Shallow seismic velocity structure beneath San Miguel volcano, El Salvador, estimated using seismic ambient noise (0.2–1.3 Hz)

Shallow seismic velocity structure beneath San Miguel volcano, El Salvador, estimated using seismic ambient noise (0.2–1.3 Hz) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shallow seismic velocity structure beneath San Miguel volcano, El Salvador, estimated using seismic ambient noise (0.2–1.3 Hz) Kevyn Enrique Pineda, Takumi Hayashida This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6100331/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Earth, Planets and Space → Version 1 posted 5 You are reading this latest preprint version Abstract San Miguel volcano is one of the most active volcanoes in El Salvador, yet its structural characteristics remain underexplored. We developed a one-dimensional seismic velocity structure model by analyzing seismic ambient noise recorded around the volcano and inverting the Rayleigh wave dispersion curves. The data were obtained from a temporary seismograph network deployed in 2014. We applied the spatial autocorrelation (SPAC) method and ambient noise seismic interferometry (ANSI), assuming the temporal and spatial uniformity of ambient noise characteristics. The SPAC method enabled the derivation of phase velocities for surface waves within the frequency range of 0.2 to 1.0 Hz. Additionaly, we estimated Rayleigh wave group velocities using ANSI, which employs Green's function derived from cross-correlating ambient noise. The resulting dispersion curve was acquired in the 1.0–1.3 Hz frequency band. The velocity model revealed four sedimentary layers, with S-wave velocities ranging from 1.0 to 2.5 km/s overlying a half-space layer. Using the obtained velocity model, we located volcano-tectonic earthquakes, resulting in more accurate hypocenter determinations. The seismicity was found to align with a deformation zone known as the San Miguel Fracture Zone, situated on the volcano's northern flank. San Miguel volcano seismic ambient noise velocity model seismic interferometry spacial autocorrelation method Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction San Miguel volcano is located in the eastern part of El Salvador and is recognized as one of the country’s most active volcanoes, having experienced 28 eruptions over the past 500 years. This symmetrical stratovolcano rises to an elevation of 2130 m, covering an area of approximately 190 km 2 (Jiménez et al., 2020 ). The volcanic composition is predominantly basaltic-andesite. However, stratigraphic evidence reveals the presence of Plinian acid deposits, attributed to Pacayal volcano, an inactive volcanic system situated to the northwest of San Miguel volcano (Fig. 1 ) (Patlan Almeida, 2012 ). Situated in the eastern part of the El Salvador Fault Zone, a tectonic deformation zone extends 150 km and 20 km in width (Alonso-Henar et al., 2014 ), San Miguel volcano is associated with the San Miguel Fracture Zone (SMFZ). This fracture zone, identified by Schiek ( 2009 ), extends from the northwest to the southern flank of the volcano (Fig. 1 ). Its structure indicates both effusive and explosive activity during the Holocene, with numerous Strombolian eruptions producing pyroclastic density currents, spatter, and ashfall from the crater, and basaltic-andesitic lava flows along its slopes (Jiménez et al., 2020 ). Throughout its historical effusive activity, 11 lava flows have been recorded, with the largest event occurring in 1699 on the southeastern slope. In contrast, the explosive activity includes at least 20 events, most of which are concentrated on the western flank of the volcano (Jiménez et al., 2018 ). In recent years, San Miguel volcano has displayed intermittent volcanic activity, including the release of ash and gas plumes. The most recent eruption in 2013 was accompanied by various seismic signals, such as volcano-tectonic earthquakes, long-period earthquakes, and occasional explosions (García, 2016 a). Additionally, pulsating gas emissions were observed, with plumes reaching altitudes of up to 200 meters. The eruption expulled juvenile material, lapilli, and scoria fragments (Scarlato et al., 2017 ). Despite this activity, the lack of detailed on the structural properties of the volcano has limited our understanding of the geological and geophysical processes within it, hindering the identification of unstable areas linked to volcanic hazards such as slope failure or lava flows. Ambient seismic noise has been widely used to investigate Earth's structure, from shallow subsurface sediments (e.g., Foti et al., 2011 ; Hayashi et al., 2022 ) to upper mantle features (e.g., Yang et al., 2008 ; Gao & Shen, 2014 ; Emry et al., 2019 ). Seismic ambient noise arises from various sources, including both human activities and natural phenomena. Human-induced noise, commonly referred to as 'cultural noise,' typically dominates the short-period band (< 1 s) (McNamara & Boaz, 2019 ). In contrast, seismic ambient noise in the intermediate period range (1–30 s) is mainly influenced by microseisms, which are generated by the interaction of ocean waves with coast and ocean floor topography (Chouet & Matoza, 2013 ). Over the past decade, numerous studies using seismic ambient noise to examine volcanoes have emerged across Latin America (e.g., Spica et al., 2015 ; Lanza et al., 2016 ; Perton et al., 2022 ). This study aims to investigate the structural properties of San Miguel volcano using seismic ambient noise data to estimate a local shallow seismic velocity model, providing critical insights for future detailed tomographic studies. Additionally, we analyze the seismicity associated with the volcano and its spatial distribution. Data This study utilized seismic ambient noise data recorded at four broadband seismic stations deployed around San Miguel volcano. Following the most recent eruption in December 2014, the Ministry of Environment and Natural Resources of El Salvador (MARN) established a temporal seismic network consisting of four sensors to monitor volcanic activity (Table 1 ). The network comprised three Trillium Compact seismometers and one Lennartz 3D seismometer with a sampling frequency of 100 Hz. All the seismometers were powered by solar panels and car batteries. Although the data were recorded using instruments from different manufacturers, it was processed together since the stations operated with similar frequency sensitivity. The network was predominantly situated on the northern side of San Miguel volcano (Fig. 1 ), with continuous observations conducted from February to April 2014. The inter-station distances within the network ranged from 1.5 to 5.5 km. Table 1 Station locations shown in Fig. 1 . Station Code Latitude Longitude Elevation (m) VSM 13.44096 -88.27220 1698 LCY 13.42135 -88.29365 997 BLLM 13.44255 -88.23876 610 RANC 13.43411 -88.28855 1248 Methods Spatial Autocorrelation method The spatial autocorrelation (SPAC) method effectively determines subsurface structural properties by assuming surface waves as the dominant component in noise records observed simultaneously across a temporary seismic array (Aki, 1957 ; Okada, 2003 ). This method employs a technique to estimate the phase velocity of surface waves through the azimuthal averaging of cross-spectra from ambient noise measurements. In this study, we aimed to derive the dispersion curves of the fundamental mode Rayleigh wave using