Machine learning thermobarometry and chemometry using amphibole and clinopyroxene: a window into the roots of an arc volcano (Mount Liamuiga, Saint Kitts)

Oliver John Higgins, Tom Sheldrake , Luca Caricchi 

The physical and chemical properties of magma govern the eruptive style and behaviour of volcanoes. Many of these parameters are linked to the storage pressure and temperature of the erupted magma, and melt chemistry. However, reliable single-phase thermobarometers and chemometers which can recover this information, particularly using amphibole chemistry, remain elusive. We present a suite of single-phase amphibole and clinopyroxene thermobarometers and chemometers, calibrated using machine learning. This approach allows us to intimately track the range of pre-eruptive conditions over the course of a millennial eruptive cycle on an island arc volcano (Saint Kitts, Eastern Caribbean). We unpick the story of Mount Liamuiga, a stratovolcano that pops its upper-crustal (2 kbar), dacitic cork at the beginning of the Lower Mansion Series eruptive sequence. This permits a progressive increase in the thermal maturity of the magma arriving at the surface from the middle to upper crust (2 – 5.5 kbar) through time. The temperature increase correlates well with matrix plagioclase chemistry, which itself displays a remarkable progression to less evolved (more anorthitic) composition in time. We find that amphibole is a reliable themobarometer (SEE = 1.4 kbar; 40 ˚C), at odds with previous studies. We suggest it is the regression strategy, as opposed to the abject insensitivity to pressure, that has hindered previous calibrations of amphibole only thermobarometers. By recognising this, we have constructed a high-resolution, quantitative picture of the magma plumbing system beneath an arc volcano.