Microseismic　event　picking　is　one　of　the　key　steps　in　seismic　processing　and　imaging.　Manually　picking　is　a　widely　used　way　to　pick　the　microseismic　events,　which　is　time-consuming.　The　standard　short-term　average/long-term　average　(STA/LTA)　is　a　traditional　method　to　pick　the　microseismic　first　arrivals,　which　would　lead　to　inaccurate　first-arrival　picks　in　case　of　low　signal-to-noise　ratio　(SNR).　We　developed　a　workflow　to　automatically　pick　the　microseismic　first　arrivals　by　using　the　feature　pyramid　networks　(FPNs).　To　train　the　proposed　model,　we　first　randomly　select　part　of　the　microseismic　traces　and　manually　pick　the　time　index　of　the　first　arrivals.　Next,　we　segment　every　selected　trace　into　two　parts　based　on　the　time　index　of　the　manual　picking　and　then　assign　each　part　a　label.　Afterward,　we　train　the　proposed　fine-tuning　FPN　model　by　using　the　training　data　and　the　corresponding　labels.　It　should　be　noticed　that　we　proposed　a　loss　function,　named　the　point-aware　loss,　for　solving　the　microseismic　first-arrival　picking　issue.　Finally,　we　predict　the　microseismic　first　arrivals　by　using　the　well-trained　fine-tuning　FPN　model.　The　numerical　examples　demonstrate　that　our　proposed　model　successfully　identifies　the　microseismic　first　arrivals.　The　microseismic　first　arrivals　predicted　by　using　our　proposed　model　are　more　robust　and　more　accurate　than　those　obtained　by　using　the　STA/LTA　and　the　encoder-decoder　network.