Comptes Rendus Géoscience

Francesco Vetere

Olivier Namur

Francois Holtz

Renat Almeev

Paola Donato

Francesco Frondini

Michele Cassetta

Alessandro Pisello

Diego Perugini

Influence of volatiles (H$_{2}$O and CO$_{2}$) on shoshonite phase equilibria

Camille Daffos

Caroline Martel

Laurent Arbaret

Rémi Champallier

Comptes RendusGéoscience — Sciences de la PlanèteSupplementary material to the article available at:https://doi.org/10.5802/crgeos.214Supplementary material: Bubble connectivity inexperimentally-sheared crystal-bearing silicic meltsCamille Daffosa, CarolineMartel ∗, a, Laurent Arbaret a and Rémi Champallier aa Institut des Sciences de la Terre d’Orléans (ISTO), Univ. Orléans, CNRS, BRGM,UMR 7327, Orléans, FranceE-mails: camille.daffos@univ-orleans.fr (C. Daffos), caroline.martel@cnrs-orleans.fr(C. Martel), laurent.arbaret@univ-orleans.fr (L. Arbaret),remi.champallier@cnrs-orleans.fr (R. Champallier)1. MethodsThe experiments consisted in three main phases:(i) synthesis of a crystal-bearing hydrated glass bymelt hydration in an internally-heated pressure ves-sel and mixing of the obtained hydrated glass withplagioclase crystals, (ii) H2O-bubble formation bydecompression in the Paterson-type apparatus, and(iii) subsequent deformation in simple shear of thethree-phase magma.1.1. Experimental methods1.1.1. Synthesis of crystal-bearing hydrated glassesThe anhydrous starting glass is a haplogranite(HPG8, prepared by Schott AG, Germany) with theeutectic composition of the quartz–albite–orthoclasesystem [Holtz et al., 1992; composition in wt%: 78.6SiO2, 12.5 Al2O3, 4.6 Na2O, 4.2 K2O). About 10 g ofthe powdered (<90 µm) anhydrous HPG8 glass wasmixed with either about 1 g or 0.5 g deionized H2O,and sealed in 20-mm diameter and 30-mm long Aucapsules. The HPG8 hydrations were performed in∗Corresponding author.internally-heated pressure vessels pressurized withargon (at ISTO) at T of 750 and 950 °C and P of400 MPa (for the capsules containing 1 g H2O) and Pof 170 MPa (for the capsules containing 0.5 g H2O).The H2O contents introduced in the capsules wereenough to ensure H2O solubilities at P of 400 MPaand 170 MPa (950 °C) according to the H2O solubil-ity model of Newman and Lowenstern [2002], i.e. 9.6and 5.0 wt% H2O, respectively. Hydration durationswere 48 to 96 h, which is long enough to homog-enize H2O by diffusion in the HPG8 glass powder,according to Zhang and Behrens [2000]. The sam-ples were isobarically quenched by shutting downthe furnace, in order to avoid H2O exsolution fromthe melt. The recovered hydrated HPG8 glasses wereground in an agate mechanical grinder to obtain a∼20-µm grain size powder, to which plagioclase crys-tals were added. We purchased commercially a pla-gioclase block of labradorite (anorthite content of50 to 70 mol%) whose composition was tested forchemical resistance at the contact of the HPG8 hy-drated melt under the elected run conditions. Thelabradorite block was reduced into pieces using ahigh-voltage pulsed power fragmentation apparatus(Selfrag, at BRGM, Orléans, France). The main advan-tage of the Selfrag is to crush rocks at the grain joints,so that we were expecting more or less elongated bits,ISSN (electronic) : 1778-7025 https://comptes-rendus.academie-sciences.fr/geoscience/2 