# Self-supervised Contrastive Learning for Volcanic Unrest Detection

Bountos Nikolaos Ioannis This work has received funding from the European
Union’s Horizon2020 research and innovation project DeepCube, under grant
agreement number 101004188.  Ioannis Papoutsis  Institute of Astronomy,
Astrophysics, Space Applications & Remote Sensing, National Observatory of
Athens  Institute of Astronomy, Astrophysics, Space Applications & Remote
Sensing, National Observatory of Athens  Dimitrios Michail  Department of
Informatics & Telematics, Harokopio University of Athens  Nantheera
Anantrasirichai  Department of Computer Science, University of Bristol

###### Abstract

Ground deformation measured from Interferometric Synthetic Aperture Radar
(InSAR) data is considered a sign of volcanic unrest, statistically linked to
a volcanic eruption. Recent studies have shown the potential of using
Sentinel-1 InSAR data and supervised deep learning (DL) methods for the
detection of volcanic deformation signals, towards global volcanic hazard
mitigation. However, detection accuracy is compromised from the lack of
labelled data and class imbalance. To overcome this, synthetic data are
typically used for finetuning DL models pre-trained on the ImageNet dataset.
This approach suffers from poor generalisation on real InSAR data. This letter
proposes the use of self-supervised contrastive learning to learn quality
visual representations hidden in unlabeled InSAR data. Our approach, based on
the SimCLR framework, provides a solution that does not require a specialized
architecture nor a large labelled or synthetic dataset. We show that our self-
supervised pipeline achieves higher accuracy with respect to the state-of-the-
art methods, and shows excellent generalisation even for out-of-distribution
test data. Finally, we showcase the effectiveness of our approach for
detecting the unrest episodes preceding the recent Icelandic Fagradalsfjall
volcanic eruption.

###### Index Terms:

deep learning, self-supervised, contrastive learning, SimCLR, volcano, InSAR,
Fagradalsfjall eruption

##  I Introduction

Globally, 800 million people live within 100 km of a volcano [1]. Improvements
in forecasting volcanic activity have been shown to reduce fatalities due to
volcanic eruptions [2]. Hence, several volcano observatories are set-up
globally, including the Geohazard Supersites and Natural Laboratories
initiative. However, a significant proportion of the $\sim$1,500 holocene
volcanoes has no ground-based monitoring, although the deformation at
volcanoes is statistically linked to eruption [3], which can be detected prior
to the event [4].

Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1,
6-day repeat-cycle, satellite allows the systematic monitoring of volcanic
unrest at a global scale. Such abundance of InSAR data have the potential to
enable observatories to monitor volcanic activity without additional costs.
Fringes detected in wrapped interferograms over volcanoes indicate the onset
of deformation, usually due to magma chamber fill-in at depth. Associating
fringes with deformation is non-trivial; atmospheric signals may also give
rise to such fringes, which may lead to false positive identifications. Their
effect is also amplified in the presence of strong topography, which is
usually the case for volcanic domes.

Recent studies have proposed the use of supervised Deep Learning (DL)
architectures to automatically detect the presence of ground deformation
triggered by volcanic unrest, within single interferograms. Anantrasirichai et
al. [5] were the first ones to use DL on short-term wrapped interferograms to
detect rapid deformation. They rely on heavy data augmentation and employ a
transfer learning strategy using AlexNet Convolutional Neural Network (CNN)
architecture, pre-trained on ImageNet [6]. Synthetically generated
interferograms, based on analytic forward models of magma chambers (e.g. Mogi,
dykes, sills), have also been used to train the same network [7], reaching 86%
F1-score on real data. Valade et al. [8] train a custom CNN architecture on
synthetic wrapped interferograms, but test it to only a few real
interferograms. A CNN workflow has also been proposed to identify surface
deformation associated with an earthquake [9], using synthetic wrapped and
unwrapped interferograms. They report an accuracy for their best model of 85%,
tested on 32 real InSAR interferograms, yet in principle earthquake induced
interferometric signals are denser and clearer than the volcanic ones.
Finally, volcanic deformation detection from a single interferogram using a
combination of synthetic and augmented real InSAR data has been performed by
Gaddes et al. [10]. They apply a VGG-based model, while they split the target
class ”deformation”, to two sub-classes: Sill/Point and Dyke and report an
overall accuracy of 83%.

