# Recent earthquakes on unmapped faults highlight hidden seismic hazards within the Golden Triangle region of Laos, Thailand and Myanmar

I Sethanant, E Nissen, E Bergman, S Oliva, L Pousse-Beltran, E Karasözen, P Pananont

► To cite this version:

I Sethanant, E Nissen, E Bergman, S Oliva, L Pousse-Beltran, et al.. Recent earthquakes on unmapped faults highlight hidden seismic hazards within the Golden Triangle region of Laos, Thailand and Myanmar. Geophysical Journal International, 2025, 241 (1), pp.606-627. 10.1093/gji/ggaf061. hal-04987265

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# Recent earthquakes on unmapped faults highlight hidden seismic hazards within the Golden Triangle region of Laos, Thailand and Myanmar

I. Sethanant Ⓓ,<fnref n="1" />,<fnref n="2" /> E. Nissen Ⓓ,<fnref n="2" />,<fnref n="3" /> E. Bergman Ⓓ,<fnref n="4" /> S.J. Oliva Ⓓ,<fnref n="2" />,<fnref n="5" /> L. Pousse-Beltran Ⓓ,<fnref n="6" /> E. Karasözen Ⓓ,<fnref n="7" /> and P. Pananont Ⓓ,<fnref n="8" />

Accepted 2025 February 6. Received 2024 December 23; in original form 2024 August 1

## SUMMARY

In the past decade, six $M_w \ge 5.5$ earthquakes struck the mountainous Golden Triangle region (Laos, Thailand, Myanmar) of the southeast India–Eurasia collision zone. The largest of them, the 2019 $M_w$ 6.2 Sainyabuli earthquake in western Laos, shook river communities, dams and a UNESCO World Heritage Site, prompting a need to understand regional earthquake potential. We used Interferometric Synthetic Aperture Radar (InSAR) data and modelling to solve for the 2019 main shock source parameters, revealing right-lateral strike-slip along a 24 km-long NNW-trending fault which has limited topographic expression and was previously unmapped. InSAR modelling of its largest ($M_w$ 5.5) aftershock in 2021 revealed a 7 km-long splay fault, also previously unrecognized. The 2022 $M_w$ 5.9 Keng Tung earthquake in the northern Golden Triangle also ruptured an unknown, NW-trending right-lateral fault conjugate to longer, NE-trending faults nearby. Collectively, this shows that the region contains faults which are little evident in global digital topography and/or obscured by vegetation but long enough to generate sizeable earthquakes that should be accounted for in seismic hazard assessments. We relocated well-recorded aftershocks and other background seismicity (1978–2023) from across the Golden Triangle using the *mloc* software. Calibrated hypocentres span focal depths of 5–24 km and are distributed away from the main InSAR-modelled fault traces, another indication of fault structural immaturity. For the three 2019–2022 InSAR-constrained events, we also obtained moment tensor solutions from regional seismic waveform inversion. InSAR-derived peak slip depths and seismological centroid depths are mostly shallow (3–5 km), while focal depths are generally located in areas of low coseismic slip near the bottom of InSAR model faults. More broadly, we estimate a regional seismogenic thickness of ~17 km (the 90 per cent seismicity cut-off depth), a crucial parameter for seismic hazard calculations and building codes. Our integration of remote-sensing and seismologic analyses could be a blueprint for assessing earthquake potential of other regions with sparse instrumentation and limited topographic fault expression.

**Key words:** Radar interferometry; Asia; Earthquake hazards; Earthquake source observations; Seismicity and tectonics; Crustal structure.

## 1 INTRODUCTION

The mountainous Shan Plateau is a region of complex faulting in the northwestern corner of the Indochina (or Sunda/Sundaland)

block within the ‘Golden Triangle’ border region of Laos, Thailand, Myanmar and China (Fig. 1). The plateau is bordered to the west by the N–S-trending, right-lateral (~20 mm yr⁻<fnref n="1" />) Sagaing fault, which accommodates northward motion of India within the oblique

<footnote n="1">¹School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Victoria 3010, Australia. E-mail: israporn.sethanant@unimelb.edu.au</footnote>

<footnote n="2">²School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8P 5C2, Canada</footnote>

<footnote n="3">³College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon 97331, USA</footnote>

<footnote n="4">⁴Global Seismological Services, Golden, Colorado 80401, USA</footnote>

<footnote n="5">⁵Department of Geophysics, Universidad de Chile, Santiago 8320000, Chile</footnote>

<footnote n="6">⁶Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, IRD, Université Gustave Eiffel, ISTERRE, F-38000 Grenoble, France</footnote>

<footnote n="7">⁷Alaska Earthquake Center, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA</footnote>

<footnote n="8">⁸Department of Earth Sciences, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Tectonic setting of the Shan Plateau within the broader India–Eurasia collision zone. Red arrows are GNSS velocities with respect to stable Eurasia from Wang & Shen (2020), showing clockwise rotation around the southeastern Himalayan syntaxis and NE–SW-oriented sinistral shear across the Shan Plateau. Black lines are active faults from Department of Mineral Resources, Thailand (DMR) and the Global Earthquake Model Foundation (Styron & Pagani 2020) data bases. White triangles are seismograph stations used in this study from the International Seismological Centre (ISC), National Earthquake Information Center (NEIC, U.S. Geological Survey) and local networks (data available in GCCEL, see Data availability). Underlying hillshaded topography and bathymetry is from the U.S. Geological Survey GTOPO30 Digital Elevation Model (see Data availability).</figcaption></figure>

Indo-Burma subduction zone. To the north and east, it is separated from the South China block by the NW–SE-trending, right-lateral (~2 mm yr⁻¹) Red River fault (Vigny et al. 2003; Shen et al. 2005; Simons et al. 2007; Wang et al. 2014b; Shi et al. 2018a). The plateau itself is criss-crossed by an array of long (up to ~400 km), NE-trending, arcuate (concave northwestwards), left-lateral faults together with a few, shorter (up to ~200 km), conjugate right-lateral faults (Fig. 2; Lacassin et al. (1998); Wang et al. (2014b, 2022)). Global Navigation Satellite systems (GNSS) velocities show that these faults together accommodate ~12 mm yr⁻¹ of SW-oriented, left-lateral shear between the stable Indochina Block and the northern Shan Plateau (Kreemer et al. 2014; Shi et al. 2018a). Though there are few firm estimates of long-term slip rates on individual NE-trending faults (e.g. Sun et al. 2017; Shi et al. 2018b, 2022), sinistral river offsets of ~5–20 km (summing to ~110–130 km across all sinistral faults in the Shan Plateau) (Lacassin et al. 1998; Wang et al. 2014b) yield average fault slip rates of ~1 mm yr⁻¹ if it is assumed that slip initiated at ~10 Ma (Shi et al. 2018a)—an age supported by magnetostratigraphic and thermochronologic data (Cao et al. 2011; Li et al. 2013).

At least twenty magnitude >6 earthquakes have been recorded across the Shan Plateau since the early twentieth century (Fig. 2), but only a few of these are attributed to known faults with any

confidence (Wang et al. 2014b), and precise epicentral locations and focal depths are mostly lacking. This reflects both the lack of dense seismic instrumentation and the difficulty of imaging surface deformation with satellite radar or optical imagery beneath the dense forest cover. Counterintuitively, the largest pair of events each ruptured NW-trending dextral faults: the 1912 May 23 $M_w$ 7.7 earthquake on the Northern Kyaukkyan fault in the western Shan Plateau (Wang et al. 2014b; Crosetto et al. 2019) and the 1988 November 6 $M_w$ ~7 doublet on the Lancang fault in the central plateau (Chen & Wu 1989; Holt et al. 1991; Wang et al. 1991) (Fig. 2, blue stars). These are the two most prominent NW-trending faults in the study area, both around ~200 km long (Wang et al. 2014b). The 1995 July 11 $M_w$ 6.8 Menglian Myanmar–China border earthquake, the first in the region to be mapped using Interferometric Synthetic Aperture Radar (InSAR), also ruptured a NW-trending dextral fault, though in this case the fault was previously unrecognized (Ji et al. 2017). Of the more prominent, NE-trending sinistral faults, the Nam Ma fault broke in the 2011 March 24 $M_w$ 6.8 Tarlay, Myanmar earthquake, generating both clear surface ruptures and clear InSAR fringes (Feng et al. 2013; Tun et al. 2014; Wang et al. 2014a), while the Mae Chan fault may have hosted the 2007 May 16 $M_w$ 6.3 Bokeo, Laos earthquake, though in this case a definitive InSAR signal is lacking (e.g. Lee & Shyu 2015). A dense, temporary network
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Mapped active faults and earthquake focal mechanisms from across the Shan Plateau. Grey beach balls are earthquakes without independent waveform modelling or InSAR analysis, gathered from the automated Global Centroid Moment Tensor (GCMT) catalogue (Dziewonski et al. 1981; Ekström et al. 2012) and the ANSS ComCat (U.S. Geological Survey, Earthquake Hazards Program 2017). They span the years 1976–2019 and are scaled by magnitude and plotted at ISC-EHB catalogue epicentres (Engdahl et al. 1998; Weston et al. 2018; Engdahl et al. 2020; International Seismological Centre 2023). Other mechanisms are coloured by centroid depths from published waveform modelling studies (Le Dain et al. 1984; Chen & Molnar 1990; Holt et al. 1991; Ma & Hao 1997; Kumar et al. 2015; Pananont et al. 2017) or peak slip depths from InSAR slip models (Feng et al. 2013; Wang et al. 2014a; Ji et al. 2017 and this paper); these depths are labelled in light blue (in km), with InSAR depths asterisked. Earthquakes in dark blue text are dextral faulting events within the Golden Triangle. Blue star is the 1912 Kyaukkyan fault earthquake epicentre (Holt et al. 1991). Black lines are active faults from DMR and Styron & Pagani (2020), and topography is from the Shuttle Radar Topography Mission 3 arcsec DEM (see Data availability). White lines are national boundaries, blue lines show major rivers and white triangles are regional seismograph stations used in this study. Abbreviations: f.—fault; MXF—Mengxing fault, SYBF—Sainyabuli fault, WF—Wan Ha fault, and the MLS—Mae Lao segment and PS—Pan segment of the Phayao fault zone.</figcaption></figure>

deployed after the 2014 May 5 M<sub>w</sub> 6.2 Mae Lao earthquake in Chiang Rai, Thailand supported coeval rupture of the conjugate Mae Lao and Pan segments of the Phayao fault zone (Pananont et al. 2014, 2017; Boonchaisuk et al. 2017). All of these earthquakes are at crustal depths. Intraslab seismicity within the subducting Indian

Plate occurs west of the Sagaing fault (Fadil et al. 2023). Locally recorded focal and centroid depths in the Mae Lao (Chiang Rai) sequence are up to ~14 and ~18 km, respectively (Pananont et al. 2017), consistent with regional centroid depths of ~2–14 km from teleseismic waveform inversions of a handful of other moderate to
large earthquakes from across the Shan Plateau (Le Dain et al. 1984; Chen & Molnar 1990; Holt et al. 1991; Ma & Hao 1997; Kumar et al. 2015; Pananont et al. 2017). Dislocation modelling of the $M_w$ 6.8 1995 Menglian and 2011 Tarlay earthquakes revealed peak slip at ~3–5 km depth and deepest slip at ~12–20 km (Feng et al. 2013; Wang et al. 2014a; Ji et al. 2017). Collectively, therefore, the limited available data support a regional seismogenic layer thickness somewhere in the range of ~10–20 km, but additional constraints on this important parameter—which in part governs how large a future earthquake can be—are clearly a high priority.