Camille Daffos et al.Supplementary Figure S1. The 50–90 µm frac-tion size of the plagioclase crystals.i.e. resembling the natural plagioclase shapes. Therecovered fragments were sieved to 50–90 µm (Sup-plementary Figure S1), before manual mixing withthe hydrated HPG8 glass powder following differ-ent weight proportions. Very minor amounts of crys-tals other than plagioclase (presumably Fe–Ti oxidesand pyroxenes) have been found in the fragments,likely coming from mineral inclusions trapped in thelabradorite block. Recalculated to volume propor-tions using a 2.7 density for labradorite, four seriesbased on their crystal contents (Φc_melt_3D) have beenchosen: 0, 21, 50, and 70 vol%.1.1.2. Synthesis of bubble-bearing magmasAbout 1.6–2.0 g of the crystal-glass mixtures weresealed in 14-mm diameter and 12-mm long Cu cap-sules in order to prevent H2O loss. The capsules weresandwiched between alumina spacers, alumina andzirconia pistons, and the whole assembly was coatedby an iron jacket (Figure 1b). The sample column wasplaced at the hot spot of the internal Mo-furnace ofthe gas-medium (argon) Paterson rig [Paterson andOlgaard, 2000; Australian Scientific Instruments PtyLtd, at ISTO; Figure 1a]. The samples were (i) pressur-ized to 240 MPa, (ii) heated to 850 °C (pressure willreach 300 MPa) at a rate of 10 °C/min and maintainedduring 15 min for thermal equilibration, then (iv) de-compressed to 50 MPa within 9 to 20 min in order togenerate H2O bubble nucleation, (v) maintained for45 min to let bubbles grow, and (vi) cooled to 650 °C(crystal-free samples) or 750 °C (crystal-bearingsamples), in order to allow further deformation with-out inconvenient jacket breakage, followed by a 15-min dwell for thermal equilibration (Figure 1c). In thePaterson rig, all samples were decompressed from300 MPa (maximum pressure allowed in the Patersonrig), so that the samples pre-hydrated at 400 MPa(∼9.6 wt% H2O) nucleated between 300 and 50 MPa[possibly during annealing at 300 MPa, although thehigh supersaturations >100 MPa required for homo-geneous nucleation in silica-rich melts would favournucleation at pressure below 300 MPa; Mourtada-Bonnefoi and Laporte, 2002]. The samples hydratedto 5.0 wt% H2O nucleated at pressure between 170and 50 MPa.1.1.3. Torsion experimentsKeeping the experimental setup as it was afterdecompression, deformation in right-lateral simpleshear was carried out at T of 650 °C (crystal-free sam-ples) or 750 °C, P of 50 MPa, and shear rate γr of2× 10−4 s−1. Torsion durations were 1 or 2 h, whichled to bulk finite shear γ of 1 and 2, respectively, [seePaterson and Olgaard, 2000, for γ calculation]; notethat in torsion, the sample centre experiences zerotorque and, therefore, zero strain; both strain andstrain rate increase radially toward the sample exte-rior. Temperature, pressure, shear stress and angu-lar displacement were live recorded on a computer.The experiment was ended by relaxing the stress, fol-lowed by an isobaric quench (shut of the furnacewhile maintaining 50 MPa confining pressure) at arate of ∼40 °C/min from 750 to 200 °C. According toMader et al. [2013], the relaxation time (λ) of a bub-ble writes as: λ = (a ×ηmelt)/σ, where a is the bub-ble’s equivalent spherical radius, ηmelt is the viscosityof the