the vertical component ambient noise records. The SPAC coefficient \(\:\rho\:\left(r,f\right)\) , a frequency-dependent function for a given inter-station distance (𝑟), is defined considering azimuthal averaging of cross-spectra: $$\:\rho\:\left(r,f\right)={\int\:}_{-\pi\:}^{\pi\:}\frac{\text{Re}\left[{S}_{0r}\left(f,\theta\:\right)\right]}{\sqrt{\left|{S}_{00}\left(f\right)\right|\text{}\left|{S}_{rr}\left(f,\theta\:\right)\right|}}d\theta\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where \(\:{S}_{0r}\) is the cross spectral density of ambient noise between two sensors placed at the center of the circle (0) and on the circumference ( r ) with azimuth angle of θ , and \(\:{S}_{00}\) and \(\:{S}_{rr}\) denote the power spectral densities of ambient noise at the center and circumference of the circle. If the ambient noise environment allows noise propagation from all azimuths, the outcome derived from processing records at only two points without azimuthal averaging aligns with Eq. (1) (Morikawa et al., 2004 ). It has been noted that the measurement of two points can be effectively substituted by conducting measurements over several hours to several days (Chavez-Garcia et al., 2006 ; Hayashi et al., 2013 ; Yokoi et al., 2021 ). Eq. (1) indicates the procedure for calculating the phase velocity \(\:\left(c\right)\) in the SPAC method, involving the analysis of ambient seismic noise and utilizing the relationship between phase velocity and the zeroth-order Bessel function of the first kind \(\:{J}_{0}\) . $$\:\rho\:\left(r,\omega\:\right)=\:{J}_{o}\left(\:\frac{r\omega\:}{c}\:\right).\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ Ambient noise seismic interferometry (ANSI) Ambient noise seismic interferometry (ANSI) involves generating a virtual seismic signal by cross-correlating seismic waveforms recorded at two stations (Wapenaar & Fokkema, 2006 ). The time derivative of the cross-correlation function from long-term noise measurements corresponds to Green's function between two distant stations, representing the Earth’s subsurface properties (e.g., Lin et al., 2008 ). Data processing basically followed the methodology proposed by Bensen et al. ( 2007 ). Using continuous recordings, we applied cross-correlation to retrieve Green’s functions between station pairs. The continuous records were divided into 1-hour segments, which were then demeaned, detrended, and corrected for instrumental response. To reduce the non-stationarity of the wavefield, normalization was applied in both the frequency and time domains using the one-bit normalization (Larose et al., 2004 ), where all positive amplitudes were replaced by + 1 and all negatives by -1. Cross-correlations were computed for every 1-hour segment for all station pairs and subsequently stacked to improve the signal-to-noise ratio (SNR). Cross-correlation functions were computed for each pair of east-west (E) and vertical (Z) components, north-south (N) and Z components, Z and E components, and combinations of E and Z components. The resulting cross-correlation functions were then transformed to decompose them into radial (R) and Z component contributions based on geometric relationships, using the following formula (Yamanaka et al., 2010 ): $$\:\left(\begin{array}{c}ZR\\\:RZ\end{array}\right)=\left(\begin{array}{cc}ZE&\:ZN\\\:EZ&\:NZ\end{array}\right)\times\:\left(\begin{array}{c}\text{s}\text{i}\text{n}\theta\:\\\:\text{c}\text{o}\text{s}\theta\:\end{array}\right),\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ where \(\:\theta\:\) indicates the azimuth angle between two sensors. To enhance the strength of surface waves in the cross-correlations functions, we employed the technique established by Takagi et al., ( 2014 ), which separates body waves and Rayleigh waves utilizing the ZR and RZ components. For the same station pair, the cross-correlation function of the RZ component, subtracted from the ZR component and divided by 2 (i.e., (ZR-RZ)/2), theoretically represents the Green's function for Rayleigh waves. We obtained the Green's function for Rayleigh waves by differentiating the cross-correlation functions of the (RZ-ZR)/2 component and determined the group velocity by applying the multiple-filter technique of Dziewonski et al. ( 1969 ), which uses narrow band-pass filters to estimate group velocities. The data from all the station pair were processed, however, not all of them produced meaningful result. The station pair with the biggest elevation difference (BLLM – VSM) failed to produce stable outcomes. Even with the above, with the remaining station pairs we obtained the group velocity corresponding to the frequency range in which the data were filtered. Both the SPAC and ANSI methods discussed in this study are designed to estimate the phase and group velocities of Rayleigh waves by processing ambient noise waveform records. The SPAC method focuses on Rayleigh waves with longer wavelengths relative to the sensor spacing, whereas the ANSI method targets Rayleigh waves with shorter wavelengths. Velocity model estimation The velocity structure was estimated using the obtained dispersion curve, which included both phase and group velocities, by implementing a joint inversion technique (Hayashida et al., 2019 ). The joint inversion algorithm employed the downhill simple technique (Nelder & Mead, 1965 ) combined with a simulated annealing method (Ingber, 1989 ). The inversion process was based on an initial model that considered P-wave velocity (Vp), S-wave velocity (Vs) variations, the number of layers, and their thicknesses. The resulting velocity structure model provided Vs values calculated from the dispersion curve, while Vp was determined using a linear relationship with Vs, as described by Ludwig ( 1970 ). Multiple velocity models were generated to compute theoretical dispersion curves, which were then compared to those derived from the ambient noise data. The value of mismatch (misfit) was determined for each of the calculated profiles. The misfit value was calculated for each profile. Iterative inversion enabled the determination of the number of iterations through a misfit threshold, ensuring the model converged with a good fit to the data. Initially, the inversion used simple initial models; however, if the misfit values were unsatisfactory, adjustments were made to the initial parameters, and an additional layer was added to the model. Table 2 presents the inversion parameters for the initial model. Table 2 Parameters of the initial model used for inversion. Layer Vp (km/s) Depth (km) Vs (km/s) Density (g/cm 3 ) 1 2.30 0.21 to 0.40 0.75 to 1.25 1.98 2 2.75 0.30 to 0.60 0.90 to 1.60 2.10 3 3.28 1.00 to 1.40 1.80 to 2.30 2.36 4 3.51 0.70 to 1.20 1.90 to 2.20 2.52 5 7.23 - 5.00 to 5.90 2.80 Results We calculated SPAC coefficients for each sensor-to-sensor pair to determine the phase velocity, using the vertical components of the ambient noise. These coefficients were then averaged across all station pairs (Fig. 2 ). The highest coherence of the curves was observed at frequencies below 0.4 Hz, with a rapid decline at frequencies above 0.5 Hz, likely due to weak ambient noise levels or the strong presence of incoherent waves (Cho & Iwata, 2021 ). The phase velocity estimation was limited at higher frequencies because multiple candidates exist in the short wavelength range. To address this, we applied the method introduced by Ekström et al. ( 2009 ), which