# A Systematic Search for Tectonic Tremor and Low-Frequency Earthquakes in the Atacama Segment of the Chilean Subduction Zone Turns Up Empty

Jannes Münchmeyer, William B Frank, Sophie Giffard-roisin, David Marsan, Frederik Tilmann, Marcos Moreno, Anne Socquet

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## To cite this version:

Jannes Münchmeyer, William B Frank, Sophie Giffard-roisin, David Marsan, Frederik Tilmann, et al. A Systematic Search for Tectonic Tremor and Low-Frequency Earthquakes in the Atacama Segment of the Chilean Subduction Zone Turns Up Empty. Journal of Geophysical Research: Solid Earth, 2025, 130 (9), pp.e2025JB031283. 10.1029/2025jb031283. hal-05350486

HAL Id: hal-05350486

https://hal.science/hal-05350486v1

Submitted on 6 Nov 2025

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# JGR Solid Earth

## RESEARCH ARTICLE

10.1029/2025JB031283

**Special Collection:**

Slow to fast earthquakes and the geology, structure, and rheology of their host subduction zones

**Key Points:**

* We systematically search for low-frequency earthquakes (LFEs) and tectonic tremors in the Atacama segment, Chile, in a 3.3 year period

* With classical and machine learning detection approaches, validated by manual inspection, we find no evidence for LFEs or tectonic tremors

* Comparing to Nankai and Cascadia, we provide constraints on the potential LFE and tremor patterns that might escape our detection

**Supporting Information:**

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

**Correspondence to:**

J. Münchmeyer,
munchmej@univ-grenoble-alpes.fr

**Citation:**

Münchmeyer, J., Frank, W. B., Giffard-Roisin, S., Marsan, D., Tilmann, F., Moreno, M., & Socquet, A. (2025). A systematic search for tectonic tremor and low-frequency earthquakes in the Atacama segment of the Chilean subduction zone turns up empty. *Journal of Geophysical Research: Solid Earth, 130*, e2025JB031283. https://doi.org/10.1029/2025JB031283

Received 28 JAN 2025

Accepted 28 AUG 2025

**Author Contributions:**

**Conceptualization:** Jannes Münchmeyer, William B. Frank, Sophie Giffard-Roisin, David Marsan, Anne Socquet

**Formal analysis:** Jannes Münchmeyer

**Funding acquisition:** Frederik Tilmann, Marcos Moreno, Anne Socquet

**Software:** Jannes Münchmeyer

© 2025. The Author(s).
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.

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## A Systematic Search for Tectonic Tremor and Low-Frequency Earthquakes in the Atacama Segment of the Chilean Subduction Zone Turns Up Empty

Jannes Münchmeyer¹,² ID, William B. Frank² ID, Sophie Giffard-Roisin¹ ID, David Marsan¹ ID, Frederik Tilmann³,⁴ ID, Marcos Moreno⁵ ID, and Anne Socquet¹ ID

¹University Grenoble Alpes, University Savoie Mont Blanc, CNRS, IRD, University Gustave Eiffel, ISTerre, Grenoble, France, ²Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA, ³GFZ, German Research Centre for Geosciences, Potsdam, Germany, ⁴Institute for Geological Sciences, Freie Universität Berlin, Berlin, Germany, ⁵Department of Structural and Geotechnical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

**Abstract** Subduction megathrusts release stress not only seismically through earthquakes, but also through creep and transient slow deformation, called slow slip events (SSEs). Understanding the interplay between fast and slow slip is essential for illuminating the deformation processes on the subduction interface. The Chilean subduction margin, while one of the most seismically active regions worldwide, has few reports of SSEs. Furthermore, there are no comprehensive reports of tectonic tremors or low-frequency earthquakes (LFEs), seismic signals typically accompanying SSEs, tracking deformation at small spatial and temporal scales. Here, we perform a systematic search for tectonic tremors and LFEs in the Atacama segment in Northern Chile, a region hosting both shallow and deep SSEs. Using dense seismic networks, we investigate 3.3 years between November 2020 and February 2024. Due to the network geometry, we focus on deep tremor and LFEs. We apply two complementary methods, envelope correlation for tremor search and deep learning detection for LFEs, to generate initial catalogs. To validate the potential detections, we use clustering, matched filtering, heuristics, and extensive manual inspection. While our initial search provides numerous candidates, after verification, we find no evidence for tectonic tremor or LFEs in the region. In contrast, our approaches successfully recover tremors and LFEs in two reference regions outside Chile with known tremor and LFE activity. Our observations show that tremors and LFEs in Northern Chile are either of lower moment rate than in other regions, have substantially longer recurrence rates, or are absent altogether, potentially due to the cold subduction.

**Plain Language Summary** Tectonic plates in a subduction zone accumulate stress due to tectonic forcing. This stress is released through different deformation processes, namely earthquakes and slow slip events (SSEs). SSEs are transient motions on the plate interface lasting from days to years releasing similar seismic moment as regular earthquakes (up to $M_w > 7.0$), but without emitting seismic waves. However, SSEs are often accompanied by seismic signals, called tremors and low-frequency earthquakes (LFEs). So far, it is unknown if SSEs are always accompanied by tremors or LFEs or not. In this study, we address this question for the Atacama segment in Northern Chile, one of the most seismically active regions worldwide. We perform a systematic search for tremors and LFEs using two complementary methods and manual inspection. We find no evidence for tremors or LFEs in the region. While our results can not fully rule out the regional existence of LFEs and tremors, we provide important constraints. We infer how big LFEs and tremors might be to still not be detectable or how rarely they need to occur. These constraints can help in building physical models of tremorgenesis and the mechanism of SSEs, improving our understanding of the interactions between slow and fast deformation in subduction zones.

## 1. Introduction

The differential movement of tectonic plates leads to a constant accumulation of stress on the plate interface. This stress is released either in the form of fast deformation, known as earthquakes, or through slow deformation (Behr & Bürgmann, 2021; Ide et al., 2007). Slow deformation can either happen constantly, in the form of creep, or transiently, as so-called slow slip events (SSE). Slow and fast deformation have repeatedly been observed to interact with each other: SSEs can accelerate into fast earthquakes and earthquakes can trigger SSEs (Itoh
**Supervision:** William B. Frank,
Sophie Giffard-Roisin, David Marsan,
Anne Socquet

**Visualization:** Jannes Münchmeyer

**Writing – original draft:**

Jannes Münchmeyer

**Writing – review & editing:**

Jannes Münchmeyer, William B. Frank,
Sophie Giffard-Roisin, David Marsan,
Frederik Tilmann, Marcos Moreno,
Anne Socquet

et al., 2023; Kato et al., 2012; Obara & Kato, 2016; Radiguet et al., 2016; Socquet et al., 2017; Uchida et al., 2016; Voss et al., 2018).

Two seismic phenomena commonly spatiotemporally correlated with SSEs are tectonic tremors and low-frequency earthquakes (LFEs). Tectonic tremors emit seismic signals without clear P and S arrivals that typically last several minutes (Obara, 2002; Wech & Creager, 2008). They are called *tectonic*, as they originate from an energy source on a plate interface. For simplicity, in this study we refer to tectonic tremor simply as tremor. Tremors are typically observable in a frequency band between the microseismic noise (>1 Hz) and roughly 10 Hz. Tremors cluster in space and time (Armbruster et al., 2014; Wech & Creager, 2011). However, as tremors are emergent without clear phase arrivals, the definition of tremor is not sharp with no agreed definition of a minimum and maximum duration of a tremor, or the impulsiveness or smoothness of the source process.

In contrast, LFEs can be more precisely defined. An LFE is an individual earthquake-like event on the plate interface with a source duration typically below 1 s, that differs from a regular earthquake in its spectrum, which is depleted in high-frequency content (Bostock et al., 2015; Katsumata & Kamaya, 2003; Q.-Y. Wang et al., 2023). In contrast to tremors, LFE recordings show P and S phase arrivals. Without the high frequencies (>10 Hz) of earthquakes, these phases are emergent, making them substantially more difficult to pinpoint than for regular earthquakes. In addition, LFEs are typically only observed at low signal-to-noise ratios (SNR), where often even visual identification is challenging. LFEs are highly repetitive, with the same source producing almost identical signals hundreds or even thousands of times, called an LFE family (Bostock et al., 2015; Frank et al., 2014; Shelly, 2017). LFE families cluster in space, that is, typically many LFE families occur within small areas. Similarly, LFEs within and across families cluster in time with events often occurring in bursts. This clustering is multi-scale, with very dense LFE bursts lasting tens of seconds to minutes, and then again several of such bursts occurring over a longer time frame (Frank et al., 2016; Shelly, 2010). Interevent times between LFEs can be shorter than their wave trains, on the order of at most seconds. It has been proposed that tectonic tremors are superposition of many closely colocated LFE sources in a short time frame (Shelly et al., 2007).