A common issue encountered by all studies is the scarcity of training data for
the positive class, i.e. interferograms with volcanic deformation. Hence, most
works cope with class imbalance via heavy data augmentation engineering and
the creation of positive class synthetic data for supervised learning.
Supervised learning though requires big curated datasets to work well. To
counter this, researchers have used classical architectures pretrained on
large unrelated datasets. We argue that transfer learning from computer vision
tasks provide less meaningful information for the task of volcanic activity
detection when compared to features learnt from related data. Indeed the
studies discussed above, report that they struggle to generalise well for
real, unseen InSAR data.

Recently, the AI community shifted its focus to resolve these shortcomings of
supervised learning, towards unsupervised/self-supervised training schemes.
The goal is to exploit the information hidden inside the data and produce
features without any human supervision that can generalize well for different
classification tasks. In remote sensing some recent works highlight the value
of these approaches [11][12].

In this letter, we propose a self-supervised, contrastive learning framework
based on SimCLR [13] to solve the deformation/non-deformation binary
classification problem, using an unbalanced, real, wrapped InSAR dataset. We
avoid using unwrapped interferograms or time-series data in order to have
faster classification response, which is particularly useful in volcano
observatories for near real-time monitoring. Our approach exploits the
abundance of unlabelled InSAR data to learn quality visual features, which can
be used by a simple linear supervised classifier for the detection task.

The contributions of our work are as follows:

  * •

We are the first to introduce a self-supervised learning framework for
volcanic activity detection.

  * •

We propose a training pipeline that does not rely on the generation of massive
augmented, synthetic or manually annotated InSAR data.

  * •

We demonstrate that training on a large unlabeled InSAR dataset in a self-
supervised manner provides more quality features than using pre-trained models
from ImageNet.

  * •

Experimental results show that models trained with this framework have the
ability to generalize better, even for InSAR data drawn from a different
distribution with respect to the training set.

  * •

We provide the first generic feature learning model for InSAR, which can be
used for different downstream tasks.

##  II Contrastive self-supervised learning pipeline

Figure 1: The proposed pipeline. First, we use unlabeled InSAR data to learn
feature representations with the SimCLR [13] self-supervised framework and
then attach a linear classifier for the supervised training with a few labels.

Our approach is set-up as an instance discrimination task where every image in
the dataset belongs to its own class. Our pipeline is a two step training
process. First, it consists of an encoder trained in a self-supervised manner,
and second of a fully connected layer attached on top of the encoder for the
supervised classification task. For the self-supervised training we utilize
the recently introduced SimCLR [13] framework. SimCLR learns representations
by trying to maximize the similarity of two augmented views of the same
example in the latent space. The main components of the adopted framework are
the following:

  * •

A stochastic data augmentation module that creates two different
transformations of the same input wrapped interferogram patch. For every patch
$x$ the augmentation module creates two augmented views
$\tilde{x}_{i},\tilde{x}_{j}$. In our experiments, we use Horizontal and
Vertical Flips, Cutout, Multiplicative Noise, Elastic Transformation, Gaussian
Blur and Gaussian Noise. Each view is generated from a random combination of
these augmentations.

  * •

An encoder $f(\cdot)$ for the representation extraction from the augmented
patches. There is no constraint on the choice of the encoder. Following [13],
we use the ResNet [14] architecture. The representation is then extracted from
the output after the average pooling layer, $h_{i}=ResNet(\tilde{x}_{i})$.

  * •

A projection head $g(\cdot)$, that maps the encoder’s representation to the
space where the contrastive loss is calculated. We, like [13] use a multilayer
perceptron (MLP) with one hidden layer and a ReLU activation function. Thus,
$z_{i}=g(h_{i})=W^{(2)}\sigma(W^{(1)}h_{i})$ , where $W^{1}$ and $W^{2}$
represent the weight matrices and $\sigma$ is the ReLU function.