The purpose of this paper is to re-evaluate the seismic potential of the Golden Triangle by investigating several new earthquakes from 2019 to 2023. We draw upon improvements in mapping subtle surface deformation signals in dense forest cover through use of the European Space Agency Sentinel-1A/1B satellites, whose shorter (6 or 12 d) revisit times provide better interferometric coherence than earlier C-band SAR platforms (Funning & Garcia 2019). We also build upon gradual improvements in regional seismic station coverage in two ways. First, we are able to estimate moment tensors of moderate ($M_w$ 5–6) earthquakes using waveforms recorded by regional seismic networks (Pananont et al. 2017; Fadil et al. 2023), where previous modelling studies relied upon teleseismic signals (Le Dain et al. 1984; Chen & Molnar 1990; Holt et al. 1991; Ma & Hao 1997). Secondly, we are able to determine more precise hypocentral parameters, not only for the best-recorded modern earthquakes but also any earlier events (going back decades) that share common seismic station picks (e.g. Walker et al. 2011; Karasözen et al. 2016, 2019). Together, the new constraints from InSAR modelling, hypocentral relocations and regional moment tensors help us estimate the regional seismogenic layer thickness, which sets the lower depth limit for slip in large earthquakes and is thus an important parameter for seismic hazard assessment. Finally, we examine the topographic expression of the faults responsible for the 2019–2023 earthquakes, which is crucial for understanding regional seismic potential. Overall, our work on the Golden Triangle region provides a blueprint for studying moderate-to-large strike-slip events and seismic hazards in other regions with sparse instrumentation.

# 2 DATA AND METHODS

## 2.1 Target earthquakes

An initial focus of our study is the $M_w$ 6.2 earthquake that struck Sainyabuli (also spelled Xayaburi or Xaignabuli) province of western Laos on 2019 November 20 at 23:50 UTC, or November 21 at 06:50 Indochina Time (Fig. 2). This was widely felt in Laos, Thailand (as distant as Bangkok, population >10 million), Myanmar, Vietnam and southern China. The 2019 earthquake lies south or east of those studied previously and thus provides vital information about the seismic potential of the southeastern Golden Triangle, a region containing potentially vulnerable populations, infrastructure and cultural sites. The earthquake risk is exemplified by the proximity of the 2019 earthquake to the Sainyabuli hydroelectric dam on the Mekong River (~40 km away) and to Luang Prabang UNESCO World Heritage Site (~90 km away). The United States Geological Survey (USGS) Advanced National Seismic System Comprehensive Earthquake Catalogue (ANSS ComCat) places the earthquake near the ~140 km-long, ~N–S-trending Sainyabuli distal fault system—which hosted at least three earthquakes in the past ~3000 yr according to paleoseismic trenching (horizontal slip rate

of 0.8 mm yr⁻¹, Nualkhao et al. 2016)—and along strike south-westwards from the ~370 km-long Dien Bien Phu fault zone, the southeasternmost of the pronounced, NE-trending sinistral faults of the Shan Plateau (Zuchiewicz et al. 2004; Lai et al. 2012; Wang et al. 2014b). Other new earthquakes of interest to us include a $M_w$ 5.7 foreshock that preceded the 2019 November 20 $M_w$ 6.2 Sainyabuli earthquake by three hours; a $M_w$ 5.5 earthquake on 2021 December 19, also in the Sainyabuli region and a pair of $M_w$ 5.9 and 5.7 earthquakes on 2022 July 21 and 2023 November 17 near Keng Tung, Myanmar (Fig. 2). These latter events lie near the ~140 and ~180 km-long, NE-trending Wan Ha and Mengxing sinistral faults in the central Shan Plateau (Lacassin et al. 1998; Wang et al. 2014b).

We investigate these five earthquakes using Sentinel-1 InSAR and seismological observations and modelling. For the 2019 $M_w$ 6.2 Sainyabuli main shock, we also invert the InSAR data to find the best-fitting source parameters and slip distribution. Independently, we perform a calibrated multi-event relocation in order to assess hypocentral distributions of the 2019–2021 Sainyabuli and 2022–2023 Keng Tung sequences and other notable well-recorded earthquakes in the region. We also model regional waveforms to estimate moment tensors with centroid depths for the Sainyabuli and Keng Tung earthquakes.

## 2.2 InSAR data processing and source modelling

We mapped surface deformation in the 2019 and 2021 Sainyabuli and 2022 and 2023 Keng Tung earthquakes using C-band (wave-length 5.55 cm) Synthetic Aperture Radar (SAR) data, acquired by the European Space Agency’s Sentinel-1A/1B satellites and processed using GAMMA software (Wegmüller et al. 2016). For each earthquake, we used scenes from one ascending track (a99 for Sainyabuli and a172 for Keng Tung) and one descending track (d62 and d135) to produce two, 12-d interferograms (Table 1). For the 2019 November 20 Sainyabuli earthquakes, we also generated a third, 6-d ascending interferogram covering the southern epicentral area; all three interferograms capture both the $M_w$ 5.7 foreshock (21:03 UTC) and the $M_w$ 6.2 main shock (23:50 UTC). InSAR topographic phase contributions were removed using the 3 arcsec (90-m) Shuttle Radar Topography Mission (SRTM) digital elevation model (Farr & Kobrick 2000; Rosen et al. 2000; Kobrick 2006; Farr et al. 2007) and the interferograms were filtered using a power spectrum algorithm (Goldstein & Werner 1998) to remove phase noise and improve the visibility of fringes. Interferograms were unwrapped using the minimum cost flow algorithm, which we found through experimentation to be most effective in this highly vegetated region, and then georectified to the Universal Transverse Mercator coordinate system (UTM zone 47N) with a 90 m pixel resolution. Lastly, we removed residual atmospheric and orbital noise using the Generic Atmospheric Correction Online Service (GACOS) (Yu et al. 2017). Correction of signals from tropospheric delay did not visually improve the interferograms, likely due to the sparse geodetic instruments in this region, and so the final interferograms post-processed in GACOS only included planar trend corrections.

Of the earthquakes investigated with InSAR, only the 2019 November 20 Sainyabuli $M_w$ 6.2 main shock (captured with its $M_w$ 5.7 foreshock) produced a sufficient deformation signal-to-noise for inverse modelling. Neither the observed deformation nor our fault model are indicative of multiple ruptures, and so we interpret both as representing only the larger main shock. We followed a common two-step inversion strategy, solving first for uniform slip
<table><caption>**Table 1.** Details of the InSAR imagery used to study the $M_w$ 6.2 2019 November 20 Sainyabuli earthquake (2019SYB), the $M_w$ 5.5 2021 December 19 Sainyabuli earthquake (2021SYB), the $M_w$ 5.9 2022 July 21 Keng Tung earthquake (2022KT) and the $M_w$ 5.7 2023 November 17 Keng Tung earthquake (2023KT). *i* is the line-of-sight (LOS) incidence angle measured from the vertical and *Az.* is the azimuth measured from north, both determined at the ANSS ComCat event epicentre. Abbreviations: a—ascending track, d—descending track.</caption><thead><tr><th>Interferogram</th><th>Track</th><th>Date 1</th><th>Date 2</th><th><i>i</i> (°)</th><th>Az. (°)</th></tr></thead><tbody><tr><td>2019SYB-01</td><td>a99</td><td>Nov 15, 2019</td><td>Nov 27, 2019</td><td>38</td><td>349</td></tr><tr><td>2019SYB-02</td><td>d62</td><td>Nov 11, 2019</td><td>Nov 24, 2019</td><td>40</td><td>191</td></tr><tr><td>2019SYB-03</td><td>a62</td><td>Nov 18, 2019</td><td>Nov 24, 2019</td><td>40</td><td>191</td></tr><tr><td>2021SYB-01</td><td>a99</td><td>Dec 10 2021</td><td>Dec 22, 2021</td><td>38</td><td>349</td></tr><tr><td>2021SYB-02</td><td>d62</td><td>Dec 7, 2021</td><td>Dec 19, 2021</td><td>40</td><td>191</td></tr><tr><td>2022KT-01</td><td>a172</td><td>Jul 19, 2022</td><td>Jul 31, 2022</td><td>44</td><td>350</td></tr><tr><td>2022KT-02</td><td>d135</td><td>Jul 16, 2022</td><td>Jul 28, 2022</td><td>38</td><td>191</td></tr><tr><td>2023KT-01</td><td>a172</td><td>Nov 11, 2023</td><td>Nov 23, 2023</td><td>44</td><td>350</td></tr><tr><td>2023KT-02</td><td>d135</td><td>Nov 8, 2023</td><td>Nov 20, 2023</td><td>38</td><td>191</td></tr></tbody></table>

on a planar, rectangular fault and thereafter for the slip distribution. To prepare the data for modelling, we first identified phase unwrapping errors as isolated patches of coherent data bordered by spurious phase jumps, and manually added or subtracted $2\pi$ (or multiples of $2\pi$) radians to these patches in order to align with surrounding, coherent data. Then, we downsampled the data using the Quadtree algorithm (Jónsson et al. 2002; Wright et al. 2003), adjusting the sampling block size and phase variance and coherence thresholds in order to densify sampling of areas with steep, coherent phase gradients while avoiding sampling of mostly decorrelated areas. The final downsampled data sets had between ~300 and ~600 points each, with very few of these lying within the patchy, decorrelated centre of the InSAR deformation pattern. Later tests in which isolated data patches were removed altogether prior to downsampling yielded no significant difference in model results.