Both LFEs and tremors typically coincide with SSEs (Frank, Radiguet, et al., 2015; Ito et al., 2007; Michel et al., 2019). Matching LFE and tremor occurrence with geodetic records, it can be shown that they trace out areas of slow slip (Bartlow et al., 2011; Bletery & Nocquet, 2020; Frank et al., 2016; Itoh et al., 2022; Wech & Bartlow, 2014). This often leads to characteristic large scale migrations of LFE or tremor activity, with migration speeds of several kilometers per day (Bletery et al., 2017). LFEs and tremors occur not only during large SSEs, but also in between these events, hinting at intermittent aseismic deformation (Frank, 2016). It is unclear whether tremors and LFEs are triggered by aseismic deformation (Frank & Brodsky, 2019) or if tremors and LFE facilitate the migration of slow slip (Itoh et al., 2024). In addition, it has been shown that the scaling between tremors/LFEs and SSEs does not follow a purely linear relationship (Frank & Brodsky, 2019; Mouchon et al., 2023). Nonetheless, tremors and LFEs are a useful tool to illuminate SSE dynamics, such as slip extent or intermittence, in particular, at scales that are too short or small to be observed geodetically (Frank, 2016; Mouchon et al., 2023).

SSEs, tremors, or LFEs have been observed in diverse subduction zones, for example, in Nankai, Alaska, Cas-
cadia, Mexico, Costa Rica, Chile, Hikurangi and Taiwan (Behr & Bürgmann, 2021). However, in some zones,
such as Northern Chile, SSEs have been observed (Klein et al., 2018; Münchmeyer, Molina-Ormazabal, Radi-
guet, et al., 2025), but no record of accompanying tremors or LFEs exist. This raises the question whether SSEs
can occur in isolation without a tremor and LFE signature. Answering this question is essential for understanding
the mechanisms generating SSEs, tremors, and LFEs. Therefore, here we systematically search for tremors and
LFEs in the Atacama segment in Northern Chile, using dense deployments spanning 3.3 years. We search for both
tremors and LFE individually, taking into account the different challenges and opportunities. Tremors, with their
longer wavetrains, are easier to identify, yet difficult to precisely locate (Wech & Creager, 2008). In contrast,
LFEs with the short, low-SNR waveforms are hard to detect, but their specific phase arrivals allow precise
location and therefore detailed insights into interface structure and dynamics (Mouchon et al., 2023).

## 2. Data and Methods

### 2.1. Study Region and Data Availability

Our study region is the Atacama segment of the Northern Chile subduction zone between 24°S and 31°S (Figure 1). The region is known to host a wide range of slow and fast seismic phenomena. The Atacama segment
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Overview map of the region visualizing the tectonic setting, the past slow slip activity, and the seismic station coverage. Stations are colored according to their seismic network, using upward triangles for permanent stations and downward triangles for temporary stations. Within the Y6 network, stations deployed for 4 months in 2023 are indicated separately. A small number of additional stations of the C1, CX, GE and RI networks outside the visualized area have been used. Colored contours show past SSEs. For the 2014 SSE (orange) we provide the 5 cm slip contours (Klein et al., 2018), for the 2020 SSEs (green) the approximate slip outline (Klein et al., 2022), for the 2020 afterslip (red) the 2 cm slip contours (Molina-Ormazabal et al., 2025), and for the 2023 SSE (purple) the 1 cm slip contours (Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025). Black dots mark major mines (M. S. Baker & Buteyn, 2016).</figcaption></figure>

hosts one of the longest standing seismic gaps along the Chilean megathrust, called the 1922 gap, after the 1922 $M_w \approx 8.5$ earthquake (Kanamori et al., 2019; Vigny et al., 2024). The segment is bordered to the North by the 1995 Antofagasta earthquake ($M_w = 8.0$) and to the South by the 2015 Illapel earthquake ($M_w = 8.3$). Between these two past rupture zones, the subducting Copiapó ridge is thought to have repeatedly served as a boundary or nucleation region for major earthquakes (Ruiz & Madariaga, 2018). While earlier results suggested that the 1922 earthquake ruptured the whole segment between the Antofagasta and Illapel rupture areas, hence the name 1922 gap, recent research suggests that the ridge also served as a boundary for the 1922 earthquake (Vigny et al., 2024) and therefore acts as a persistent seismic barrier. This is consistent with the low degree of interseismic locking (<0.6) inferred from continuous GNSS records (González-Vidal et al., 2023; Métois et al., 2016; Yáñez-Cuadra et al., 2022). Münchmeyer, Molina-Ormazabal, et al. (2025) provide a detailed description of the seismicity in the area.

In addition to its role in the nucleation and arrest of megathrust ruptures, the region around the subducting Copiapó ridge is the only area in our study region known to host SSEs. Both shallow and deep SSE have been reported in the area. For shallow SSEs, only one certain detection exists, occurring in September 2023 (Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025). Another shallow SSE in 2006 has been proposed, yet geodetic station coverage is insufficient to fully verify this detection (Ojeda et al., 2023). The shallow SSE in 2023 had a magnitude $M_w = 6.6$ and lasted for approximately one month (Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025). This SSE was accompanied by migrating seismic swarm activity illuminating the rupture process. In 2020, a seismic sequence with cumulative moment $M_w = 7.1$ triggered afterslip propagating northwards outside
the rupture area for more than 50 km (Molina-Ormazabal et al., 2025). Similar to the 2023 SSE, this afterslip was accompanied by seismic warm. This suggest that seismic swarms mark transient slow slip, similar to observations in other regions (Nishikawa & Ide, 2018). As swarm activity has repeatedly been observed during the past 50 years, it has been proposed that the region hosts recurring shallow SSEs of different sizes (Comte et al., 2002; Marsan et al., 2023; Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025; Ojeda et al., 2023). Further downdip, deep SSEs with a recurrence period of ~5 years and a typical duration of more than 1 year have been observed (Klein et al., 2018, 2022). For the best-studied event in 2014, a magnitude of $M_w$ = 6.9 and a duration of more than a year have been reported (Klein et al., 2018). With moment rates around 3 * 10¹² Nm/s (shallow) and 6 * 10¹¹ Nm/s (deep), these SSEs are at the lower end of typical tectonic SSE moment rates (Ide & Beroza, 2023). There is one report of tectonic tremor in the region of the Copiapó ridge (Pastén-Araya et al., 2022). Yet these tremors only occur on a single day and do not coincide with an SSE. We revisit this report in the discussion in Section 4.2.

Here, we use a dense seismic network comprised of 193 seismic stations (triangles in Figure 1). We focus on the time range between the deployment of the first temporary stations in November 2020 and the removal of the last stations in February 2024. As not all networks have been running at the same time, the typical number of available stations per day is between 60 and 120 (Figure S1 in Supporting Information S1). The stations consist of a mix of broadband instruments, short-period seismometers, and geophones, with each instruments providing adequate coverage for the typical tremor band (1–10 Hz). A more detailed description of the seismic networks is provided in Münchmeyer, Molina-Ormazabal, et al. (2025), where the data set is used to build a dense catalog of regular earthquakes. As this study uses exclusively onshore instrumentation, we focus on deep tremor and LFEs. However, our methods are designed to be sufficiently general to detect shallow events as well, yet with lower sensitivity.

## 2.2. Reference Regions With Known Tremor/LFE Activity

To test the sensitivity of our methods in identifying LFEs and tremors from continuous waveforms, we apply our workflows to two reference regions with known tremor and LFE activity, Cascadia and Nankai. We focus on periods of intense tremor and LFE activity. Compared to the study region in Chile, we consider smaller spatial extents and time periods in the two reference regions. This is sufficient for the sensitivity test and substantially reduces computational demand. We analyze the differences in station density and seismic noise conditions of all three regions in the discussion.

The first reference region is Southern Vancouver Island, Cascadia, Canada. The region has long been known to host SSEs (Dragert et al., 2001), tectonic tremors (Wech & Creager, 2008), and LFEs (Bostock et al., 2015). For this region, we study the time period between 15/01/2021 and 15/02/2021, as during this period a tremor migration occurred underneath the study region. We use all stations from the Canadian seismic network between 48.0°N and 49.3°N and between 125.9°W and 122.7°W. In total, we use 13 seismic stations.

The second reference region is Nankai, Japan. Similar to Cascadia, there are extensive reports of SSEs, tectonic tremors, and LFEs (Kato & Nakagawa, 2020; Shelly et al., 2006; Takemura et al., 2023). We use data from HiNet, a dense network of borehole seismometers. Similar to Cascadia, we selected a time period with known tremor activity, in this case from 25/05/2012 to 15/06/2012. We focus on the region between 32.8°N and 34.5°N and between 132°E and 134°E, using 36 HiNet stations.

