AUTOMATING THE PARAMETRIZATION OF MULTI-STAGE ITERATIVE SOURCE SEPARATION WITH PRIORS USING MACHINE-LEARNING

Systems and methods may use machine learning to automate the parameterization process for multi-stage iterative source separation. Seismic signals that are generated by a plurality of sources are received by a plurality of sensors within a field as a blended signal. An automated machine learning model that has been trained on blended and unblended signals determines if the incoming blended signal has a relatively high or low signal to noise ratio and then selects a threshold value based on the detected signal to noise ratio. The blended signal is then separated according to the source of the seismic data. A seismic image based on the separated seismic data is then generated which can then be used to adjust one or more control parameters in a machine or tool within the field.