Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Xiao, Zhuowei; Wang, Jian; Liu, Chang; Li, Juan; Zhao, Liang; Yao, Zhen‐Xing

doi:10.1029/2020jb021444

Cited by 40 publications

(23 citation statements)

References 54 publications

(69 reference statements)

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“…The ESPRH workflow is built based on serval pre-existing methods (Figure 1). It consists of three stages: 1) the earthquake detection and phase picking stage via EqT (Mousavi et al, 2020), S-EqT (Xiao et al, 2021), and PickNet (Wang et al, 2019) models; 2) the earthquake association stage by REAL (Zhang et al, 2019) method; 3) the earthquake location stage with HypoInverse (Klein, 2002) and HypoDD (Waldhauser and Ellsworth, 2000) methods.…”

Section: Methodsmentioning

confidence: 99%

“…The Earthquake Transformer (EqT) (Mousavi et al, 2020) model achieves the state-of-art performance of ~99% precision, ~99% recall rate, and ~0.01 s mean absolute error for picking P and S phases on the STanford EArthquake Dataset (STEAD) (Mousavi et al, 2019a), outperforming all the other popular models. Xiao et al, 2021 proposed the Siamese Earthquake Transformer (S-EqT) (Xiao et al, 2021) model to address the false-negative issue in the EqT model. However, due to the limitation of the training set distribution (e.g., 92% of seismograms in the STEAD dataset are within 110 km epicenter distance), the phase picking precision of both EqT and S-EqT would decrease on seismograms with epicenter distances larger than 110 km.…”

Section: Introductionmentioning

confidence: 99%

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Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

Huang

Xiao

et al. 2022

Front. Earth Sci.

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Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. However, the precise and rapid building of submarine earthquake catalogs is challenging due to the following facts: (i) intense noise in ocean seismic data; (ii) the sparse seismic network; (iii) the lack of historical near-field observations. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. The ESPRH workflow integrates Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking, PickNet for phase refinement, REAL for earthquake association and rough location, and HypoInverse, HypoDD for precise earthquake relocation. We apply ESPRH to the continuous data recorded by an array of 12 broadband Ocean Bottom Seismographs (OBS) near the Challenger Deep at the southern-most Mariana subduction zone from Dec. 2016 to Jun. 2017. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

Huang

Xiao

et al. 2022

Front. Earth Sci.

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“…In this section, we compare the EdgePhase model with three other multi-station DL models that combine the feature embeddings from individual stations with an aggregation module (van den Ende & Ampuero, 2020; Xiao et al, 2021;W. Zhu et al, 2021).…”

Section: Comparison To Other Multi-station DL Modelsmentioning

confidence: 99%

EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

Feng

Mohanna

Meng

2022

Geochem Geophys Geosyst

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“…To declare a detection within the continuous data, the model has to surpass a certain threshold of the prediction value. Even though the problem of earthquake detection is quite straightforward, the existing models are usually developed for specific purposes and/or in specific conditions, and suffer from false detections (Perol et al 2018;Lomax et al 2019;Wu et al 2019;Mousavi et al 2019;Magrini et al 2020;Zhu & Beroza 2018;Ross et al 2018;Mousavi et al 2020;Yang et al 2020;Majstorović et al 2021;Xiao et al 2021;Saad et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Interpreting convolutional neural network decision for earthquake detection with feature map visualization, backward optimization and layer-wise relevance propagation methods

Majstorović

Giffard‐Roisin

Poli

2022

Geophysical Journal International

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Summary In the recent years, the seismological community has adopted deep learning (DL) models for many diverse tasks such as discrimination and classification of seismic events, identification of P- and S- phase wave arrivals or earthquake early warning systems. Numerous models recently developed are showing high accuracy values, and it has been attested for several tasks that DL models perform better than the classical seismological state-of-art models. However, their performances strongly depend on the DL architecture, the training hyperparameters, and the training datasets. Moreover, due to their complex nature, we are unable to understand how the model is learning and therefore how it is making a prediction. Thus, DL models are usually referred to as a “black-box”. In this study we propose to apply three complementary techniques to address the interpretability of a convolutional neural network (CNN) model for the earthquake detection. The implemented techniques are: feature map visualisation, backward optimisation and layer-wise relevance propagation. Since our model reaches a good accuracy performance (97 per cent), we can suppose that the CNN detector model extracts relevant characteristics from the data, however a question remains: can we identify these characteristics? The proposed techniques help to answer the following questions: How is an earthquake processed by a CNN model? What is the optimal earthquake signal according to a CNN? Which parts of the earthquake signal are more relevant for the model to correctly classify an earthquake sample? The answer to these questions help understand why the model works and where it might fail, and whether the model is designed well for the predefined task. The CNN used in this study had been trained for single-station detection, where an input sample is a 25 seconds three-component waveform. The model outputs a binary target: earthquake (positive) or noise (negative) class. The training database contains a balanced number of samples from both classes. Our results shows that the CNN model correctly learned to recognize where is the earthquake within the sample window, even though the position of the earthquake in the window is not explicitly given during the training. Moreover, we give insights on how a neural network builds its decision process: while some aspects can be linked to clear physical characteristics, such as the frequency content and the P- and S- waves, we also see how different a DL detection is compared to a visual expertise or an STA/LTA detection. On top of improving our model designs, we also think that understanding how such models work, how they perceive an earthquake, can be useful for the comprehension of events that are not fully understood yet such as tremors or low frequency earthquakes.

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Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Abstract: Earthquake detection and phase picking are essential and challenging problems in seismology, contributing substantially to building earthquake catalogs (M.

Cited by 40 publications

References 54 publications

Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

Interpreting convolutional neural network decision for earthquake detection with feature map visualization, backward optimization and layer-wise relevance propagation methods

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