## 2.3. Tremor and LFE Detection Workflows

We developed workflows for tremor and LFE detection combining different classical and machine learning methods with manual inspection. Here, we provide a general overview of these workflows, while we provide all additional technical detail in Appendix A.

The basis for our tremor detection is the envelope correlation method of Wech (2021). For this method, we calculate the signal envelopes in the tremor band (1–8 Hz) at each station, a proxy for the seismic moment rate. We correlate the envelopes between stations in 5 min windows to identify spatially coherent signals. If the envelopes across multiple stations are well correlated, we estimate differential travel times from the cross-correlations and locate events using a grid search. Due to the size of the study region, we subdivide the region into multiple grid cells. From these initial detections, we remove all detection coinciding with cataloged
earthquakes (Münchmeyer, Molina-Ormazabal, et al., 2025), as these likely correspond to earthquake rather than tremor sources. As tremors occur in bursts, we use clustering with DBScan and only keep events in clusters, thereby removing spurious detections (Wech, 2021).

For LFE detection, we adapt a classical earthquake detection workflow consisting of phase picking, phase association, and location. We use the deep learning LFE phase picker from Münchmeyer et al. (2024). We associate the picks using PyOcto (Münchmeyer, 2024). We locate the events with NonLinLoc (Lomax et al., 2000) using the 3D velocity model from Münchmeyer, Molina-Ormazabal, et al. (2025). As the low SNR waveforms of LFEs makes the analysis of individual LFEs challenging, we use all detected LFE candidates as a basis for template matching. This way, we generate a family of similar detections for each candidate event. This allows us to analyze the group behavior of the family and the stacked waveform, which have a better SNR than individual detections. Similar to the tremor detection described above, we perform the template matching in multiple space-time cells. As it is likely that closely located candidate templates will detect similar families of events, we remove duplicates among the template results using the temporal behavior of their event families.

We manually inspect the remaining clusters to analyze three diagnostic features (Figure 2). First, we identify whether the occurrence times throughout the day suggest an anthropogenic source, for example, mining, or a non-anthropogenic source (Figures 2a and 2b). Second, we identify if the signal shows no clear phase arrivals, typical for a surface wave, one phase arrival, or two phase arrivals (Figures 2c–2e). Third, we classify if the recurrence pattern of the event family exhibits the typical temporal clustering of LFEs (Frank et al., 2016; Shelly, 2010), has a bimodal recurrence, or another type of random behavior such as the Poissonian-like interevent distribution of regular earthquakes (Figures 2f–2h). To this end, we analyze the distribution of time windows between consecutive events. We expect an LFE to be non-anthropogenic, show two phase arrivals, and have a typical LFE recurrence pattern. To ensure we do not miss LFE families, we err on the side of caution and consider a candidate event family to be LFE-like if in doubt. For example, we assign two phase arrivals if at least some station stacks show two clear arrivals.

## 3. Results

### 3.1. Tremor Candidates

To identify tremor candidates, we apply the envelope correlation to the data from our 3.3 years study period, identifying 430,000 tremor candidates in total. Note that many candidates will have been counted multiple times due to the overlapping search regions. Of these detections, 230,000 coincide with cataloged earthquakes, leaving 200,000 candidates (Figure 3a). These tremor candidates are distributed throughout the whole study region with an along-dip extent between 200 and 300 km. The distribution loosely mirrors the station coverage, that is, the along dip extent is wider in regions with wider station coverage.

The majority of detections occur isolated in space, time, or both. After applying DBScan and only retaining clusters with at least 5 members, only 12,816 tremor candidates remain in 1156 clusters. Of these clusters 147 have an average depth shallower than 10 km, suggestive of surface sources such as mining activity. Four-hundred forty seven clusters have an average depth deeper than 95 km, which is close to the lower boundary of the search region set at 100 km. These events are substantially deeper than expected for tremors and are likely caused by deep or teleseismic sources. We exclude both these very shallow and very deep clusters, leaving 562 clusters. We show selected waveform examples from these remaining clusters in Figure 4.

Of the 562 clusters, 44 occur offshore around 25.5°S within 3 weeks between 07/03/2023 and 27/03/2023. These detections correspond to the airgun shots of an active seismic experiment within the cruise SO297 of the RV Sonne (Lange et al., 2023). Depth and longitude estimates for these shots are highly unreliable, including detections down to more than 90 km depth, as we use an S wave velocity model, while the actual waveforms of airguns are dominated by P waves. The waveforms of these detection show a very regular 60 s periodicity (Figure 4a). Figure S2 in Supporting Information S1 shows that these detections trace out the track of the ship during the cruise. This highlights that our tremor detection approach has better resolution in latitude direction than in longitude direction, because the events are occurring offshore outside the network. We do not continuously detect the airgun shots, missing detections in particular when the ship is far offshore.

After removing the cruise-related clusters, we manually inspect the waveforms of all remaining clusters. All but 9 clusters show impulsive onset and are related to mining, regional, or teleseismic events. Mine blasts are
<figure><img src="image_5.png" /><figcaption>**Figure 2.** The three features used for LFE classification with typical examples. (a, b) Distribution of events over time of day, identifying anthropogenic and non-anthropogenic sources. (c–e) Wave type and number of identifiable phase arrivals. Note that for the actual classification, we are basing our assessment on all available stations and not a single station. The three colors show the three components with a vertical offset for better visibility. Waveforms are filtered between 1 and 8 Hz. (f–h) Recurrence time plots with log-scaled interevent times on the y axis. All examples in the figure are from candidate event families in Northern Chile. In (g), we highlight the two typical recurrence time bands.</figcaption></figure>

characterized by a strong surface wave content, likely explaining why they are missing from the reference earthquake catalog. They occur predominantly during daytime. The regional earthquakes often feature S minus P times in excess of 30 s and are therefore located outside the study area of the earthquake catalog used in our automated exclusion procedure of earthquakes. Apparent tremor detections are caused by both body wave coda and surface waves of these events. Many of these regional events originate from the highly active and deep Jujuy cluster around 24°S (Valenzuela-Malebrán et al., 2022). One cluster can be directly related to the arrival of the P wave from the 2023 Kahramanmaras earthquake (Figure 4d), possibly related to the source duration and directivity of that event (Jia et al., 2023). Of the remaining 9 clusters, we identified 4 clusters that correspond to non-blasting activities in the mines Las Cenizas, Las Luces, and Franke (Figure 4e). These clusters are characterized by locations close to the mines, regularly repeating impulses, and activation only during daytime.

Three clusters occur in close proximity to each other on 2021-12-11 between 2 and 3 p.m. (UTC). Upon close inspection, these clusters are caused by a mixture of three sources: mine blasting; the teleseismic P wave arrivals
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Tremor candidates in Northern Chile detected using envelope correlation. (a) Detected tremor candidates excluding detections matching cataloged earthquakes. Black triangles indicate seismic stations. (b) All tremor candidates contained in DBScan clusters with at least five members. (c) Same as (b) but without tremor candidates in shallow (<10 km) and deep (>95 km) clusters. We note that none of the candidates have been identified as an actual tectonic tremor. In all plots, we add a small scatter (Gaussian with standard deviation 0.03°) to the candidate locations to reduce overlap caused by the quantization of the grid search (4 km). We indicate the approximate locations of the incoming Copiapó (CR) and Taltal (TR) ridges.</figcaption></figure>

from a magnitude 5.1 earthquake in the South Sandwich Islands (OT 14:35:44 UTC), a magnitude 5.7 earthquake about 40 km north of La Serena (OT 14:54:19 UTC).

The remaining two clusters occur on 2023-06-23 between 21:20 and 21:50 (UTC) and on the subsequent day between 04:00 and 06:00 (UTC), both near the coast around 27.5°S. The first cluster consists of numerous detections within an extended signal of 20 minute duration. The signal is roughly cigar shaped with a slow increase in amplitude over 15 min and a subsequent decay over 5 min (Figure 5) with a peak amplitude of about $5 * 10^{-7}$ m/s. The signal is strongest between about 3 and 10 Hz, with stations near the source showing signal up to 20 Hz (Figure S3 in Supporting Information S1). At the closest station, MB19, additional even higher frequency burst are visible. At low-noise stations, the signal can clearly be identified even at a distance of 200 km.

For further inspection, we locate the signal using a modified envelope correlation, using a 20 min correlation window to leverage the full signal shape. The most likely event location is close to shore at a depth of 3 km, the shallowest depth considered in the envelope correlation (Figure S4 in Supporting Information S1). For the epicenter, the uncertainty ellipse trends South-Westward, away from the most dense part of the network. The events 7 hr later are similar in terms of shape and frequency content, but each individual signal is shorter, only about 200 s long (Figures S6–S8 in Supporting Information S1). The peak amplitudes are about 4 times lower. The most likely locations are close to the location of the preceding 20 min signal. The combination of similar signal characteristics and close temporal proximity suggests that both clusters are caused by the same source process.

