# Self-Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks

Joachim Rimpot, Clément Hibert, Jean-philippe Malet, Germain Forestier, Jonathan Weber

## ► To cite this version:

Joachim Rimpot, Clément Hibert, Jean-philippe Malet, Germain Forestier, Jonathan Weber. Self-Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks. Journal of Geophysical Research. Machine Learning and Computation, 2025, 2 (4), 10.1029/2025jh000958. [https://doi.org/10.1029/2025jh000958](https://doi.org/10.1029/2025jh000958)

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Distributed under a Creative Commons Attribution 4.0 International License
## RESEARCH ARTICLE

10.1029/2025JH000958

**Key Points:**

* We propose a self-supervised clustering workflow for the exploration of seismic data sets applicable to networks up hundreds of stations

* Our method retrieve microseismic signals of landslide and regional earthquakes from more than 10 millions windows of seismic data

* The workflow enables automated data exploration without event detection and with minimum knowledge of the local and regional seismicity

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

J. Rimpot,
jrimpot@unistra.fr

**Citation:**

Rimpot, J., Hibert, C., Malet, J.-P., Forestier, G., & Weber, J. (2025). Self-supervised learning based clustering workflow for exploring seismological data from dense networks. *Journal of Geophysical Research: Machine Learning and Computation, 2*, e2025JH000958.
https://doi.org/10.1029/2025JH000958

Received 15 AUG 2025

Accepted 26 OCT 2025

**Author Contributions:**

**Conceptualization:** Joachim Rimpot, Clément Hibert

**Investigation:** Joachim Rimpot

**Methodology:** Joachim Rimpot, Clément Hibert, Jean-Philippe Malet, Germain Forestier, Jonathan Weber

**Supervision:** Clément Hibert, Jean-Philippe Malet

**Visualization:** Joachim Rimpot, Clément Hibert, Jean-Philippe Malet

**Writing – original draft:** Joachim Rimpot

© 2025 The Author(s). *Journal of Geophysical Research: Machine Learning and Computation* published by Wiley Periodicals LLC on behalf of American Geophysical Union.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_2.png" /></figure>

# Self-Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks

Joachim Rimpot<fnref n="1" /> ID, Clément Hibert<fnref n="1" />,<fnref n="2" /> ID, Jean-Philippe Malet<fnref n="1" />,<fnref n="2" /> ID, Germain Forestier<fnref n="3" /> ID, and Jonathan Weber<fnref n="3" /> ID

**Abstract** A key challenge in environmental seismology is processing seismic data to study source physics, natural and human-induced forcings, and geological structures such as landslides, glaciers, and volcanoes. Seismic arrays with dozens of stations have expanded data set sizes, and this, combined with signal complexity and high noise levels, makes it difficult to analyze using traditional event detection and labeling methods especially for low-energy or rare events. Clustering continuous data offer a comprehensive method for exploring the data sets and detecting all relevant events. In this study, we present a clustering workflow based on self-supervised learning (SSL) designed to handle data sets ranging in size from thousands to millions of events. This approach enables automated clustering of continuous data from seismic arrays containing dozens of stations. When applied to the “Marie-sur-Tinée” landslide data set, our workflow processed 10 millions 30-s windows and identified four main families: Potential Endogenous Landslide Seismic Events, Potential Regional Earthquakes, Potential Rainfall-Induced Signals, and Noise. Despite the overall consistency, some noise remained in the event-related clusters highlighting areas for further improvement in clustering methods. Nevertheless, the proposed SSL-based clustering workflow shows great potential for an efficient exploration of seismic data sets of millions of events and could be a solution for the blind exploration of similarly large data sets.

**Plain Language Summary** Analyzing seismic activity helps scientists better understand natural events such as landslides and earthquakes by studying both micro and macro-seismicity. In this study, we developed a method to automatically group large amounts of seismic data into categories without requiring prior knowledge of the types of seismic sources and of the seismotectonic context of the region. This method uses self-supervised learning to process continuous seismological data flows from a data set consisting of 69 stations. When applied to data from the “Marie-sur-Tinée” landslide, it grouped over 10 million short time windows into four categories: microseismic events, regional earthquakes, rainfall signals, and other events. Although some grouping errors remained in the final groups, this new approach shows promise for quickly analyzing large amounts of seismic data and could lead to improved models for documenting natural hazards.

## 1. Introduction

Environmental seismology rely on large data streams to advance the understanding of source physics, natural and anthropogenic forcings, and mechanisms of geological structures (e.g., landslides, glaciers, volcanoes) and related hazards. In recent years, dense seismic arrays, consisting of dozens of temporary stations deployed over several weeks, have greatly expanded the amount of data available (e.g., available dense network data sets, Almendros, 2023; Gautier, 2021; Young et al., 2023). These arrays make it possible to study local microseismic sources, including rare events and events whose released energy is weak leading to low signal-to-noise ratios. However, analysis of the large data sets generated by these arrays is challenging. Event catalogs are required to characterize seismic sources, but traditional methods that rely on event detection and expert labeling are no longer sufficient due to the volume of data, the complexity of the signals, and the high levels of noise especially in environmental contexts such as landslides, volcanoes, or glaciers (Aster & Winberry, 2017; Langet & Silverberg, 2023; McNutt & Roman, 2015; Podolskiy & Walter, 2016; Provost et al., 2018; Vouillamoz et al., 2018; Xin et al., 2024).

<footnote n="1">¹Institut Terre et Environnement de Strasbourg (ITES), CNRS UMR 7063, Université de Strasbourg, Strasbourg, France,</footnote>

<footnote n="2">²Ecole et Observatoire des Sciences de la Terre (EOST), CNRS UAR 830, Université de Strasbourg, Strasbourg, France,</footnote>

<footnote n="3">³Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), UR 7499, University of Haute-Alsace, Mulhouse, France</footnote>
Writing – review & editing:

Joachim Rimpot, Clément Hibert, Jean-
Philippe Malet, Germain Forestier,
Jonathan Weber

A fundamental step in seismological data analysis is event detection that is identifying signals of interest within continuous waveforms. Traditional approaches include the STA/LTA detectors (Allen, 1982), the PSPicker (Baillard et al., 2014), and more recent machine learning based approaches such as PhaseNet (Zhu & Ber- oza, 2019), EQTransformer (Mousavi et al., 2020), or the low-frequency earthquake detector of Münchmeyer et al. (2024). These algorithms have been proven to be effective, but their performances are limited by the training databases and parameter choices on which they rely. In fact, existing data sets only represent a subset of seismic environments and signal types (e.g., the STEAD data set Mousavi, Sheng, et al., 2019). Therefore, detection performances can decrease in noisy environmental contexts or when facing previously unseen event character- istics. This restricts the ability of detection algorithms to provide exhaustive catalogs of events.

Following the step of detecting signals of interest is the classification step. It consists in classifying the detected
signals into meaningful classes. In the recent years, this step has also been extensively addressed with the help of
machine learning, and approaches can broadly be divided into supervised and unsupervised strategies. The su-
pervised approaches rely on manually labeled data sets in order to train classification models. Recent studies,
using Random Forest approaches (Breiman, 2001), obtained high classification accuracy. Maggi et al. (2017) and
Hibert et al. (2017) each achieved over 90% classification accuracy while characterizing Piton de la Fournaise
volcano events. Pirot et al. (2024) achieved similar results on regional classification of large ice-quakes in
Greenland, and Domel et al. (2023) achieved 87% accuracy on marine seismological data. The unsupervised
approaches do not rely on labeled data sets but serve as features extractors for later tasks such as clustering tasks
exploring the extracted feature space (e.g., Jenkins et al., 2021; Morin et al., 2024; Mousavi, Zhu, et al., 2019).
Mousavi, Zhu, et al. (2019) applied a deep embedded clustering to distinguish detected teleseismic from local
detected earthquakes, and Jenkins et al. (2021) used a combination of neural networks and Gaussian mixture
models to cluster detected impulsive signals from Antarctica. Recently, Morin et al. (2024) proposed to use the
handcrafted seismic features proposed by Provost et al. (2017) as the input of a Gaussian Mixture Model for the
seismic detected events of the rock cliff near the Gros-Morne village, Canada. This led to three clusters whose
possible sources are linked with the anthropogenic activity, the meteorological activity, and the rockfall activity.