To estimate the geometry of the 2019 Sainyabuli main shock, we solved the Okada (1985) equations to invert for the strike, dip, rake, location, length, top and bottom depths and uniform slip of a rectangular fault plane embedded in an elastic half-space with Lamé parameters $\lambda$ and $\mu$, both $3.2 \times 10^{10}$ Pa. Each parameter is solved nonlinearly except for slip. We also jointly inverted for three nuisance parameters for each interferogram: a wholesale translation in line-of-sight (LOS) displacement to account for the unknown displacement at the unwrapping reference point, and east-west and north-south linear ramps to account for residual orbital ramps. The two descending interferograms were weighed half of that of the ascending interferogram in the inversion to balance the LOS displacements from both satellite viewing geometries. We used the downhill simplex algorithm (Press et al. 1992) to minimize misfits between observed and modelled surface displacements. We provided generous search bounds for each fault plane parameter (Table 2), with median values taken either directly from the observed InSAR deformation pattern (fault position, strike and length), from the ANSS ComCat automated main shock focal mechanism (dip, rake) or using the Visible Earthquake synthetic interferogram tool (top depth, bottom depth, slip) (Funning & Cockett 2012) (see Data availability). To ensure that the global minimum misfit solution was obtained, we used a Monte Carlo procedure to restart the inversion 100 times using starting parameters selected randomly from within these bounds (Wright et al. 1999; Funning et al. 2005; Elliott et al. 2012). Finally, we repeated the inversion using narrower search bounds centred upon the converging source parameters from the first iteration. These narrower bounds confirmed that we found a global minimum. Using these minimum misfit fault parameters, we generated synthetic interferograms for visual comparison with the data. We also considered a more complex fault geometry by adding a

second model fault and solving for both geometries simultaneously. However, this did not improve the fit visually or quantitatively compared to the preferred, one-fault model.

Next, we inverted for the slip distribution of the 2019 Sainyabuli main shock. To allow for tapered slip, we first extended the uniform slip model fault plane by a few kilometres along strike (to 24 km), up dip (to the surface) and down dip (to 13 km). The latter value is based on depth resolution tests in which we repeated the uniform slip inversion with bottom depths fixed at increments either side of the minimum misfit value (9 km); resulting forward model interferograms are visually indifferent over the range 6–12 km. In addition, our independent calibrated relocation (described later) shows that within two hours of the main shock, aftershock focal depths range from 7 to 13 km. The extended fault plane was subdivided into 2 km × 2 km patches and the slip distribution calculated using a Laplacian smoothing operator (Wright et al. 2004; Funning et al. 2005) and a slip positivity constraint (Bro & De Jong 1997). We manually chose the smoothing parameter that best balances our desire for a realistically smooth slip distribution with the model misfit. Finally, as for the uniform slip inversion, synthetic interferograms were produced for comparison with observations.

The 2021 $M_w$ 5.5 Sainyabuli and 2022 $M_w$ 5.9 Keng Tung earthquakes generated smaller InSAR fringe patterns with insufficient deformation signal-to-noise for slip inversion. Instead, we produced uniform slip forward models using fixed parameters estimated either directly from the InSAR data (fault strike, length), from the ANSS ComCat automated focal mechanism (dip, rake), or from the Visible Earthquake synthetic interferogram tool (top depth, bottom depth, slip) (Funning & Cockett 2012). Through iteration, we then adjusted these parameters until we achieved a satisfying fit to the data. We also generated forward models using the alternate nodal plane, in order to verify our distinction of fault and auxiliary plane. Interferograms capturing the 2023 $M_w$ 5.7 Keng Tung earthquake do not exhibit a clear InSAR deformation signal. However, to be confident of our interpretation, we assessed the expected earthquake signal using forward models, similar to above.

## 2.3 Earthquake relocation

To reduce the ~10 km epicentral and focal depth uncertainties typical of routine, automated global catalogues (e.g. Weston et al. 2011), we relocated hypocentres of the 2019–2021 Sainyabuli and 2022–2023 Keng Tung earthquakes and their largest aftershocks. These were incorporated within a larger cluster of earthquakes (referred to here as the Golden Triangle cluster) spanning the southern and
<table><caption>**Table 2.** Earthquake source parameters of the five earthquakes studied here, from our study (regional seismic waveform modelling, and where available, InSAR modelling solutions) and from other automated catalogues. For the 2019 Sainyabuli main shock InSAR model, we also provide parameter search bounds (see the text). For all InSAR solutions, longitude and latitude refer to the centre point of the InSAR model fault surface projection. For the deviatoric moment tensor solutions, the best solutions are reported, and the mean and one standard deviation are listed in the brackets (see Tables S1 and S2 for the inversion reference points and moment tensor solution components).</caption><thead><tr><th>Model type</th><th>Longitude (°)</th><th>Latitude (°)</th><th>Strike (°)</th><th>Dip (°)</th><th>Rake (°)</th><th>Length (km)</th><th>Top (km)</th><th>Bottom (km)</th><th>Centroid (km)</th><th>Slip (m)</th><th>M<sub>0</sub> (N·m)</th><th>M<sub>w</sub></th></tr></thead><tbody><tr><td colspan="13"><strong>2019 Sainyabuli foreshock</strong></td></tr><tr><td colspan="13"><em>This study: Regional waveform modelling</em></td></tr><tr><td>Deviatoric</td><td>101.33</td><td>19.37</td><td>175<br>(198 ± 63)</td><td>56<br>(66 ± 11)</td><td>-158<br>(-129 ± 75)</td><td>-</td><td>-</td><td>-</td><td>3.6<br>(4.2 ± 1.2)</td><td>-</td><td>2.78 × 10<sup>17</sup></td><td>5.6</td></tr><tr><td colspan="13"><em>Other mechanisms:</em></td></tr><tr><td>GCMT</td><td>101.41</td><td>19.44</td><td>345</td><td>88</td><td>-152</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>4.05 × 10<sup>17</sup></td><td>5.7</td></tr><tr><td>USGS Mww</td><td>101.25</td><td>19.45</td><td>161</td><td>75</td><td>-177</td><td>-</td><td>-</td><td>-</td><td>20</td><td>-</td><td>3.97 × 10<sup>17</sup></td><td>5.7</td></tr><tr><td>USGS Mwb</td><td>101.25</td><td>19.45</td><td>160</td><td>79</td><td>-177</td><td>-</td><td>-</td><td>-</td><td>14</td><td>-</td><td>3.26 × 10<sup>17</sup></td><td>5.7</td></tr><tr><td colspan="13"><strong>2019 Sainyabuli main shock</strong></td></tr><tr><td colspan="13"><em>This study: InSAR inverse modelling</em></td></tr><tr><td>Search bounds</td><td>101.438 ± 6 km</td><td>19.446 ± 6 km</td><td>170 ± 20</td><td>70 ± 20</td><td>-160 ± 40</td><td>20 ± 10</td><td>5 ± 5</td><td>10 ± 10</td><td>-</td><td>1.0*</td><td>-</td><td>-</td></tr><tr><td>Uniform slip</td><td>101.417</td><td>19.438</td><td>166</td><td>76</td><td>-167</td><td>16</td><td>2</td><td>8</td><td>5<sup>†</sup></td><td>0.7</td><td>2.48 × 10<sup>18</sup></td><td>6.2</td></tr><tr><td>Distributed slip</td><td>101.417</td><td>19.438</td><td>166</td><td>76</td><td>-167</td><td>24</td><td>0</td><td>14</td><td>5<sup>†</sup></td><td>1.0<sup>†</sup></td><td>2.48 × 10<sup>18</sup></td><td>6.2</td></tr><tr><td colspan="13"><em>This study: Regional waveform modelling</em></td></tr><tr><td>Deviatoric</td><td>101.37</td><td>19.35</td><td>174<br>(177 ± 31)</td><td>67<br>(70 ± 7)</td><td>-141<br>(-136 ± 42)</td><td>-</td><td>-</td><td>-</td><td>4.2<br>(4.0 ± 0.7)</td><td>-</td><td>1.35 × 10<sup>18</sup></td><td>6.1</td></tr><tr><td colspan="13"><em>Other mechanisms:</em></td></tr><tr><td>GCMT</td><td>101.44</td><td>19.36</td><td>163</td><td>84</td><td>-153</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>1.88 × 10<sup>18</sup></td><td>6.1</td></tr><tr><td>USGS Mww</td><td>101.36</td><td>19.45</td><td>169</td><td>65</td><td>-155</td><td>-</td><td>-</td><td>-</td><td>18</td><td>-</td><td>2.28 × 10<sup>18</sup></td><td>6.2</td></tr><tr><td>USGS Mwb</td><td>101.36</td><td>19.45</td><td>164</td><td>55</td><td>-167</td><td>-</td><td>-</td><td>-</td><td>4</td><td>-</td><td>1.49 × 10<sup>18</sup></td><td>6.1</td></tr><tr><td colspan="13"><strong>2021 Sainyabuli aftershock</strong></td></tr><tr><td colspan="13"><em>This study: InSAR forward modelling</em></td></tr><tr><td>Uniform slip</td><td>101.381</td><td>19.517</td><td>330</td><td>77</td><td>-179</td><td>7</td><td>1</td><td>5</td><td>3<sup>†</sup></td><td>0.3</td><td>2.76 × 10<sup>17</sup></td><td>5.6</td></tr><tr><td colspan="13"><em>This study: Regional waveform modelling</em></td></tr><tr><td>Deviatoric</td><td>101.35</td><td>19.50</td><td>339<br>(254 ± 88)</td><td>86<br>(84 ± 6)</td><td>140<br>(10 ± 150)</td><td>-</td><td>-</td><td>-</td><td>3.1<br>(3.1 ± 0.5)</td><td>-</td><td>1.93 × 10<sup>17</sup></td><td>5.5</td></tr><tr><td colspan="13"><em>Other mechanisms:</em></td></tr><tr><td>GCMT</td><td>101.41</td><td>19.48</td><td>337</td><td>83</td><td>162</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>2.32 × 10<sup>17</sup></td><td>5.5</td></tr><tr><td>USGS Mww</td><td>101.31</td><td>19.56</td><td>336</td><td>77</td><td>-179</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>2.09 × 10<sup>17</sup></td><td>5.5</td></tr><tr><td>USGS Mwb</td><td>101.31</td><td>19.56</td><td>154</td><td>86</td><td>-176</td><td>-</td><td>-</td><td>-</td><td>9</td><td>-</td><td>1.32 × 10<sup>17</sup></td><td>5.3</td></tr><tr><td colspan="13"><strong>2022 Keng Tung main shock</strong></td></tr><tr><td colspan="13"><em>This study: InSAR forward modelling</em></td></tr><tr><td>Uniform slip</td><td>99.908</td><td>21.135</td><td>148</td><td>66</td><td>-174</td><td>11</td><td>2</td><td>6</td><td>4<sup>†</sup></td><td>0.4</td><td>6.16 × 10<sup>17</sup></td><td>5.8</td></tr><tr><td colspan="13"><em>This study: Regional waveform modelling</em></td></tr><tr><td>Deviatoric</td><td>99.88</td><td>21.06</td><td>151<br>(176 ± 73)</td><td>77<br>(80 ± 6)</td><td>-172<br>(-69 ± 98)</td><td>-</td><td>-</td><td>-</td><td>4.9<br>(5.0 ± 0.7)</td><td>-</td><td>5.94 × 10<sup>17</sup></td><td>5.8</td></tr><tr><td colspan="13"><em>Other mechanisms:</em></td></tr><tr><td>GCMT</td><td>99.95</td><td>21.14</td><td>329</td><td>72</td><td>179</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>9.6 × 10<sup>17</sup></td><td>5.9</td></tr><tr><td>USGS Mww</td><td>99.88</td><td>21.15</td><td>151</td><td>66</td><td>-176</td><td>-</td><td>-</td><td>-</td><td>16</td><td>-</td><td>1.03 × 10<sup>18</sup></td><td>5.9</td></tr><tr><td>USGS Mwb</td><td>99.88</td><td>21.15</td><td>152</td><td>85</td><td>-175</td><td>-</td><td>-</td><td>-</td><td>10</td><td>-</td><td>6.52 × 10<sup>17</sup></td><td>5.8</td></tr><tr><td colspan="13"><strong>2023 Keng Tung main shock</strong></td></tr><tr><td colspan="13"><em>This study: Regional waveform modelling</em></td></tr><tr><td>Deviatoric</td><td>99.29</td><td>21.00</td><td>166<br>(173 ± 26)</td><td>76<br>(79 ± 4)</td><td>-168<br>(-158 ± 49)</td><td>-</td><td>-</td><td>-</td><td>10.1<br>(9.0 ± 1.5)</td><td>-</td><td>3.15 × 10<sup>17</sup></td><td>5.6</td></tr><tr><td colspan="13"><em>Other mechanisms:</em></td></tr><tr><td>GCMT</td><td>99.37</td><td>21.16</td><td>346</td><td>86</td><td>162</td><td>-</td><td>-</td><td>-</td><td>12</td><td>-</td><td>5.43 × 10<sup>17</sup></td><td>5.8</td></tr><tr><td>USGS Mww</td><td>99.33</td><td>21.22</td><td>339</td><td>76</td><td>-179</td><td>-</td><td>-</td><td>-</td><td>20</td><td>-</td><td>3.92 × 10<sup>17</sup></td><td>5.7</td></tr><tr><td>USGS Mwb</td><td>99.33</td><td>21.22</td><td>169</td><td>77</td><td>180</td><td>-</td><td>-</td><td>-</td><td>6</td><td>-</td><td>3.72 × 10<sup>17</sup></td><td>5.6</td></tr></tbody></table>