extends the frequency range for measuring phase velocity by accounting for the zero-crossing frequencies in the SPAC coefficients. Consequently, we determined the phase velocity in the frequency range of 0.1 to 1.0 Hz. Figure 3 presents the derived cross-correlation functions. In the analysis, we considered both the causal and acausal parts of Green’s function. The quality of all dispersion curves was carefully evaluated, focusing on positive and negative time symmetry. As a result, we obtained group velocity within the frequency range of 1.0 to 1.3 Hz (Fig. 4 a). The final velocity model, derived from the dispersion curve, phase and group velocities, consists of four layers overlying a homogeneous half-space, extending to a depth of 3.2 km beneath the surface of the San Miguel volcano (Fig. 4 b). Table 3 summarizes the results of the calculated velocity structure. The model includes a superficial layer with a thickness of 0.3 km, a P-wave velocity (Vp) of 2.4 km/s, and an S-wave velocity (Vs) of 1.0 km/s. In the deepest layer, Vp exceeds 7.4 km/s and Vs is over 5.5 km/s, though the thickness of this layer is undetermined due to limited control of deeper structures. The results in the deepest layer should be interpreted with caution since our dispersion curves data was limited at low frequencies. However, multiple inversions were performed to ensure the parameters in the final layer do not affect the resulting velocity model. Table 3 Parameters of the obtained velocity structure in San Miguel volcano. Thickness (km) P-wave velocity (km/s) S-wave velocity (km/s) 0.30 2.41 1.00 0.46 2.77 1.33 1.18 3.45 1.95 0.89 3.44 1.94 - 7.45 5.55 Volcano-tectonic earthquake location We located earthquakes associated with the volcano to validate our calculated velocity model. A waveform shape analysis was employed to distinguish volcanic seismicity from tectonic sources. Since San Miguel volcano exhibits variable volcanic seismicity (García, 2016 ), our analysis focused on Volcano-Tectonic (VT) earthquakes, characterized by impulsive P-wave arrivals, which aid in their identification. The events were selected using the Python open-source tool Repeating Earthquakes Detector (RedPy) (Hotovec-Ellis, 2023 ). RedPy is designed for the automated identification of repeating earthquakes, which are distinguished by similar waveforms within continuous seismic data. Event selection was based on applying a cross-correlation threshold and requiring a minimum number of stations to identify a common seismic event. We set the minimum cross-correlation value to 0.8 and applied RedPy to one-day data. This resulted in the identification of a primary family of 11 events. We manually picked only P-phase arrivals from the detected events due to the difficulty in observing clear S-wave arrivals. We applied the obtained velocity model for the location procedure using the hypocenter program from SEISAN software (Havskov & Ottemöller, 1999 ). The selected earthquakes were also located using the velocity model proposed by Marroquín ( 1998 ) to validate our results (Fig. 5 a). Challenges arose during the location process due to ambiguous phase arrivals and complex waveform patterns, particularly for distant stations with low SNR. The hypocenter determination relied on data from only four stations, and misinterpretation of phases could lead to significant errors. Nevertheless, the earthquake hypocenters were determined with a root mean square (RMS) error of less than 0.5. It should be noted that the azimuthal gap may strongly influence the location accuracy in the southeastern flank of the volcano. The epicenters are situated on the northern flank of the San Miguel volcano (Fig. 5 ), with a focus distribution beneath the volcanic edifice at shallow depths, ranging from approximately 0.5 to 4.0 km. The proposed velocity model allowed for determining locations with lower RMS (Fig. 5 b). Although there are minor differences in the epicenter positions, the most significant distinction lies in depth, wherein the proposed velocity model reduced the scatter in event depths. The located earthquakes on the volcano's northern flank align with the position of the San Miguel Fracture Zone (SMFZ). Conclusions We investigated the structural characteristics of San Miguel volcano by calculating the Rayleigh-wave dispersion curve using the employment of the SPAC method and ANSI. These ambient noise analysis methods required assumptions about the nature of seismic ambient noise, such as wavefield equipartition and the homogeneous distribution of noise sources. However, the extended observation period helped mitigate the limitations posed by these assumptions. The combined application of the SPAC and ANSI techniques enabled us to extend the frequency range of the Rayleigh-wave dispersion curves (0.2–1.3 Hz). The extended frequency content of the dispersion curves allowed for obtaining a velocity model with higher resolution in both shallow and deep layers. Our study of seismic ambient noise resulted in the first velocity model for San Miguel volcano, addressing a gap in geophysical research where little prior investigation exists. The model consists of four layers overlying a half-space, with Vp ranging between 2.4 km/s and 3.4 km/s, Vs between 1.0 km/s and 1.9 km/s, and a maximum depth of 2.8 km. While our Vs results beneath San Miguel volcano are too coarse to identify specific geological units or magma plumbing systems, this preliminary model provides a foundation for more detailed studies that incorporate the elastic properties of the volcanic system. The applyication of this velocity structure allowed for more precise determination of the locations and depths of seismic activity. This information can enhance monitoring efforts at San Miguel volcano, where accurate seismic event locations are crucial for understanding the underlying geological and geophysical processes. Abbreviations ANSI Ambient Noirse Seismic Interferometry MARN Minister of Environment and Natural Resources of El Salvador RedPy Repeating Earthquakes Detector SMFZ San Miguel Fracture Zone SNR Signal Noise Ratio SPAC Spatial Autocorrelation RMS Root Mean Square Vp P-wave velocity Vs S-wave velocity VT Volcano-tectonic Declarations The authors must provide the following sections under the heading “Declarations”. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The data are not available as they belong to the Ministry of Environment and Natural Resources of El Salvador. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Master's Program in Disaster Management Policy at the National Graduate Institute for Policy Studies (GRIPS) and the Building Research Institute (BRI), organized by the Japan International Cooperation Agency (JICA). Authors' contributions Study design: Takumi Hayashida Data analysis and interpretation: Kevyn Pineda and Takumi Hayashida Manuscript writing: Kevyn Pineda Critical revision of the manuscript content: Takumi Hayashida Acknowledgements The authors thank the Ministry of Environment and Natural Resources of El Salvador for cooperating in this research. The author expresses gratitude to the Japan International Cooperation Agency (JICA) for the opportunity to conduct this research within the framework of the “Seismology, Earthquake Engineering, and Tsunami Disaster Mitigation" program, conducted in collaboration with the National Graduate Institute for Policy Studies and the Building Research Institute. References Aki K (1957) Space and time spectra of stationary stochastic waves, with special reference to microtremors. 