@article {chrapkiewicz_magma_2022,
	title = {Magma Chamber Detected Beneath an Arc Volcano With Full-Waveform Inversion of Active-Source Seismic Data},
	journal = {Geochemistry, Geophysics, Geosystems},
	volume = {23},
	number = {11},
	year = {2022},
	note = {_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022GC010475},
	month = {10/2022},
	pages = {e2022GC010475},
	abstract = {Arc volcanoes are underlain by complex systems of molten-rock reservoirs ranging from melt-poor mush zones to melt-rich magma chambers. Petrological and satellite data indicate that eruptible magma chambers form in the topmost few kilometres of the crust. However, very few chambers have ever been definitively located, suggesting that most are too short-lived or too small to be imaged, which has direct implications for hazard assessment and modeling of magma differentiation. Here we use a high-resolution technology based on inverting full seismic waveforms to image a small, high-melt-fraction magma chamber that was not detected with standard seismic tomography. The melt reservoir extends from \~{}2 to at least 4 km below sea level (b.s.l.) at Kolumbo{\textemdash}a submarine volcano near Santorini, Greece. The chamber coincides with the termination point of the recent earthquake swarms and may be a missing link between a deeper melt reservoir and the high-temperature hydrothermal system venting at the crater floor. The chamber poses a serious hazard as it could produce a highly explosive, tsunamigenic eruption in the near future. Our results suggest that similar reservoirs (relatively small but high-melt-fraction) may have gone undetected at other active volcanoes, challenging the existing eruption forecasts and reactive-flow models of magma differentiation.},
	keywords = {full-waveform inversion, Kolumbo volcano, magma chamber, magmatic system, Santorini volcanic field, volcano tomography},
	issn = {1525-2027},
	doi = {10.1029/2022GC010475},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022GC010475},
	author = {Chrapkiewicz, K. and Paulatto, M. and Heath, B. A. and Hooft, E. E. E. and Nomikou, P. and Papazachos, C. B. and Schmid, F. and Toomey, D. R. and Warner, M. R. and Morgan, J. V.}
}
@article {krischer_automated_2018,
	title = {Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {123},
	number = {7},
	year = {2018},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2017JB015289},
	pages = {5902{\textendash}5928},
	abstract = {We present a new anisotropic seismic tomography model based on a multiscale full seismic waveform inversion for crustal and upper-mantle structure from the western edge of North America across the North Atlantic and into Europe. The gradient-based inversion strategy utilizes the adjoint state method coupled with an L-BFGS quasi-Newton optimization scheme. To improve the handling of large data quantities in the context of full seismic waveform inversions, we developed a workflow framework automating the procedure across all stages, enabling us to confidently invert for waveforms from 72 events recorded at 7,737 unique stations, resulting in a total of 144,693 raypaths, most of them with three-component recordings. The final model after 20 iterations is able to explain complete waveforms including body as well as surface waves of earthquakes that were not used in the inversion down to periods of around 30 s. The model is complemented by a detailed resolution analysis in the form of 3-D distributions of direction-dependent resolution lengths.},
	keywords = {data management, full-waveform inversion, inverse problems, optimization, seismology, workflow},
	issn = {2169-9356},
	doi = {10.1029/2017JB015289},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2017JB015289},
	author = {Krischer, Lion and Fichtner, Andreas and Boehm, Christian and Igel, Heiner}
}
@article {261,
	title = {Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico},
	journal = {GEOPHYSICS},
	volume = {83},
	year = {2018},
	month = {07/2018},
	pages = {B167{\textendash}B181},
	abstract = {We present a case study of gas hydrate quantification using dense short-offset multichannel seismic (MCS) and sparse long-offset ocean-bottom-seismometer (OBS) data in lease block Green Canyon 955 (GC955), Gulf of Mexico (GOM), where the presence of gas hydrate was interpreted using logging while drilling (LWD) data acquired by the GOM Gas Hydrate Joint Industry Project Leg II expedition. We use frequency-domain full-waveform inversion (FWI) of seven OBS gathers to invert for a P-wave velocity model of an approximately 7 km long MCS profile connecting two LWD sites, GC955-H and GC955-Q. We build the starting model for FWI using traveltime inversion (TI) of the MCS and OBS data. In addition, we use the TI model for depth migrating the MCS stack. At the LWD sites, we constrain the hydrate saturation (Sgh ) using sonic and resistivity logs. Unfortunately, as is typical of seismic quantification problems, the FWI model resolution is not sufficient to extrapolate the LWD-based Sgh . Therefore, we apply Backus averaging to the sonic log, at 60 m wavelength, bringing it within approximately 8\% of the FWI model and make the assumption that averaging the sonic log is same as redistributing the gas hydrate within the Backus wavelength. In this manner, instead of Sgh , the FWI model is able to estimate the total gas hydrate volume. In the end, we use the FWI model and the migrated stack to constrain the locations and bulk volumes of free gas and gas hydrate. Our results demonstrate that with careful processing, reasonable estimates on locations and bulk volumes of submarine gas hydrate accumulations can be achieved even with sparse seismic data that are not adequate for amplitude-based assessments.},
	keywords = {full-waveform inversion, hydrates, ocean-bottom node},
	issn = {0016-8033},
	doi = {10.1190/geo2017-0414.1},
	url = {https://library.seg.org/doi/10.1190/geo2017-0414.1},
	author = {Wang, Jiliang and Jaiswal, Priyank and Haines, Seth S. and Hart, Patrick E. and Wu, Shiguo}
}
