Query: deep learning for subduction

title: Probing Slow Earthquakes With Deep Learning
abstract: Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.

title: From Classical Earthquake Location to Deep Learning based Phase Detection and Picking using Seismic Data from the Northern Chile Subduction Zone

title: Towards assessing the link between slow slip and seismicity with a Deep Learning approach
abstract: <p>The deployment of increasingly dense geophysical networks in many geologically active regions on the Earth has given the possibility to reveal deformation signals that were not detectable beforehand. An example of these newly discovered signals are those associated with low frequency earthquakes, which can be linked with the slow slip (aseismic slip) of faults. Aseismic fault slip is a crucial phenomenon as it might play a key role in the precursory phase before large earthquakes (in particular in subduction zones) during which the seismicity rate grows as well as does the ground deformation. Geodetic measurements, e.g. the Global Positioning System (GPS) are capable to track surface deformation transients likely induced by an episode of slow slip. However, very little is known about the mechanisms underlying this precursory phase, in particular regarding to how slow slip and seismicity relate./p><p>The analysis done in this work focuses on recordings acquired by the Japan Meteorological Agency in the Boso area, Japan. In the Boso peninsula, interactions between seismicity and slow slip events can be observed over different time spans: regular slow slip events occur every 4 to 5 years, lasting about 10 days, and are associated with a burst of seismicity (Hirose et al. 2012, 2014, Gardonio et al. 2018) whereas an accelerated seismicity rate has been observed over decades that is likely associated with an increasing shear stress rate (i.e. tectonic loading) on the subduction interface (Ozawa et al. 2014, Reverso et al. 2016, Marsan et al. 2017)/p><p>This work aims to explore the potential of #160;Deep Learning #160;for better characterizing the interplay between seismicity and ground surface deformation. The analysis is based on a data driven approach for building a model for assessing if a link seismicity #8211; surface deformation exists and to characterize the nature of this link. This has potentially strong implications, as (small) earthquakes are the prime observable, so that better understanding the seismicity rate response to potentially small slow slip (so far undetected by GPS) could help monitoring those small slow slip events. The statistical problem is expressed as a regression between some features extracted from the seismic data and the GPS displacements registered at one or more stations./p><p>The proposed method, based on a Long Short Term Memory (LSTM) neural network, has been designed in a way that it is possible to estimate which features are more relevant in the estimation process. From a geophysical point of view, this can provide interesting insights for validating the results, assessing the robustness of the algorithms and giving insights on the underlying process. This kind of approach represents a novelty in this field, since it opens original perspectives for the joint analysis of seismic aseismic phenomena with respect to traditional methods based on more classical geophysical data exploration./p>

title: Uncovering the physical controls of deep subduction zone slow slip using supervised classification of subducting plate features
abstract: Deep slow slip events (SSEs) at subduction zones have significantly contributed to refining our understanding of the megathrust earthquake cycle at the brittle ductile transition. However, the specific combination of factors that determine their occurrence has not yet been fully explored. Here we evaluate the contribution of several of these characteristics using globally mapped geophysical data that are used as proxies for physical properties of the subducting plate. This is performed by classifying 25 km wide, trench parallel segments into binary classes based on the observation (or lack thereof) of deep, short or long term SSEs. The five characteristics explored here include subducting plate age, sediment thickness, relative plate velocity, slab dip, and plate surface roughness. We use these characteristics to train six Machine Learning models based on different learning algorithms: Gaussian Naive Bayes, Logistic Regression, Linear Discriminant Analysis, Random Forest, Support Vector Machine, and K Nearest Neighbour. Short term SSE models show that subducting plate age, relative velocity, and sediment thickness have the strongest predictive power with the first two characteristics negatively correlating and sediment thickness positively correlating with SSE occurrence, respectively. These results are consistent with a conceptual model where slow slip is controlled by conditions favoring the enduring release (and possible storage) of fluids near the source region. However, the relationship between these features and elevated pore fluid pressures is not established here and further evidence is needed to validate this hypothesis. We then use a final model constructed as a weighted average of the best performing models to make predictions on the probability of SSE occurrence, with predicted short term SSE occurrence in South America, the Aleutians, Sumatra, Vanuatu and Solomon, as well as long term SSE occurrence in the Aleutians, Izu Bonin, Kuril Kamchatka, Mariana, and Tonga Kermadec. Overall, long term SSE models do not perform as well as the short term SSE models which may indicate that long term SSEs are controlled by a different and/or extended set of physical characteristics than the short term SSEs.