Based on the observed characteristics, we suggest the signals are not caused by tectonic tremors. First, the clusters occur in isolation within the 3.3 years time period, which does not fit the typical recurrence of any known tremor in other regions. Second, the content of high frequency signals is rather strong for a tremor, in particular the signals above 20 Hz, given that, assuming an interface process, the closest station is still at least 40 km (the interface depth) from the source. Third, while the source depth has substantial uncertainty, it is most likely shallower than the plate interface that locates at almost 40 km depth in this region (Münchmeyer, Molina-Ormazabal, et al., 2025). While we are not able to uniquely identify a source process for the signal, we
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Vertical-component waveforms from signals identified as tremor candidates. All waveforms have been bandpass-filtered between 1 and 10 Hz, the typical tremor frequency band. Labels on the top right indicate the SEED identifier of the trace. Note that we classified these waveforms not solely based on the single station waveform, but incorporating data from further stations, wider frequency bands, and additional context information.</figcaption></figure>

suggest that it is occurring on or near the ocean floor. A potential candidate would be a turbidity current in the area below the outlet of the Copiapó river where a sediment fan has been mapped (Warwel et al., 2025). Similar seismic signatures have been observed for other turbidity current events, including the clustered occurrence, (M. L. Baker et al., 2024) and for mass wasting events on land (Clare et al., 2024; Cook et al., 2021).

After this manual inspection of the initially detected tremor candidates, no compelling candidates remain.

## 3.2. LFE Candidates

Using a deep learning pipeline developed to detect LFEs within continuous seismic waveforms (Münchmeyer et al., 2024), we find a total of 18,640 LFE candidates in the study area (Figure 6). On average, each event has 13.6
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Emergent 20 min signal on 2023-06-23, likely related to a marine or seafloor process. The inferred location is not compatible with plate boundary faulting like a tremor. Each panel shows the vertical component waveforms for a station between 26.5°S and 28.5°S for the time from 2023 to 06–23 21:22:00 UTC to 2023-06-23 21:47:00 UTC. Spectrograms for the same stations and time range are available in Figure S3 in Supporting Information S1. We removed the instrument response and bandpass filtered the waveforms between 1 and 8 Hz. All stations are scaled equally. Stations are ordered by their epicentral distance to the most likely source of the 20 min signal estimated with envelope correlation (Figure S4 in Supporting Information S1). A station map is provided in Figure S5 in Supporting Information S1.</figcaption></figure>

associated picks, with an almost equal split into P and S picks. The epicenters are distributed primarily along a band of about 100 km East-West extent starting slightly east of the coastline and ranging from 25°S to 29.5°S. Two shallow offshore clusters are detected around 25.5°S and 27.5°S. The majority of detections locates between the surface and 60 km depth. Detections at larger depths only occur in significant numbers in the northeastern portion of the study region. No clear structures are visible in the epicentral or hypocentral distributions, however,
<figure><img src="image_9.png" /><figcaption>**Figure 6.** LFE candidates in Chile detected using the deep learning pipeline (Münchmeyer et al., 2024). Each dot represents an individual LFE candidate colored according to the depth. There is a small number of detections at larger depths and outside the selected map region. In (a), we provide the 30 and 55 km depth contours as approximate depth range for tremors and LFEs in other regions (Münchmeyer, Molina-Ormazabal, et al., 2025). The dashed lines in (a) delineate the different cross-sections in (b–e). The dashed black lines in (b–e) indicates the slab model from Münchmeyer, Molina-Ormazabal, et al. (2025) at the center of the profile. We show the topography at the center of the profile. Topography is exaggerated by a factor of 4, bathymetry is not exaggerated. We indicate the approximate locations of the incoming Copiapó (CR) and Taltal (TR) ridges. We note that we identify none of the LFE candidates as a true detection.</figcaption></figure>

the detection method is known to produce high picking uncertainties and thereby large location uncertainties, particularly in depth (Münchmeyer et al., 2024).

Looking at the temporal evolution of the detection rate, we see that its first order behavior is determined by the number of available stations (Figure 7). The highest average number of detections occurs between August 2021 and March 2022, the time during which both the XZ and Y6 networks were deployed (Figure 1). Another time span of high detection rates occurs in early 2023, the time when two dense networks of geophones were deployed to record offshore active seismic experiments. Detection rates during this time are potentially further increased by false detections related to the airgun shots. In comparison to periods of intense tremor activity in regions where LFEs and tremor regularly occur, the detection rates we find in northern Chile are low (Münchmeyer et al., 2024). In addition, the dynamic range, that is, the ratio between days with low activity and days with high activity with similar network coverage, is lower than in the regions examined by Münchmeyer et al. (2024), suggesting a high base rate of false detections.

We observe a clear spike in event counts on 12 August 2021 that slowly decreases over the next days to weeks (Figure 7). The increased activity occurs throughout the study region and is not localized. Through waveform inspections and comparison to global seismic catalogs, we identified this spike as related to teleseismic activity around the South Sandwich Islands. Beginning with the 12 August 2021 $M_w = 7.5/8.1$ earthquake doublet (Jia et al., 2022), a large number of detections occurs within P waves of earthquakes beneath the South Sandwich Islands (Figure 7b). The decay in apparent LFE activity following the mainshocks correlates well with the decay
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Temporal behavior of the LFE candidates detected in Chile. (a) In black, daily LFE count throughout the study period. In orange, daily number of active seismic stations. (b) Zoom-in of panel a around the South Sandwich EQ sequence. (c) Hourly LFE candidate count by time of day.</figcaption></figure>

in aftershock activity. We identified aftershocks of the South Sandwich sequence down to $M_w \approx 5.0$. Upon further manual inspection, we identified further detections related to other teleseismic P arrivals, for example, from events offshore Japan. Nonetheless, these detections alone do not account for all detections.

Figure 7c breaks down the activity by time of day to identify potential anthropogenic sources of the detections. There is a general trend for higher detections at night, pointing at higher detection capabilities due to lower nighttime noise. However, there is a clear spike in activity in the daytime between 1 and 4 p.m. local time. This spike in activity in the early afternoon is consistent with the typical blasting schedule of open pit mines in the area (Münchmeyer, Molina-Ormazabal, et al., 2025), suggesting that some detections can be attributed to mine blasting.

To further investigate the detected candidates, we perform template matching and manual classification. After duplicate removal, we inspect a total of 581 potential LFE families (Figure 8). Figure 9 shows typical event classes detected. A large number of families can be related to anthropogenic sources (Figure 9 first and second columns). Many of these are characterized by surface type waveforms. Furthermore, most families show a bimodal recurrence pattern, that is, they have two characteristic return periods. This pattern is common for heavy machinery that is operated temporarily, in particular, in the open pit mines. This differs from the burst-like recurrence of typical LFE families as these typically show intermediate return times as well. Only few families show burst-like recurrence, including a number of families around the Copiapó ridge. Looking at the number of phase arrivals, the most common case is the complete absence of clearly identifiable arrivals. This is consistent with the high number of surface wave type examples. In particular in the South, we detect a substantial number of events with a single arrival. For many of these detections, the amplitudes are largest on the vertical component, suggesting that these families might be related to teleseismic P wave arrivals (Figure 9 third column). For some of these families, we identified the source region of the teleseismic waves, with a particularly high occurrence of waves from the South Sandwich islands region. Only a handful of all families feature two clear phase arrivals. No
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Summary of the classification results of LFE candidate families after template matching. Each panel visualizes one feature. Each dot marks one template, that is, one LFE candidate from the initial deep learning detections. The colors indicate whether the template fits the criterion not at all (blue) or fully (yellow). In-between values are possible, because a template might have been classified differently between the different grid cells and time blocks that it belongs to. Gray dots have been added for templates that have not been manually inspected because they did not match with any cluster or no member of the cluster had a sufficient number of detections. We indicate the approximate locations of the incoming Copiapó (CR) and Taltal (TR) ridges.</figcaption></figure>

family jointly fulfills all criteria we set for an LFE detection, that is, non-anthropogenic, two phase arrivals, and burst-like recurrence.

