000005887 001__ 5887
000005887 005__ 20241203110535.0
000005887 0247_ $$2DOI$$a10.1093/gji/ggac469
000005887 037__ $$aSCART-2022-0113
000005887 100__ $$aYates, Alexander
000005887 245__ $$aAssessing Similarity in Continuous Seismic Cross-Correlation Functions Using Hierarchical Clustering: Application to Ruapehu and Piton de La Fournaise Volcanoes
000005887 260__ $$c2023
000005887 520__ $$aPassive seismic interferometry has become a popular technique towards monitoring. The method depends on the relative stability of background seismic sources in order to make repeatable measurements of subsurface properties. Such stability is typically assessed by examining the similarity of cross-correlation functions through time. Thus, techniques that can better assess the temporal similarity of cross-correlation functions may aid in discriminating between real subsurface processes and artificial changes related variable seismic sources. In this study, we apply agglomerative hierarchical clustering to cross-correlation functions computed using seismic networks at two volcanoes. This allows us to form groups of data that share similar characteristics and also, unlike common similarity measures, does not require a defined reference period. At Piton de la Fournaise (La Réunion island), we resolve distinct clusters that relate both to changes in the seismic source (volcanic tremor onset) and changes in the medium following volcanic eruptions. At Mt Ruapehu (New Zealand), we observe a consistency to cross-correlation functions computed in the frequency band of volcanic tremor, suggesting tremor could be useful as a repeatable seismic source. Our results demonstrate the potential of hierarchical clustering as a similarity measure for cross-correlation functions, suggesting it could be a useful step towards recognizing structure in seismic interferometry data sets. This can benefit both decisions in processing and interpretations of observed subsurface changes.
000005887 594__ $$aNO
000005887 6531_ $$aseismology
000005887 6531_ $$avolcanology
000005887 6531_ $$avolcano-seismology
000005887 6531_ $$aambient noise
000005887 6531_ $$amachine learning
000005887 6531_ $$aclassification
000005887 6531_ $$aclustering
000005887 6531_ $$aeruption
000005887 700__ $$aCaudron, Corentin
000005887 700__ $$aLesage, Philippe
000005887 700__ $$aMordret, Aurélien
000005887 700__ $$aLecocq, Thomas
000005887 700__ $$aSoubestre, Jean
000005887 773__ $$c472–489$$pGeophysical Journal International$$v233$$y2023
000005887 85642 $$ahttps://doi.org/10.1093/gji/ggac469
000005887 8560_ $$fthomas.lecocq@observatoire.be
000005887 8564_ $$s1475444$$uhttp://publi2-as.oma.be/record/5887/files/ggac469fig3.png$$yCluster output using 0.1–1.0 Hz cross-correlation functions (CCFs) for station-pair UV05-UV12 at Piton de la Fournaise (blue line in Fig. 1a). (a) Normalized spectral width measurement. Lower values indicate a more coherent seismic wavefield dominated by fewer seismic sources. Dashed white lines show frequency range of CCFs. Triangles above (red) indicate eruption start times (b) Correlogram showing amplitudes of CCFs at different lag times (red = positive, blue = negative). Dashed black lines show part of CCFs used in clustering. (c) Location of clusters in time, colour-coded according to dendrogram output in (e). (d) Apparent velocity changes, colour-coded according to correlation coefficient (CC) computed between 10-d stacks and the reference stack period (shaded grey). Vertical red bars denote timing of eruptive activity. (e) Dendrogram, with clusters defined at a distance threshold of 0.2 (dashed-red line). (f) Mean value of spectral width as a function of frequency within time period of each cluster. (g) Normalized frequency spectrum of CCFs within each cluster, computed from averaged CCF. Legend shows Scale Factor (SF) to convert from non-normalized spectrum. (h) Average of CCFs within each cluster at positive lag times. (i) Mean coherence, averaged over CCF frequency range, between different clusters at positive lag times, computed using moving window of 10-s length with 0.5 s step (95 per cent overlap).
000005887 8564_ $$s17315$$uhttp://publi2-as.oma.be/record/5887/files/ggac469fig3.gif?subformat=icon$$xicon$$yCluster output using 0.1–1.0 Hz cross-correlation functions (CCFs) for station-pair UV05-UV12 at Piton de la Fournaise (blue line in Fig. 1a). (a) Normalized spectral width measurement. Lower values indicate a more coherent seismic wavefield dominated by fewer seismic sources. Dashed white lines show frequency range of CCFs. Triangles above (red) indicate eruption start times (b) Correlogram showing amplitudes of CCFs at different lag times (red = positive, blue = negative). Dashed black lines show part of CCFs used in clustering. (c) Location of clusters in time, colour-coded according to dendrogram output in (e). (d) Apparent velocity changes, colour-coded according to correlation coefficient (CC) computed between 10-d stacks and the reference stack period (shaded grey). Vertical red bars denote timing of eruptive activity. (e) Dendrogram, with clusters defined at a distance threshold of 0.2 (dashed-red line). (f) Mean value of spectral width as a function of frequency within time period of each cluster. (g) Normalized frequency spectrum of CCFs within each cluster, computed from averaged CCF. Legend shows Scale Factor (SF) to convert from non-normalized spectrum. (h) Average of CCFs within each cluster at positive lag times. (i) Mean coherence, averaged over CCF frequency range, between different clusters at positive lag times, computed using moving window of 10-s length with 0.5 s step (95 per cent overlap).
000005887 8564_ $$s17774$$uhttp://publi2-as.oma.be/record/5887/files/ggac469fig3.jpg?subformat=icon-180$$xicon-180$$yCluster output using 0.1–1.0 Hz cross-correlation functions (CCFs) for station-pair UV05-UV12 at Piton de la Fournaise (blue line in Fig. 1a). (a) Normalized spectral width measurement. Lower values indicate a more coherent seismic wavefield dominated by fewer seismic sources. Dashed white lines show frequency range of CCFs. Triangles above (red) indicate eruption start times (b) Correlogram showing amplitudes of CCFs at different lag times (red = positive, blue = negative). Dashed black lines show part of CCFs used in clustering. (c) Location of clusters in time, colour-coded according to dendrogram output in (e). (d) Apparent velocity changes, colour-coded according to correlation coefficient (CC) computed between 10-d stacks and the reference stack period (shaded grey). Vertical red bars denote timing of eruptive activity. (e) Dendrogram, with clusters defined at a distance threshold of 0.2 (dashed-red line). (f) Mean value of spectral width as a function of frequency within time period of each cluster. (g) Normalized frequency spectrum of CCFs within each cluster, computed from averaged CCF. Legend shows Scale Factor (SF) to convert from non-normalized spectrum. (h) Average of CCFs within each cluster at positive lag times. (i) Mean coherence, averaged over CCF frequency range, between different clusters at positive lag times, computed using moving window of 10-s length with 0.5 s step (95 per cent overlap).
000005887 905__ $$apublished in
000005887 980__ $$aREFERD