MSc Internship: Data-driven inverse problem of gravimetric data with neural learning in geophysical exploration

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Within a collaboration between Inria Team Makutu and Orano, we propose a 6-month MSc internship on inverse problems in Earth imaging, with a focus on approaches based on neural models. Gravity surveys provide a means of characterizing the subsurface by detecting variations in the Earth’s gravitational field arising from contrasts in rock density. Subsurface density contrasts can be associated with hydrothermal alteration, and are employed as indicators of geological environment hosting a mineral of interest. In this internship, we consider the inverse problem of imaging 3D density profiles of the Earth subsurface from ground gravimetric data measuring gravitational acceleration. The internship will focus on machine-learning-based approaches to gravity inversion. In particular, we will investigate both surrogate-based inversion methods, in which neural models are used to approximate the forward or inverse mapping, and direct neural approaches that learn an approximation of the inverse mapping from gravity observations to subsurface density models. Depending on the work progress, complementary data-sets, such as electromagnetic data, may be considered. In a data- and machine-learning-driven perspective, we aim to develop an in-house computational framework for neural models to image subsurface density distributions from gravity data. The choice of methodology and neural architectures