While no family can be clearly identified as an LFE, we acknowledge that the distinction between burstlike activity and bimodal activity is subjective. Therefore, to further rule out LFE detections, we manually pick P and S phase arrivals for all events with two arrivals and locate these events. Of the located families, the majority falls into the Los Colorados mine (28.3°S, 70.8°W) or the Candelaria mine (27.5°S, 70.3°W). Two families locate around 27.48°S 70.97°W at 14 km depth about 7 km offshore (Figure 9 last column). One family locates around 26.94°S 70.86°W at only 0.1 km depth about 6 km offshore. These events locate significantly shallower than the interface, even taking the depth uncertainties (8–13 km) into account, and are shallower than the shallowest regular seismicity in these regions (Münchmeyer, Molina-Ormazabal, et al., 2025). Given the location
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Waveforms (top), recurrence plots (center) and time of day histograms (bottom) of potential LFE families. Each column represents one family, with a label on top. The waveform plots show ZNE components (top to bottom). Note that we only show a selected station per family, while we used multiple stations for classification. The recurrence plots show the interevent time between two consecutive detections. The time-of-day histograms have been normed for visibility, while event counts differ between the examples. The teleseismic P-arrival family corresponds to earthquakes in the South-Sandwich islands, with the aftershock sequence of the $M_w = 8.1$ earthquake on 12 August 2021 clearly visible in the recurrence plot (middle).</figcaption></figure>

uncertainties, the events might be located on the shoreline and at the surface/seafloor, consistent with the strong surface wave content. All three families show higher event rates during the daytime (early morning to early afternoon), suggesting anthropogenic origins of the signals, potentially related to fishery or other boating. Regardless of their exact origin, these signals and their locations are not consistent with LFE sources. The location analysis therefore confirms that none of the candidate detections have typical LFE characteristics.

# 4. Discussion

## 4.1. Comparing Regional Detection Sensitivity

Several factors determine the detection sensitivity of LFE and tremor detection workflows. In the following, we compare the two major ones, network coverage and SNR, between our study regions in Chile and two reference regions, Cascadia and Nankai. We applied our workflows to these reference regions where they successfully recovered tremors and LFEs (Text S1 in Supporting Information S1). We note that we design our workflows to be independent of the amount of data or only have expected improvements in the sensitivity with longer duration, that is, we ensure that the sensitivity due to the changes in study area and duration do not negatively impact the comparison. For tremor detection, the methods is independent of the study duration, with the longest range dependencies coming from the clustering on a scale of hours. For the LFE detections, only the template matching is
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Slab geometries (Hayes et al., 2018), station coverage, and tremor detections in Cascadia, Nankai and Northern Chile. We indicate stations with white triangles. The median distance to the fifth closest station is 60 km in Cascadia, 37 km in Japan, and 27 km in Chile. For Cascadia and Nankai, we show tremors detected in this study using black dots. As in Figure S17 in Supporting Information S1, we add a small scatter to the individual tremor locations. Black dashed lines show the 30 and 55 km isodepth lines, a wide estimate of the typical interface depth of tremor-genic areas.</figcaption></figure>

dependent on the amount of data. As longer study durations will lead to potentially more detections and better waveform stacks, no degradation from longer durations is expected.

Comparing the station densities, we observe the highest density in Chile, followed by Nankai and then Cascadia (Figure 10). As a quantitative measure, we estimate the median distance to the fifth closest station for each station in the network, given we require at least five stations for the envelope correlation. This distance is 60 km in Cascadia and 37 km in Japan. In Chile, the distance varies with station coverage (Figure S1 in Supporting Information S1): 49 km (ANILLO phase I), 43 km (ANILLO phase II), 37 km (ANILLO + DEEPtrigger), 64 km (DEEPtrigger), 46 km (DEEPtrigger + IMO), 34 km (DEEPtrigger + IMO + SO297). With only the DEEPtrigger network (03/2022–12/2022), station coverage is unbalanced, with good coverage in the center and south of our study area, including the Copiapó ridge, and low coverage around the Taltal ridge in the North. This will likely lead to decreased detection performance north of 26.5°S during this time period. In conclusion, during most of the time the station density is between the ones in Cascadia and Japan, with an overall denser coverage of the Copiapó ridge, where the history of slow slip events makes tremors and LFEs particularly likely.

The signal amplitude at a station is determined by the combination of source effects and path effects, with the latter dominated by geometric spreading and attenuation along the source-receiver path. A first order proxy for the effects of spreading and attenuation is the distance between stations and source regions, which depends on station coverage and slab geometry. In both Cascadia and Nankai, deep tremors occur within a band between roughly 30 and 55 km interface depth (Figure 10). The tremors in Nankai occur on average slightly further downdip than in Cascadia. Given the deeper along-dip extent of interplate seismicity in Chile than in the other regions, and the comparatively large depth of the deep SSEs (~60 km), it should be assumed that potential deep tremors would occur at greater depth (potentially up to ~65 km) in Chile. Dense station coverage extends down to slab depth above 90 km, covering the full zone of the reported deep SSEs (Klein et al., 2022) and the transition zone from brittle to ductile deformation as imaged by regular seismicity (Münchmeyer, Molina-Ormazabal, et al., 2025). While the greater hypocentral depth will increase the event-station distance, this depth difference between the
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Comparison of the background noise across the three regions. At low frequencies, the noise levels are similar in Chile and Nankai and higher in Cascadia. At higher frequencies, the noise is lowest in Nankai and at a similar, higher level for Cascadia and Chile. For each stations, we calculate the root mean square (RMS) of the signal in four distinct frequency bands in non-overlapping 60 s windows. We then select the 90th percentile among all time windows to get an estimate of the high noise condition at each station while not being affected by short transients. The histograms show the distributions of these percentile values among the different stations. We normalize the histograms using the station count for better visibility.</figcaption></figure>

regions is smaller than the interstation spacing. Therefore, hypocentral distances will be more strongly controlled by the epicentral distances and less by the potential difference in hypocentral depth, suggesting only mildly increased attenuation in Chile compared to the reference regions. Furthermore, attenuation in Northern Chile depends generally more strongly on the epicentral distance than on the event depth (Münchmeyer et al., 2020, e.g., Figure 9). As the 30 km depth contour of the plate interface coincides with the coastline, shallower tremors might be difficult to detect, instead requiring offshore instrumentation.

As the denominator of the SNR, we evaluate the background noise in the typical tremor band (Figure 11). At frequencies between 0.5 and 1.0 Hz, Chile and Nankai show similar noise distributions, while the noise in Cascadia has roughly double the root mean squared (RMS). At higher frequencies, the noise in Nankai is systematically lower than in Chile, with a median factor of 2.4 at frequencies between 4 and 8 Hz. The lower noise level in Nankai is likely due to the borehole installations of the HiNet stations. As large parts of the ambient noise field are composed of surface waves, the borehole stations are particularly useful at higher frequencies. At higher frequencies, the noise levels in Chile approach the ones in Cascadia, with similar median noise levels between 4 and 8 Hz. Due to the very different environmental conditions and seismic background rates, we suspect that the origins of these noise fields are different. Nonetheless, they should have comparable impact on the detection sensitivity.

In addition to SNR and station coverage, the actual shape of the waveform will be relevant for tremor and LFE detection. The envelope correlation method employed for tremor search depends on the low-frequency coherence of the envelope across stations. In our study, we low-pass filter our envelopes at 0.2 Hz, that is, we only consider slow variations in the envelope. For such variations to become incoherent across stations, a complex subsurface structure or strong site effects are required. There is no evidence for either of the two in Chile, as shown by tomography results (Pastén-Araya et al., 2022) and by low station residuals in magnitude scale calibration (Münchmeyer, Molina-Ormazabal, et al., 2025). We thus assume that envelope coherence across stations does not differ substantially between the studied regions. The impact of waveform shape on LFE detection is substantially more difficult to analyze, due to the low interpretability of the underlying deep learning model. However, two indicators suggest that the deep learning model is transferable across regions. First, transferability for the LFE detection model was explicitly tested for four regions, including our two reference regions (Münchmeyer et al., 2024). In particular, these results show that the model can correctly detect LFEs in regions without any examples from these regions required in training. Second, phase pickers for earthquakes are transferable across regions, even though in-region performance is slightly superior to cross-region performance (Münchmeyer et al., 2022). We therefore suggest that in general the model should be able to identify LFEs in Chile, if the SNR conditions are similar.

Putting together the factors above, we provide a rough estimates for the size of tremors and LFEs that could exist and that might be detectable with our methods in Chile. In comparison to Cascadia, we expect to be able to detect
tremors of the same magnitude, or even slightly smaller. This is due to the superior station density and slightly more favorable noise conditions in Chile. In comparison to Nankai, we should be able to detect tremors with at least about twice the velocity amplitude. Under similar spectral characteristics, this would roughly correspond to twice the moment rate. We estimate this factor from the difference in noise levels in the least favorable band, that is, 4–8 Hz. Typical moment rates for tremors, LFEs, and SSEs, are on the range of 10¹¹ to 10¹³ Nm/s (Aguiar et al., 2009; Ide et al., 2008). Note that such moment rates for SSEs are geodetic long-term averages, which can be exceeded by several orders of magnitude on short time scales (Frank & Brodsky, 2019). Consistent with our findings on sensitivity, typical observed moment rates are higher in Cascadia than in Nankai (Ide & Beroza, 2023). This suggests that we should be able to detect tremors with moment rates in this range.

