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Novel hybrid artificial neural network based autopicking workflow for passive seismic data

Microseismic monitoring is an increasingly common geophysical tool to monitor the changes in the subsurface. Autopicking involving phase arrival detection is a common element in microseismic data processing schemes and is necessary for accurate estimation of event locations as well as other workflows such as tomographic or moment tensor inversion, etc. The quality of first arrival picking is dependent on the actual seismic waveform, which in turn is related to the near surface and subsurface structure, source type, noise conditions, environmental factors, and monitoring array design, etc. We have developed a new hybrid autopicking workflow which makes use of multiple derived attributes from the seismic data and combines them within an artificial neural network framework. An evolutionary algorithm scheme is used as the network training algorithm. The autopicker has been tested and its applicability has been validated using a synthetically modelled seismic source, with promising results. In this work, we share the basic workflow and different attributes that have been tested with this algorithm for a synthetic data set to provide a framework for independent implementation, use and validation. We also compare the results obtained using the new neural network based autopicking routine with very robust contemporary autopicking algorithms in use within the industry.