hydrated melt, and σ is the surface tension atthe bubble-liquid interface. Considering a = 10 and100 µm (from the sample analyses), ηmelt = 106.3 Pa·s[HPG8 melt with 2.9 wt% H2O at 50 MPa and 750 °C;Hess and Dingwell, 1996], and σ= 0.2 N/m at 50 MPa[Bagdassarov et al., 2000], λ would be about 2 and17 min, respectively. At 700 °C, ηmelt = 106.9 Pa·s andλ becomes 7 and 66 min for bubble radius of 10 and100 µm, respectively. During quenching, about 42 sare spent between 750 and 700 °C, so that we inferthat in our experiments, the small 10-µm radius bub-bles fully relaxed (returned to sphericity), whereasthe larger 100-µm radius bubbles experienced lim-ited relaxation during quenching. After experiment,Camille Daffos et al. 3Supplementary Figure S2. After run sample preparation for analyses. (a) Cutting, (b) polish section withone outer piece of the sample, and (c) bore of a 3-mm cylinder in the middle piece.the sample column was extracted from the vessel andthe extent of deformation was verified by the progressof the pen-markers between the non-deformed (pis-ton) and deformed (sample) parts. The iron jacketwas removed to release the Cu-capsule containingthe sample. Note that shear stress could not be de-termined in-situ during deformation due to the lowviscosity (high temperature) of the samples, so thatbulk viscosity could not be measured.1.2. Analytical methods1.2.1. Microstructural analysisAfter experiment, the Cu capsule containing thesample was cut following two sections parallel tothe shear plane, in order to expose their longitudi-nal tangential surfaces, which represent the planes ofmaximum shear strain [Paterson and Olgaard, 2000](Supplementary Figure S2a). One of the two sectionswas placed in epoxy, polished, and carbon coated fortwo-dimensional (2D) observations using a scanningelectron microscope (SEM; Merlin Compact Zeiss atISTO) (Supplementary Figure S2b).The sample polished sections being about 5–10 mm long by 10–15 mm wide and often show-ing microtextural heterogeneities, the sample anal-ysis was performed by processing 5 to 10 thumb-nail images that covered the whole section. TheSEM images were segmented using the SPO2003image-processing software [Launeau and Robin,1996, Launeau and Cruden, 1998], in order to de-termine the average 2D bubble and crystal contents(accuracy of ±3 area% determined on repeated mea-surements), bubble and crystal sizes, shapes, andorientations (following the method of intercepts;[Launeau and Robin, 1996], and bubble and crystalnumber densities, as follows:• Crystal-free bubble content: Φb_melt_2D =(bubble area)/(bubble area + glass area) ×100;• 2D-bulk porosity: Φb_bulk_2D = (bubblearea)/(bubble area + glass area + crystalarea) × 100;• 2D-bubble-free crystal content: Φc_melt_2D =(crystal area)/(crystal area + glass area) ×100;• 2D-bulk crystal content: Φc_bulk_2D = (crys-tal area)/(bubble area + glass area + crystalarea) × 100;• 2D-bulk glass content: Φg_bulk_2D = glassarea/(bubble area + glass area + crystal area)× 100;• Crystal-free areal bubble number density:BND = (number of bubbles)/(glass area).• Bubble-free areal crystal number density:CND = (number of crystals)/(glass area).