## 4.2. The 20th September 2019 Tremor Reports

While no large-scale tremor catalog exists for the region, Pastén-Araya et al. (2022) report tremor detections around the subducted Copiapó ridge. Analyzing 8 months of data, the authors identified nine tremor occurrences, all on 20 September 2019 between 00:39 and 03:52 (UTC) before the beginning of our study period. For detecting the tremors, the authors used a very similar envelope correlation method to the one presented here, however, with slightly different preprocessing steps and a 1D velocity model. In addition, all detections have been manually inspected to exclude examples with clear P and S arrivals. Pastén-Araya et al. (2022) infer tremor locations toward the downdip end of the interface seismicity band or within the transition zone. The most likely depth estimates are slightly above the plate interface, even though there is substantial uncertainty.

Here, we reprocess the data from the study of Pastén-Araya et al. (2022) for 20 September 2019 between 00:00 and 04:00 (UTC). As before and as in Pastén-Araya et al. (2022), we use the envelope correlation method by Wech (2021), but now with the preprocessing described in Pastén-Araya et al. (2022). We note that small differences might remain due to the exact timing of waveform windows and aspects of the preprocessing not described in the original publication. For higher location accuracy we use the 3D velocity model from Müchmeyer, Molina-Ormazabal, et al. (2025). With this processing, we were able to reproduce six of the detections from Pastén-Araya et al. (2022). We visualize their waveform envelopes, locations and location uncertainties in Figures S10–S15 in Supporting Information S1. Out of the six detections, four have their most likely depth at 103 km, the deepest depth considered in our search. This suggests that they are caused by signals with near-vertical incidence from below the study region, such as teleseismic or longer-range regional sources. The remaining two examples locate at 59 km depth, with the most likely depth range between 40 and 80 km, and at 43 km but with an uncertainty ellipse including almost the complete search range. The epicenters show a wide scatter both onshore and offshore.

Given the characteristics of the reported signals and our reprocessing, it seems uncertain if the detections can be attributed to tectonic tremors. First, our reprocessed locations are not compatible with a source on the plate interface. The four deep locations suggests signals from outside the network or deeper below the network. The remaining two detections are both still deeper than the plate interface, which is only included in the uncertainty ellipse of the event with the poorest depth constraint. Second, that tremors would only occur within a 4 hr window of the full 8 months study period seems unlikely. Typical tremor sequences in other regions consist of tremors over days to weeks (Behr & Bürgmann, 2021). In addition, our study did not produce further detections in this region with a similarly dense and substantially wider seismic network spanning 3.3 years. Based on this information, we consider it unlikely that the detections from Pastén-Araya et al. (2022) are tectonic tremors, even though we are not able to identify the origin of these events. While, for example, teleseismic sources might cause the signals, it remains unclear why these detections should only occur on a single day either.

## 4.3. Limitations of Our Detection Methods

Our study has not found any evidence of either tectonic tremors or LFEs in the Atacama segment. However, this conclusion relies on several assumptions about the nature of LFEs and tremors that are important to revisit to explore the limitations of our study. The first assumption is that the tremors can be detected with the envelope correlation method. For this, the tremors need to be substantially above the noise in the frequency band from 1 to 8 Hz, and have a duration that can reasonably dominate a 300 s correlation window. In addition, the tremor source process needs to be sufficiently non-smooth in time to provide robust differential times that can be identified through cross-correlation. Second, to pass our clustering criteria, the tremors need to occur in bursts, because we
would miss isolated tremors by design given that we filter detections with DBScan (Wech, 2021). We note that our clustering criteria are loose. We require 5 detections in a cluster, yet each potential tremor source is in two adjacent search cells and correlation windows overlap by 50%. In practice, a single tremor with a few minute duration could already fulfill these criteria. Third, we assume that tectonic tremors occur on the plate interface or in its immediate vicinity. Of these assumptions, probably the most questionable point is a sufficient SNR, combining signal moment rate, noise conditions, and station coverage, as discussed in Section 4.1. It is therefore possible, that a study with, for example, borehole sensors or array processing on a more dense network, might be able detect tremors if they exist.

Similar to the tremor analysis, potential missed LFE detections might originate from both the initial detection step and the subsequent reprocessing. As discussed previously, it is likely that the deep learning model would detect LFEs in Chile, yet hard to validate with certainty. For the template matching of LFE candidates, we assume that LFEs are repeating, with a requirement of at least 100 detections of the same family. This bar is low, compared to the observed repeat rates in other regions, but we can not rule out the existence of LFEs with lower repeat rate (Bostock et al., 2015; Frank et al., 2014; Kato & Nakagawa, 2020; Shelly, 2017). Low SNR might lead to both missed initial detections and apparent low repeat rates. However, it is worth pointing out that at the signal-to-noise conditions in the reference regions, the templates were of sufficient quality.

### 4.4. Implications of the Missing Tremor and LFE Detections

We propose four potential explanations for the lack of tremor and LFE detection in our search: (i) tremors/LFEs have smaller moment rates than in other regions; (ii) the region hosts tremors/LFEs but none happened during our study period; (iii) the region does not host any tremors/LFEs; (iv) tremors/LFEs have different characteristics in this region than in others.

For option (i), it is possible that we are not able to detect LFEs and tremors because they would be hidden below the noise. Frank and Brodsky (2019) show that LFE moment rate scales with the rate of geodetic slip and demonstrate its variations reflect the intermittence of slow deformation (Jolivet & Frank, 2020). Similarly, Ide and Beroza (2023) suggest that SSEs, tremors and SSEs, while at different scales, follow a consistent scaling with a region-dependent upper bound on their moment rate. As we showed in our analysis of the station geometries and noise conditions, we expect to see deep tremors with moment rates above $10^{12}$ Nm/s to $10^{13}$ Nm/s. Yet the SSEs in the region only have a moment rate between $6 * 10^{11}$ Nm/s (2014 deep SSE (Klein et al., 2018)) and $3 * 10^{12}$ Nm (2023 shallow SSE, Münchmeyer, Molina-Ormazabal, Radiguet, et al. (2025)), close to the lower boundary of our detection capabilities. While these are only two positive observations, it is noteworthy that continuous GNSS observations have not identified SSEs with higher moment rate, suggesting that such events are, at least, rare. In addition, it is unclear to which degree the slip during the observed SSEs is continuous or intermittent. It is therefore possible, that tremors and LFEs might have moment rates below our detectability threshold. For shallow tremors, this scenario is more likely, as no offshore instrumentation was employed, lowering the detection sensitivity offshore.

Option (ii), the possibility that LFEs and tremors occur in Chile but not during the 3.3 years time period analyzed here, is supported by the timing of deep SSEs in the catalog. Deep SSEs have been observed in 2014 and 2020, with further possible detections in 2005 and 2009 (Klein et al., 2022). Our 3.3 years study period falls into the gap between 2020 and the next expected recurrence around 2025. In addition, in the northern part of the study area there is a temporary gap with lower station density during this 3.3 years study period (Figure S1 in Supporting Information S1). However, a multi-year quiescence of tremors and LFEs in a region generally hosting such events would be unique among the subduction zones with known tremor activity. Well studied regions, like Nankai, Guerrero, or Cascadia, show numerous tremor and LFE detections in between the larger burst associated with detected SSEs (Frank, 2016; Kato & Nakagawa, 2020; Michel et al., 2019). On the other hand, modeling studies suggest that complete tremor/LFE quiescence between SSEs is possible with arbitrarily long temporal gaps between successive tremor/LFE periods (B. Wang & Barbot, 2023).

This leads to option (iii), that the Atacama region is not hosting LFEs and tremors at all. While most regions hosting SSEs also have records of tremors or LFEs, some examples, do not have a comprehensive record of tremors or LFEs associated with them (Montgomery-Brown & Syracuse, 2015; Mouchon et al., 2023). Several
possible causes have been proposed, including fluid availability, thermal properties, pressure, and temperature (Montgomery-Brown & Syracuse, 2015).

