Microseismic source imaging using physics-informed neural networks with hard constraints: a field application

Xiaojuan Huang, Tariq Ali Alkhalifah

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Microseismic source imaging is crucial for event location both on the exploration and the seismological scales due to its high accuracy and high resolution. There exists a challenge to source imaging in the case of common sparse observation and irregular geometry. Our recently proposed direct imaging method via physics-informed neural networks with hard constraints has already shown great potential in solving such a problem on synthetic data. Here, we further show the effectiveness of this method by means of the application to the Real hydraulic fracturing data. Specially, we have slightly modified the workflow by adding preprocessing and using the reference frequency loss function with causality implementation to obtain reasonable and reliable source locations. The field examples show that our method can correctly locate the source with physics-guided training signals in a label-free manner.
Original languageEnglish (US)
Title of host publication84th EAGE Annual Conference & Exhibition
PublisherEuropean Association of Geoscientists & Engineers
StatePublished - 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-29
Acknowledgements: The authors thank KAUST and the DeepWave Consortium sponsors for supporting this research, Microseismic Inc. for the use of the Arkoma data, and Hanchen Wang and Fu Wang for discussing the field data preprocessing. We would also like to thank the SWAG group for the collaborative environment.


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