central Shan Plateau and going as far back as 1978. Other major events in the cluster include the 2007 May 16 $M_w$ 6.3 Bokeo, Laos; 2011 March 24 $M_w$ 6.8 Tarlay, Myanmar; and 2014 May 5 $M_w$ 6.1 Mae Lao, Thailand earthquakes.

The relocation was performed with the program *mloc* (e.g. Bergman *et al.* 2023), which is based on the HypocentralID Composition method in Jordan & Sverdrup (1981). Like all relative event location codes, *mloc* utilizes reported seismic phase arrival

<footnote>*Parameters that are linearly inverted without search bounds</footnote>

<footnote>†Uniform slip model: central depth of fault plane</footnote>

<footnote>‡Distributed slip model: peak slip and peak slip depth</footnote>
times from stations that observed two or more events within a lim-
ited source region to improve relative locations. *mloc* also incor-
porates two different procedures which can minimize the absolute
location bias of the entire cluster due to unknown Earth struc-
ture; this is termed ‘calibration’. For the Golden Triangle cluster,
we used the ‘direct calibration’ procedure, which takes advantage
of a unique characteristic of the hypocentroidal decomposition al-
gorithm, the separation of the relocation problem into two parts,
estimation of relative locations (cluster vectors) and estimation of
the absolute location of the hypocentroid (the geometric mean of
all event hypocentre parameters). By adding the cluster vectors to
the hypocentroid, absolute hypocentres are obtained for all events
in the cluster. These hypocentres are considered calibrated only if
the hypocentroid has been estimated in a manner that minimizes
the biasing effect of unknown Earth structure, that is, lateral hetero-
geneity within the source region sampled by the ray paths that are
used. In direct calibration, this involves the use of different arrival
time data sets for the cluster vectors and the hypocentroid. First,
all arrival times (of phases observed at a given station from two or
more events) at all distances are used to solve for the cluster vectors.
Secondly, the hypocentroid is estimated from all arrival times for
any event in the cluster that are within a specified distance, cho-
sen to include most crustal direct-arriving phases (*Pg* and *Sg*) and
exclude most Moho-refracted phases (*Pn* and *Sn*). In effect, the
hypocentroid is treated as a single event location problem using all
near-source arrivals from all events in the cluster. By using data
only from close stations, the location bias from unmodelled lateral
heterogeneity is minimized.

The distance up to which arrival time data is utilized for direct calibration depends on the availability of nearby data and the quality of the station azimuthal distribution. We used arrival times reported by the International Seismological Centre (ISC), augmented with regional and local data from the Thai Meteorological Department (TMD), Department of Mineral Resources Thailand (DMR) and temporary stations deployed by Pananont et al. (2017) (see Data availability). These local-regional data sets were crucial in the direct calibration analysis.

The ~200 km dimensions of the Golden Triangle cluster (Fig. 3) is greater than usual for calibrated relocation studies (Bergman et al. 2023), increasing the likelihood of encountering lateral heterogeneity and thus compromising the effort to minimize location bias to some extent. One approach to reducing this problem is by exploiting *mloc*'s capability to use a local velocity model for calculating traveltimes of *Pg*, *Sg*, *Pn* and *Sn* (we use it out to 17° epicentral distance). We started with the global 1-D ak135 model (Kennett et al. 1995) and adjusted crustal-upper mantle velocities and layer thicknesses by requiring a close fit to the observed move out of arrival times from the calibrated hypocentroid (Table 3). The thickness of the crust (37 km) is based on the fit to *Pn* and *Sn* arrivals after the cluster has a preliminary calibration based on the direct crustal phases. The effects of lateral heterogeneity can be seen in the traveltime residuals in Fig. 3(c). They are notable but not severe.

The final challenge is that the sparse instrumentation in the re-
region results in weak azimuthal control (for direct calibration) for
each localized grouping of events ('sub-cluster') within the overall
cluster. For example, the 2014 Mae Lao earthquakes lacked local
station coverage to the northwest, the 2019 Sainyabuli earthquakes
required local stations to the north, east and south, and the 2022
Keng Tung event needed local stations to the north and east. To
overcome this challenge, we combined these subclusters and other
suitably recorded events in the region, exploiting their connection

through common regional and teleseismic stations and thus improv-
ing overall azimuthal control to the point where direct calibration
was possible.

We found it helpful to relocate the 2014 Mae Lao subcluster inde-
pendently first, since its rich TMD data set and the well-distributed
temporary stations from Pananont et al. (2017) helped stabilize
the relocation prior to adding the remaining subclusters. Arrival
time data, picked by ourselves from a waveform catalogue from
Pananont et al. (2017), comprised 227 events recorded from 24
permanent stations from TMD and DMR at regional distances, and
from 11 temporary stations deployed within 24 hr of and 30 km
from the main shock rupture. We included other available data of
the 2014 Mae Lao sequence from ISC, TMD and DMR at all dis-
tances. We manually assessed data quality, re-picked arrival time
data for multiply-reported phases and dropped events with spurious
phase picks, large arrival time scatter or azimuthal gaps of > 200°,
thus ensuring sufficient azimuthal coverage for constraining clus-
ter vectors. The calibrated events of the 2014 Mae Lao subcluster
are published in the Global Catalogue of Calibration Earthquake
Locations (See Data availability).

The remaining subclusters were then incorporated into a full re-
location, adopting similar data quality assessments. Initial runs of
the full inversion provided unstable solutions for the 2014 Mae Lao,
2019 Sainyabuli and 2022 Keng Tung main shocks. We found that
dominant teleseismic data of the 2014 Mae Lao event in the west-
ern epicentral hemisphere (mostly in Europe) produced longitudi-
nal bias, shifting locations eastward. We fixed this by incrementally
adding data into the cluster, at first using data only out to 15° epicen-
tral distance for the cluster vectors. Then we added to the data set
only the highest quality arrival time readings from greater distances,
first up to 30° then up to 45°. Lastly, in the direct calibration inver-
sion, we calculated the absolute locations using a relatively large
threshold of 1.2° for the hypocentroid (Fig. 3b). This higher epicen-
tral distance limit ensured sufficient close-in and well-distributed
arrival time data for azimuthal control and cluster stability. InSAR
fault models of the Sainyabuli and Keng Tung earthquakes (from
our study) and of the 2011 Tarlay earthquake (Feng et al. 2013;
Wang et al. 2014a), which are well distributed across the region,
helped verify the relocated epicentres.