• Bubble long and short axes; bubble eccen-tricity: R = (long axis)/(short axis); the radius(a) equivalent to the bubble surface; bubbleorientation to the horizontal (θb).1.2.2. X-ray Computed Tomography (XCT)After the removal of the two longitudinal tangen-tial sections for the 2D analysis, the sample rest wasbored perpendicularly to the shear plane (0 strain4 Camille Daffos et al.in centre; Supplementary Figure S2c) in order to getan oriented 3-mm core sample for three-dimensional(3D) analysis using XCT (Phoenix NanoTOM at ISTO).The XCT images were reconstructed using a Phoenix3D software with a voxel resolution of 1.4-µm side.A qualitative analysis of the whole core sample wasperformed in order to segment the glass+crystal ma-trix, the isolated bubbles, and the bubbles connectedto the outside of the core sample, using the com-mercial software VGStudio Max. Smaller sample vol-umes (cubes of 600 and 1400-µm side) were definedto qualify bubble size and spatial distribution, as wellas to quantify the 3D bulk porosity (Φb_bulk_3D) andthe volume content of bubbles connected to the sam-ple border (Φb_connect_3D), using the commercial soft-ware Blob3D. The porosity measurements,Φb_bulk_3Dand Φb_connect_3D, were determined with an accu-racy of ±3 vol%. Because the small size of the ex-perimental samples precludes porosity 3D measure-ment iterations, the assumption made here is thatthe volumes on which the measurements have beenmade are representative of the whole experimentalsample.1.2.3. Glass H2O contentsThe H2O contents of the starting hydrated glasseswere checked using an elemental analyser (Ther-moFlash2000 at ISTO), given with a precision of±0.15 wt%. The results confirmed the H2O solubil-ity data from [Newman and Lowenstern, 2002], i.e.H2O contents of 9 to 10 wt% for the 400-MPa hydra-tions and ∼5 wt% for the 170-MPa hydrations. As theelemental analyser is a destructive method (samplepyrolyze), the H2O contents of the residual glassesfrom the run products were analysed using an elec-tron microprobe (EMP; Cameca SX Five at ISTO) fol-lowing the “by-difference” method of Devine et al.[1995], and using HPG8 glass standards with knownH2O contents [samples from Martel et al., 2000]. Theanalysed elements were Si, Al, Ca, Na, K, with alka-lis analysed first, in order to minimize loss under theelectron beam. The EMP conditions were set to an ac-celeration voltage of 15 kV, a beam current of 6 nA,peak/background counting times of 10/5 s for all el-ements, and a defocussed beam of 10× 10 µm size.The analytical errors on major elements were calcu-lated at ∼1% (relative) for SiO2, Al2O3 and CaO, and5% for Na2O and K2O, and ±0.7 wt% on the H2O con-tent.Some of the residual glasses were double-checkedby microRaman spectroscopy. The Andor Shamrock500i spectrometer is coupled with a Nikon ECLIPSENi-U microscope uses a long working distance Nikonobjective (100×, numerical aperture of 0.8), whichallows lateral resolution of 1–2 µm2. A monochro-matic 532 nm Coherent Genesis MX SLM laser wasused as exciting source (output laser power was setto 300 mW). The 1200 lines/mm grating was used,providing two spectral windows spanning 200 to1500 cm−1 (silicate network) and 2895 to 3800 cm−1(OH stretching region). For each analysis, the acqui-sition time was 60 s. In order to determine H2O con-tent, the HPG8 glass standards were measured in