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Geophys J Int 174(1):235–248 Yokoi T, Hayashida T, Pokharel T, Shresta S, Timsina C, Bhattarai S, Sharma R, Nepali D (2021) Broadband microtremor array exploration in Kathmandu Valley, Nepal. 17th World Conference on Earthquake Engineering, 17WCEE, 12pp, Paper No.1d-0125 Supplementary Files GraphicalAbstract.png Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Earth, Planets and Space → Version 1 posted Reviewers agreed at journal 22 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 13 Mar, 2025 First submitted to journal 11 Mar, 2025 Editorial decision: Major Revision 03 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6100331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431793892,"identity":"d1afb9ec-0faa-4f3e-8f7f-34989b4a6baf","order_by":0,"name":"Kevyn Enrique Pineda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACCQbGBgYGNhATyPgApNjYidbCxtjAOANEMxPUAgJsEMzMA+IQ0iI5u7ntwYcyOznz+c3Nn21+bZPnY2Zg/PAxB7cWaZmD7YYzziUbyxxjbDDO7btt2MbMwCw5cxtuLXISiW3SvG3MiTOAfknO7bnNCNTCxsxLSMvftnqwlsOWPbftCWqRBmlhbDsM0tLYzPDjdiJBLZJzDrZJ9pw7bizBltjM2NtwO7mNmbEZr18kbrc/k/hRVi0nwXz88Ycff27bzm9vPvjhIx4t0IiBAsY2MNmARz26FoY/+BWPglEwCkbByAQACldMKviRwlIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5470-2759","institution":"Ministerio de Medio Ambiente y Recursos Naturales","correspondingAuthor":true,"prefix":"","firstName":"Kevyn","middleName":"Enrique","lastName":"Pineda","suffix":""},{"id":431793893,"identity":"b6c4f0a5-4833-454e-9271-40b2568685e8","order_by":1,"name":"Takumi Hayashida","email":"","orcid":"https://orcid.org/0000-0003-1285-1577","institution":"International Institute of Earthquake Engineering and Seismology","correspondingAuthor":false,"prefix":"","firstName":"Takumi","middleName":"","lastName":"Hayashida","suffix":""}],"badges":[],"createdAt":"2025-02-25 00:56:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6100331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6100331/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40623-025-02288-5","type":"published","date":"2025-10-14T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79585385,"identity":"3dd0c838-5b80-4bda-8510-a50a3b89c178","added_by":"auto","created_at":"2025-03-31 12:24:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5874261,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic map of San Miguel volcano and the surrounding seismic station location. The dashed black line represents the San Miguel Fault Zone, while the white line indicates the interstation paths. Data recorded by the seismic stations marked with red triangles were used for seismic ambient noise analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/8f1d74d16cc86cfb83d02c4a.png"},{"id":79585377,"identity":"117d9fcd-4109-4c0c-a79e-11e9254f7cbb","added_by":"auto","created_at":"2025-03-31 12:24:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202197,"visible":true,"origin":"","legend":"\u003cp\u003eAveraged SPAC coefficients as a function of frequency for each station pair. The color indicates the interstation distance for each station pair.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/dda74e5a91d1b12960dfe00f.png"},{"id":79586399,"identity":"bb70311a-de8b-4e89-9709-aee0864715bd","added_by":"auto","created_at":"2025-03-31 12:32:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":700304,"visible":true,"origin":"","legend":"\u003cp\u003eStacked cross-correlations for all station pairs, sorted by interstation distance.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/09707d53db9576d4e94916ba.png"},{"id":79585379,"identity":"a0e4b496-a562-495b-8e58-72c415484408","added_by":"auto","created_at":"2025-03-31 12:24:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210182,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Phase and group velocities of Rayleigh wave. (b) Velocity model calculated for San Miguel volcano. The final velocity model is shown by the black line, with the misfit value represented by the color in the color bar. Vs was estimated from the inversion of the dispersion curves, while Vp was obtained using a linear relation with Vs.\u003c/p\u003e","description":"","filename":"Figura4.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/4bd9e2d39987e1c093c3fff7.png"},{"id":79585383,"identity":"98319e7e-002e-4109-a92e-d97c76a8d62d","added_by":"auto","created_at":"2025-03-31 12:24:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3845333,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of hypocenter distribution of the located seismicity around the San Miguel volcano. The dotted black line represents the San Miguel Fracture Zone.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/ebdcfb62cd419aefddd3aeab.png"},{"id":93956104,"identity":"35dbfc5c-3101-40ac-b984-9b9757e71c57","added_by":"auto","created_at":"2025-10-20 16:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16462226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/ea382f63-ed3c-4722-9817-0701d8941810.pdf"},{"id":79586396,"identity":"85f092fe-a7bf-4b35-82ae-54faa0ccbf9b","added_by":"auto","created_at":"2025-03-31 12:32:15","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":303406,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-6100331/v1/18325b49d7a476d807f3e8cc.png"}],"financialInterests":"","formattedTitle":"Shallow seismic velocity structure beneath San Miguel volcano, El Salvador, estimated using seismic ambient noise (0.2–1.3 Hz)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSan Miguel volcano is located in the eastern part of El Salvador and is recognized as one of the country\u0026rsquo;s most active volcanoes, having experienced 28 eruptions over the past 500 years. This symmetrical stratovolcano rises to an elevation of 2130 m, covering an area of approximately 190 km\u003csup\u003e2\u003c/sup\u003e (Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The volcanic composition is predominantly basaltic-andesite. However, stratigraphic evidence reveals the presence of Plinian acid deposits, attributed to Pacayal volcano, an inactive volcanic system situated to the northwest of San Miguel volcano (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Patlan Almeida, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSituated in the eastern part of the El Salvador Fault Zone, a tectonic deformation zone extends 150 km and 20 km in width (Alonso-Henar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), San Miguel volcano is associated with the San Miguel Fracture Zone (SMFZ). This fracture zone, identified by Schiek (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), extends from the northwest to the southern flank of the volcano (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Its structure indicates both effusive and explosive activity during the Holocene, with numerous Strombolian eruptions producing pyroclastic density currents, spatter, and ashfall from the crater, and basaltic-andesitic lava flows along its slopes (Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Throughout its historical effusive activity, 11 lava flows have been recorded, with the largest event occurring in 1699 on the southeastern slope. In contrast, the explosive activity includes at least 20 events, most of which are concentrated on the western flank of the volcano (Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, San Miguel volcano has displayed intermittent volcanic activity, including the release of ash and gas plumes. The most recent eruption in 2013 was accompanied by various seismic signals, such as volcano-tectonic earthquakes, long-period earthquakes, and occasional explosions (Garc\u0026iacute;a, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003ea). Additionally, pulsating gas emissions were observed, with plumes reaching altitudes of up to 200 meters. The eruption expulled juvenile material, lapilli, and scoria fragments (Scarlato et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite this activity, the lack of detailed on the structural properties of the volcano has limited our understanding of the geological and geophysical processes within it, hindering the identification of unstable areas linked to volcanic hazards such as slope failure or lava flows.