TY  - JOUR
A1  - Chakraborty, Megha
A1  - Quinteros Cartaya, Claudia
A1  - Li, Wei
A1  - Faber, Johannes
A1  - Rümpker, Georg
A1  - Stöcker, Horst
A1  - Srivastava, Nishtha
T1  - PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
T2  - Artificial Intelligence in Geosciences
N2  - Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
KW  - First-motion polarity
KW  - Earthquake waveforms
KW  - Convolutional
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/78286
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-782860
SN  - 2666-5441
VL  - 3
SP  - 46
EP  - 52
PB  - Elsevier
CY  - Amsterdam
ER  -