  * •

The contrastive loss estimation. Given a set $\\{\tilde{x}_{k}\\}$ which
contains, among others, a positive pair of augmented interferogram patches
$\tilde{x}_{i}$ and $\tilde{x}_{j}$, the contrastive loss aims to guide our
algorithm to identify $\tilde{x}_{j}$ in $\\{\tilde{x}_{k}\\}_{k\neq i}$ for a
given $\tilde{x}_{i}$.

The training pipeline can be seen in Figure 1 and is as follows. From a batch
of size N we create 2N samples using the augmentation process defined above.
The augmented InSAR patches created from the same original patch serve as a
positive example and the rest 2(N-1) patches in the batch constitute the
negative examples. The process can be summarized with the following graph:
$x_{k}\xrightarrow[]{t\in
T}\tilde{x}_{i,j}^{k}\xrightarrow[]{f(\cdot)}h_{i,j}^{k}\xrightarrow[]{g(\cdot)}z_{i,j}^{k}$,
where T is the set of augmentations. We define the similarity function
$sim(x,y)$ as the cosine similarity between $x$ and $y$. For each minibatch we
attempt to minimize the following contrastive loss function:

$l_{i,j}=-log\frac{e^{sim(z_{i},z_{j})/\tau}}{\sum_{k=1}^{2N}\mathbbm{1}_{[k\neq
i]}e^{sim(z_{i},z_{k})/\tau}},$ (1)

where $\mathbbm{1}_{[k\neq i]}\in\\{0,1\\}$ is $1$ if $k\neq i$ and $0$
otherwise and $\tau$ is a temperature parameter. $\tau$ scales the input and
expands the range of values of the cosine similarity. We set $\tau=0.5$ and
the final loss is calculated on all positive pairs in a mini batch.

Finally, using the encoder that was trained in a self-supervised manner we
proceed with the classification task, by freezing the parameters of the
encoder and attach a trainable linear classifier on top. We propose an
oversampling approach for the supervised classification task, where we
randomly choose a class from which the next sample will be drawn. For a batch
size N, this process takes place N times. In this approach one sample might be
seen more than once in each epoch, thus preventing the domination of the
larger class.

##  III Experiments

All data and code presented in this section are published at the project’s
repository: (https://github.com/ngbountos/DeepCubeVolcano).

###  III-A Datasets

We use only real data that come from two different sources, which we symbolize
with S1 and C1 (Table I). S1 dataset was provided by the authors of [5] and
[7], and contains Sentinel-1 wrapped InSAR patches from 16 volcanoes globally.
The S1 dataset is highly imbalanced, containing a large number of negative
examples but very few positive ones ($\sim$2%). C1 was manually collected by
us from the LiCSAR online InSAR repository [15] over 5 volcanoes: Taal, Cerro
Azul, Fagradalsfjall, Etna and Ale Bagu, and is much more balanced. We use
only the S1 dataset for training our models, while we create two different
test datasets. The first one contains 64 balanced samples drawn from S1, and
the entire C1 dataset serves as a, second, evaluation dataset.

TABLE I: Overview of the datasets used in this work. For the self-supervised task, the positives and negatives of S1 are employed together without labels. C1 is used only for evaluation. Data Source | Train | Test | Total
---|---|---|---
- | Positive | Negative | Positive | Negative |
S1 | 150 | 7386 | 32 | 32 | 7600
C1 | - | - | 404 | 365 | 769

The two test sets S1 and C1 are quite different. Figure 2 shows examples of
deformation and non-deformation samples from both sources. C1 test dataset is
highly diverse as we have included 1) wrapped interferograms from both
descending and ascending viewing geometries, 2) unfiltered and Goldstein
phase-filtered interferograms, and 3) pure phase-only interferograms and
interferograms where the phase is overlaid with SAR amplitude. S1 dataset is
much more harmonised and therefore S1 and C1 are considered to be drawn from
distributions with different characteristics with respect to noise level and
visual features. Such challenging test samples are used to evaluate the
generalization performance of our self-supervised approach.

