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
BT  - examples from the Hengill Geothermal Field, Iceland
JF  - 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
U6  - https://doi.org/10.1093/gji/ggab511
SN  - 0956-540X
SN  - 1365-246X
VL  - 229
IS  - 2
SP  - 999
EP  - 1016
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Beyreuther, Moritz
A1  - Hammer, Conny
A1  - Wassermann, Joachim
A1  - Ohrnberger, Matthias
A1  - Megies, Tobias
T1  - Constructing a hidden Markov Model based earthquake detector: application to induced seismicity
JF  - Geophysical journal international
N2  - The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia.
KW  - Time-series analysis
KW  - Neural networks
KW  - fuzzy logic
KW  - Seismic monitoring and test-ban treaty verification
KW  - Volcano seismology
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-246X.2012.05361.x
SN  - 0956-540X
VL  - 189
IS  - 1
SP  - 602
EP  - 610
PB  - Wiley-Blackwell
CY  - Malden
ER  -