA/LTA ratio above a pre-defined
cut-off threshold
are interpreted as a phase arrival.
Traditional autopicking methods using manual
feature extraction all suffer from similar
limitations in their approach.
They often struggle to pick out S-phases,
as the S-phase start time is often hidden
within the P-wave coda.
Also, these methods can contain many parameters
that need fine-tuning, as seismic wave propagation
is highly heterogeneous and varies significantly
with location.
Artificial intelligence in the form of machine
learning demonstrates promise in overcoming
these difficulties.
Such algorithms automatically optimise the
features most appropriate for classification
of seismic phases.
This significantly increases the accuracy
of S-phase picks, reduces the need for manual
fine-tuning, and is much faster than conventional
methods.
The algorithm employed to characterise phase-arrivals
is a specific version of a neural network.
This class of machine learning algorithms
is inspired by biological neural networks,
being the basic structure of human and animal
brains.
An artificial neural network consists of interconnected
neurons, which can be trained to recognise
specific pattern.
This is used for example to categorise photos
of animals to their actual species.
Within this process, neurons receive and process
signals and send it to other neurons.
The artificial neurons are typically summarised
into layers, which perform different kinds
of weighted transformations or filters on
their inputs.
Thus, signals travel from a first input layer
into others and finally arrive at the last
output layer.
Those transformations extract specific features
of the unknown input data that characterise
for example the appearance of a cat.
Artificial neural networks build the framework
for these operations.
No predefined criteria are fed into the system
to decide how the categorisation is done.
By training the network with large amounts
of categorised data,
it is able to build its own logic.
Such a training with classified data is called
supervised learning.
The application is a sequential process.
At first, the network architecture is derived.
It defines how many layers build the neural
network and how they process the data.
Secondly, the network is trained on a classified
dataset.
For our example this means, that for each
photo of a cat
the correct species is known beforehand.
Then each pixel value will feed into an input
neuron.
Within the network architecture, the neurons
are multiplied with a weight parameter and optimised.
This allows to extract input features most
appropriate for classification.
At this stage the model architecture is saved,
along with the final weights for each neuron.
Finally, the trained model can be applied
to unknown data.
When applied to new data the weights in the
network architecture are fixed and are not
allowed to be optimised any more.
A special form of those neural networks are
convolutional neural networks
– CNNs for short.
They use convolution operations in place of
other matrix transformations within the layers
and are typically applied in the sense of
so-called supervised learning algorithms.
These convolutions reduce and condense the
data until the most appropriate features to
characterise an animal are apparent.
For this purpose a feature map is built.
A feature map is created by passing a convolutional
filter window over the input data and is iteratively
optimised through backpropagation.
During this, the values in the convolutional
filter are being iterated until the error
in the CNN prediction is at a minimum when
compared against
the true classification of the data.
If trained correctly, the convolutional filters
should therefore be extracting the most appropriate
local features from the input data to characterise
the picture of the cat.
This and other components are used to build
the neural network.
Repeated convolutional layers enable the network
to perform a more abstract model of decision-making
or detect more complex features.
Pooling operations and other regularisation parameters
reduce the overall dimensionality of the model.
Thus, they reduce the redundant components
of the feature maps not associated with classification.
Provided with enough examples and trained
sufficiently, the algorithm should now be
able to detect new photos of cats or other
animals.
Those concepts can directly be applied to
seismic phase determination.
At first, the neural network is trained with
manually picked phase arrivals.
Then the convolutional filters are iteratively
optimised until they extract the most appropriate
features to characterise the different phases.
Jack Woollam and his colleagues tested the
trained CNN against common STA/LTA using the
data set from northern Chile partly shown
in the beginning.
For this purpose, they applied a two-stage
approach.
Firstly, they used the STA/LTA method to
detect the presence of a seismic event.
Then, they extracted a window around this
event and stored it as a waveform snippet.
Secondly, they picked specific arrival times
of different seismic P- and S-phases using
STA/LTA and CNN independently.
This is the result of the CNN picking.
They used each set of picks to perform a 1D relocalisation
of the seismic events.
The more accurate the picks are, the smaller
the standard deviations of the traveltime
residuals are expected to be.
The figure shows those standard deviations
for the CNN picks on the left and the ones
achieved by STA/LTA on the right.
The overall residual distribution is broader
for the STA/LTA picks and narrower for CNN,
especially for the picks of S-waves.
This indicates, that CNN outperforms STA/LTA
and exceeds the accuracy of widely used pickers,
while being orders of magnitude faster than
traditional methods.
Thus, classified seismological data from earthquake
catalogues are a perfect application for supervised-learning
based algorithms such as a CNN.
In this video you learned how convolutional
neural networks – CNNs – can be applied
to pick seismic phase arrivals.
Conventional autopickers, like STA/LTA methods,
are typically based on manual feature extraction
and often lack for accuracy in determining
S-Phase arrivals hidden in a P-wave coda.
CNNs overcome this problem and are much faster
as well.
You learned that neural networks consist of
interconnected neurons, which are grouped
into layers.
CNNs are a special form of neural networks,
which use convolutional operations in place
of general matrix multiplication within the
layers.
Those layers are trained to filter and reduce
the incoming data to its appropriate features.
Once trained, the neural network can be applied
to new waveform data to determine phase arrivals.
Jack Woollam and his colleagues showed that
the accuracies of CNN-picked arrivals exceed
those of automatic picks of feature based
methods like STA/LTA.
Their results highlight the promise of such
methods in solving seismological problems
using large amounts of data, which are more
and more available worldwide.