thesame analytical conditions as the samples. The Ra-man spectra were proceeded following the method ofBehrens et al. [2006].1.3. Calculations1.3.1. Gas fractionThe gas fraction equation of Jaupart and Alle-gre [1991] calculates the gas volume (αmelt) resultingfrom degassing of a H2O-saturated melt in closed-system. The equation writes as follows:αmelt = 1/{1+ρgas/[(Cwi −Cw f )×ρliq]}×100 (1)where ρgas is the H2O density at T and final P cal-culated after Burnham et al. [1969], ρliq is the meltdensity taken as 2400 kg/m3, Cwi and Cw f are theH2O solubilities at initial and final pressures, re-spectively, calculated after Newman and Lowenstern[2002]. The uncertainty associated with the αmelt cal-culation mostly depends on the precision of the meltH2O contents (Cwi and Cw f ). Assuming an uncer-tainty of ±0.7 wt% on the EMP “by-difference” H2Omeasurements suggests uncertainties of ±3 vol% onαmelt calculations.1.3.2. Bulk viscosityThe bulk viscosity (ηbulk) of our samples werecalculated using the Equation (3.2) of Truby et al.[2015] that gives the relative viscosity (ηr , with ηr =ηbulk/ηmelt) of a three-phase suspension of bubblesand crystals in a Newtonian liquid undergoing steadyflow. The model adopts an “effective medium” ap-proach in which the bubbly liquid is treated as a con-tinuous medium which suspends the crystals. Themodel is valid for a low bubble capillary number (C a)Camille Daffos et al. 5regime. C a describes the ratio of the viscous stressto the restoring stress and writes as follows: C a =(ηmelt ×γr )/(σ/a), where a is the bubble’s equivalentspherical radius. For γr of 2×10−4 s−1, bubble radiusof 10 and 100 µm, σ of 0.2 N/m [Bagdassarov et al.,2000], and ηmelt of 106.3 Pa·s (HPG8 liquid with 2.9wt% H2O at 750 °C; [Hess and Dingwell, 1996], C a is∼0.02 and 0.2, respectively, which satisfies the low C aregime (C a < 1) required for the viscosity calculation.The equation writes:ηr = (1−Φb_melt_3D/100)−1×(1−Φc_bulk_3D/Φmax)−2 (2)where Φmax is the particle maximum packing of66 vol% [Truby et al., 2015].Supplementary Figure S3. 3D bubble size distributions.6 Camille Daffos et al.Supplementary Figure S4. Crystal breakage. The red squares highlight crystal breakage in the unde-formed (γ = 0) sample containing 50 vol% crystals (bottom left) and associated crystal size histogramshowing a large population of 0–20 µm diameter crystals (bottom right), in contrast to non-broken crys-tals in the undeformed sample containing 10 vol% crystals (upper left; same scale bar) associated to alower frequency of 0–20 µm crystals (upper right).ReferencesBagdassarov, N. S., Dorfman, A., and Dingwell, D. B.(2000). Effect of alkalis, phosphorus, and wateron the surface tension of haplogranite melt. Am.Mineral., 85, 33–40.Behrens, H., Roux, J., Neuville, D. R., and Siemann, M.(2006). Quantification of dissolved H2O in silicateglasses using confocal microRaman spectroscopy.Chem. Geol., 229, 96–112.Burnham, C. W., Holloway, J. R., and Davis, N. F.(1969). Thermodynamic properties of water to1000 °C and 10,000 bars. Geol. Soc. Am. Spec. Pa-pers, 132, 1–96.Devine, J. D., Gardner, J. E., Brack, H. P., Layne, G. 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Bubble connectivity in experimentally-sheared crystal-bearing silicic melts