\u003c/p\u003e \u003cp\u003eAmbient seismic noise has been widely used to investigate Earth's structure, from shallow subsurface sediments (e.g., Foti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to upper mantle features (e.g., Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gao \u0026amp; Shen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Emry et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Seismic ambient noise arises from various sources, including both human activities and natural phenomena. Human-induced noise, commonly referred to as 'cultural noise,' typically dominates the short-period band (\u0026lt;\u0026thinsp;1 s) (McNamara \u0026amp; Boaz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, seismic ambient noise in the intermediate period range (1\u0026ndash;30 s) is mainly influenced by microseisms, which are generated by the interaction of ocean waves with coast and ocean floor topography (Chouet \u0026amp; Matoza, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Over the past decade, numerous studies using seismic ambient noise to examine volcanoes have emerged across Latin America (e.g., Spica et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lanza et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Perton et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study aims to investigate the structural properties of San Miguel volcano using seismic ambient noise data to estimate a local shallow seismic velocity model, providing critical insights for future detailed tomographic studies. Additionally, we analyze the seismicity associated with the volcano and its spatial distribution.\u003c/p\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eThis study utilized seismic ambient noise data recorded at four broadband seismic stations deployed around San Miguel volcano. Following the most recent eruption in December 2014, the Ministry of Environment and Natural Resources of El Salvador (MARN) established a temporal seismic network consisting of four sensors to monitor volcanic activity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The network comprised three Trillium Compact seismometers and one Lennartz 3D seismometer with a sampling frequency of 100 Hz. All the seismometers were powered by solar panels and car batteries. Although the data were recorded using instruments from different manufacturers, it was processed together since the stations operated with similar frequency sensitivity.\u003c/p\u003e \u003cp\u003eThe network was predominantly situated on the northern side of San Miguel volcano (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with continuous observations conducted from February to April 2014. The inter-station distances within the network ranged from 1.5 to 5.5 km.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStation locations shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation Code\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.44096\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.27220\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1698\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCY\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.42135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.29365\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLLM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.44255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.23876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRANC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.43411\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.28855\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1248\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n \n\n \u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eSpatial Autocorrelation method\u003c/h2\u003e\u003cp\u003eThe spatial autocorrelation (SPAC) method effectively determines subsurface structural properties by assuming surface waves as the dominant component in noise records observed simultaneously across a temporary seismic array (Aki, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Okada, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This method employs a technique to estimate the phase velocity of surface waves through the azimuthal averaging of cross-spectra from ambient noise measurements. In this study, we aimed to derive the dispersion curves of the fundamental mode Rayleigh wave using the vertical component ambient noise records.\u003c/p\u003e\u003cp\u003eThe SPAC coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\left(r,f\\right)\\)\u003c/span\u003e\u003c/span\u003e, a frequency-dependent function for a given inter-station distance (𝑟), is defined considering azimuthal averaging of cross-spectra:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\rho\\:\\left(r,f\\right)={\\int\\:}_{-\\pi\\:}^{\\pi\\:}\\frac{\\text{Re}\\left[{S}_{0r}\\left(f,\\theta\\:\\right)\\right]}{\\sqrt{\\left|{S}_{00}\\left(f\\right)\\right|\\text{}\\left|{S}_{rr}\\left(f,\\theta\\:\\right)\\right|}}d\\theta\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{0r}\\)\u003c/span\u003e\u003c/span\u003e is the cross spectral density of ambient noise between two sensors placed at the center of the circle (0) and on the circumference (\u003cem\u003er\u003c/em\u003e) with azimuth angle of \u003cem\u003eθ\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{00}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{rr}\\)\u003c/span\u003e\u003c/span\u003e denote the power spectral densities of ambient noise at the center and circumference of the circle. If the ambient noise environment allows noise propagation from all azimuths, the outcome derived from processing records at only two points without azimuthal averaging aligns with Eq.\u0026nbsp;(1) (Morikawa et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It has been noted that the measurement of two points can be effectively substituted by conducting measurements over several hours to several days (Chavez-Garcia et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hayashi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yokoi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Eq.\u0026nbsp;(1) indicates the procedure for calculating the phase velocity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(c\\right)\\)\u003c/span\u003e\u003c/span\u003e in the SPAC method, involving the analysis of ambient seismic noise and utilizing the relationship between phase velocity and the zeroth-order Bessel function of the first kind \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{J}_{0}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\rho\\:\\left(r,\\omega\\:\\right)=\\:{J}_{o}\\left(\\:\\frac{r\\omega\\:}{c}\\:\\right).