TY  - JOUR
A1  - Nooshiri, Nima
A1  - Bean, Christopher J.
A1  - Dahm, Torsten
A1  - Grigoli, Francesco
A1  - Kristjansdottir, Sigriour
A1  - Obermann, Anne
A1  - Wiemer, Stefan
T1  - A multibranch, multitarget neural network for rapid point-source inversion in a microseismic environment
T2  - Geophysical journal international
N2  - Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M <= 1.6) earthquakes at the Hellisheioi geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity.
KW  - Neural networks
KW  - fuzzy logic
KW  - Computational seismology
KW  - Induced seismicity
KW  - Earthquake source observations
Y1  - 2021
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64030
SN  - 0956-540X
SN  - 1365-246X
VL  - 229
IS  - 2
SP  - 999
EP  - 1016
PB  - Oxford Univ. Press
CY  - Oxford
ER  -