Yves Moussallam

Clive Oppenheimer

Bruno Scaillet

Comptes RendusGéoscience — Sciences de la PlanèteSupplementary material to the article available at:https://doi.org/10.5802/crgeos.158Supplementary material: A novel approach tovolcano surveillance using gas geochemistryYves Moussallam ∗, a, b, Clive Oppenheimer c and Bruno Scaillet da Lamont-Doherty Earth Observatory, Columbia University, New York, USAb Department of Earth and Planetary Sciences, American Museum of Natural History,New York, USAc Department of Geography, University of Cambridge, Downing Place, CambridgeCB2 3EN, UKd ISTO, 7327 Université d’Orléans-CNRS-BRGM, 1A rue de la Férollerie, 45071 Orléanscedex 2, FranceE-mails: yves.moussallam@ldeo.columbia.edu (Y. Moussallam), co200@cam.ac.uk(C. Oppenheimer), bscaille@cnrs-orleans.fr (B. Scaillet)∗Corresponding author.ISSN (electronic) : 1778-7025 https://comptes-rendus.academie-sciences.fr/geoscience/2 Yves Moussallam et al.Supplementary Table S1. Literature compilation of volcanic gas composition measurementsC. R. 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Comptes RendusGéoscienceSciences de la PlanèteNikolai M. Shapiro, Michel Campillo, Anne Obermann and Andrew CurtisForeword to New developments in passive seismic imaging and monitoringVolume 356, Special Issue S4 (2024), p. 1-3Online since: 2 December 2024Issue date: 20 January 2025Part of Special Issue: New Developments in Passive Seismic Imaging and MonitoringGuest editors: Michel Campillo (University Grenoble-Alpes, Institut des Sciences de laTerre, Grenoble, France),Andrew Curtis ( School of GeoSciences, University of Edinburgh, Scotland),Anne Obermann (Swiss Seismological Service, ETH, Zurich, Switzerland) and NikolaiShapiro (CNRS, Institut des Sciences de la Terre, Grenoble, France)https://doi.org/10.5802/crgeos.281This article is licensed under theCreative Commons Attribution 4.0 International License.http://creativecommons.org/licenses/by/4.0/C EN T R EMER S ENN EThe Comptes Rendus. Géoscience — Sciences de la Planète are a member of theMersenne Center for open scientific publishingwww.centre-mersenne.org — e-ISSN : 1778-7025Comptes Rendus. Géoscience — Sciences de la Planète2024, Vol. 356, Special issue S4, p. 1-3https://doi.org/10.5802/crgeos.281ForewordNew Developments in Passive Seismic Imaging and MonitoringForeword to New developments in passive seismicimaging and monitoringNikolai M. Shapiro ,a, Michel Campillo ,a, Anne Obermann ,b and Andrew Curtis ,ca Institut des Sciences de la Terre, University Grenoble Alpes, CNRS, University SavoieMont Blanc, IRD, University Gustave Eiffel, Grenoble, Franceb Swiss Seismological Service, ETH Zurich, Switzerlandc School of GeoSciences, University of Edinburgh, Edinburgh EH9 3FE, UKE-mails: nikolai.shapiro@univ-grenoble-alpes.fr (N. M. Shapiro),michel.campillo@univ-grenoble-alpes.fr (M. Campillo), anne.obermann@sed.ethz.ch(A. Obermann), andrew.curtis@ed.ac.uk (A. Curtis)Funding. ERC Advanced Grants F-Image, SEISMAZE, FaultScan.During the last two decades, imaging and mon-itoring methods based on cross-correlations of am-bient seismic noise have been extensively developedand have become widely used at different scales andin different natural and human-controlled environ-ments. Application of these methods has been fa-cilitated by improved availability of data from largeand dense seismic networks and by several open soft-ware packages. At the same time, noise-based imag-ing and monitoring is far from becoming just a setof “standard” and “routine” methods. As discussedin this special issue, the origin and nature of theseismic noise wavefields, their correlation propertiesas well as their sensitivity to the medium structureand changes, remain areas of active study. Improv-ing their understanding is necessary in order to re-fine the existing methods and to develop new ap-proaches for imaging and monitoring. Therefore,noise-based passive seismology remains a dynamicfield of research with some first-order problems yetto be solved. In this context, 13 years after the firstthematic issue of Comptes Rendus Géoscience onpassive seismic noise-based imaging and monitoring[e.g., Campillo et al., 2011] we introduce a new spe-cial issue devoted to this topic.Most passive noise-based seismic imaging andmonitoring methods are based on the so-called“noise cross-correlation theorem” [e.g., Lobkis andWeaver, 2001, Wapenaar, 2004, Roux et al., 2005,Campillo, 