Optimized AI identification for faults in northern slope,central Sichuan Basin
The latest studies suggest strike-slip faults developed in northern slope of central Sichuan Basin.With steep plane and small throw,these faults are not apparent.It is hard to rapidly and accurately identify them running through targets of the deep-seated Lower Paleozoic due to low signal-to-noise ratio(SNR)of seismic data.So,an optimized AI technique has been used for fault identification.Firstly,background simulation is performed to obtain the residual error between local background energy data and original seismic data,thereby enhancing the signal manifest of minor faults beneath stratigraphic reflection.Secondly,the SNR is improved by means of structure-oriented filtering which makes both continuity of seismic event and fault features more pronounced.Finally,AI technique is used for this identification.Results demonstrate that this technique is not only greatly time saving in artificial interpretation,but ex-hibits strong noise resistance for redundant suppression with effect,improves the SNR,identifies the minor faults that are difficult to be detected using conventional attributes,and makes the prediction on strike-slip faults'distribution and intersection even better.
Central Sichuan paleoupliftNorthern slopeLower PaleozoicStrike-slip faultAI fault identificationOptimizationMi-nor fault
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