Figure 2: Sample interferogram patches from both data sources. The first and
second rows contain samples from S1 and C1 sources respectively. C1 dataset is
diverse - from left-to-right and for the deformation class: 1) Cerro Azul
unfiltered, interferometric phase only, 2) Etna descending interferogram,
Goldstein filtered, phased and amplitude overlay, and 3) Taal, ascending
interferogram, water masked interferometric phase, Goldstein filtered.

###  III-B Contrastive learning model performance

We evaluate the performance of the encoder trained with our proposed self-
supervised approach (Section II) and compare it to the pretrained features
from ImageNet [6]. We experiment with different scales of the ResNet
architecture and utilize the linear evaluation protocol, i.e. we freeze the
encoder parameters and fine-tune a simple linear classifier on top of the
network. The linear evaluation protocol is a standard way to assess the
quality of the learnt representations [16]. To speedup convergence for the
self-supervised stage, we initialize the weights of the encoder with the
parameters learnt from ImageNet. We then retrain all layers with the self-
supervised method for 200 epochs using an unlabelled version of the S1
dataset. At the fine-tuning step we apply the oversampling approach (Section
II) to the labeled S1 dataset. No oversampling is performed for the self-
supervised stage, as it assumes no class knowledge.

Table II shows how ResNet architectures with different capacities compare in
the two different test setups. The models we examine are ResNet18, ResNet34
and ResNet50. It is noteworthy that the results using the features learnt from
the contrastive learning framework were obtained after only 1 to 3 epochs of
fine-tuning the linear classifier. We found that setting the learning rate
between $0.001$ and 0.005 works best. On the contrary, the networks that used
the pre-trained encoder from ImageNet required 75 epochs to converge. We set
the learning rate to 0.001. For the fine-tuning step we use a batch size of
112. At the pre-training stage, we use the largest possible batch size
depending on the architecture. We set the batch size to 32 for ResNet50 and
112 for ResNet18 and ResNet34.

We conduct two additional experiments using ResNet50, our best performing
backbone encoder. First, we train SimCLR from scratch using random
initialization for 300 epochs to show that the performance gain comes from
self-supervised training on InSAR data alone. We publish this model on the
project’s repository. Second, we test MoCo [16], a different self-supervised
approach to show that performance gain can be achieved from different self-
supervised methods as well.