However, both supervised and unsupervised approaches generally depend on the initial detection stage to provide input events. This two-step approaches can be inefficient in environmental seismology as the endogenous microseismicity of environmental contexts is very complex (McNutt & Roman, 2015; Podolskiy & Walter, 2016; Provost et al., 2018) with varying events durations (few seconds to dozens of minutes or more), frequency contents (from low frequency content <50 Hz to high frequency content ≈100 Hz), and amplitude values, which can either be close to the noise level or have a high signal-to-noise ratio. Under these conditions, standard pickers struggle to detect all events, and the resulting catalogs remain incomplete. Therefore, continuous exploration of the seismological stream of data is required to ensure an exhaustive and more precise characterization of the endogenous seismicity of environmental contexts.

By analyzing continuous seismic data streams, it becomes possible to characterize local seismic activity in its entirety, allowing more effective detection of weakly represented events or events with low signal-to-noise ratios than traditional event detection and labeling approaches. Supervised methods enable continuous exploration of seismological data sets (e.g., Chmiel et al., 2021; Wenner et al., 2020), but, because they rely on an initial labeled catalog, these approaches are not suitable for exhaustive exploration of continuous seismological records. Recently, unsupervised approaches for continuous data set exploration have been investigated (Chai et al., 2025; Moreau et al., 2022; Rimpot et al., 2024; Seydoux et al., 2020; Steinmann et al., 2022). Seydoux et al. (2020) applied a deep scattering network to a one-day, three-component data stream from a single station near Nuugaatsiaq, Greenland to identify precursors to the June 2017 landslide. Using a 17-day-long data stream, the authors demonstrated the ability of this approach to retrieve precursory accelerators in a larger data set. Similarly, Steinmann et al. (2022) combined a deep scattering network with hierarchical clustering to discover earthquakes in a 2-day continuous seismic stream from a single station near the North Anatolian Fault, Turkey. Moreau et al. (2022) extended the deep scattering approach to explore a 1-month continuous record from a network of five geophones deployed on fast ice in the Van Mijen Fjord, Svalbard, Norway. They further used the resulting clusters to invert waveforms and estimate ice thickness. Rimpot et al. (2024) analyzed continuous records from two stations successively deployed for about 3 weeks each near the active area of the Fani Maoré submarine volcano, Mayotte, Indian Ocean. The authors highlighted synchronized cluster activations whose signals could be linked to two undocumented eruptions. Finally, Chai et al. (2025) applied two unsupervised machine learning algorithms, Uniform Manifold Approximation and Projection (UMAP, McInnes et al., 2018) for dimensionality
reduction, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN, McInnes et al., 2017) for clustering on the low-dimensional embedding to 59 days of continuous data from two stations near an industrial facility in Texas, United States. Their analysis yielded five and seven clusters, including those associated with vehicle-traffic-related signals.

The methodologies presented in recent studies (Chai et al., 2025; Moreau et al., 2022; Rimpot et al., 2024; Seydoux et al., 2020; Steinmann et al., 2022) offer valuable perspectives for the analysis of continuous microseismic data sets. However, several critical challenges remain to be addressed. First, effective processing in high noise level environments, such as those encountered in landslides, glaciers, and volcanoes (e.g., Provost et al., 2018), remains an open issue. Second, the capacity to handle the substantial data volumes produced by dense seismic networks is essential. In the context of identifying potential precursory signals associated with landslide events, we do not consider the study by Seydoux et al. (2020) as directly targeting microseismicity, since the considered seismic station was located 30 km away from the landslide site, thereby limiting its sensitivity to high-frequency locally sourced seismic events. In contrast, the studies by Moreau et al. (2022) and Rimpot et al. (2024) did analyze continuous records of microseismic events in a natural context, respectively, from a glacier and a submarine volcano. Nevertheless, both study areas are relatively isolated from anthropogenic and environmental noise. Specifically, in Moreau et al. (2022), only background noise and transient events caused by human activity mainly due to a field experiment involving students and the deployment of seismic nodes were identified. In Rimpot et al. (2024), the deployment of two underwater stations near the submarine volcano offered a natural shielding against most anthropogenic and surface environmental noise. Finally, (Chai et al., 2025) proposed to explore an industrial facility data set mainly composed of events, which sources are human-related.

Regarding the data volume, Seydoux et al. (2020) and Steinmann et al. (2022) processed 17-day and 2-day long records, respectively. In contrast, Moreau et al. (2022) analyzed approximately 5 months of continuous recording at a single site (derived from five stations, each recording for 1 month), Rimpot et al. (2024) examined about 1.5 months of continuous data, and Chai et al., 2025 explored 2 months of continuous records. Dense seismic networks, however, produce significantly larger data sets by continuously recording at numerous stations over extended periods, resulting in volumes equivalent to several years of single-station data. For instance, Gautier (2021) deployed 35 stations for 1 month, corresponding to 2 years and 11 months of single-station continuous data, while Young et al. (2023) deployed 329 nodes over 23 days, yielding more than 20 years of equivalent single-station records. To date, none of the existing approaches have demonstrated the capability to exhaustively process continuous data sets of the size produced by dense seismic arrays. Furthermore, the methods employed by Moreau et al. (2022), Rimpot et al. (2024), and Chai et al., 2025 are constrained by computational limitations. In the first study, data from five stations were downsampled from 1,000 Hz to 250 Hz to reduce computational costs, thereby limiting the exhaustibility of the analysis. In the second, a density-based clustering algorithm (DBSCAN, Ester et al., 1996) was applied, which is known to encounter performance issues when handling large-scale data sets (Ali et al., 2010). In the third, a dimensionality reduction technique (UMAP) was applied to 10-s segments consisting of 15,003 data points. To the best of our knowledge, no studies have explored the use of millions of samples (as generated by a dense network) with segments of 15,000 data points or more, depending on the chosen window size and sampling rate. Therefore, the feasibility of this approach remains unproven for the exploration of large-volume continuous microseismological data. However, we highlight potential challenges related to computational limits and the complexity of the resulting low-dimensional embeddings. In particular, representing millions of samples in only a few dimensions can produce highly intricate embeddings, which may hinder clustering algorithms from forming consistent and meaningful groups. Consequently, the development of new strategies is imperative for the effective analysis of continuous microseismic data acquired from dense seismic networks particularly in complex and noise-prone environments such as landslides.

