A computer-implemented method for interpreting geophysical data utilising an Artificial Neural Network, ANN, performed by electronic operations executed by a computing device, comprising: performing a training processing step on at least one training-data set, comprising the steps of: a. fa) - generating a first label-data by segmenting said at least one training-data set into different regions of at least a first region, representing a known first region having at least one identified geological feature, and a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region, and generating at least one dynamic score for each one of said first label-data, each one corresponding to a distance value from any one of said at least one identified geological feature; b. generating a first ANN model output for a dynamically adaptable Region of Interest, ROI, of said first label-data utilising corresponding said at least one dynamic score of said first label-data, said dynamically adaptable ROI including said first and second region, but ignoring or excluding said third region; c. ter- generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; d. generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; e. repeating steps c and d until a predetermined condition is met, providing a final ANN model output; applying said final ANN model output as ANN model to a seismic target-data set, obtained from a geophysical survey of a subterranean environment, generating a desired output data configured to provide a visual representation of said at least one identified geological feature identified in said seismic target-data set. performing a training processing step on at least one training-data set, comprising the steps of: a. fa) - generating a first label-data by segmenting said at least one training-data set into different regions of at least a first region, representing a known first region having at least one identified geological feature, and a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region, and generating at least one dynamic score for each one of said first label-data, each one corresponding to a distance value from any one of said at least one identified geological feature; b. generating a first ANN model output for a dynamically adaptable Region of Interest, ROI, of said first label-data utilising corresponding said at least one dynamic score of said first label-data, said dynamically adaptable ROI including said first and second region, but ignoring or excluding said third region; c. ter- generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; d. generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; e. repeating steps c and d until a predetermined condition is met, providing a final ANN model output; a. fa) - generating a first label-data by segmenting said at least one training-data set into different regions of at least a first region, representing a known first region having at least one identified geological feature, and a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region, and generating at least one dynamic score for each one of said first label-data, each one corresponding to a distance value from any one of said at least one identified geological feature; b. generating a first ANN model output for a dynamically adaptable Region of Interest, ROI, of said first label-data utilising corresponding said at least one dynamic score of said first label-data, said dynamically adaptable ROI including said first and second region, but ignoring or excluding said third region; c. ter- generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; d. generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; e. repeating steps c and d until a predetermined condition is met, providing a final ANN model output; applying said final ANN model output as ANN model to a seismic target-data set, obtained from a geophysical survey of a subterranean environment, generating a desired output data configured to provide a visual representation of said at least one identified geological feature identified in said seismic target-data set.