2006, Gouédard et al., 2008] which statesthat the time derivative of cross-correlation of anideal fully diffuse wavefield converge to the Green’sfunction of the medium in which the waves are prop-agating. The fully diffuse wavefield can be definedeither as being composed of all possible medium vi-bration modes with equally partitioned energy, or lo-cally as composed of isotropic combination of planewaves, or as generated by homogenously distributedrandom sources. Neither of these definitions appliesto the real Earth’s seismic noise whose main sourcesare inhomogeneously distributed over the surface.This makes direct application of the “full” cross-correlation theorem to real seismological data ques-tionable, and requires that we further improve ourunderstanding of seismic noise cross-correlations todevelop accurate methods.So far, the surface wave part of Green’s functionshas been most reliably reconstructed from the cor-relations of the ambient seismic noise. Since ini-tial demonstration of this possibility [e.g., ShapiroISSN (electronic): 1778-7025 https://comptes-rendus.academie-sciences.fr/geoscience/2 Nikolai M. Shapiro et al.and Campillo, 2004, Shapiro et al., 2005, Sabra et al.,2005a,b] a family of methods known as AmbientNoise Surface Wave Tomography (ANSWT) has beendeveloped and successfully applied in many studies[e.g., Ritzwoller et al., 2011, Shapiro, 2018]. A system-atic application of this approach to image the Euro-pean Alps is presented by Paul et al. [2024]. The au-thors show how methodological advances of this ap-proach evolving from simple isotropic group velocitytomography to wave-equation based inversions, andthose based on trans-dimensional Bayesian formal-ism and including anisotropy, significantly improvedknowledge of the structure of the crust and shallowmantle beneath the Alps-Apennines system.Giammarinaro et al. [2024] use two-dimensionalsimulations of wave propagation to investigate thelateral resolution power of an alternative approachto ANSWT: seismic Rayleigh wave focal spot imaging.They demonstrate that the station configuration canbe tuned to improve image quality and properties,and that high-quality data from dense networks canresult in super-resolution.Lavoué et al. [2024] investigate the applicability ofthe ANSWT at small scales, i.e., those of a single sed-imentary basin, to resolve the near-surface structurefor estimation of site amplifications required for seis-mic hazard models. They show that, while ANSWTresults reproduce well the main geological structuresof the basin, they have limited capability to accu-rately predict the numerical amplification near thebasin edges and other locations with significant 3Dwave propagation effects. This allows the authors tosuggest perspectives for future improvement of AN-SWT, that shows promise for site effect assessment inlow- to moderate-seismicity contexts.Boué and Tomasetto [2024] investigate how theteleseismic body waves are generated by oceanicforcing on the Earth’s surface, resulting in a spa-tially inhomogeneous distribution of microseismicsources. The authors show that, despite the inherentcomplexity of these noise sources, cross-correlationbased methods applied to properly selected pairsof stations result in the isolation of coherent wavesfor imaging applications and propose a workflowbased on ocean sea state models to extract robustinterferences.Influence of the heterogeneous distribution ofnoise sources on the accuracy of noise-based seis-mic monitoring is studied by Stehly et al. [2024] whoperform a single station analysis at all available Eu-ropean broadband stations. They show that at shortperiods (<3 s), the noise field in Europe is domi-nated by surface waves coming from two sources:(1) the north Atlantic Ocean dominating during win-ters, and (2) the Adriatic and Aegean Seas increas-ing in summer. The interplay of these two sourceregions leads to time and space dependent conver-gence of the coda part of cross-correlations, and thusin across-Europe variations of the accuracy and tem-poral resolution of detected seismic velocity changes.Other important aspects of the noise-based seis-mic monitoring are addressed by Canel et al. [2024]who investigate the physical mechanisms that couldexplain the seismic velocity changes measured fromthe noise cross-correlations in the vicinity of activefault zones. The authors perform a set of numeri-cal experiments to test a simple model