2.4 Waveform modelling

Finally, we estimated moment tensors for the 2019 M<sub>w</sub> 5.7 and M<sub>w</sub> 6.2 Sainyabuli foreshock and main shock, the 2021 M<sub>w</sub> 5.5 Sainyabuli earthquake, and the 2022 M<sub>w</sub> 5.9 and 2023 M<sub>w</sub> 5.7 Keng Tung earthquakes, by inverting long-period waveforms filtered to 0.02–0.05 Hz from stations within ~ 10° epicentral distance. The resulting source parameters and centroid depths provide independent checks of our InSAR modelling results. Green’s functions were computed via the frequency-wave number integrator method (Saikia 1994) for the 1-D velocity model derived during the *mloc* relocation process in Section 2.3 (Table 3). We performed the probabilistic inversion and optimization with the open-source Pyrocko software (Heimann et al. 2018), and used Bayesian bootstrapping (with *n* = 100) to produce uncertainty estimates for various model parameters. The parameter search for centroid location allowed N–S and E–W source shifts of up to 30 km from our *mloc* relocated epicentres or the surface projection of InSAR centroid slip (Table S1), and source depths of 1–30 km. Since isotropic source components are not expected in this strike-slip-dominated, non-volcanic study area, we solved for the deviatoric moment tensor.
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Direct calibration ray paths and traveltime plots. (a) Map of event-station ray paths (red vectors), and local-regional stations that are included (white triangles) and excluded (grey triangles) from the direct calibration in calculating absolute locations. Open circles are calibrated epicentres. The two red circles show 1.0° and 2.0° radii centred upon the cluster hypocentroid (the average location of all events in the cluster). (b) Observed and modelled arrival times at local and regional distances out to 4°. Modelled curves are from the customized velocity model in Table 3. The vertical dashed line indicates the ray path length limit of 1.2° used in the direct calibration step. The phases include: red crosses—Pg; cyan crosses—P; green crosses—Pn; red circles—Sg; cyan circles—Lg; green circles—Sn. (c) The arrival time residuals within 1.2° epicentral distance. The solid and dashed red lines represent the average traveltime residual for each phase; the values annotated at each line.</figcaption></figure>

<table><caption>**Table 3.** 1-D velocity model used in the *mloc* relocation. The crust and upper mantle velocities are customized based on fitting arrival time data while below 120 km depth are velocities from the global ak135 model (Kennett *et al.* 1995).</caption><thead><tr><th>Depth range (km)</th><th><i>P</i> (km s<sup>-1</sup>)</th><th><i>S</i> (km s<sup>-1</sup>)</th></tr></thead><tbody><tr><td>0.0–10.0</td><td>5.40</td><td>3.35</td></tr><tr><td>10.0–25.0</td><td>5.65</td><td>3.45</td></tr><tr><td>25.0–37.0*</td><td>6.70</td><td>3.85</td></tr><tr><td>37.0*–77.5</td><td>7.85</td><td>4.40</td></tr><tr><td>77.5–120.0</td><td>7.85–8.05</td><td>4.40–4.50</td></tr></tbody></table>

# 3 THE 2019–2021 SAINYABULI, LAOS EARTHQUAKES

## 3.1 The 2019 Sainyabuli main shock kinematics

All three interferograms show far-field deformation fringes hinging around a ~25 km-long, ~N–S-trending, largely incoherent region (Fig. 4, Fig. S1, left hand column). Decorrelation in this area is likely caused by a combination of steep deformation gradients and strong ground shaking near the earthquake source, exacerbated by mountainous topography. The ascending interferogram displays four lobes with more fringes visible west of the partially decorrelated zone than east of it (Fig. 4a); unwrapped LOS displacements

peak at ~13 cm away from the satellite in the southwest lobe and ~9 cm towards the satellite in the northwest lobe (Fig. 4d). The two descending interferograms show asymmetric double lobes, with more fringes east of the decorrelated zone than west of it (Fig. 4g, Fig. S1) and peak LOS displacements of ~11 cm towards and ~11 cm away from the satellite (Fig. 4j). The contrast in the location of the dominant fringes between the ascending and descending interferograms is characteristic of roughly N–S-trending strike-slip faults (Funning *et al.* 2005), with the sense of displacement indicating right-lateral motion.

Our uniform slip InSAR inverse modelling supports rupture of a 16 km-long, steeply WSW-dipping right-lateral strike-slip fault that strikes SSE across the partially decorrelated zone, with a small component of normal slip (strike, dip and rake are 166°, 76° and −167°, respectively; Table 2). This geometry is therefore consistent with our first-order observations of the data interferograms. The model fault has 0.7 m of slip and top and bottom depths of 1.8 and 8.3 km, respectively. Distributed slip inversion on an extended fault plane (see Data and methods) yielded peak slip of 1.0 m at ~5 km depth and only up to 0.3 m of model slip at the surface (Fig. 5a). Slip is concentrated at the centre of the model fault plane between 3 and 7 km depth. To quantify this shallow slip deficit (SSD), we averaged the slip values across all subfault patches in each row, and normalized the mean against the peak average slip. SSD is then calculated as one minus the normalized slip of the surficial

<footnote>*Estimated Moho depth in this study</footnote>
November 20, 2019 M<sub>w</sub> 6.2 Sainyabuli mainshock

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Observed (left column), distributed slip inverse model (middle column) and residual (right column) interferograms of the 2019 M<sub>w</sub> 6.2 Sainyabuli main shock. (a–f) show the ascending interferogram and (g–l) the descending one; (a–c, g–i) are wrapped and (d–f, j–l) unwrapped with red indicating motion towards the satellite. In the middle column, the thick black line and dashed rectangle indicate the InSAR model fault surface projection and corner coordinates, respectively, and the arrow shows the slip vector azimuth. The black star is our relocated epicentre (see Table 4). The third InSAR data set only partially captured the rupture area and is shown in Fig. S1.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 5.** (a) 3-D view of our InSAR distributed slip model for the 2019 Mw 6.2 Sainyabuli main shock. Each sub-fault patch is 2 km by 2 km. The black star represents our relocated hypocentre (see Table 4). (b) Depth profiles of average (bottom axis) and normalized slip (top axis), plotted against the central depth of each subfault patch.</figcaption></figure>

model fault patches, expressed as a percentage. The averaged and normalized slip across the surficial row of our model fault are 0.11 m and 0.20, respectively yielding a SSD of 80 per cent (Fig. 5b).

The synthetic interferograms match the data well, with root mean square error in LOS displacement of 0.8 cm (Fig. 4, middle column) and far-field, long wavelength residual fringes not necessarily greater than atmospheric noise (Fig. 4, right column). However, unwrapped descending interferograms display shorter wavelength residual deformation parallelling the model fault within the region of partial decorrelation (Fig. 4 I, Fig. S1). These residuals may reflect undersampling of InSAR data points and/or phase unwrapping errors in this area. However, we tested the effect of near-fault data sampling on model results and found that including more data points from isolated InSAR data patches near the fault centre neither impacted the model fault geometry nor reduced the residuals. Another possibility is that these residuals arise from our model assumptions of a planar fault. We tested this by inverting the InSAR data with two model fault segments, end-to-end with a change in strike, but the solution did not improve the quantitative or visual fits. We therefore prefer the simpler one-fault geometry.

Our multi-event relocation places the main shock hypocentre along the northern half of the InSAR model fault, and at 12 km ± 4 km depth (Table 4, Figs 6, 7, 8g, h). Together, this suggests an upward and partially bilateral rupture. Our centroid depth from waveform modelling is shallower with a minimum misfit value of 4.2 km and a mean and standard deviation of 4.0 km ± 0.7 km (Table 2, Fig. 9b). The centroid depth is therefore in close agreement with the InSAR peak slip depth at ~5 km. Our waveform model moment tensor solution has a SSE-trending nodal plane with strike, dip and rake values of 174° (mean 177° ± 31°), 70° (70° ± 7°) and

-141° (-136° ± 42°), respectively (Table 2, Fig. 9b). Our InSAR model fault strike and dip lie within a few degrees of these values, and while there is a greater discrepancy in rake, the InSAR value of -166° still lies within the seismological uncertainties.

## 3.2 The 2021 Sainyabuli earthquake

Ascending and descending interferograms of the 2021 Sainyabuli earthquake both show two deformation lobes, aligned NNW-SSE and ~5-10 km in diameter (Fig. 10, left column). These are proximal to the 2019 Sainyabuli earthquake, of which we therefore consider this event to be a late aftershock. A small, central area of decorrelation was presumably caused by strong ground shaking or steep phase gradients, but the interferograms are otherwise mostly coherent. In the wrapped interferograms the lobes contain 1-2 fringes (Figs 10a, e), and in the unwrapped interferograms LOS displacements reach ~9 cm (Figs 10c, g). The lobes in the ascending interferogram have an opposite sense of LOS displacement and are positioned on the opposite (western) side of the decorrelated area than those in the descending interferogram, a pattern consistent with approximately NNW-trending right-lateral strike-slip faulting (Funning et al. 2005).

InSAR forward models illustrate that of the two seismological nodal planes, only the NNW-striking right-lateral fault can reproduce the observed fringe pattern (Fig. 10, right column; see Fig. S2 for the second nodal plane test), consistent with our first-order interpretations. This fault is located just west of and oblique to (strike 330°) the northern portion of the 2019 main shock fault, but dips in the opposite direction, 77° northeastward (Table 2). The observed
<table><caption>**Table 4.** Relocated hypocentres of the five largest events studied here, and three other events discussed. Area of confidence (conf.) ellipse refers to the epicentral uncertainties. For full calibrated locations of the whole cluster, see Supplemental datafile S2.</caption><thead><tr><th>Event</th><th>Yr/mo/d</th><th>Hr:min:s<br>(UTC)</th><th>Longitude<br>(°)</th><th>Latitude<br>(°)</th><th>Area of conf.<br>ellipse (km²)</th><th>Focal depth<br>(km)</th></tr></thead><tbody><tr><td>2007 Bokeo main shock</td><td>2007/05/16</td><td>08:56:12.28</td><td>100.76472</td><td>20.56756</td><td>3.6</td><td>13 (+6, -2)</td></tr><tr><td>2011 Tarlay main shock</td><td>2011/03/24</td><td>13:55:10.66</td><td>99.90313</td><td>20.69534</td><td>2.6</td><td>15 ± 4</td></tr><tr><td>2014 Mae Lao main shock</td><td>2014/05/05</td><td>11:08:42.11</td><td>99.70969</td><td>19.69162</td><td>4.1</td><td>11 ± 4</td></tr><tr><td>2019 SYB foreshock</td><td>2019/11/20</td><td>21:03:56.23</td><td>101.20787</td><td>19.44473</td><td>3.3</td><td>13 ± 4</td></tr><tr><td>2019 SYB main shock</td><td>2019/11/20</td><td>23:50:41.50</td><td>101.37618</td><td>19.48227</td><td>2.3</td><td>12 ± 4</td></tr><tr><td>2021 SYB aftershock</td><td>2021/12/19</td><td>21:06:12.20</td><td>101.30785</td><td>19.56231</td><td>3.7</td><td>8 ± 4</td></tr><tr><td>2022 KT main shock</td><td>2022/07/21</td><td>17:07:25.71</td><td>99.84235</td><td>21.13601</td><td>3.0</td><td>20 ± 4</td></tr><tr><td>2023 KT main shock</td><td>2023/11/17</td><td>01:37:08.88</td><td>99.27014</td><td>21.21042</td><td>4.1</td><td>14 ± 4</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Earthquake relocation station coverage and main results. (a) Regional index map (same bounds as Fig. 2) showing seismograph stations at local and regional distances used to calculate the relocation hypocentroid (white triangles) and other stations used to estimate relative locations in the cluster (grey triangles). Background colours are tectonic terranes and their suture boundaries, digitized from Metcalfe (2021). (b) Close-in map showing the distribution of our relocated epicentres (circles, coloured by year of earthquake and scaled by magnitude) with respect to InSAR model faults (red and purple lines), mapped faults (black lines) from DMR and Styron & Pagani (2020) (see Data availability), cities (black squares), World Heritage Sites (yellow diamonds) and major dams along Mekong River (blue rectangles) from Ang et al. (2023).</figcaption></figure>

fringes are reproduced reasonably well using uniform slip of 0.3 m at 1–5 km depth, a little shallower on average than the main shock inverse model slip plane.