Large data sets generated by seismic networks are difficult to analyze comprehensively particularly without prior knowledge of the possible seismic activity. Recently, self-supervised learning (SSL) approaches have been explored (Kinzel et al., 2024; T. Liu, Alavi, et al., 2024; Song et al., 2024; Yang et al., 2023) as they are effective in learning meaningful representations from unlabeled data. Kinzel et al. (2024) applied the SimCLR network (T. Chen et al., 2020) to cluster time series of detected events from the Neumayer Observatory (Antarctica), achieving 88% accuracy in separating hand-labeled groups while also revealing meaningful clusters of previously unlabeled events. Yang et al. (2023) proposed time series contrastive clustering (TSCC), which achieved a clustering accuracy of 98.07% in distinguishing microseismic events from noise. The authors of Song et al. (2024) introduced an SSL workflow based on SimCLR for learning meaningful features from labeled data sets, reaching a
classification score of 87.14%. They then applied the trained model to another mining-related data set and obtained a classification score of 86.70%. The authors highlighted the fast convergence of SimCLR (15 epochs) and an improvement of the classification score of more than 11% compared to their supervised deep learning model. Finally, T. Liu, Alavi, et al., 2024 leveraged an SSL approach inspired by language models by proposing a foundation model, SeisLM (Seismic Language Model). In their article, the authors emphasized the potential of SSL for pretraining foundation models that can later be fine-tuned for downstream tasks such as event detection, phase picking, onset time regression, and foreshock-aftershock classification. SeisLM demonstrated strong performance, surpassing task-specific approaches. However, although pretraining relied on detected events without labels, the fine-tuning step still requires labeled data sets, limiting the approach by the availability and exhaustibility of existing catalogs. Although these four studies rely on detected events, they provide promising insights into the potential of SSL approaches for extracting meaningful information from microseismological records.

SSL techniques offer a promising way to cluster large data sets produced by dense seismic networks. In order to scale unsupervised clustering approach to very large seismic data sets and allow the exploration of millions of events, we propose an SSL-based clustering workflow for automatically clustering continuous seismic data sets without the need for a priori expert knowledge or pre-detection of events. This approach leverages time and time-frequency representations of seismological data, which are commonly used by seismologists as intuitive ways to visualize and analyze the data. We apply our methodology to the data set of the “Marie-sur-Tinée” landslide, which consists of 10 million 30-s length windows of seismic data recorded with a network of 69 temporary stations deployed over a 72-day period. First, we detail the steps of the SSL-based clustering workflow. Then, we present the results obtained for the “Marie-sur-Tinée” landslide data set. This includes the representation of the outcomes of the SSL model and the clustering workflow. Finally, we offer a characterization of the clusters, identifying four main families: Potential Endogenous Landslide Seismic Events (PELSE), Potential Regional Earthquakes (PREQ), Potential Rainfall-Induced Signals (PRIS), and other events, which are not related to the three main families.

## 2. Methodology

The proposed clustering workflow (Figure 1) integrates a self-supervised learning method for feature extraction with two clustering algorithms: k-means (Lloyd, 1982; MacQueen, 1967) and agglomerative hierarchical clustering (Johnson, 1967). This allows direct clustering in the feature space. In addition, the workflow can be applied iteratively, allowing for improved characterization of different sections of the data set over time. Iterations are used to refine the clustering results by focusing on specific clusters of interest identified among the automatically extracted ones. This process helps to filter out noise (e.g., nonseismic signals) and reduce the data set size allowing for a more targeted and accurate analysis.

### 2.1. Step 1: Representation of the Input Data

The choice of the representation of the input data for unsupervised learning algorithms is often determined by the used models. Seydoux et al. (2020) and Steinmann et al. (2022) proposed to use continuous three-component seismic traces as inputs to a deep scattering network. In contrast, Kinzel et al. (2024) and Yang et al. (2023) used time series of predetected events for contrastive learning models. Rimpot et al. (2024) further explored the use of images as input representation for Siamese network, providing a synoptic view of the data set, highlighting its complexity by representing the different type of signals in an organized manner.

As shown in Figure 1, our proposed workflow uses imaged seismic data as input based on the representation used by seismologists to label events. This input data representation allows combining several information as the temporal and frequency ones useful to distinguish events classes. These images are constructed following the method of Rimpot et al. (2024). Nonoverlapping 30s-length windows are used to generate the images with the upper portion showing the spectrogram and the lower portion showing the corresponding seismic trace. The spectrogram represents the full frequency range to capture a wide range of events from low to high frequencies. The waveform can be displayed as raw or filtered data to provide specific information to the model. The range of colors used in the spectrogram was manually chosen based on expert knowledge of the type of sources expected to be uncovered, an initial examination of the data, and a comprehensive understanding of the contexts. The color palette is consistent across all the images of the data set. Concerning the waveform representations, no clipping in
<figure><img src="image_3.png" /><figcaption>**Figure 1.** SSL-based clustering workflow highlighting the different steps of the proposed self-supervised learning based method. The possible iterative approach serving at refining the results is also explained.</figcaption></figure>

amplitude was realized. Therefore, the y-axis values depend on the maximum amplitude value of the 30s-length window of data. Additional data representations can be included as subplots or new images if they improve event discrimination.

## 2.2. Step 2: Self-Supervised Learning

The proposed workflow is based on an SSL algorithm. SSL models are designed to learn meaningful features from unlabeled data by generating implicit labels through data augmentation. These models encode the input data in a high-dimensional latent space called the embedding. Contrastive learning approaches are a type of SSL, where the model learns meaningful features by contrasting the feature representation of similar pairs of input data (positive pairs) and dissimilar pairs of input data (negative pairs). Among the many SSL algorithms, BYOL (Bootstrap Your Own Latent, Grill et al., 2020) belongs to a family of contrastive learning networks that includes networks such as SimCLR (T. Chen et al., 2020), SimSiam (X. Chen & He, 2021), and Moco (He et al., 2020). Although SimCLR and Moco rely on both positive (similar) and negative (dissimilar) samples, BYOL and SimSiam work without the need for negative pairs, which is advantageous when exploring unlabeled data sets.
We thus investigated SSL algorithms that do not require negative samples, such as BYOL and SimSiam. Although both are in the field of contrastive learning, they differ in their approach. SimSiam and BYOL focus on maximizing the similarity between the resulting views of positive pairs, which are created by augmenting the input data. Common data augmentations for images include horizontal and vertical flips, rotations, and color jittering. In SimSiam, two augmented views of the same input are compared and the model is trained to maximize their similarity. BYOL uses a similar process but involves two networks: one network (the online network) learns to match its representations to the output of a second network (the target network). We tested both the BYOL and SimSiam models on a subdataset. By comparing the training loss and the collapse level (a phenomenon where the model produces uniform outputs), which are displayed on the Figure S1 in Supporting Information S1, we selected BYOL as it had a more stable training loss and because SimSiam presents a high collapse level.

The BYOL model uses a ResNet-18 backbone (He et al., 2016) for feature extraction with randomly initialized weights. It is trained for 50 epochs with a batch size of 256 image pairs. Each image size is 256 × 256 pixels. The output embedding of the model represents each image as a 512-dimensional vector, which forms the latent space or feature space for the subsequent clustering steps.

## 2.3. Step 3: Hierarchical Clustering

The SSL-trained model produces a 512-dimensional embedding that captures the similarity between events in the data set. Rimpot et al. (2024) proposed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm (Van der Maaten & Hinton, 2008) to reduce the embedding to 2 dimensions before applying density-based clustering to this lower-dimensional space. In contrast, Kinzel et al. (2024) proposed to apply a centroid-based clustering algorithm directly on the original feature space. To fully exploit the rich information of the trained model, we follow the approach of Kinzel et al. (2024) and apply the k-means algorithm (Lloyd, 1982; Mac-Queen, 1967) to the 512-dimensional embedding. The k-means algorithm is used as an initial filtering step to reduce the number of information to a limited number of clusters, which centroids are then clustered with a hierarchical agglomerative clustering approach to obtain finer clustering results. The choice of the k-means al-gorithm was motivated by its ability to explore our high-dimensional embedding compared to other clustering approaches as DBSCAN and HDBSCAN (McInnes et al., 2017).