title: Deep learning slow earthquakes in Cascadia and universal frictional characteristics
abstract: Slow earthquakes and associated tremor are common to most subduction zones, taking place down dip from the neighboring locked zone where megathrust earthquakes occur. We report observation of quasi continuous tremor that quantifies the slow slip rate at all times. By training a convolutional neural network to detect known tremor on a single station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The model trained in Cascadia recognizes tremor in other subduction zones and the San Andreas Fault at Parkfield. The generalization to other fault zones and different tectonic environments suggests that the frictional characteristics underlying slow slip and tremor on faults are universal, as posited from experiment and theory. 1 ar X iv :1 90 6. 08 03 3v 1 ph ys ic s. ge oph 1 9 Ju n 20 19

title: Timeliness of earthquake magnitude estimation from the prompt elasto gravity signal using Deep Learning
abstract: <p>Mass redistribution during large earthquakes produces a prompt elasto gravity signal (PEGS) that travels at the speed of light and can be observed on seismograms before the arrival of P waves. PEGS carries information about earthquake magnitude and the temporal evolution of seismic moment, therefore it could be used to both improve the accuracy of current early source estimation systems and speed up early warning. However, PEGS has been detected for only a handful of very large earthquakes so far, and its potential use for operational early warning remains to be established. In this work, we study the timeliness of magnitude estimation for subduction earthquakes in Japan using PEGS waveforms by means of Deep Learning and Bayesian uncertainty analysis. Given the paucity of PEGS observations, we train the model on a database of synthetic seismograms augmented with empirical noise in order to simulate more realistic waveforms. We use about 80 stations from the Japanese F Net network and from networks with data available through IRIS./p><p>Under this experimental setup, we find that our model is able to track the moment release for earthquakes with a final Mw above 8.0, with a system latency that depends on the signal to noise ratio of PEGS. The application of our model to the Mw=9.1 Tohoku Oki earthquake shows a latency of about 50 s after which the model is able to track well the evolving Mw of the earthquake. After about 2 minutes from the earthquake origin time, a reliable estimate of its final Mw is obtained. Similar performances in terms of timeliness of final Mw estimation are observed for the relatively smaller Hokkaido earthquake (Mw=8.1) although with higher uncertainty./p><p>Our results highlight the potential of PEGS to enhance the performance of existing tsunami early warning systems where estimating the magnitude of very large earthquakes within few minutes is vital./p><p>#160;/p>

title: Earthquake detection and location for Earthquake Early Warning Using Deep Learning
abstract: Earthquake Early Warning System (EEWS) is a warning system that provides information about the estimated S wave arrival time, which can cause significant and destructive seismic energy using the information carried by the P wave. Technological advances in analyzing data supported by big data, the interconnection between networks, and high performance computing systems in the era of the 4.0 industrial revolution have posed challenges to process and analyze earthquake early warning using modern seismological techniques. Early identification of earthquake events is the key to time efficiency to accelerate the dissemination of information. Here, we implement deep learning for early detection and classification of the earthquake P wave and noise signals using raw historical data from 3 component BMKG single station (2014 2020) in the subduction zone of West Sumatra. The feature selection of the waveform is only selected for earthquakes distance in the cluster close to the station centroid. Statistically, the results of training and testing show good and convergent performance. This result is a preliminary study of deep learning, which is targeted at the classification of earthquakes p wave and noise signals and its association to estimate early earthquake location using 3 component record channels.