According to our hypocentral relocation, the 2021 earthquake initiated at 8 km ± 4 km depth, a little deeper than the InSAR

forward model fault plane (Table 4, Figs 7, 8g, h). Our moment tensor inversion yields a best centroid depth of 3.1 km (mean 3.1 km ± 0.5 km), consistent with the centre of the InSAR model fault (Table 2, Fig. 9c). The waveform model implies NNW-trending right-lateral strike-slip kinematics similar to the InSAR model, with
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Relocated focal depths. (a) Calibrated epicentres of all 158 events in our relocation cluster coloured by focal depth. All available InSAR model faults are plotted as thick lines, in solid red (this study) or purple (Feng *et al.* 2013; Wang *et al.* 2014a; Lee & Shyu 2015). Faults include: DBPF—Dien Bien Phu fault, MCF—Mae Chan fault, MXF—Mengxing fault, NMF—Nam Ma fault, SYBF—Sainyabuli fault, WF—Wan Ha fault, and the MLS—Mae Lao segment and PS—Pan segment of the Phayao fault zone. (b) Histogram of the overall focal depth distribution illustrating our interpreted seismogenic thickness of 17 km (D90).</figcaption></figure>

best strike, dip and rake values of 339° (mean 254° ± 88°), 86° (84° ± 6°) and 140° (10° ± 150°), respectively.

## 3.3 Other earthquakes of the Sainyabuli sequence

The 2019 November 20 $M_w$ 5.7 foreshock is relocated ~20 km west of the main shock fault (Fig. 8g), confirming our initial interpretation that the observed InSAR deformation represents the main shock only. Its moment tensor has similar nodal planes and centroid depth to the main shock (Table 2, Fig. 9a), with the absence of a clear InSAR signal despite the shallow (~4 km) centroid depth perhaps implying that the N–S nodal plane represents the fault. The calibrated hypocentre is deeper at ~13 km, implying rupture upwards from near the base of the fault (Table 4, Fig. 8h).

Relocated aftershocks of the Sainyabuli sequence are mostly distributed northwest of the main shock (Figs 7a, 8g, and Supplemental datafile S2). Early aftershocks in 2019 are aligned along a northwesterly trend, along strike from the InSAR model fault, but later ones in 2020–2021 fall tentatively along a ~15 km-long north-easterly trend, hinting at a conjugate structure involved later in the aftershock sequence (Figs 6, 8g). Calibrated aftershock focal depths range from 5 to 24 km, with uncertainties for each individual depth estimated at ± 4 km (Fig. 8h). The deepest is therefore twice the depth of the 2019 main shock rupture plane.

## 4 THE 2022 AND 2023 KENG TUNG, MYANMAR EARTHQUAKES

The 2022 July 21 $M_w$ 5.9 and 2023 November 17 $M_w$ 5.7 earthquakes are relocated to ~50 km east and ~80 km west of the Keng Tung town in the central Shan Plateau (Fig. 2). While they share the same geographic name, the two earthquakes belong to distinct sequences.

The 2022 July 21 $M_w$ 5.9 Keng Tung earthquake interferograms are more decorrelated than those of the Sainyabuli earthquakes, but a pair of small (~5–10 km diameter) deformation lobes can still be made out in both ascending and descending frames (Figs 11a, e). The signal yields at most 6–7 cm of LOS displacement (Figs 11c, g), with the sense of displacement indicative of either NE-trending left-lateral or NW-trending right-lateral strike-slip kinematics. InSAR forward modelling shows that the NW-trending right-lateral slip plane is the more likely of the two (Fig. 11, right column; see Fig. S3 for the second nodal plane test) (Table 2, rake –174°). The model fault is ~11 km long, strikes 148°, dips 66° southwestwards and has uniform slip of 0.4 m across a relatively shallow depth range of 2–6 km (Table 2).

Our relocated hypocentre for the 2022 Keng Tung earthquake lies west of the InSAR model fault, consistent with the SW-dipping geometry, but is significantly deeper at 20 km ± 4 km (Figs 8a, b, and Table 4). Our waveform model centroid depth of 4.9 km (mean 5.0 km ± 0.7 km) is in close agreement with our InSAR forward model (Table 2). Together, these data suggest that the earthquake
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Zoomed in maps and histograms for distinct sequences: (a–b) 2022–2023 Keng Tung, (c–d) 2007 Bokeo and 2011 Tarlay; (e–f) 2014 Mae Lao; and (g–h) 2019–2021 Sainyabuli, with focal depths of the largest events (thick outlined circles) labelled in parentheses on the maps and in black text on histograms. Maps also show InSAR model faults as thick lines, in solid red (this study) or purple (Feng *et al.* 2013; Wang *et al.* 2014a). The (a) Keng Tung and (g) Sainyabuli regions include our waveform moment tensors, with red lines representing the best mechanism and fuzziness illustrating uncertainty. The histograms include the overall depth distribution (from Fig. 7b) in grey, best centroid depths in blue (see Table 2), and InSAR model bottom depths as dashed red (this study) and purple (published models) lines. Historic events prior to 2007 are excluded from the histogram of each sequence. Grey circles in panel (e) and focal mechanism in panel (f) are from Pananont *et al.* (2017). Faults: DBPF—Dien Bien Phu fault, MCF—Mae Chan fault, MXF—Mengxing fault, NMF—Nam Ma fault, SYBF—Sainyabuli fault, WF—Wan Ha fault, and the MLS—Mae Lao segment and PS—Pan segment of the Phayao fault zone.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 9.** (a–e) Our regional moment tensor solutions of the five earthquakes. In each panel, the left column shows the station distribution in terms of distance and azimuth. The centre of the plot corresponds to the origin of the search space (see Table S1). Blue circles are scaled according to the weighting factor of the corresponding station’s transverse component in the misfit function. The central column is a fuzzy moment tensor illustration of solution uncertainty, with *P* wave radiation patterns of every ensemble solution stacked. If the variability of the ensemble solutions is small, the fuzzy plot has clearly separated black and white fields, consistent with the nodal lines of the global best solution (indicated in red). The right column is a histogram showing the distribution of modelled centroid depths displayed as Gaussian kernel densities (curved solid red line) with vertical solid and dashed red lines giving the median and mean values, the pink shaded areas denoting the 68 per cent and 90 per cent confidence intervals and minimum and maximum values (from narrowest to widest), the plot range indicating the searched parameter space and the vertical grey line indicating the reference values (InSAR centroid slip depth or relocated focal depth, see Table S1).</figcaption></figure>

nucleated in and ruptured upwards from a region of low eventual slip (e.g. Wei et al. 2015; Karasözen et al. 2016). The waveform model has a NW-trending nodal plane with strike, dip and rake values of 151° (mean 176° ± 73°), 77 (80° ± 6°) and −172 (−69° ± 98°), respectively, consistent with InSAR. Relocated aftershocks of the 2022 Keng Tung earthquake are concentrated around the ends of the InSAR model fault. However, a few events are located east of it along a northeastward trend, conjugate to the InSAR fault but consistent with the strike of the nearby left-lateral Wan Ha fault. Aftershocks exhibit focal depths ranging from 11 to 23 km, with individual uncertainties of ± 3–4 km, nucleating at greater depths than the main shock rupture plane (Fig. 8b).

InSAR data for the 2023 November 17 $M_w$ 5.7 Keng Tung earthquake do not show clear deformation signals in either the wrapped or unwrapped interferograms, even after correcting for tropospheric delays (Fig. 12, left column). In the unwrapped interferograms, however, the slight positive to negative change in LOS displacement, close to our relocated epicentre, may be indicative of the main shock deformation (Figs 12d, j). We conducted forward modelling of NW-trending and NE-trending nodal planes to investigate which could be the causative fault (Fig. 12, middle and right columns). The fault geometry of the forward model was taken from the ANSS ComCat (dip, rake), estimated from the InSAR data (strike, length), and obtained from independent earthquake relocation analysis (top and bottom depths), and we assumed a slip value of 0.3 m to allow for agreement with the seismic moment. Our results show that neither fault orientation produces a satisfying fit (visually or statistically) with the InSAR data, suggesting that the focal depth is likely below the limits of InSAR detection for an earthquake of this size (i.e. ~10 km depth; Funning & Garcia 2019).

Our relocated hypocentre places this earthquake on an unmapped fault in mountainous topography west of Keng Tung, nucleating at 14 km ± 4 km depth (Table 4, Figs 6, 8a, b). Our waveform modelling reveals a consistent centroid depth of 10 km (mean 9.0 km ± 1.5 km) (Table 2). Although InSAR forward models cannot resolve the fault geometry, the orientation of the two possible nodal planes are in agreement with the focal mechanisms, exhibiting strike, dip and rake values of 166° (mean 173° ± 26°), 76 (79° ± 4°) and −168 (−158° ± 49°), respectively. Although the main shock ruptured an unmapped fault, the NW-striking nodal plane parallels a structural fabric evident in the topography (Fig. 8a). Our catalogue also includes one aftershock of this earthquake, ~9 km to the southeast (Fig. 8a), with a similar focal depth of 14 km ± 4 km (Supplemental datafile S2).

# 5 OTHER NOTABLE EARTHQUAKES

## 5.1 The 2014 Mae Lao, Thailand earthquake

The 2014 May 5 $M_w$ 6.1 Mae Lao earthquake in northern Thailand and several of its aftershocks were included in our relocation cluster. We found that the main shock initiated at 11 km ± 4 km depth (Table 4), and that aftershock focal depths range from 5 to 20 km, each with uncertainties of 1.4–4 km (Figs 8e, f, and Supplemental datafile S2). The northeasterly lineament of the distribution of our relocated events is roughly consistent with, but slightly oblique to the ENE-striking pattern resolved by Pananont et al. (2017), shown as grey circles in Fig. 8(e). Regional broad-band waveform inversion in Pananont et al. (2017) suggests a main shock centroid depth of 12 km (Fig. 8f), similar to our calibrated focal depth (Table 4).
<figure><img src="image_11.png" /><figcaption>**Figure 10.** Observed (left column) and forward model (right column) interferograms of the 2021 $M_w$ 5.5 Sainyabuli aftershock, shown both wrapped (a–b, e–f) and unwrapped (c–d, g–h), with red indicating motion towards the satellite. The thick black line is our inferred forward model fault, the dashed rectangle marks its corner coordinates and the arrow indicates the slip vector azimuth. The black star is our relocated epicentre (see Table 4). See Fig. S2 for the forward model test of the second nodal plane.</figcaption></figure>

The regional moment tensor solution from Pananont *et al.* (2017) also indicates a NNW-striking, right-lateral rupture plane. We were unable to study this sequence using Sentinel-1 InSAR data, which first became available a few months after the main shock.

