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We present a novel, deep-learning based method -- dubbed Galactic-SeismologySubstructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search forsubstructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies ona combined application of Siamese Neural Networks to transform the phase spaceinformation and the K-means algorithm for the clustering. As a validation test,we apply GS$^{3}$ Hunter to a subset of the Feedback in Realistic Environments(FIRE) cosmological simulations. The stellar streams and substructures thusidentified are in good agreement with corresponding results reported earlier bythe FIRE team. In the same vein, we apply our method to a subset of local halostars from the Gaia Early Data Release 3 and GALAH DR3 datasets, and recoverseveral, previously known dynamical groups, such as Thamnos 1+2, Hot ThickDisk, ED-1, L-RL3, Helmi 1+2, and Gaia-Sausage-Enceladus, Sequoia, VRM, Cronus,Nereus. Finally, we apply our method without fine-tuning to a subset of K-giantstars located in the inner halo region, obtained from the LAMOST Data Release 5(DR5) dataset. We recover three, previously known structures (Sagittarius,Hercules-Aquila Cloud, and the Virgo Overdensity), but we also discover anumber of new substructures. We anticipate that GS$^{3}$ Hunter will become auseful tool for the community dedicated to the search of stellar streams andstructures in the Milky Way (MW) and the Local group, thus helping advance ourunderstanding of the stellar inner and outer halos, and of the assembly andtidal stripping history in and around the MW.

Galactic-Seismology Substructures and Streams Hunter with LAMOST and Gaia. I. Methodology and Local Halo Results