\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch3\u003eAmbient noise seismic interferometry (ANSI)\u003c/h3\u003e\u003cp\u003eAmbient noise seismic interferometry (ANSI) involves generating a virtual seismic signal by cross-correlating seismic waveforms recorded at two stations (Wapenaar \u0026amp; Fokkema, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The time derivative of the cross-correlation function from long-term noise measurements corresponds to Green's function between two distant stations, representing the Earth’s subsurface properties (e.g., Lin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData processing basically followed the methodology proposed by Bensen et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Using continuous recordings, we applied cross-correlation to retrieve Green’s functions between station pairs. The continuous records were divided into 1-hour segments, which were then demeaned, detrended, and corrected for instrumental response. To reduce the non-stationarity of the wavefield, normalization was applied in both the frequency and time domains using the one-bit normalization (Larose et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), where all positive amplitudes were replaced by + 1 and all negatives by -1. Cross-correlations were computed for every 1-hour segment for all station pairs and subsequently stacked to improve the signal-to-noise ratio (SNR).\u003c/p\u003e\u003cp\u003eCross-correlation functions were computed for each pair of east-west (E) and vertical (Z) components, north-south (N) and Z components, Z and E components, and combinations of E and Z components. The resulting cross-correlation functions were then transformed to decompose them into radial (R) and Z component contributions based on geometric relationships, using the following formula (Yamanaka et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\left(\\begin{array}{c}ZR\\\\\\:RZ\\end{array}\\right)=\\left(\\begin{array}{cc}ZE\u0026amp;\\:ZN\\\\\\:EZ\u0026amp;\\:NZ\\end{array}\\right)\\times\\:\\left(\\begin{array}{c}\\text{s}\\text{i}\\text{n}\\theta\\:\\\\\\:\\text{c}\\text{o}\\text{s}\\theta\\:\\end{array}\\right),\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e indicates the azimuth angle between two sensors. To enhance the strength of surface waves in the cross-correlations functions, we employed the technique established by Takagi et al., (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which separates body waves and Rayleigh waves utilizing the ZR and RZ components. For the same station pair, the cross-correlation function of the RZ component, subtracted from the ZR component and divided by 2 (i.e., (ZR-RZ)/2), theoretically represents the Green's function for Rayleigh waves.\u003c/p\u003e\u003cp\u003eWe obtained the Green's function for Rayleigh waves by differentiating the cross-correlation functions of the (RZ-ZR)/2 component and determined the group velocity by applying the multiple-filter technique of Dziewonski et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1969\u003c/span\u003e), which uses narrow band-pass filters to estimate group velocities. The data from all the station pair were processed, however, not all of them produced meaningful result. The station pair with the biggest elevation difference (BLLM – VSM) failed to produce stable outcomes. Even with the above, with the remaining station pairs we obtained the group velocity corresponding to the frequency range in which the data were filtered. Both the SPAC and ANSI methods discussed in this study are designed to estimate the phase and group velocities of Rayleigh waves by processing ambient noise waveform records. The SPAC method focuses on Rayleigh waves with longer wavelengths relative to the sensor spacing, whereas the ANSI method targets Rayleigh waves with shorter wavelengths.\u003c/p\u003e\u003ch3\u003eVelocity model estimation\u003c/h3\u003e\u003cp\u003eThe velocity structure was estimated using the obtained dispersion curve, which included both phase and group velocities, by implementing a joint inversion technique (Hayashida et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The joint inversion algorithm employed the downhill simple technique (Nelder \u0026amp; Mead, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) combined with a simulated annealing method (Ingber, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The inversion process was based on an initial model that considered P-wave velocity (Vp), S-wave velocity (Vs) variations, the number of layers, and their thicknesses. The resulting velocity structure model provided Vs values calculated from the dispersion curve, while Vp was determined using a linear relationship with Vs, as described by Ludwig (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1970\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple velocity models were generated to compute theoretical dispersion curves, which were then compared to those derived from the ambient noise data. The value of mismatch (misfit) was determined for each of the calculated profiles. The misfit value was calculated for each profile. Iterative inversion enabled the determination of the number of iterations through a misfit threshold, ensuring the model converged with a good fit to the data. Initially, the inversion used simple initial models; however, if the misfit values were unsatisfactory, adjustments were made to the initial parameters, and an additional layer was added to the model. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the inversion parameters for the initial model.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of the initial model used for inversion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVp (km/s)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepth (km)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVs (km/s)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDensity (g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 to 0.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 to 1.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 to 0.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 to 1.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 to 1.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 to 2.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 to 1.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 to 2.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00 to 5.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe calculated SPAC coefficients for each sensor-to-sensor pair to determine the phase velocity, using the vertical components of the ambient noise. These coefficients were then averaged across all station pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest coherence of the curves was observed at frequencies below 0.4 Hz, with a rapid decline at frequencies above 0.5 Hz, likely due to weak ambient noise levels or the strong presence of incoherent waves (Cho \u0026amp; Iwata, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The phase velocity estimation was limited at higher frequencies because multiple candidates exist in the short wavelength range. To address this, we applied the method introduced by Ekstr\u0026ouml;m et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which extends the frequency range for measuring phase velocity by accounting for the zero-crossing frequencies in the SPAC coefficients. Consequently, we determined the phase velocity in the frequency range of 0.1 to 1.0 Hz.