This week we want to show you an example of the 4th Lundin model 'Lundin_GeoLab_SimpleDenoise". We believe this model is applicable to almost all datasets. Unlike the AJAX...
This week we want to show you an example of the 4th Lundin model 'Lundin_GeoLab_SimpleDenoise". We believe this model is applicable to almost all datasets. Unlike the AJAX model , the SimpleDenoise model preserves the amplitude - frequency content. It only removes random noise.
We applied 3 noise reduction filters and compare these with the input data (PSTM processed) on one inline and one crossline. The 3 filters are:
Dip-Steered Median Filter with a stepout of 2 AJAX, a trained Machine Learning model (3D Unet) from AkerBP (Lundin-Geolab) available in OpendTect's library of pre-trained models that has learned to reduce noise, enhance lateral continuity, and optimize frequency content. DeSmile, another trained Machine Learning model (3D Unet) from AkerBP (Lundin-Geolab) available in OpendTect's library of pre-trained models. DeSmile has learned to reduced migration smiles artefacts. Share your thoughts in the comments below and let us know which filter you prefer!
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