Wang *et al.* (2014a). Aftershock focal depths range from 10 to 18 km, with individual uncertainties spanning between 3 and 8 km (Supplemental data file S2). In general, these aftershocks all occur deeper than the main area of concentrated slip revealed by InSAR (Feng *et al.* 2013; Wang *et al.* 2014a).

## 5.2 The 2011 Tarlay, Myanmar earthquake

The 2011 March 24 $M_w$ 6.8 Tarlay earthquake ruptured the major, NE-trending Nam Ma fault in Myanmar (Fig. 6). It was a destructive event, killing at least 74 people, damaging or destroying over 700 homes rendering >3000 people homeless and impacting at least 18 000 people (United Nations Office for the Coordination of Humanitarian Affairs 2011, last accessed 2024 February 28). Its left-lateral mechanism and slip distribution were constrained using InSAR and SAR pixel tracking (Feng *et al.* 2013; Wang *et al.* 2014a). The earthquake generated a ~30 km-long surface rupture with shallow offsets of up to ~2 m, peak slip of at least ~4 m concentrated at ~3–5 km depth (implying a ~50 per cent shallow slip deficit), and major slip down to ~10 km depth.

Our earthquake relocation reveals that the main shock initiated at ~15 km depth (Table 4), a few kilometres below the InSAR model fault bottom depth of 12 km in Wang *et al.* (2014a), and at the western end of the InSAR model fault, suggesting that the earthquake ruptured unilaterally from west to east (Figs 8c, d). Large aftershocks are distributed mostly at the fault endpoints and within the surrounding rock volume, consistent with InSAR range offsets which show a ~1 km wide zone of off-fault deformation

## 5.3 The 2007 Bokeo, Laos earthquake

The 2007 May 16 $M_w$ 6.3 Bokeo earthquake occurred close to the western section of the Mae Chan fault in western Laos (Fig. 6). Published focal mechanism (ANSS ComCat) of the main shock suggested either a NW-striking right-lateral or a NE-striking left-lateral rupture plane, the latter consistent with kinematics of the Mae Chan fault. Additionally, Lee & Shyu (2015) observed an apparent deformation signal in InSAR imagery, and used forward modelling to suggest an ~18 km-long rupture with 0.7 m of slip distributed between 4 and 8 km depth.

However, the relocated main shock epicentre, with its 4 km² area of confidence ellipse, occurs at least 20 km west of the InSAR model fault in Lee & Shyu (2015), albeit along on the same Mae Chan fault (Table 4). There is no clear surface deformation signal in the InSAR imagery at this location. Our calibrated focal depth reveals that the main shock initiated at 13 km (+6,–2 km), and aftershock focal depths range from 6 to 21 km, with individual uncertainties of 3–9 km (Supplemental datafile S2). These are distributed away from the main shock in all directions—some occurring along and off the
<figure><img src="image_12.png" /><figcaption>**Figure 11.** Observed (left column) and forward model (right column) interferograms of the 2022 *M*<sub>w</sub> 5.9 Keng Tung earthquake, shown both wrapped (a–b, e–f) and unwrapped (c–d, g–h), with red indicating motion towards the satellite. The thick black line is our inferred forward model fault surface projection, the dashed rectangle marks its corner coordinates and the arrow is the slip vector azimuth. The black star is our relocated epicenter (see Table 4). See Fig. S3 for the forward model test of the second nodal plane.</figcaption></figure>

mapped surface trace of the Mae Chan fault, and others along other, shorter mapped secondary faults (Figs 8c, d).

Our relocations therefore suggest that the InSAR signal interpreted by Lee & Shyu (2015) as coseismic is something else. There may be a number of factors at play in this misidentification. First, the noisy and decorrelated InSAR data hinder the ability to identify coseismic signals accurately. Secondly, the main shock may have ruptured a fault conjugate to the Mae Chan fault, to which the satellite look direction might be insensitive. Thirdly, the main shock may have ruptured at too great a depth for a strong InSAR signal. The focal depth of the 2007 Bokeo main shock is strongly constrained by teleseismic depth phases at 13 km (global minimum). A secondary local minimum in the relocation is estimated at approximately 17 km; this deeper focal depth supports the latter scenario.

# 6 DISCUSSION

## 6.1 Seismogenic layer thickness and earthquake rupture mechanics

In continental earthquake belts, the seismogenic layer refers to the brittle part of the crust or lithosphere capable of sustaining seismic rupture. Its thickness is a key parameter in seismic hazard analyses, setting the depth limit for slip in major earthquakes and thus partly controlling rupture area and seismic moment. The base of the

seismogenic layer can be estimated in a variety of ways: by inverting for coseismic slip distributions in the largest earthquakes, by modelling interseismic locking depths using geodesy, by analysing seismic tomographic or magnetotelluric data sets or by determining precise focal or centroid depths for large seismicity data sets. In the last case, it is often parametrized as ‘D90’, defined as the depth above which 90 per cent of seismicity occurs (e.g. Zeng et al. 2022; Ellis et al. 2023; Seebeck et al. 2023).

In our 158 event relocated earthquake catalogue, the 90th percentile focal depth is 17 km. This lies between InSAR-derived estimates for the deepest coseismic slip in the largest modern earthquakes in this region: ~20 km in the 1995 *M*<sub>w</sub> 6.8 Menglian earthquake (Ji et al. 2017) and ~12 km in the 2011 *M*<sub>w</sub> 6.8 Tarlay earthquakes (Feng et al. 2013; Wang et al. 2014a). Our D90 estimate of 17 km is also consistent with the 10–21 km range of geodetically derived locking depths and well-constrained centroid depths along the southern Sagaing fault, west of our study area (e.g. Maurin et al. 2010; Pananont et al. 2017; Tin et al. 2022; Fadil et al. 2023). It is somewhat deeper than the locking depths of 5–15 km along the southern Red River fault, east of our study area (Lu et al. 2021), but magnetotelluric data show a resistive fault zone extending to ~20 km depth, more consistent with our D90 (Ye et al. 2022). In the 2014 Mae Lao, Thailand sequence—the earthquakes with the best local station coverage within the Golden Triangle region—the deepest aftershock occurred at 18 km depth according to regional waveform inversion (Pananont et al. 2017), also in close agreement with our interpreted seismogenic thickness.
<figure><img src="image_13.png" /><figcaption>**Figure 12.** Observed (left column) and forward model (middle and right columns) interferograms of the 2023 *M*<sub>w</sub> 5.7 Keng Tung event, shown both wrapped (a–c, g–i) and unwrapped (d–f, j–l), with red indicating motion towards the satellite. Observed interferograms (left column) and forward models of both nodal planes (middle and right columns) show that InSAR cannot distinguish the causative fault in this instance. Dashed rectangles are the model faults in map view, with thick black lines along their surface projections and arrows denoting the slip vector azimuth. The black star is our relocated epicenter (see Table 4).</figcaption></figure>
In the continents, crustal seismicity is thought to be limited to a threshold temperature of ~350 °C (Jackson *et al.* 2021), in close agreement with our D90 of 17 km assuming a 20 °C km⁻¹ geothermal gradient. Our results imply that half to a third of the average regional crustal thickness of 35–45 km is seismogenic (Searle & Morley 2011; Noisagool *et al.* 2014). To place our estimated D90 of 17 km in the context of other global continental shear zones, it is at the deeper end of the ~13–17 km range of values estimated for the eastern California shear zone (Zeng *et al.* 2022), but slightly shallower than values of ~20–25 km for eastern Turkey and the South Island of New Zealand (Turkelli *et al.* 2003; Duman *et al.* 2018; Ellis *et al.* 2023). The seismogenic layer in the Shan plateau thus appears to be of intermediate thickness among comparable continental plate boundary zones.

Our D90 estimate provides additional context for our study of the 2019 $M_w$ 6.2 Sainyabuli and 2022 $M_w$ 5.9 Keng Tung earthquakes, which can now be seen to have ruptured only the upper half of the seismogenic layer (Figs 8b, h). This potentially leaves the lower half as an ongoing seismic hazard. Similar inferences were made following the 2003 $M_w$ 6.6 Bam, Iran earthquake, where coseismic slip was concentrated at depths shallower than ~10 km, but locally recorded aftershocks illuminated a seismogenic layer of double that thickness (Funning *et al.* 2005; Jackson *et al.* 2006). Other major earthquake sequences that illustrate similar vertical partitioning of the continental seismogenic layer include the 2008–2009 Qaidam, China earthquakes (Elliott *et al.* 2011) and the 1981 and 1998 Golbaf-Sirch, Iran earthquakes (Berberian *et al.* 2001). This further emphasizes that seismic hazard in the immediate vicinity of a recent earthquake should not be assumed to be reduced.

Finally, the largest earthquakes studied here illustrate other aspects of the mechanics of coseismic rupture. In these earthquakes, the peak slip and centroid depths from InSAR and waveform modelling are generally shallower than calibrated focal depths from relocation, implying that seismic slip propagated upwards (Fig. 8). For example, the 2019 Sainyabuli main shock nucleated at 12 km ± 4 km near the base of the InSAR model fault and well below concentrated slip at 3–7 km depth, while the 2022 Keng Tung main shock initiated at 20 km ± 4 km, much deeper than the uniform slip forward model fault at ~2–6 km (Figs 8b, h). While acknowledging the limited local-distance station coverage near Keng Tung, our calibrated focal depth is still at least several kilometres deeper than the InSAR-modelled fault, indicating nucleation within a zone of low eventual slip. Confidence in this interpretation is provided by observations of other, better recorded earthquakes. For instance, the 2014 $M_w$ 6.1 South Napa earthquake in California nucleated at ~11 km but eventual slip concentrated at just 0–5 km depth (Wei *et al.* 2015). The 2011 $M_w$ 5.9 Simav earthquake in Turkey similarly nucleated at 18 km depth, much below the InSAR rupture plane at ~6–11 km (Karasözen *et al.* 2016). These discrepancies between deep hypocentres and shallow coseismic slip may reflect a tendency for earthquakes to nucleate within a creeping zone near the base of the seismogenic layer (Wei *et al.* 2015).