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the derived cross-correlation functions. In the analysis, we considered both the causal and acausal parts of Green\u0026rsquo;s function. The quality of all dispersion curves was carefully evaluated, focusing on positive and negative time symmetry. As a result, we obtained group velocity within the frequency range of 1.0 to 1.3 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe final velocity model, derived from the dispersion curve, phase and group velocities, consists of four layers overlying a homogeneous half-space, extending to a depth of 3.2 km beneath the surface of the San Miguel volcano (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the results of the calculated velocity structure. The model includes a superficial layer with a thickness of 0.3 km, a P-wave velocity (Vp) of 2.4 km/s, and an S-wave velocity (Vs) of 1.0 km/s. In the deepest layer, Vp exceeds 7.4 km/s and Vs is over 5.5 km/s, though the thickness of this layer is undetermined due to limited control of deeper structures. The results in the deepest layer should be interpreted with caution since our dispersion curves data was limited at low frequencies. However, multiple inversions were performed to ensure the parameters in the final layer do not affect the resulting velocity model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of the obtained velocity structure in San Miguel volcano.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-wave velocity (km/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS-wave velocity (km/s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVolcano-tectonic earthquake location\u003c/h2\u003e \u003cp\u003eWe located earthquakes associated with the volcano to validate our calculated velocity model. A waveform shape analysis was employed to distinguish volcanic seismicity from tectonic sources. Since San Miguel volcano exhibits variable volcanic seismicity (Garc\u0026iacute;a, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), our analysis focused on Volcano-Tectonic (VT) earthquakes, characterized by impulsive P-wave arrivals, which aid in their identification.\u003c/p\u003e \u003cp\u003eThe events were selected using the Python open-source tool Repeating Earthquakes Detector (RedPy) (Hotovec-Ellis, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). RedPy is designed for the automated identification of repeating earthquakes, which are distinguished by similar waveforms within continuous seismic data. Event selection was based on applying a cross-correlation threshold and requiring a minimum number of stations to identify a common seismic event. We set the minimum cross-correlation value to 0.8 and applied RedPy to one-day data. This resulted in the identification of a primary family of 11 events.\u003c/p\u003e \u003cp\u003eWe manually picked only P-phase arrivals from the detected events due to the difficulty in observing clear S-wave arrivals. We applied the obtained velocity model for the location procedure using the hypocenter program from SEISAN software (Havskov \u0026amp; Ottem\u0026ouml;ller, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The selected earthquakes were also located using the velocity model proposed by Marroqu\u0026iacute;n (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) to validate our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Challenges arose during the location process due to ambiguous phase arrivals and complex waveform patterns, particularly for distant stations with low SNR. The hypocenter determination relied on data from only four stations, and misinterpretation of phases could lead to significant errors. Nevertheless, the earthquake hypocenters were determined with a root mean square (RMS) error of less than 0.5. It should be noted that the azimuthal gap may strongly influence the location accuracy in the southeastern flank of the volcano.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe epicenters are situated on the northern flank of the San Miguel volcano (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with a focus distribution beneath the volcanic edifice at shallow depths, ranging from approximately 0.5 to 4.0 km. The proposed velocity model allowed for determining locations with lower RMS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Although there are minor differences in the epicenter positions, the most significant distinction lies in depth, wherein the proposed velocity model reduced the scatter in event depths. The located earthquakes on the volcano's northern flank align with the position of the San Miguel Fracture Zone (SMFZ).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe investigated the structural characteristics of San Miguel volcano by calculating the Rayleigh-wave dispersion curve using the employment of the SPAC method and ANSI. These ambient noise analysis methods required assumptions about the nature of seismic ambient noise, such as wavefield equipartition and the homogeneous distribution of noise sources. However, the extended observation period helped mitigate the limitations posed by these assumptions. The combined application of the SPAC and ANSI techniques enabled us to extend the frequency range of the Rayleigh-wave dispersion curves (0.2\u0026ndash;1.3 Hz). The extended frequency content of the dispersion curves allowed for obtaining a velocity model with higher resolution in both shallow and deep layers.\u003c/p\u003e \u003cp\u003eOur study of seismic ambient noise resulted in the first velocity model for San Miguel volcano, addressing a gap in geophysical research where little prior investigation exists. The model consists of four layers overlying a half-space, with Vp ranging between 2.4 km/s and 3.4 km/s, Vs between 1.0 km/s and 1.9 km/s, and a maximum depth of 2.8 km. While our Vs results beneath San Miguel volcano are too coarse to identify specific geological units or magma plumbing systems, this preliminary model provides a foundation for more detailed studies that incorporate the elastic properties of the volcanic system.\u003c/p\u003e \u003cp\u003eThe applyication of this velocity structure allowed for more precise determination of the locations and depths of seismic activity. This information can enhance monitoring efforts at San Miguel volcano, where accurate seismic event locations are crucial for understanding the underlying geological and geophysical processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmbient Noirse Seismic Interferometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMARN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinister of Environment and Natural Resources of El Salvador\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRedPy\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRepeating Earthquakes Detector\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMFZ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSan Miguel Fracture Zone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSignal Noise Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpatial Autocorrelation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eP-wave velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eS-wave velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVolcano-tectonic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eThe authors \u003cem\u003emust\u003c/em\u003e provide the following sections under the heading \u0026ldquo;Declarations\u0026rdquo;.