### 2.3.1. k-Means Clustering

k-means is a centroid-based clustering algorithm that works iteratively, typically using Euclidean distance, though any suitable distance measure can be applied. Initially, k-means assigns as many centroids in the feature space as the user specifies. Each data point is then assigned to the nearest centroid. In subsequent iterations, the centroids are recalculated as the arithmetic mean of the samples within each cluster, and cluster membership is updated. This means that a sample may belong to one cluster in the first iteration and to another cluster in later iterations. The process stops either when a maximum number of iterations is reached or when the centroids stabilize. k-means aims to minimize the sum of the squared Euclidean distances between samples and their assigned centroids. In our study, k-means was run with a maximum of 300 iterations using a scalable k-means++ centroids initialization.

Determining the appropriate number of clusters *k* in unsupervised clustering methods such as k-means is a challenging task particularly in the context of high-dimensional feature spaces generated by the BYOL SSL model. Although several techniques have been proposed in the literature, Schubert (2023) emphasizes the inherent difficulties in accurately identifying the true number of clusters in synthetic case-based data sets. Although methods such as the Calinski-Harabasz variance ratio criterion (Caliński & Harabasz, 1974), the Gap statistic (Tibshirani et al., 2001), and the Bayesian Information Criterion (BIC) (Schwarz, 1978) have shown greater efficiency in recovering the original number of clusters in the synthetic case-based data sets, Schubert (2023) concludes that “we must accept that there is no ‘optimal’ solution in cluster analysis.”

Given the complexity of the return embedding of SSL model, combining hundreds of features for thousand to millions samples, we suggest setting a high *k* (e.g., 1,000, 3,000, 5,000) much larger than the expected number of clusters. This results in clusters of manageable size (<10,000 samples) presenting consistency. We implemented k-means using the GPU-optimized version available in the Rapids-cuML library (Raschka et al., 2020).
### 2.3.2. Agglomerative Clustering

In addition to the k-means approach, we applied agglomerative hierarchical clustering to merge the clusters resulting from k-means in a structured hierarchical manner (Figure 1). This step aims to reduce the total number of clusters to a more manageable set, making subsequent analysis and interpretation more efficient. Hierarchical clustering has already been explored in seismology. For example, Steinmann et al. (2022) applied hierarchical clustering directly to the feature matrix generated by a deep scattering model. By truncating the dendrogram at a distance level, the authors obtained 16 clusters identified as belonging to four families of clusters corresponding to monochromatic signals, anthropogenic signals, ambient seismic noise, and potential seismic signals such as earthquakes. Our approach proposes an alternative truncation mode allowing the selection of clusters at different distances or node levels. This alternative offers a finer selection of clusters compared to distances and levels truncations, allowing the identification of clusters of rare events, which present characteristics making them inconsistent with other groups.

In our workflow, we use the agglomerative hierarchical clustering algorithm of the Scikit-learn library (Pedregosa et al., 2011). This algorithm implements a bottom-up approach based on the Ward linkage method (Ward, 1963), which minimizes the variance within merged groups at each step using Euclidean distances. Initially, the k-means cluster centroids are treated as individual elements, which are then merged with others according to the Ward criterion. This hierarchical approach allows us to identify groups of k-means clusters that exhibit internal consistency.

By truncating the resulting dendrogram, the number of final clusters can be significantly reduced compared to the initial number of k-means clusters. We propose a truncation strategy based on the inconsistency coefficient for each node in the dendrogram. The inconsistency coefficient normalizes the height (linkage distance) of a node by the mean and standard deviation of the heights of the nodes below it in the dendrogram. More precisely, considering a node $i$, at a linkage distance $d_i$, we defined $M_i$ the set of merged nodes below the considered node $i$ for which the linkage distances are defined as $D_i = \{d_j | j \in M_i\}$. Therefore, the inconsistency coefficient at a node $i$, $R_i$, can be defined as follows:

$$R_i = \frac{d_i - \mu_i}{\sigma_i}, \qquad (1)$$

with

$$\mu_i = \frac{1}{N} \sum_{d \in D_i} d, \quad \text{and} \quad \sigma_i = \frac{1}{N-1} \sqrt{\sum_{d \in D_i} (d - \mu_i)^2} \qquad (2)$$

The truncation method based on the inconsistency coefficient allows a robust selection of clusters at several hierarchical levels. We propose to use an inconsistency threshold of 2, which results in fewer truncations while maintaining high cluster consistency.

## 3. Data Set

To evaluate the potential of the proposed SSL-based clustering workflow, we analyze the data set from the “Marie-sur-Tinée” landslide (Figure 2) located in the Southeast French Alps between the Vésubie and Tinée valleys. This landslide was triggered by the Alex storm in October 2020, which affected several regions in Europe and resulted in more than 600 mm of rainfall in less than 24 hr in these valleys (Carrega & Michelot, 2021). Following this triggering event, the landslide was monitored with a maximum of 69 temporary seismic stations (IGU-16HR 3C 5 Hz SmartSolo sensors, provided by the DENSAR-EOST service). The stations were deployed for a total of 72 days. Data acquisition took place between 18 April 2021 and 28 June 2021. In the first phase, from April 18 to 19 May 2021, 69 seismic stations were deployed (blue markers, Figure 2). From May 20 to 28 June 2021, a station swap was performed and 40 stations were installed (yellow markers, Figure 2). All the deployed station have a sampling rate of 500 Hz.

This data set presents significant challenges due to the complexity of the local microseismicity and the large amount of generated data to be processed. The preparation of the input images resulted in a total of 10,365,846
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Landslide "Marie-sur-Tinée," located near Marie-sur-Tinée in the French Alps. The blue dots display the locations of the 69 temporary stations installed from 18 April 2021 to 19 May 2021. The yellow dots identify the second set of station located at the top of the landslide and active from 20 May 2021 to 28 June 2021. The stations used to compose the training set are marked with magenta dots.</figcaption></figure>

images using nonoverlapping 30s-length windows. The spectrograms represented in the input images include frequency information up to the Nyquist frequency of 250 Hz, and no filtering was applied to the raw waveforms.

The 10 millions seismic imaged signals are not directly used to train the SSL model due to the excessive computational time. The time required to train such a model depends on several factors including the number of images in each batch (batch size), the number of training epochs, and the number of samples used. To reduce the computation time while maintaining a reasonable batch size and number of epochs, we selected a subset of the data set for training. This training set consists of all the 1,966,581 images from 13 seismic stations (magenta markers, Figure 2), which were selected to ensure a comprehensive coverage of the network and to include a wide range of local seismic sources. The model is trained over 50 epochs using a batch size of 256 image pairs.

# 4. Results

## 4.1. Visualization of SSL Results

Training unsupervised learning models for blind exploration of a data set presents challenges in evaluating training consistency. To evaluate the trained model and the reliability of the resulting embeddings, we visualize the output embedding of the training set in two dimensions using a t-SNE transformation (Figure 3). The inset in the upper right corner shows the t-SNE two-dimensional embedding where each black point corresponds to one of the 2 millions training samples. The embedding shows distinct small patches especially in the upper half. In contrast, the lower half of the embedding appears more diffuse with fewer distinct patches. In addition, we observe a curved structure of points along the right side of the embedding.