title: Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning
abstract: Although infrequent, large earthquakes (Mw8+ can be extremely damaging and occur on subduction and intraplate faults worldwide. Earthquake early warning (EEW) systems aim to provide advanced warning before strong shaking and tsunami onsets. These models estimate earthquake magnitude by the early metrics of waveforms, relying on empirical scaling relationships of abundant past events. However, both the rarity and complexity of great events make it challenging to characterize them, and EEW algorithms often underpredict magnitude and the resulting hazards. Here we propose a model, M LARGE, that leverages the power of deep learning to characterize crustal deformation patterns of large earthquakes in real time. We generate realistic rupture scenarios and use these to train a model that directly measures earthquake magnitude from ground displacements. M LARGE successfully performs reliable magnitude estimation on the testing dataset with an accuracy of 99% for simulated events and for five damaging historical earthquakes in the Chilean Subduction Zone. Unlike existing models which focus on the final earthquake magnitude, M LARGE tracks the evolution of the source process and can make faster and more accurate magnitude estimates, frequently before rupture is complete. M LARGE significantly outperforms currently operating EEW algorithms.

title: Identification of Low Frequency Earthquakes on the San Andreas Fault With Deep Learning
abstract: Slow slip events occur frequently on the shallow and deep extents of subduction megathrusts and on transform faults throughout the world (e.g. Behr Burgmann, 2021) In slow slip events, the fault slip velocity is ~1 to 2 orders of magnitude above the background plate rate and orders of magnitude below typical earthquakes, hence slow slip has only a very weak seismic manifestation. Commonly, slow slip is accompanied by low frequency earthquakes (LFEs) LFEs manifest as low amplitude, emergent signals. Pand S wave arrivals are sometimes visible in individual traces but are most apparent in waveform stacks  LFEs are also depleted in high frequency content relative to traditional earthquakes of similar size. This depletion could be due to slip speeds that are slower than typical earthquakes, attenuation along the path, or some combination of the two (Bostock et al. 2015, 2017; Hawthorne et al. 2019; Littel et al. 2018; Thomas et al. 2016)

title: Identifications of shallow slow earthquakes based on deep learning in the Nankai trough
abstract: Recent development of seismic and geodetic observations has brought us to find a new earthquake category as low frequency earthquakes (LFEs) LFEs generally deplete radiated energy in higher frequencies, while rich in longer frequency components compared with ordinary earthquakes of similar size. LFEs show tremor like waveforms continuing longer than several minutes. Investigations of the source processes of LFEs will help us to understand a diversity of earthquake generation processes and frictional properties along faults, especially for those located in subduction zones. We have studied shallow LFEs that occurred off the Kii peninsula, observed by DONET ocean floor observation network. Our studies have depended on visual inspections to detect LFEs because LFEs were not detected by the automatic event detection system of DONET because of the emergent onsets. In order to monitor LFE activities, it is necessary to develop automatic detection systems which also distinguish LFEs from ordinary earthquakes. In this study, we introduce our ongoing project to develop an automatic detection system of LFE based on AI using machine learning approach. As described above, LFE signals are characterized by frequency contents and signal duration. Therefore, we use running spectrum images for the detections which can represent differences in the frequency contents as well as signal duration. Based on image recognitions of running spectrum, we try to detect and distinguish LFE signals from ordinary earthquakes and background noise.  We use a deep convolution neural network (CNN) for the detections of LFE signals from continuous images of running spectrum. We use running spectrum images of DONET records, which are periodically created every hour for data quality control. Since low frequency components from smaller LFEs are below noise level, we use plots of higher frequency components between 2 10 Hz. The spectrum image was converted to gray scale with its intensity proportional to logarithm of the signal power. Adjusting the image size suitable for the processing and connecting hourly plot, one day spectrum images were created.