\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not available as they belong to the Ministry of Environment and Natural Resources of El Salvador.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Master\u0026apos;s Program in Disaster Management Policy at the National Graduate Institute for Policy Studies (GRIPS) and the Building Research Institute (BRI), organized by the Japan International Cooperation Agency (JICA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: Takumi Hayashida\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: Kevyn Pineda and Takumi Hayashida\u003c/p\u003e\n\u003cp\u003eManuscript writing: Kevyn Pineda\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript content: Takumi Hayashida\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Ministry of Environment and Natural Resources of El Salvador for cooperating in this research. The author expresses gratitude to the Japan International Cooperation Agency (JICA) for the opportunity to conduct this research within the framework of the \u0026ldquo;Seismology, Earthquake Engineering, and Tsunami Disaster Mitigation\u0026quot; program, conducted in collaboration with the National Graduate Institute for Policy Studies and the Building Research Institute.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAki K (1957) Space and time spectra of stationary stochastic waves, with special reference to microtremors. Bull Earthq Res Inst 35:415\u0026ndash;456\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso-Henar J, \u0026Aacute;lvarez-G\u0026oacute;mez JA, Martinez-D\u0026iacute;az JJ (2014) Constraints for the recent tectonics of the El Salvador Fault Zone, Central America Volcanic Arc, from morphotectonic analysis. 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Bull Seismol Soc Am 106(4):1438\u0026ndash;1445\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarose E, Derode A, Campillo M, Fink M (2004) Imaging from one-bit correlations of wideband diffuse wave fields. J Appl Phys 95(12):8393\u0026ndash;8399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin F-C, Moschetti MP, Ritzwoller MH (2008) Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps. Geophys J Int 173(1):281\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig WJ (1970) The Manila Trench and West Luzon Trough\u0026mdash;III. Seismic-refraction measurements. Deep Sea Res Oceanogr Abstracts 17(3):553\u0026ndash;571\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarroqu\u0026iacute;n G (1998) Seismic properties of the crust in the volcanic chain of El. 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Earth and Planetary Science Letters, 421, 27\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakagi R, Nakahara H, Kono T, Okada T (2014) Separating body and Rayleigh waves with cross terms of the cross-correlation tensor of ambient noise. J Geophys Research: Solid Earth 119(3):2005\u0026ndash;2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWapenaar K, Fokkema J (2006) Green\u0026rsquo;s function representations for seismic interferometry. Geophysics 71(4):SI33\u0026ndash;SI46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamanaka H, Chimoto K, Moroi T, Ikeura T, Koketsu K, Sakaue M, Nakai S, Sekiguchi T, Oda Y (2010) Estimation of surface-wave group velocity in the southern Kanto area using seismic interferometric processing of continuous microtremor data. BUTSURI-TANSA (Geophys Explor) 63(5):409\u0026ndash;425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Li A, Ritzwoller MH (2008) Crustal and uppermost mantle structure in southern Africa revealed from ambient noise and teleseismic tomography. Geophys J Int 174(1):235\u0026ndash;248\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokoi T, Hayashida T, Pokharel T, Shresta S, Timsina C, Bhattarai S, Sharma R, Nepali D (2021) Broadband microtremor array exploration in Kathmandu Valley, Nepal. 17th World Conference on Earthquake Engineering, 17WCEE, 12pp, Paper No.1d-0125\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"San Miguel volcano, seismic ambient noise, velocity model, seismic interferometry, spacial autocorrelation method","lastPublishedDoi":"10.21203/rs.3.rs-6100331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6100331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSan Miguel volcano is one of the most active volcanoes in El Salvador, yet its structural characteristics remain underexplored. We developed a one-dimensional seismic velocity structure model by analyzing seismic ambient noise recorded around the volcano and inverting the Rayleigh wave dispersion curves. The data were obtained from a temporary seismograph network deployed in 2014. We applied the spatial autocorrelation (SPAC) method and ambient noise seismic interferometry (ANSI), assuming the temporal and spatial uniformity of ambient noise characteristics. The SPAC method enabled the derivation of phase velocities for surface waves within the frequency range of 0.2 to 1.0 Hz. Additionaly, we estimated Rayleigh wave group velocities using ANSI, which employs Green's function derived from cross-correlating ambient noise. The resulting dispersion curve was acquired in the 1.0\u0026ndash;1.3 Hz frequency band. The velocity model revealed four sedimentary layers, with S-wave velocities ranging from 1.0 to 2.5 km/s overlying a half-space layer. Using the obtained velocity model, we located volcano-tectonic earthquakes, resulting in more accurate hypocenter determinations. The seismicity was found to align with a deformation zone known as the San Miguel Fracture Zone, situated on the volcano's northern flank.\u003c/p\u003e","manuscriptTitle":"Shallow seismic velocity structure beneath San Miguel volcano, El Salvador, estimated using seismic ambient noise (0.2–1.3 Hz)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 12:24:10","doi":"10.21203/rs.3.rs-6100331/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-22T06:24:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T22:54:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-13T11:39:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth, Planets and Space","date":"2025-03-11T23:52:24+00:00","index":"","fulltext":""},{"type":"decision","content":"Major Revision","date":"2025-03-03T20:06:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92c800e4-b4c7-46a5-a829-751cd8faaf1a","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:04:35+00:00","versionOfRecord":{"articleIdentity":"rs-6100331","link":"https://doi.org/10.1186/s40623-025-02288-5","journal":{"identity":"earth-planets-and-space","isVorOnly":false,"title":"Earth, Planets and Space"},"publishedOn":"2025-10-14 15:58:24","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-03-31 12:24:10","video":"","vorDoi":"10.1186/s40623-025-02288-5","vorDoiUrl":"https://doi.org/10.1186/s40623-025-02288-5","workflowStages":[]},"version":"v1","identity":"rs-6100331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6100331","identity":"rs-6100331","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}