TABLE II: Experiment results on both test sets. ACC, FP, TP, FN, TN, F1, P and R, stand for overall accuracy, false positives, true positives, false negatives, true negatives, f1-score, Precision and Recall, respectively.  |  |  |  |  | S1 |  |  |  |  |  |  |  | C1 |  |  |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Model | ACC | FP | TP | FN | TN | F1 | P | R | ACC | FP | TP | FN | TN | F1 | P | R
ResNet18-ImageNet | 81% | 12 | 32 | 0 | 20 | 0.841 | 0.727 | 1 | 64% | 0 | 132 | 272 | 365 | 0.491 | 1 | 0.326
ResNet18-SimCLR | 84% | 9 | 31 | 1 | 23 | 0.860 | 0.775 | 0.968 | 70% | 0 | 178 | 226 | 365 | 0.611 | 1 | 0.440
ResNet34-ImageNet | 82% | 10 | 31 | 1 | 22 | 0.848 | 0.756 | 0.968 | 70% | 3 | 181 | 223 | 362 | 0.615 | 0.983 | 0.448
ResNet34-SimCLR | 82% | 11 | 32 | 0 | 21 | 0.853 | 0.744 | 1 | 91% | 4 | 339 | 65 | 361 | 0,907 | 0.988 | 0.839
ResNet50-ImageNet | 82% | 10 | 31 | 1 | 22 | 0.848 | 0.756 | 0.968 | 63% | 1 | 125 | 279 | 364 | 0.471 | 0.992 | 0.309
ResNet50-SimCLR | 85% | 8 | 31 | 1 | 24 | 0.872 | 0.794 | 0.968 | 91% | 10 | 347 | 57 | 355 | 0.911 | 0.971 | 0.858
ResNet50-SimCLR-Scratch | 85% | 8 | 31 | 1 | 24 | 0.872 | 0.794 | 0.968 | 86% | 2 | 306 | 98 | 363 | 0.859 | 0.993 | 0.757
ResNet50-Moco | 79% | 13 | 32 | 0 | 19 | 0,831 | 0.711 | 1 | 82% | 0 | 267 | 137 | 365 | 0.795 | 1 | 0.660
AlexNet | 82% | 9 | 30 | 2 | 23 | 0.844 | 0.769 | 0.937 | 72% | 49 | 244 | 160 | 316 | 0.699 | 0.832 | 0.603
VGG16 | 85% | 6 | 29 | 3 | 26 | 0.865 | 0.828 | 0.906 | 64% | 39 | 171 | 233 | 326 | 0.556 | 0.814 | 0.423
ViT-ImageNet | 90% | 3 | 29 | 3 | 29 | 0.906 | 0.906 | 0.906 | 59% | 5 | 96 | 308 | 360 | 0.343 | 0.950 | 0.343
DenseNet121-ImageNet | 82% | 9 | 30 | 2 | 23 | 0.844 | 0.769 | 0.937 | 54% | 0 | 53 | 351 | 365 | 0.231 | 1 | 0.131
InceptionV4-ImageNet |  92% | 4 | 31 | 1 | 28 | 0.924 | 0.885 | 0.968 | 69% | 23 | 196 | 208 | 342 | 0.628 | 0.894 | 0.485

Additionally, we compare our models with the state-of-the-art - all published
methods use supervised approaches. We train AlexNet’s and VGG16’s final layer
for 50 epochs, while keeping the rest of the layers freezed, with the ImageNet
pre-trained weights. We also examine three more methods, popular in computer
vision i.e Vision Transformer (ViT), DenseNet121 and Inception-V4 (Table II).
We use oversampling and random rotation augmentation for all methods.

###  III-C Discussion

The results summarised at Table II show that the models trained with the
SimCLR method performed better or comparable with the respective ones pre-
trained with ImageNet, for each test dataset and for each ResNet encoder
architecture. This is significant; it highlights the fact that training in a
self-supervised approach with 7,536 unlabeled samples only (Table I), drawn
from a distribution of wrapped interferograms, provides better quality
features than using models pre-trained in a supervised way from $\sim$1.5
million ImageNet images. It is even more impressive that our proposed self-
supervised learning technique produced models that required only 1-3 epochs of
finetuning to achieve these results, versus the 75 epochs needed for the
Imagenet pre-trained model, for the same task.

The major enhancement in our approach however, shows itself at the C1 dataset.
The high quality of the learnt InSAR data representations is clearer in the
second half of Table II that summarises the experiments on the C1 test
dataset, which is drawn from a distribution with different characteristics
with respect to the training set (Section III-A). While for S1 dataset the
best ImageNet and SimCLR models provide 92% and 85% overall accuracy
respectively, for C1 dataset the corresponding accuracies are 70% and 91%. The
supervised ImageNet model struggles to resolve the required features from the
new InSAR data distribution and overall, this large performance gap underlines
the ability of the self-supervised model to generalize better. To further
validate this we construct 731 synthetic interferograms using SyInterferoPy
[10] over a collection of subaerial volcanoes. Deformation patterns include
those due to dykes, sills, and Mogi sources. Our ResNet50-SimCLR model reaches
88.9%, while ResNet50-ImageNet 69.9% and InceptionV4-ImageNet 66.2% true
positive rate respectively. Again the self-supervised learnt features
generalize better.

The last five rows of Table II provide a comparison between ResNet50-SimCLR,
our best performing encoder, and architectures on the same task, proposed by
the state-of-the-art studies of Section I and other popular methods used in
computer vision. Again, the proposed method generalizes better, as seen in the
C1 part of the table. On S1 all models perform well with Inception-V4
achieving the best accuracy. On C1, our method reaches 91% overall accuracy
and 0.911 F1 score based on the linear evaluation protocol.