LFE and tremor occurrences have been linked to fluid rich environments detected through geophysical imaging (Behr & Bürgmann, 2021; Delph et al., 2018; Kato et al., 2010). In Northern Chile, one main source of fluids, sediments entering into the interface at the trench, is very weak, with sediment thickness of only about 200 m at the trench (van Keken et al., 2011). Nonetheless, other indicators highlight that fluids are present all over the Northern Chilean subduction. The region hosts recurrent swarm activity, typically linked to fluid migrations (Holtkamp et al., 2011; Marsan et al., 2023; Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025; Ojeda et al., 2023), shallow SSEs, linked to reduced normal stress from fluids (Bangs et al., 2023; Danré et al., 2022; Farge et al., 2021; Frank, Shapiro, et al., 2015; Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025; Warren-Smith et al., 2019), strong intraplate activity in the subducting plate, linked to dehydration in the blueschist to eclogite transition, and mantle wedge seismicity, caused by fluid intrusion into the cold mantle wedge (Münchmeyer, Molina-Ormazabal, et al., 2025). These fluids can enter the subducting plates due to fractures from the numerous outer rise earthquakes observed in the region (Münchmeyer, Molina-Ormazabal, et al., 2025). Additionally, in the Copiapó ridge, seamounts enter the subduction, which have repeatedly been linked to fluid incursions through both sediment transport and plate fractures (Bangs et al., 2023; Chesley et al., 2021; Gase et al., 2023; Shaddox & Schwartz, 2019; Sun et al., 2020). Seismic imaging studies found elevated vp/vs ratios in the potentially tremorgenic zones around the Copiapó ridge, suggestive of fluids on the interface (Pastén-Araya et al., 2022). However, even without fluids it is possible to model SSEs through geo-metric complexities (Romanet et al., 2018) or thermally activated friction (B. Wang & Barbot, 2023).

The role of the temperature of a subduction zone for SSE, tremor, and LFE occurrence is debated (Montgomery-Brown & Syracuse, 2015). Most tremor detections stem from hot subduction zones, such as Nankai or Cascadia. It has been proposed that hot subduction favors tremorgenesis as larger amounts of fluids are released in meta-morphic reactions at shallower depth than in cold subduction zones (Behr & Bürgmann, 2021; Condit et al., 2020). Abers et al. (2017) relate the thermal state of a subduction zone to the hydration of the mantle wedge, suggesting that hotter subduction zones have a higher abundance of fluids. As tremors and LFEs have been linked to reduced normal stress due to fluid pressure, this could explain why these regions might have favorable conditions for tremors and LFEs. However, some cold subduction zones host tremor and LFEs, for example, Hikurangi (Aden-Antoniów et al., 2024). Therefore, it is unclear what influence the temperature conditions in northern Chile might have on the observed absence of recurring LFEs and tremors.

While aseismic slip rates might lead to tremors/LFEs with moment rates below our detection sensitivity, as discussed above, they might also cause a complete absence of tremors and LFEs. The link between tremor/LFE signals and SSEs is complex, with tremors appearing in parallel to aseismic deformation (Michel et al., 2019), tremors fronts following aseismic deformation (Bletery et al., 2017), and temporary quiescence of tremors during aseismic deformation (Wech & Bartlow, 2014). Several studies proposed that too low (Frank & Brodsky, 2019; Frank et al., 2014; Wech & Bartlow, 2014) or too high (Montgomery-Brown & Syracuse, 2015) slip rates might lead to a lack of tremors and LFEs. Both the deep and shallow SSEs around the Copiapó ridge have slip rates around $10^{-8}$ m/s (Klein et al., 2018; Münchmeyer, Molina-Ormazabal, Radiguet, et al., 2025), a typical value for tremorgenic SSEs Montgomery-Brown and Syracuse (2015). However, this geodetic estimate is a long-term average, not resolving potential intermittence of slip rates that might be essential for tremor- and LFE-genesis (Frank & Brodsky, 2019).

Looking at the occurrence of LFEs and tremors, it is worth not only considering macroscopic features but also looking at the microstructure. Fine-scale tremor and LFE maps show that the events do not occur on the full interface, but rather in distinct and long-term stable patches with diameters of only few kilometers (Armbruster et al., 2014; Shelly et al., 2009). This suggests structural heterogeneities on the interface, with only certain patches fulfilling the structural requirements of tremorgenesis. Mouchon et al. (2023) linked this segmentation to the non-linearity in scaling between SSE and LFE activity in Guerrero, Mexico, showing that parts of the interface accommodating slow slip might not be able to accommodate LFEs. While the exact structural properties of these patches are not fully understood (Behr & Bürgmann, 2021), the observed segmentation suggests that it is possible that a subduction zone does not host the structures required for LFEs and tremors at all. This would suggest that the northern Chilean subduction zone could host SSEs that are not accompanied by tremor or LFEs.
Lastly, option (iv), the possibilities that LFE/tremor have fundamentally different characteristics in Northern Chile, is essentially a wild card. As discussed in the previous section, our detection algorithms rely on assumptions about the repeat rate, clustering, spectra and signal shapes of the LFEs/tremors. While our comparison study shows that these assumptions are valid and justified in reference regions, such as Nankai and Cascadia, and consistent with the majority of models proposed for tremors and LFEs, it is possible that they are not valid in Northern Chile. We address this problem by applying two complementary methods targeting both tremors and LFEs. Nonetheless, we are unable to rule out the existence of tremors or LFEs with substantially different characteristics in Northern Chile.

## 5. Conclusion

In this study, we performed a systematic search for tectonic tremors and LFEs in the Atacama segment (24°S to 31°S) in Northern Chile. We search for both tremors and LFEs with complementary methods to account for the different characteristics of these two signal classes. Despite the documented SSE cases, we did not detect any compelling evidence for tremor or LFE activity in the region. While for a small number of supposed detections of tremors and LFEs we are not able to identify the source with certainty, their characteristics make a tectonic source on the interface unlikely. In contrast, the identical workflows applied to Cascadia and Nankai successfully recover both LFE and tremor activity, together with their characteristic migration patterns. We note that due to our network geometry, located exclusively onshore, our results focus on deep tremor and LFEs, while shallow events might be missed due to their higher distance to the network.

While our results can not fully rule out tremors or LFEs in the region, they provide bounds on their possible moment rates and recurrence times. These results are an important constraint for modeling studies, for example, studying the impact of temperature and fluid availability on the existence of tremor and LFEs. In addition, they serve as a basis for developing and testing scaling relationships for SSEs, tremors, and LFEs. Lastly, our results provide guidance for which instrumentation and methods might be required to identify potential lower magnitude tremors in Chile and beyond.

## Appendix A: Tremor and LFE Detection Details

### A1. Tremor Detection

We detect and locate tremor candidates using the envelope correlation method of Wech (2021). The method correlates envelopes of vertical seismograms between multiple stations. These envelopes are a proxy for the energy rates recorded at each station over time. For a tremor, we expect a similar development of the energy rate with a moveout consistent with seismic wave propagation. The method by Wech (2021) identifies the number of station pairs with correlation values above a threshold (here 0.7). For each of these pairs, a differential travel time is identified by maximizing the correlation value. If at least 5 station pairs exceed the correlation threshold, a grid search is used to locate the source of the coherent signal. For the grid search, the method assumes an S wave moveout speed.

To apply the envelope correlation method to tremor detection, we follow the preprocessing of Wech (2021). First, we bandpass filter the data between 1 and 8 Hz. Second, we calculate the signal envelope, lowpass filter it at 0.2 Hz and downsample the envelope to 2 Hz. For grid search, we use the 3D velocity model from Münchmeyer, Molina-Ormazabal, et al. (2025) for Chile and 1D velocity models for Cascadia and Nankai. In all cases, we use S-wave velocities. In all regions we use a grid spacing of 4 km in all axes and a search depth up to 100 km, well below the typical depth range of tectonic tremors. We apply the tremor search to 5 min windows with an overlap of 2.5 min between consecutive windows.

Due to the size of the study area in Chile, we split the region by latitude into bands of 1° width. We overlap the bands by 0.5°, implying that each potential source is contained in two bands. We process each band individually, using all stations within the band. Due to the generous overlap between neighboring bands, this should not lead to a notable reduction in detection sensitivity. Similar subdivisions are not necessary for the Cascadia or Nankai regions due to their smaller size.

While we aim to identify tremors, the envelope correlation method will detect any signal that leads to coherent envelopes across multiple stations. In a seismically active region like Northern Chile, the method will identify
earthquakes of sufficient size. To remove earthquake detections, we compare the tremor candidates to a dense earthquake catalog (Münchmeyer, Molina-Ormazabal, et al., 2025). We remove all detections that have an earthquake at an epicentral distance less than 100 km within the detection window or within the minute preceding it. We use this large distance threshold to account for the low location precision of the envelope correlation, in particular, at the boundary of the study region. Nonetheless, even with this loose criterion it is statistically unlikely to exclude a detection based on an unrelated earthquake, that is, we do not expect to introduce substantial amounts of false negatives.

