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During the RESOLVE project ("High‐resolution imaging in subsurface geophysics: development of a multi‐instrument platform for interdisciplinary research"), continuous surface displacement and seismic array observations were obtained on Glacier d'Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d'Argentière. Using multiple bandpass‐filtered copies of the continuous seismic waveforms, we compute energy‐based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on‐ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity. Plain Language Summary: Alpine glaciers are a major component in the dynamic cryospheric environment. They are characterized by a multitude of processes occurring side by side, including but not limited to melt water flow, crevasse formation, and frictional basal sliding of the ice mass over the rigid and obstructive bedrock. Each of these processes generates distinctive acoustic signals that can be recorded by seismic instruments and the changing on‐ice motions are resolvable with Global Positioning System. Considering the rapidly changing glacial environment, there is an increasing need for reliable models to predict glacial dynamics to properly assess any associated hazard. Understanding basal sliding is of particular interest to this problem. Investigated here is how to overcome the challenge of describing glacier sliding using seismic signals since the records often contains multiple "loud" signals originating from associated surface processes within the glacier. To uncover specific processes occurring at the ice‐bedrock interface, we design a machine learning model to incorporate signals recorded on the glacier to predict the on‐ice surface motions. The results provide valuable insights into the spatiotemporal dynamics of an active Alpine glacier with the potential to contribute to a better understanding of the driving mechanisms of glacier sliding. Key Points: Seismic and Global Positioning System (GPS) data are examined to study physical processes controlling glacial basal motionDecision tree model uses beamforming catalog and statistical features of time series to constrain correlations with GPS recorded motionsModel features indicate glacial on‐ice velocity is modulated by basal motions [ABSTRACT FROM AUTHOR]