The main part of Figure 3 shows this t-SNE transformed embedding where the points are replaced by their corresponding input images. For visual clarity and readability, only a small subset of the 2 millions input images (e.g., 1301 images) are shown. This representation allows evaluating the consistency of the patches within the t-SNE embedding. By comparing the input images with the patches highlighted in the inset, we can identify the types of signals that correspond to the regions of the embedding.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** t-SNE transformation of the training set embedding (1966581, 512). The top right inset represents the t-SNE two dimensional embedding (1966581, 2) and the colors represent specific regions of this 2D embedding discussed in the article. The main figure is an illustration of this transformed embedding representing the input images instead of black dots. For visual readability, only 1301 input images are represented.</figcaption></figure>

In Region 1, located at the top of the embedding, we observe signals with waveforms characterized by multiple impulsive arrivals. Focusing on the spectrograms, these signals exhibit varying frequency content but share a common high-energy, monofrequency signal at 55 Hz. Region 2 contains signals with very short, high-amplitude peaks in the waveform. These peaks display high frequencies ranging from above 50–200 Hz with irregular time occurrence and frequency content when comparing peaks within the same input image. Adjacent to Region 2, Region 3 consists of impulsive, low-frequency signals (below 75 Hz) with durations ranging from a few seconds (2 s) to several dozen seconds. Region 4 contains signals whose waveforms generally lack impulsive arrivals except for a few isolated cases where such arrivals appear at noise levels. The frequency content of these signals ranges from 20 to 100 Hz with a prominent high-energy signal between 40 and 50 Hz. On the right side of the
embedding, Region 5 contains signals with waveforms closely resembling those of Region 1. The signals in Region 5 also feature a 55 Hz mono-frequency signal, but their spectrograms show more energy around this frequency compared to Region 1. Region 6 groups signals with frequencies between 50 and 200 Hz but without a dominant energy peak at any specific frequency. The mono-frequency signal at 55 Hz is present in Region 6 signals though with lower energy than in Regions 1 and 5. The signals in Region 7 display waveform amplitudes at the noise level with a frequency content limited to frequencies below 100 Hz. Region 8 shows signals with spikes in their waveforms, and the spectrograms reveal that the highest energy levels are associated with frequencies above 50 Hz. Lastly, Region 9, shaped as a curve in the t-SNE embedding, groups signals resembling background noise but with more energetic frequencies below 100 Hz. Examples of signals from these nine regions are shown in Figure S2 of Supporting Information S1.

## 4.2. Clustering Results

After a visual inspection of the model training performances based on the t-SNE visualization, the entire data set of 10 millions images can be encoded with the trained model to obtain the final embedding. This embedding is then used as the input for the clustering step. For the clustering procedure, we set the k-means k parameter to 5,000 and the inconsistency level to 2 resulting in 860 clusters.

### 4.2.1. Polar Dendrogram Representation

The polar dendrogram representing the 860 final clusters is shown in Figure S3 of Supporting Information S1. To better visualize the organization of the different groups, Figure 4 shows a truncated version of the polar dendrogram with up to 130 clusters. Each node thumbnail in the figure represents the input image closest to the centroid of the elements under the node based on Euclidean distances. A first analysis reveals an organization based primarily on the main content of the spectrograms.

In fact, the group of green-yellow spectrograms (node A) is more closely associated with ambient seismic noise, whereas the group of blue spectrograms (node B) corresponds to short and impulsive signals. The first major separation in the middle of the polar dendrogram (between nodes A and B) appears to distinguish events with nonspecific frequency content (node A) from those with either high (>100 Hz, e.g. node F), low (<50 Hz, e.g. node E), and in between ([50 Hz, 150 Hz], e.g. node H) frequency information (node B).

Several distinct groups can be identified below node B. Nodes C and D represent signals with low frequency content and complex waveforms. Node E groups events with higher energy in the spectrogram. Node F contains very short, impulsive signals with high frequency content, whereas node G contains impulsive signals with higher energy in the spectrograms than the signals under the node F. Signals under node H present similar waveforms to those under node C with slightly higher frequency contents and more energy than the node C signals. Signals associated with node I are characterized by being very short and impulsive waveforms and having a low frequency content. Node J contains low frequency complex signals, whereas node K represents signals similar to those found under node A. The polar dendrogram shown in Figure 4 already shows consistent groupings from the very first nodes. Deeper exploration of the dendrogram reveals increasingly pure signal groups.

### 4.2.2. Clusters From the Iterative Approach

The proposed workflow aims to provide an effective solution for exploring very large data sets. However, due to the large variety of type of signals (Figures 3 and 4), a clustering matching all the possible signal classes is impossible to achieve. The iterative approach allows to refine the characterization of the data set step by step. In the first iteration, the entire data set is considered yielding the results shown in Figure 4. After manually characterizing the 860 clusters based on our expertise and published articles studying similar microseismicity (Provost et al., 2018; Vouillamoz et al., 2018), we identified three main families: Noise, very short high frequency signals (potential rainfall-induced signals) and potential geohazard related events (events with characteristics similar to microseismic events and regional earthquakes). A second iteration is then applied to further investigate the previously identified clusters of events of interest. For these potential geohazard related events, we recreated figures while limiting the frequency range from 2 to 50 Hz in the spectrogram.

We retrained the model using these 156,642 new images and ran the clustering workflow again resulting in the extraction of 53 new clusters of interest. These new clusters were then manually classified into three categories:
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Truncated polar dendrogram of the 860 initial clusters obtained at the first iteration. The complete figure is available on the Figure S3 in Supporting Information S1.</figcaption></figure>

Noise, PELSE, and PREQ. As represented in the Figure 5a, the main family of PREQ obtained after the iteration 2 is composed of numerous very short and high frequency glitches. To clean the PREQ main family of the glitches signals, a third iteration was performed on the 28,075 images composing it. This led to 168 new clusters from which we identified clusters belonging to the main families Noise, PELSE, and PREQ. The impact of the third iteration on the PREQ main family can be observed in the Figure 5, where the temporal distributions and the maps showing the station activations are displayed for both iterations. Finally after the three iterations, the 10 millions images were clustered in four main families: Noise, PRIS, PELSE, and PREQ. The details on how the clusters of
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Comparison of the results obtained by the second iteration and the third iteration. (a) Input images examples from the cluster 34 of the second iteration. (b) Input images examples for the cluster 157 of the third iteration. (c) Temporal distribution of the PREQ main family after the second iteration (gray) and the third iteration (red). (d) Map showing the activation of the stations over the network for the PREQ main family of the second iteration. (e) Map showing the activation of the stations over the network for the PREQ main family of the third iteration.</figcaption></figure>

each iteration are grouped in the main families are available on the Tables S1–S4 in Supporting Information S1. The clusters serving as input of the iteration are highlighted in the Table S5 of Supporting Information S1.

4.3. Temporal Distribution of the Clusters

Plotting the temporal distribution of the clusters provides insight into the type of their potential seismic sources. The temporal activation for the clusters obtained from the three clustering iterations are shown in Figure 6. Initially, a daily periodicity is observed in the activation of many clusters. In particular, clusters in the range *I1_C1–I1_C380* and *I1_C703–I1_C860* show a nighttime periodicity, whereas most clusters from *I1_C417* to *I1_C697* display a daytime periodicity. Although this periodicity can be observed, the periodic pattern remains
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Temporal distribution of the clusters obtained within the three clustering iterations. Clusters obtained with the first iteration are noted I1_#, clusters from the second iteration are noted I2_#, and clusters for the last iteration are noted I3_#.</figcaption></figure>

somewhat unclear. In addition, we observe clusters with sporadic activation, such as those in ranges I1_C381–I1_C416 and I1_C698–I1_C702. These clusters with sporadic activation likely correspond to microseismic events, earthquakes, or weather-related events. Clusters from the second and third iterations do not show
any periodicity, except for the first few days, from 18 April 2021 to 24 April 2021. By analyzing both the temporal activation of each cluster and its image content, it is possible to create the four main families and highlight the clusters of interest.