To further reduce the number of false detections, we cluster the detections using DBScan, keeping only events occurring within clusters Wech (2021). This method uses the burst-like recurrence of tremors We cluster the events based on their epicenter and the origin time, equating 1 hr to 20 km. We do not use the depth for clustering as it is not well-constrained. We use DBScan with an epsilon of 20 km and a minimum number of 5 samples for a cluster. We only retain detections within the clusters and discard isolated detections. For Chile, we perform clustering on the concatenated detections from all subregions. As this might lead to duplicate detections, the clustering parameters are effectively less strict in Chile than in the other regions.

## A2. LFE Detection

Our LFE detection workflow builds upon the deep learning LFE detector from Münchmeyer et al. (2024). The picker identifies P and S arrivals of LFEs at regional distances using a convolutional neural network architecture. The model has been trained on combined data from Cascadia (Canada/USA), Nankai (Japan), Guerrero (Mexico), and the San Andreas fault (USA). Münchmeyer et al. (2024) have shown that the model is able to discover LFEs both inside the training regions and when applied to regions not included in training. This suggests that the model might also be able to identify potential LFE signals in Northern Chile.

Similar to Münchmeyer et al. (2024), we integrate the picker into a detection workflow modeled after traditional earthquake detection. First, we process the continuous data with the deep learning model to obtain potential P and S picks using SeisBench (Woollam et al., 2022). We use a picking probability threshold of 0.1/0.15/0.15 (Cascadia/Chile/Nankai) for both P and S waves and an overlap of 50% of the input window between subsequent applications of the model. The different thresholds are motivated by the experience from the original model development (Münchmeyer et al., 2024). Second, we associate the picks using the PyOcto associator (Münchmeyer, 2024). We require at least 13/10/12 total phase picks per event and 2/3/4 stations with both P and S pick. We use region-dependent 1D velocity models for association (Bostock et al. (2015) for Cascadia, Münchmeyer, Molina-Ormazabal, et al. (2025) for Chile, JMA2001 for Japan). Third, we locate the LFE candidates using NonLinLoc (Lomax et al., 2000). For location, we use the 1D velocity models for Cascadia and Nankai and the 3D velocity model from Münchmeyer, Molina-Ormazabal, et al. (2025) for Chile. We remove events with onset time uncertainties above 0.85/0.75/1.0 s.

Individual LFE waveforms are usually difficult to analyze visually due to their low SNR. In particular, it can be difficult to decide whether an individual detection constitutes an LFE or not. To alleviate this problem, we exploit the repetitiveness of LFE sources (Frank et al., 2014; Shelly, 2017). To this end, we perform template matching on our detections using the fast matched filter code (Beaucé et al., 2017). Due to the size of our study region, a template matching on all cells jointly would not be effective, as only the stations closest to the LFE source are expected to record its signals. Instead, we subdivide the region into 19 overlapping cells of 1° by 1° (Figure S16 in Supporting Information S1). To account for changing station coverage we furthermore split our study into 7 time blocks (splitting on 2020-12-01, 2021-03-15, 2021-08-01, 2022-03-01, 2022-12-15, 2023-02-15, 2023-06-15, 2024-03-01). See Figure 7 for the stations counts per period.

For each space-time cell, we create a set of templates. First, we identify the most promising set of stations by identifying the stations with the highest number of phase arrivals for events within this cell. Note that the stations may locate outside the cell, which occurs in particular for offshore cells. We select up to 10 stations with the highest phase count, excluding stations with less than 10 phase arrivals in total. For each event within the spatial cell (even if outside the time range of the cell), we export a template. Each template contains three component waveforms from the selected stations, bandpass-filtered between 1 and 8 Hz and resampled to 20 Hz. The templates start 3 s before the S arrival and end 5 s after. We use the picked S arrivals and supplement them with predicted travel times in case of missing picks. We exclude events that are lacking waveforms at more than 2
stations. We perform the same template export for Cascadia and Nankai, however, due to the size and duration of the reference analysis, we only use a single time block and one spatial cell (Cascadia)/two spatial cells (Nankai).

We perform template matching individually for each cell. We match all events in the cell against continuous waveforms for the duration of the cell. We normalize the correlation curve with the median absolute deviation (MAD) within 4 hr windows. All correlation values exceeding 8 * MAD are treated as detections, with a minimum separation of 5 s between two detections of the same template. For each cell, we obtain a list of detections with the matching template, the time, and the normalized correlation value. Furthermore, for each template we calculate a waveform stack by normalizing and summing up all its detections.

The output of the template matching allows for more elaborate analysis than the original detections. First, the stacked waveforms of a repetitive event have better SNR, making both visual and automatic inspection of the templates more feasible. Second, we can now study the group behavior of the detections, such as how the rate of repeats or the change of activity level with time of day. However, as the total number of detections still makes a fully manual inspection infeasible, we further group and filter the templates. As LFEs are highly repetitive, it is likely that many templates identified the same families and will have highly similar matches. To reduce work in manual inspections, we merge such templates. To this end, we compute detection counts in non-overlapping 1 hr bins and calculate cross-correlations between the detection rate of each template within a cell. We merge all families with correlation values above 0.6, obtaining clusters of families. For each cluster, we only analyze the template with the highest number of detections as this template is likely to be the most expressive, provide the best signal-to-noise ratio in the waveform stacks, and the clearest recurrence patterns. We note that this approach might merge different LFE families because different families often show similar activity patterns. However, as the goal of our study is not to obtain comprehensive LFE catalogs but to identify whether a region hosts LFEs at all, this more aggressive duplication removal is acceptable. To further reduce the number of clusters, we only keep clusters that contain at least two templates and at least one template with more than 100 detections. This simplification relies on the assumption that LFE families have high numbers of repeats, making it highly unlikely that the original catalog found exactly one detection within a family.

For each cell, we manually inspect the remaining clusters. For each cluster, we focus on three diagnostic plots describing the template with the most detections: the 3 component waveform stacks at all stations, a recurrence time plot throughout the time range of the cell, and a histogram of activity by day. We classify the clusters based on four features (Figure 2). First, we identify if the time of day histogram shows a clear anthropogenic signature. For example, many families show the characteristic timing of mine blasts or other mining related activities, such as elevated rates during the day with particular spikes in the early and late afternoon. Second, we classify whether the event has a surface-wave-like waveform, with a narrow band of frequency content and with a smooth envelope. Third, we count the number of clearly identifiable phase arrivals in the waveform stacks. We do not require that this number of arrivals is visible at all stations, but label examples as containing two arrivals if they are visible at least at one station. Fourth, we classify the recurrence pattern into three classes: LFE type, dense bursts, and clear separation between detections; bimodal distributions of detection times; other such as Poissonian recurrence typical for earthquakes. For an LFE family, we would expect two phase arrivals with an LFE type recurrence and without an anthropogenic or surface wave signature. For any feature, if in doubt, we assign the label expected for an LFE to include the family in later, more thorough, manual inspections. We provide visual examples of the different feature manifestations in Figure 2.

## Data Availability Statement

For Chile, we use seismic data from the C, C1 (Universidad de Chile, 2012), CX (GFZ German Research Centre For Geosciences & Institut Des Sciences De L'Univers-Centre National De La Recherche CNRS-INSU, 2006), IU (Albuquerque Seismological Laboratory/USGS, 2014), GE (GEOFON Data Centre, 1993), RI, Y6 (Tilmann et al., 2021), XZ (Socquet et al., 2020), 2V (Bertrand Potin et al., 2019), 3V (Moreno et al., 2022, under embargo until experiment ends), and 9C (Zigone et al., 2019) networks. For Cascadia, we use seismic data from the CN network (Natural Resources Canada, 1975). The HiNet data for Japan is available from the NIED at https://www.hinet.bosai.go.jp/. The envelope correlation code from Wech (2021) is available at https://github.com/awech/enveloc. The reference earthquake catalog for the Atacama region is available in (Münchmeyer, Molina-Ormazabal, Marsan, et al., 2025).
Acknowledgments

This work has been partially funded by the
European Union under the grant agreement
n°101104996 ("DECODE") and the ERC
CoG 865963 DEEP-trigger. Views and
opinions expressed are however those of
the authors only and do not necessarily
reflect those of the European Union or
REA. Neither the European Union nor the
granting authority can be held responsible
for them. We thank INPRES for making
the data from the RI network available to
us. We thank INPRES and CIGEOBIO
(Universidad Nacional de San Juan) for the
data from the stations DOCA and PEDE.
We thank everyone involved in the
deployment of the seismic and geodetic
networks and the data management. The
computations presented in this paper were
performed using the GRICAD
infrastructure (https://gricad.univ-
grenoble-alpes.fr), which is supported by
Grenoble research communities. Some of
the data used are provided by the Epos-
France research infrastructure (www.epos-
france.fr - Résif until 2023), and some of
the seismological instruments used to
collect data are those of Epos-France's
Sismob mobile seismological park
(sismob.epos-france.fr) (INSU-CNRS).

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