In addition, our pipeline did not make use of massive augmented, or
synthetically generated datasets, as opposed to the state-of-the-art
approaches (Section I). Equally important, the achieved performance has been
reached with a training set containing just 150 manually annotated InSAR
patches with deformations. The potential from exploiting large unlabelled
InSAR datasets in a self-supervised approach is at least promising. Given the
recent availability of online repositories, such as LiCSAR used in this work,
that contain and produce hundreds of interferograms over volcanoes globally,
the deployment of this pipeline on a large distributed system, increasing the
effective batch size is a natural next step. In fact, greater computational
resources lead to better models. Using larger batch sizes, training longer and
optimizing the stochastic augmentations used for SimCLR, improves the
performance of contrastive learning [13]. Furthermore, our analysis showed
that Elastic Transformation is especially important for data augmentation,
potentially due to the nature of fringe patterns within InSAR data, either
these patterns are related to deformation, atmospheric disturbances,
topographical errors, or orbital ramps, etc. In our work we also use the
maximum batch size possible depending on the model’s architecture and
available memory.

##  IV Fagradalsfjall volcanic eruption case study

We apply our approach on Fagradalsfjall volcano at Reykjanes Peninsula,
Iceland. We focus on two recent unrest episodes. Triggered by dyke intrusions,
the inflation episodes took place in mid-January 2020, and early March 2021.
The latter episode led to an effusive eruption, on 19 March 2021, – the first
known eruption on the peninsula in about 800 years.

Figure 3: Visualization of ResNet50 activations on Fagradalsfjall volcano.
Pink represents the area that affected the network’s decision the most. Both
unrest episodes are shown in chronological order. $Pr_{SimCLR}$ and Expert are
the predictions made by our method and the InSAR expert, respectively
(1=positive, 0=negative).

In order to simulate a real, working product, we merge all our datasets and
collect some extra InSAR patches to finetune our models and produce a quality
classifier. We unfreeze the fully connected layer as well as layer 3 and 4 of
the ResNet50 SimCLR encoder. We train for 2 epochs and reduce the learning
rate to $0.0005$. In total the new training set contains 614 deformation and
7872 non deformation patches. We feed the network with two time-series of
wrapped interferograms, one for each unrest event.

Figure 3 presents the model classification decision for each single
interferogram vis-à-vis the decision from an InSAR expert. In addition, the
figure shows the areas of the patch that affect the most the decisions of the
final, fully connected layer. This visualization was produced using the Class
Activation Mapping (CAM) technique [17]. There are two key observations drawn
from this use case. First, the model trained with the proposed method focuses
on the correct patterns (the fringes) of the interferogram patch. Second, the
pipeline correctly captures the start of both unrest episodes, triggering a
potential alarm.

##  V Conclusion

In this work, we implemented a pipeline to train binary classification models
for volcanic unrest detection utilizing unlabelled InSAR datasets, thus
without the the need to create huge labelled datasets or generate error-prone
synthetic data. We provided proof for the superiority of the self-supervised
learnt features when compared to models pre-trained from ImageNet, and the
ability of our approach to generalise effectively even for out-of-distribution
test samples. Our approach outperforms state-of-the-art supervised methods.

Volcanic unrest early warning is of major importance for civil protection
authorities and volcano observatories. Setting-up alert mechanisms enhances
response effectiveness and allows for scientists to deploy critical in-situ
monitoring equipment to assess more accurately volcanic hazard. We highlighted
that this could be implemented in the case of the 2020-2021 Fagradalsfjall
volcano unrest and eruption.

Finally, we believe that there is much potential in our self-supervised
approach, given the abundance of InSAR data produced regularly by the
Sentinel-1 missions. Exploiting the information they contain in a self-
supervised way, while labelling only a small subset paves the way towards a
global volcanic unrest detection system, but may also be applicable to a
plethora of other remote sensing applications and tasks.

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