## 4.4. Analysis of the Four Main Families

Research in environmental seismology focuses on microseismic events, which provide valuable information about the geological structures under study. Microseismic events are typically classified into categories such as rockfalls, granular flows, micro-failures, or microquakes. Provost et al. (2018) introduced a standard typology for microseismic events specifically for endogenous seismicity in landslides. The authors classified microseismic events into three main types: “slopequakes” (including low- and high-frequency events, hybrids, precursory signals, and tremor-like signals), “rockfalls,” and “granular flows” (both dry and wet). Each of these event types occurs in landslide contexts and can be distinguished based on frequency content, duration, and waveform characteristics (Provost et al., 2018). By analyzing hundreds of events from the same class at different sites, Provost et al. (2018) also highlighted the variability within event types. For example, events within the same category may have different characteristics from one landslide to another or even within the same landslide, making event classification more complex.

The study of environmental seismicity also often reveals a wide variety of recorded signals, which sources are linked to the human activity or the fauna. Vouillamoz et al. (2018) characterized the signals from the Super-Sauze and Pechgraben debris slides, identifying a large variety of seismic and microseismic events, including regional earthquakes, rockfalls, low and high frequency quakes as well as events caused by aircraft, human footsteps, and ambient environmental noise. Similarly, as shown in Figure 7, the endogenous seismicity at the “Marie-sur-Tinée” landslide exhibits significant diversity.

Extracting several hundred clusters from such a diverse data set allows to obtain consistent clusters; however, many clusters represent similar events (Figure 4). To better characterize the seismicity of the “Marie-sur-Tinée” landslide, despite the large diversity of the signals (Figures 3 and 7), we manually analyzed and grouped the clusters based on both their image content and their temporal activation patterns (Figure 6). The obtained four main families are discussed in the following sections.

The temporal activation of these four groups is shown in Figure 8 with a time step of 30 min for visual clarity. For all four groups, we note the presence of a higher number of events during the period when 69 stations were active due to the presence of a higher number of data.

### 4.4.1. Noise

The Noise group is the largest of the four main families and includes all signals not classified in the other groups. Noise clusters exhibit significant signal diversity, because they represent various sources. Figure 7 illustrates input images for events N1–N5, which belong to the Noise group.

First, the N1 background noise event is characterized by the absence of energetic or impulsive seismic signals in the waveform and has a uniform frequency content over time. This results in a “white” spectrogram that reflects both environmental background noise and the instrumental noise of the seismic station. Event N2 shows glitches, which are short and high-energetic signals that cover a wide range of frequencies and are often associated with high amplitudes in the waveform. The Noise group also includes possible environmental noise as the N3 events, which can be caused by wind, storms, creeks, or other environmental factors (Vouillamoz et al., 2018). Finally, the Noise main family includes anthropogenic signals such as human footsteps (event N4) and motor-like (event N5). Footsteps are typically short, impulsive, energetic, and repetitive with either high or low frequencies. Motor-like signals are easily identified by their characteristic mono-frequency noise, as seen in event N5. When a motor moves, frequency variations appear in the spectrogram. In cases of multiple motors (e.g., helicopters) or motor harmonics, horizontal lines at different frequencies may be observed as shown in the spectrogram of event N5.

The Noise group includes all the signals recorded at the landslide site except those categorized as Potential Rain, PELSE, and PREQ events. This explains the temporal activation pattern of the group (Figure 8a), which closely follows the number of active seismic stations with only minor deviations when other clusters are activated. However, the spatial activation of the Noise group (Figure 9a) shows differences between the upper and lower
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Examples of events clustered in each of the four main families Noise, PRIS, PELSE, and PREQ.</figcaption></figure>

parts of the landslide. Stations located in the upper part, close to the sliding area, are more active than others
probably due to a nearby hiking trail, which increases the recording of human footstep. In addition, these stations
may be more exposed to record noise sources (airplanes, helicopters) due to the topography of the site. Finally,
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Temporal distribution of the final four main families. (a) Noise. (b) PRIS. (c) PELSE. (d) PREQ.</figcaption></figure>

exposure to wind and other meteorological conditions depends on the installation state of the seismic stations. Some stations might be less buried due to site conditions (rocks, roots, etc.) or their installation conditions and location might have evolved in time due to the landslide activity. This makes some stations more susceptible to record weather-induced noise leading to variations in recorded signals and subsequent clustering.

### 4.4.2. Potential Rainfall-Induced Signals

The second largest group is the PRIS main family. This group contains events characterized by short, impulsive, and high frequency signals as observed in events PRIS1 to PRIS6 (Figure 7). As illustrated by the Figure 7, these events are grouped together despite variations in noise levels from one event to another. The activation pattern of the PRIS main family is shown in Figure 8b and shows a highly punctual temporal activation. These activations are greatly explained by the rainfall data recorded by the weather station at the landslide site (Figure 8b) with a correlation coefficient of 0.50 that increases to 0.58 if not considering the period for which the weather station was not operational. The only cases where this main family present strong activations without corresponding rainfall data occur when the weather station was not operational, confirming that these events may be related to rainfall.

The spatial activation pattern (Figure 9b) shows uniform activation at most stations, although three stations located in the lower part of the landslide show a greater number of events than the rest of the stations. We suppose that this increased activation could be due to the quality of station installation, which may differ from a station to another. Stations that are less deeply buried due to the site conditions (rocks, roots) are more exposed to weather conditions, making them more susceptible to detecting rainfall related signals.

### 4.4.3. Potential Endogenous Landslide Seismic Events

The PELSE main family consists of clusters derived from the second and third iterations of clustering. This group includes events with characteristics similar to rockfalls (Figure 7; PELSE1-PELSE3), slopequakes (PELSE4-PELSE6), granular flows (PELSE7-PELSE12), and events similar to noise (PELSE13-PELSE15).

Events PELSE1 through PELSE3 exhibit short, impulsive wave arrivals with low-frequency content and limited duration. These events have a prominent amplitude peak in the middle of the event waveform. Their frequency content is mainly below 50 Hz, and their duration is less than 30 s. PELSE4 through PELSE6 is characterized by brief, highly impulsive, low-frequency arrivals with a waveform that exhibits an exponential decay. These events
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Maps showing how active are the station for each of the four main families. The color represents the number of events recorded by each station divided by the station's number of active days. This allows to correct the second deployment effect. (a) Noise main family. (b) PRIS main family. (c) PELSE main family. (d) PREQ main family.</figcaption></figure>

are very short (<5 s) with frequencies below 50 Hz for PELSE5 and PELSE6 and extending up to 100 Hz for PELSE4. Events PELSE7 to PELSE12 also have multiple peaks in their waveforms and have durations similar to those of PELSE1 to PELSE3. However, their spectrograms display more continuous information compared to the spectrograms of events PELSE1 to PELSE3. The frequency content with the higher level of energy is mainly below 50 Hz, although frequencies up to 75 Hz are observed (PELSE7, PELSE8, and PELSE12). These events exhibit a “cigar-shaped” spectrogram. Finally, events PELSE13 to PELSE15 share characteristics with noise events N1 and N3.