of a cohesivegranular medium and to study the relationship be-tween the damage and velocity of elastic waves. Theyshow that the microscopic deformation of cohesivediscrete media quickly becomes very heterogeneouswith a small amount of damage inducing a strong de-crease in the elastic velocity. As a consequence, theysuggest that monitoring the wave velocities in suchmedia could measure subtle transient deformationprocesses, such as earthquake initiation phases.AcknowledgmentsThe content of the present thematic Issue was dis-cussed during the 6th edition of the school on PassiveImaging and Monitoring in Wave Physics: From Seis-mology to Ultrasound, that was held on April 18–22,2022 at the Institut d’Études Scientifiques de Cargèsein Corsica, France. We thank the contributors of thespecial issue and the participants to the workshopas well as all people who contributed to its logis-tics. The workshop and the organization of this issuewere made possible by the support of CNRS (France),and Université Grenoble Alpes (Grenoble, France).The authors acknowledge support from the ERC Ad-vanced Grants F-Image, SEISMAZE, and FaultScan.ReferencesBoué, P. and Tomasetto, L. (2024). Opportune de-tections of global P-wave propagation from micro-seisms interferometry. C. R. Géosci., 356(S4), 85–100.Nikolai M. Shapiro et al. 3Campillo, M. (2006). Phase and correlation in ‘ran-dom’ seismic fields and the reconstruction of thegreen function. Pure Appl. Geophys., 163, 475–502.Campillo, M., Sato, H., Shapiro, N. M., and van derHilst, R. D. (2011). New developments on imagingand monitoring with seismic noise. C. R. Géosci.,343(8–9), 487–495.Canel, V., Campillo, M., Jia, X., and Ionescu., I. R.(2024). Damage in cohesive granular materials:simulations and geophysical implications. C. R.Géosci., 356(S4), 125–145.Giammarinaro, B., Tsarsitalidou, C., and Hillers, G.(2024). Investigating the lateral resolution of theRayleigh wave focal spot imaging technique us-ing two-dimensional acoustic simulations. C. R.Géosci., 356(S4), 41–57.Gouédard, P., Stehly, L., Brenguier, F., et al. (2008).Cross-correlation of random fields: mathematicalapproach and applications. Geophys. Prospect., 56,375–393.Lavoué, F., Froment, B., Gélis, C., et al. (2024). Po-tential and limitations of noise-based surface-wavetomography for numerical site effect estimation: acase study in the French Rhône valley. C. R. Géosci.,356(S4), 59–84.Lobkis, O. I. and Weaver, R. L. (2001). On the emer-gence of the Green’s function in the correlations ofa diffuse field. J. Acoust. Soc. Am., 110(6), 3011–3017.Paul, A., Pedersen, H. A., Bodin, T., Kästle, E., Soergel,D., Alder, C., Lu, Y., and Nouibat, A. (2024). Method-ological advances in seismic noise imaging of theAlpine area. C. R. Géosci., 356(S4), 5–39.Ritzwoller, M. H., Lin, F.-C., and Shen, W. (2011).Ambient noise tomography with a large seismicarray. C. R. Géosci., 343(8), 558–570.Roux, P., Sabra, K. G., Kuperman, W. A., and Roux,A. (2005). Ambient noise cross correlation in freespace: Theoretical approach. J. Acoust. Soc. Am.,117(1), 79–84.Sabra, K. G., Gerstoft, P., Roux, P., Kuperman, W. A.,and Fehler, M. C. (2005a). Extracting time-domainGreen’s function estimates from ambient seismicnoise. Geophys. Res. Lett., 32, article no. L03310.Sabra, K. G., Gerstoft, P., Roux, P., Kuperman, W. A.,and Fehler, M. C. (2005b). Surface wave tomogra-phy from microseisms in Southern California. Geo-phys. Res. Lett., 32, article no. L14311.Shapiro, N. M. (2018). Applications with surfacewaves extracted from ambient seismic noise. InNakata, N., Gualtieri, L., and Fichtner, A., edi-tors, Seismic Ambient Noise. Cambridge UniversityPress, Cambridge.Shapiro, N. M. and Campillo, M. (2004). Emergenceof broadband Rayleigh waves from correlations ofthe ambient seismic noise. Geophys. Res. Lett., 31,article no. L07614.Shapiro, N. M., Campillo, M., Stehly, L., and Ritz-woller, M. H. (2005). High resolution surface wavetomography from ambient seismic noise. Science,307, 1615–1618.Stehly, L., Delouche, E., Tomasetto, L., and Ranjan,P. (2024). Dynamic of seismic noise sources inthe Mediterranean Sea: implication for monitoringusing noise correlations. C. R. Géosci., 356(S4), 101–124.Wapenaar, K. (2004). Retrieving the elastodynamicGreen’s function of an arbitrary inhomogeneousmedium by cross correlation. Phys. Rev. Lett.,93(25), article no. 254301.

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