## 6.2 Fault structural immaturity and implications for seismic hazards

Shallow slip deficit (SSD) expresses the common discrepancy in InSAR model slip distributions between peak slip (normally inferred at depths of a few kilometres) and surface slip (e.g. Fialko *et al.* 2005; Dolan & Haravitch 2014). Dolan & Haravitch (2014) suggested that earthquakes on structurally immature faults are characterized by larger SSDs (in which only ~50–60 per cent of

slip reaches the surface) compared to those on mature faults. Our slip model for the 2019 $M_w$ 6.2 Sainyabuli main shock has an SSD of 80 per cent, consistent with a structurally immature fault (e.g. Dolan & Haravitch 2014). However, we acknowledge that this value is also influenced by other factors such as the quality of near-field geodetic data (e.g. Xu *et al.* 2016) and modelling choices including use of an elastic half-space (e.g. Ragon *et al.* 2018; Marchandon *et al.* 2021). Therefore, other indicators are needed to ascertain the structural maturity of the host faults. The surface projection of our InSAR model fault is inconspicuous in the topography, although it is subparallel to and across strike from nearby mapped traces of the Sainyabuli fault zone (Fig. 8g). A wide zone of aftershock seismicity is another characteristic of a newly developing fault (Perrin *et al.* 2021), and our relocated Sainyabuli aftershocks are distributed 1–10 km west and up to 6 km north of the InSAR model fault, with one event located as far as 17 km away (Fig. 8g). Since our InSAR model fault dips steeply eastwards, it appears that the aftershocks illuminate various secondary fault strands. We conclude that observations of the 2019 Sainyabuli sequence are indeed indicative of young, immature faulting. However, we acknowledge that in regions with such high rates of precipitation and erosion, even some structurally mature faults may not preserve offsets in the topography (e.g. Reitman *et al.* 2023).

While interferograms of the 2022 $M_w$ 5.9 Keng Tung earthquake are noisier than those of the Sainyabuli earthquake, our InSAR uniform slip forward modelling (top depth 2 km) suggests that it did not rupture to the surface. While the earthquake ruptured an unmapped NW-trending fault, this parallels a distinct, northwesterly structural grain evident in the local topography (Fig. 8a). The 2023 $M_w$ 5.7 Keng Tung main shock also co-locates along a subtle, NW-trending topographic lineament, although we were unable to distinguish the fault plane from the auxiliary plane due to the lack of a clear InSAR signal. It is likely that this earthquake ruptured deeper than can be resolved with InSAR. Together, these observations imply that the Keng Tung region, like Sainyabuli, contains unmapped faults with little topographic expression but which are capable of hosting significant earthquakes. Though the wet climate is likely a factor, the Golden Triangle region shares this characteristic with other regions of distributed deformation and secondary faulting, such as eastern California (e.g. Sethanant *et al.* 2023).

Looking more broadly across the Golden Triangle region, while NE-trending sinistral faults are the most pronounced in the landscape, our results demonstrate that several recent $M_w$ 5.5–6.2 events occurred on NW- to NNW-trending right-lateral faults (Fig. 7a). The 1995 July 11 $M_w$ 6.8 Menglian earthquake, located ~90 km NW of Keng Tung (Fig. 2), shows that even larger earthquakes are possible on NW-striking faults that are little evident in the topography (Ji *et al.* 2017). Faults in the central and northern Shan Plateau do not seem to align with Tertiary terrane boundaries, such as the NNE-trending suture zones between the Chiang Mai-Chiang Rai suture zone and Sukhothai Terranes (Fig. 6). This suggests that the largest inherited structures evident in the regional geology do not influence the pattern of earthquakes. Geological inheritance may also play a greater role in the southern Shan Plateau, in northwestern Thailand and north central Laos, where several mapped faults are parallel to the major terrane boundaries (e.g. Searle & Morley 2011; Chansom *et al.* 2022).

Our association of at least three newly identified faults with significant recent earthquakes could spur improvements to local seismic hazard estimations. To include these as fault sources in probabilistic seismic hazard analysis (PSHA), detailed fault mapping coupled with paleoseismic investigations and seismic catalogues will be required to obtain fault slip and earthquake rates. However, in the
interim until such rates are available, our work provides useful information for hazard practitioners working in SE Asia. First, our results for the Sainyabuli and Keng Tung sequences could help improve area source hazard models and fine tune regional ground motion models. Secondly, our new constraints on the regional, seismicity-defined seismogenic thickness (D90) help define expected rupture lengths and ground shaking distributions for earthquakes of given magnitudes.

## 7 CONCLUSIONS

We characterize recent large earthquakes in the Golden Triangle region and reevaluate regional seismic potential. The 2019 $M_w$ 6.2 Sainyabuli main shock, its 2021 $M_w$ 5.5 aftershock and the 2022 $M_w$ 5.9 Keng Tung earthquake all ruptured previously unmapped, ~10–20 km long, NW-trending right-lateral strike-slip faults in the southern and central Shan Plateau, conjugate to the more pronounced NE-trending left-lateral faults in this region. These events exhibited rupture behaviours that may be linked to fault structural immaturity, including limited topographic expression along the model fault surface projections, high shallow slip deficits (80 per cent in the 2019 Sainyabuli main shock) and widely distributed aftershocks. In the 2019 Sainyabuli earthquake, peak slip and centroid depths of 4–5 km from InSAR and regional waveform inversion are much shallower than the 12 km calibrated focal depth from relocation, implying nucleation of the earthquake within a low slip zone near the bottom of the InSAR fault. We establish for the first time a seismogenic thickness of ~17 km, derived from the 90 per cent seismicity cut-off depth (D90) of our 158 calibrated hypocentres. This also suggests that the deeper half of the seismogenic zone beneath the Sainyabuli earthquake remains a potential hazard for future ruptures. Across the Shan Plateau, short, hidden, immature faults, many of them conjugate to the longer, mature faults, are also capable of generating sizeable earthquakes and should not be overlooked in seismic hazard calculations, especially for the designing and retrofitting of nearby hydroelectric dams and cultural sites. Finally, our study provides a blueprint for multi-event earthquake relocation in sparsely instrumented regions. We find that problematic azimuthal gaps can be reduced by incorporating multiple earthquake sequences into a single relocation cluster, with InSAR observations of the largest events an important, independent check on the resulting epicentral distribution.

We kindly thank Santawat Sukrungsri (Thai Meteorological Department) and Dr Harley Benz (USGS) for their immense support in providing regional arrival time and waveform data sets. The authors are grateful to Dr Tuna Onur and Ivan Wong for their insights on seismic hazard assessments for emerging faults. We thank Win Shwe Sin Oo and Dr. Wei Shengji (Nanyang Technological University) for their support of this work and stimulating discussion. This study is partly supported by the Diana and Martin Hocking Graduate Scholarship (University of Victoria) awarded to IS.

## SUPPORTING INFORMATION

Supplementary data are available at *GJIRAS* online.

## DATA AVAILABILITY

Sentinel-1 SAR images were obtained from the Alaska Satellite Facility (https://search.asf.alaska.edu). The Visible Earthquakes InSAR tool was used for preliminary estimates of fault geometries

(https://visible-earthquakes.appspot.com, last accessed July 2022). InSAR displacements were corrected for residual linear trends and atmospheric noise using the Generic Atmospheric Correction Online Service (Yu et al. 2017) (http://www.gacos.net). For maps and InSAR topographic phase correction, we used the Shuttle Radar Topography Mission 3 arc second Digital Elevation Model (Farr & Kobrick 2000; Rosen et al. 2000; Kobrick 2006; Farr et al. 2007) obtained via https://topex.ucsd.edu/gmtsar/demgen/ and https://opentopography.org, and the USGS Global 30 Arc-Second Elevation Model accessed via https://earthexplorer.usgs.gov. InSAR inversion and forward modelling codes were kindly provided by the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). Regional mapped faults (longitudes and latitudes within 97.5°–102.8° and 13°–22°, respectively) were obtained from the Department of Mineral Resources, Thailand (DMR) through Weerachat Wiwegwin (personal communication) and from https://gis.dmr.go.th/DMR-GIS/. Global active faults (outside the aforementioned bounds) are from the Global Earthquake Model Foundation’s Global Active Faults Database (Styron & Paganini 2020). Focal mechanisms were gathered from the automated GCMT catalogue (Dziewonski et al. 1981; Ekström et al. 2012) (https://globalcmt.org/CMTsearch.html) and the ANSS ComCat (U.S. Geological Survey, Earthquake Hazards Program 2017) (https://doi.org/10.5066/F7MS3QZH). Initial earthquake epicentres were obtained from the International Seismological Centre (ISC), Engdahl-van der Hilst-Buland Bulletin (Engdahl et al. 1998; Weston et al. 2018; Engdahl et al. 2020; International Seismological Centre 2023) (https://doi.org/10.31905/PY08W6S3). Earthquake arrival times were also gathered from the ISC Bulletin (https://doi.org/10.31905/D808B830), ANSS ComCat (https://doi.org/10.5066/P95R8K8G), Incorporated Research Institutions for Seismology (http://ds.iris.edu/ds/nodes/dmc/data), the Thai Meteorological Department (TMD), DMR Thailand (Santawat Sukrungsri, personal communication) or picked by ourselves from a waveform catalogue from Pananont et al. (2017). Station locations are from the International Registry of Seismograph Stations through the ISC (https://doi.org/10.31905/EL3FQQ40), USGS NEIC, TMD, DMR and the Provincial China Network (Sun Ruomei, personal communication). Waveforms used in our moment tensor inversion are from the EarthScope Consortium Web Services (https://service.iris.edu/), including the following seismic networks: GE, GEOFON Data Centre (1993); IC, Albuquerque Seismological Laboratory (ASL)/USGS (1992); IU, Albuquerque Seismological Laboratory/USGS (2014); MM, Department of Meteorology and Hydrology - National Earthquake Data Center (2016); TM (TMD Seismic Network). Relocated events of the independent 2014 Mae Lao cluster is available at the Global Catalogue of Calibrated Earthquake Locations (GC-CEL) via https://www.sciencebase.gov/catalog/item/6385574dd34ed907bf779994, and relocation of the full cluster of this study and station locations are publicly available at https://www.sciencebas e.gov/catalog/item/6385574dd34ed907bf779994. The locations of dams in Fig. 6 are from The Mekong dam data base, publicly available at https://doi.org/10.21979/N9/ACZIJN (Ang et al. 2023).

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