The temporal activation of the PELSE main family is shown in Figure 8c. Apart from the first 5 days, no periodic pattern in the activation is visible. Furthermore, no clear correlation is observed between these events and precipitation. The spatial distribution of activation (Figure 9c) provides insight into the source locations of these events. Active stations are expected to be located in the upper part of the landslide area, near the top of the sliding zone. Figure 9c shows that three of the seven most active stations are located near the upper part of the sliding area, one is above, at the top of the scarp near a hiking trail, and the remaining three are situated in the lower part of the landslide. One is near the base of the sliding area, while the other two, including the most active station, are on the western side near a farm.

According to Provost et al. (2018), rockfall events generally have frequencies above 10 Hz although low-energy signals below 10 Hz can occasionally occur. In cases where stations are located close to the impact site, frequencies can reach up to 100 Hz. Rockfall events may have various durations depending on the morphology of the studied site. PELSE1, PELSE2, and PELSE3 are consistent with the characteristics of rockfall events as defined by Provost et al. (2018). Of the various types of slopequake events described by Provost et al. (2018), the primary characteristic include short duration (<5 s) and frequencies ranging from 5 to 50 Hz with the possibility of higher frequencies depending on site conditions. Slopequake events often exhibit a triangular-shaped spectrogram with higher frequencies observed at the beginning and gradually decreasing with time. These characteristics are
consistent with events PELSE4, PELSE5, and PELSE6. Dry granular flow events, as described by Provost et al. (2018), exhibit frequencies between 1 and 35 Hz and can last for up to 500 s. Wet granular flows, on the other hand, have higher frequency content (up to 100 Hz if the station is close to the flow path) and can last for several hours. PELSE7 to PELSE12 are similar in frequency content to dry granular flows. However, their durations are much shorter than those reported by Provost et al. (2018). This difference may be explained by the topography of the landslide, where long-lasting granular flows are unlikely. PELSE7 to PELSE12 may also represent hybrid events, combining characteristics of both rockfalls and granular flows, and are likely related to the ongoing activity of the landslide.

#### 4.4.4. Potential Regional Earthquakes

The PREQ main family contains the smallest number of events. This group consists of regional earthquake events (PREQ1 to PREQ4 in Figure 7), quarry blast events (PREQ5 and PREQ6) but also events presenting the characteristic of granular flows for PREQ7 and events whose source is undefined (PREQ8). The regional earthquakes and quarry blast have similar characteristics, including an impulsive first arrival and comparable frequency content. However, for quarry blasts, the amplitude of the waveform decreases with time, whereas for earthquakes, the S-wave arrivals generally have higher amplitudes (Renouard et al., 2021). The temporal activation of this group, shown in Figure 8d, appears random except for the first 5 days. During the data acquisition, on June 8 and 9, 2021, a seismic crisis occurred in the Vésubie valley during which an earthquake of magnitude 2.9 was recorded. In this specific case, where the earthquake occurred close to the landslide area, a clear correlation can be observed between the temporal activation of the “Potential EQ” group and the earthquake event (Figure 8).

The station activation map (Figure 9d) reveals that two stations recorded slightly more events than the rest of the network. Upon closer examination of the events within the main family, these two stations were found to have recorded signals that could belong to the PELSE main family as they present similar signal characteristics and they are localized on the top of the landslide near the scarp. Excluding these two stations from the network activation reveals a more homogeneous pattern consistent with the expected distribution of earthquake events (Figure 9d).

Reference earthquake data from the BCSF RéNASS Catalog are shown in the Figure S4 of Supporting Information S1. Each event shown is an input image from station 01 × 02, and only events detected by this station were used as reference events. A range of earthquake characteristics are observed. Some events are short with an impulsive first arrival and no clear S-waves (e.g., REQ1), whereas others are similarly short but have a clear S-wave (e.g., REQ15). Both of these examples show high energy relative to the noise level, resulting in deep blue spectrograms surrounding the signal of interest. In contrast, events such as REQ2, REQ12, and REQ21 produce signals that are close to the noise level as seen by station 01 × 02. In addition, long-duration, low-frequency earthquakes (e.g., REQ5) and events with extended codas (e.g., REQ17 and REQ23) are also observed.

The diversity of these earthquake types makes it difficult to create a pure earthquake cluster when analyzing over 10 million images. However, Figure S4 in Supporting Information S1 also highlights the clustering results of these reference earthquakes. A blue frame indicates that at least one window from a station was clustered in the PREQ main family, whereas a yellow frame indicates that at least one window from a station was clustered in the PELSE main family and none in the PREQ group. By examining the events clustered in the PELSE group, it is clear that event's shape and noise level are factors that influence clustering. Specifically, long-duration events, codas, or events with higher noise levels tend to be clustered in the PELSE group because the model is not explicitly trained to identify codas or low-frequency events in noisy conditions.

# 5. Discussion

## 5.1. Discussion on the Methodology

Our proposed SSL-based clustering workflow offers a novel solution for clustering large volume continuous seismological data sets generated by dense networks using image representations. By representing continuous seismic data as images that show both temporal and frequency information from 30-s windows of the signal, we feed the model with information it exploits to generate a consistent embedding that facilitate the extraction of clusters of similar signals. This image-based approach, which combines waveform and spectrogram representations, enhances the model's ability to distinguish between different inputs. For signals with similarly shaped
waveforms, the spectrogram provides critical complementary information, which becomes essential for accu-
rately determining signal similarity or dissimilarity and vice versa. However, these images are static represen-
tations of continuous data, and the spectrogram color range was manually calibrated to enhance the visibility of all
potential event types. Although this adjustment facilitates visual inspection, it may also influence the clustering
results by emphasizing certain time-frequency features over others. Consequently, the image generation process
becomes a critical step especially in the context of exhaustive and blind data set exploration, where selecting an
appropriate color scale can be particularly challenging.

Alternative strategies have been explored in the literature. For instance, studies such as Yang et al. (2023) and Kinzel et al. (2024) have proposed contrastive learning approaches for clustering time series of detected seismic events. Another potential approach involves using features specifically designed for seismic signals as described by Provost et al. (2017), as input for contrastive learning models. However, contrastive learning techniques applied to time series must address the challenge of effective data augmentation. In computer science, Wen et al. (2020) and Iglesias et al. (2023) conducted surveys and developed a taxonomy of time series augmentation techniques, whereas Z. Liu, Alavi, et al. (2024) proposed a method for selecting the most suitable augmentation strategies based on the data set. Adapting these augmentation methods to make them effective for seismic signals as well as designing specific augmentations for seismic signals is very challenging. Addressing this data augmentation challenge for seismic time series involves several complex considerations: How can the intrinsic seismic properties be preserved during waveform augmentation? What threshold of augmentation can be considered optimal without compromising data integrity? Furthermore, how should this threshold be defined and is it sufficient for effective model training? In the case of contrastive learning, should augmentation techniques preserve physical meaning? These questions are important for the advancement of deep learning methods in seismology especially contrastive learning approaches. They are key to advancing transfer learning applications that aim to develop global foundation models. These foundation models would be capable of classifying seismic events across a wide range of sources from macro-seismicity to microseismicity. Addressing these challenges is essential for the development of such models. The potential of time series in SSL will be further explored in future research.

