Due　to　the　difficulty　in　locating　high-resistance　grounding　faults,　this　paper　proposes　a　novel　fault　location　method　for　HVdc　transmission　lines　by　considering　double-end　unsynchronized　using　Hilbert-Huang　transform　and　one-dimensional　convolutional　neural　network　(1D-CNN).　After　the　fault　signal　is　collected　at　both　ends,　the　proposed　method　can　achieve　high-precision　fault　location,　requiring　only　the　two　ends　data　transmission　without　time　synchronization.　After　Empirical　Mode　Decomposition　(EMD),　the　high-frequency　components　of　the　double-terminal　fault　signals　are　connected　in　series　to　make　a　characteristic　waveform.　This　waveform　contains　characteristics　of　different　fault　types　and　distances,　which　can　be　learned　by　CNN.　The　trained　CNN　can　then　be　used　to　achieve　fault　location　effectively.　As　a　comparison,　two　fault　location　methods　based　on　traditional　traveling　wave　and　machine　learning　are　introduced.　Electromagnetic　transient　simulation　software　PSCAD/EMTDC　has　been　used　to　carry　out　various　types　of　fault　simulation　on　the　±　500　kV　HVdc　transmission　system.　The　results　show　that　the　proposed　method　can　reliably　and　accurately　locate　line　faults　under　fault　resistance　up　to　5200　Ω.　©　2018　IEEE.