The present manuscript highlights the potential of SSL-based contrastive learning approaches to extract mean-
ingful information from large volume data set presenting a wide variety of signals. In fact, the proposed SSL-
based approach leveraged the information contained in our input images to extract meaningful features allow-
ing to obtained consistent clustering results. As no specific tuning in the model and in the clustering strategy is
relative to prior-knowledge on the seismicity of our data set; therefore, applications to other data sets are likely to
be effective if the information contained in the input images are sufficiently discriminative. In addition to blind
and exhaustive exploration of continuous data sets, the proposed approach can also be used for the character-
ization of specific types of events by including experts knowledge-driven information on the input images (e.g.,
focusing on a specific frequency range, masking the too low or too high energy values in the spectrogram), thus
avoid the computationally heavy task to define and train an event-detector specifically designed for the
considered type of events. Finally, as being based on images, this methodology could also be considered for
alternative data formats as fiber optics distributed acoustic sensing data (e.g., Huynh et al., 2022) or to 2D
Interferometric synthetic-aperture radar (InSAR) (e.g., Eriksen et al., 2017) as well as nonseismic inputs (e.g., a
data set of optical microscope volcanic ashes proposed by Benet et al. (2025)).

**5.2. Discussion on the Main Results**

Applying our workflow to the blind exploration of 10,365,846 images representing 30s-length windows of data resulted in four main clusters: Noise, PRIS, PELSE, and PREQ. These clusters were manually created from 860 clusters obtained in the first iteration, 53 clusters in the second iteration and 168 clusters of the third iteration. The PELSE main family is derived from the second and the third iterations while the PREQ main family is only derived from the third iteration.

The advantages of the iterative approach are illustrated in Figure 5, where we compare the outcomes for the PREQ main family after two and three iterations. Examining clusters within the PREQ main family across these iterations (iteration 2 in Figure 5a and iteration 3 in Figure 5b) reveal a significant improvement in cluster consistency. Specifically, the glitch signals present in the PREQ main family in iteration 2 are entirely removed by iteration 3. Additionally, the temporal distribution in PREQ-iteration 3 main family demonstrates a clear
reduction in both the number of recorded signals and the number of active days aligning with the PREQ main family definition: Potential Regional Earthquakes (known as punctual events). Further, Figures 5d and 5e present station activation maps across the network. In the PREQ-iteration 2 main family, two stations show significantly higher number of signal per number of active days compared to others (Figure 5d). In contrast, the PREQ-iteration 3 main family exhibits a balanced distribution in the number of signals per active days, with only two stations showing a slightly higher number of signals per number of active days, localized near the landslide scarp.

Despite the large size of the data set, our workflow was also able to extract a cluster of rainfall-induced signals strongly correlated with precipitation data from the weather station at the "Marie-sur-Tinée" landslide (Figure 8). The PELSE and PREQ main families also include all reference earthquakes from the BCSF ReNASS Catalog (Figure S4 in Supporting Information S1), while highlighting new events as PREQ2 (Figure 7), which is not present in the reference catalog. Although both groups contain events consistent with their definition (rockfalls and granular flows in the PELSE group and earthquakes and quarry blasts in the PREQ group), they also contain nonrelated signals (noise signals for PELSE and granular flow signals for PREQ). This is likely due to the similarities between noise sources and microseismic signals as well as similarities between high energy long-duration earthquake signals and granular flow signals both on the spectrogram and the waveform representations. This is a limitation of the use of images especially when exploring a large data set with a wide frequency range.

## 6. Conclusion

Clustering of large data sets is a challenging but essential task in seismology. Numerous studies have focused on developing strategies to facilitate the exploration of data sets and the creation of event catalogs, providing valuable insights into understanding geological structures. However, with the increasing use of dense seismic arrays and expanded site monitoring, seismologists have to deal with much larger data sets. Our proposed SSL-based clustering workflow is designed to automatically and comprehensively cluster data sets from thousands to millions of events. In the first step, an SSL model is trained on images derived from 30-s data windows. We then apply k-means clustering to the output of the model, which produces clusters with centroids that are then used as input for agglomerative clustering. The end result is a reduced set of consistent clusters that can be manually grouped into broader categories.

When applied to the large data set of the "Marie-sur-Tinée" landslide, our workflow produced a total of 1,081 clusters through three iterations allowing the creation of four main families of events. The t-SNE visualization provides a synoptic and comprehensive view of the data set, confirming the robustness of the training process by highlighting the organization of the data set. The polar dendrogram further illustrates the relationships between clusters and confirms the consistency of the grouping. In addition, the temporal evolution of the four main families shows a coherent activation pattern with respect to event classes. These main families are labeled Noise, Potential Rainfall-Induced Signals, Potential Endogenous Landslide Seismic Events, and Potential Earthquakes. In particular, the PRIS group shows a correlation with precipitation data from the "Marie-sur-Tinée" landslide. The PELSE main family includes signals from rockfalls, slopequakes, and granular flows. The PELSE and PREQ main families include all reference earthquakes from the BCSF ReNASS catalog, whereas PREQ also includes earthquakes not present in the BCSF ReNASS catalog as PREQ2 in Figure 7.

Despite the overall clustering success, the PELSE and PREQ main families are not completely pure. Both groups contain signals that belong to the other main families. For example, the PELSE main family includes noise signals that resemble rockfalls, such as human footsteps as well as noise with characteristics similar to possible environmental noise tremors. The PREQ main family contains granular flows signals, which are confused with high-energy long-duration earthquake signals due to similarities in the spectrogram and waveform. These confusions might be the result of the use of images as input data for the SSL model. Indeed, images are static representations of the temporal and spectral information that could lead to confusions if discriminant information are not clearly highlighted in the images. New representations of the data need to be considered for both addressing these confusions and going further in the development of deep learning models for seismology, such as foundation models. Alternative to images are time series, as already explored in some studies (Kinzel et al., 2024; Yang et al., 2023), for which data augmentations strategies need to be further explored.

Our SSL-based clustering workflow paves the way for rapid and blind exploration of seismological data sets. This enables rare of weak events discoveries leading to a better documentation of the local endogenous seismicity. This
recent approach show promise in freeing up significant time for seismic event analysis and advancing knowledge in environmental seismology by limiting the manual labeling task at the cluster level as opposed to time-consuming task of labeling individually detected events in large data sets. Less than 6 days were necessary to train the SSL models and perform the clustering of each iteration and analyze the results. The creation of the main families required a total of approximately 4 hr for analyzing the clusters resulting from the three iterations. Ultimately, the ability to perform automated blind exploration resulting in easily labeled consistent clusters could contribute to the development of foundational models trained on millions of seismic and microseismic events. Such models would enable seismologists to analyze vast amounts of seismic data with greater speed and accuracy, facilitating a deeper understanding of complex geological structures such as landslides, glaciers, and volcanoes.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

The raw seismological data from the “Marie-sur-Tinée” landslide will be soon available through the RESIF datacenter. For more information about the raw seismological data, please contact Clément Hibert (hibert@unistra.fr). However, the 1.9 millions images used to train the model in the present work are available on the EasyData repository (EasyData Repository, 2025). The scripts of SSL and clustering are available on GitLab (GitLab Repository, 2025).

## Acknowledgments

This work is funded by the Agence Nationale de la Recherche (ANR)-Projet ANR-20-CE01-0006 “HighLand: Landslides and Climate Change in highly Sensible Environments: Seismology, Earth Observation and Artificial Intelligence.” The authors would like to acknowledge the High Performance Computing Center of the University of Strasbourg for supporting this work by providing scientific support and access to computing resources. Part of the computing resources were funded by the Equipex Equip@Meso project (Programme Investissements d'Avenir) and the CPER Alsac calcul/Big Data. The authors thank the Editor, Associate Editor, and Reviewers for their valuable reviews helping to improve the clarity and the quality of the present article.

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