Artificial Neural Networks as Emerging Tools for

Artificial Neural Networks as Emerging Tools for EarthquakeDetection

Otilio Rojas1,2, Beatriz Otero3, Leonardo Alvarado4,2, Sergi Mus3, Ruben Tous3

1 Barcelona Supercomputing Center (BSC), Barcelona,Spain

2 Universidad Central de Venezuela (UCV), Caracas,Venezuela

3 Universitat Politecnica de Catalunya (UPC), Barcelona,Spain

4 Fundacion Venezolana de Investigaciones Sismologicas (Funvisis), Caracas,Venezuela

[email protected], [email protected]

Abstract. As seismic networks continue to spreadand monitoring sensors become more efficient, theabundance of data highly surpasses the process-ing capabilities of earthquake interpretation analysts.Earthquake catalogs are fundamental for fault systemstudies, event modellings, seismic hazard assessment,forecasting, and ultimately, for mitigating the seismicrisk. These have fueled the research for theautomation of interpretation tasks such as eventdetection, event identification, hypocenter location, andsource mechanism analysis. Over the last forty years,traditional algorithms based on quantitative analyses ofseismic traces in the time or frequency domain, havebeen developed to assist interpretation. Alternatively,recent advances are related to the application of ArtificialNeural Networks (ANNs), a subset of machine learningtechniques that is pushing the state-of-the-art forward inmany areas. Appropriated trained ANN can mimic theinterpretation abilities of best human analysts, avoidingthe individual weaknesses of most traditional algorithms,and spending modest computational resources at theoperational stage. In this paper, we will survey thelatest ANN applications to the automatic interpretationof seismic data, with a special focus on earthquakedetection, and the estimation of onset times. For acomparative framework, we give an insight into the laborof human interpreters, who may face uncertainties in thecase of small magnitude earthquakes.

Keywords. P and S seismic waves, earthquakehypocenters, supervised, unsupervised and semisuper-vised, deep and convolutional neural networks, trainingand testing data sets.

1 Introduction

Large-magnitude earthquakes may induce strongground shaking, surface ruptures, landslides,liquefaction and Tsunamis, being any of thesedirect or indirect causes of human depths andsevere economic losses. Fortunately, earthquakemagnitude and frequency are inversely propor-tional, so events of large and moderate magnitudesare scarce on seismic catalogs compared toearthquakes of low magnitudes. However, humanor automatic detection by traditional techniquesmay fail for low-magnitude events, in case oftraces with poor signal-to-noise ratios or recordingoverlapping events. Even though, small-magnitudeearthquakes are not usually harmful for livesand civil structures, they must be also recordedin seismic catalogs. These catalogs are usedin seismic hazard assessment to map theexpected ground motion under a given earthquake

Computación y Sistemas, Vol. 23, No. 2, 2019, pp. 335–350doi: 10.13053/CyS-23-2-3197

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magnitude, and must influence building codes. Inaddition, the detection of small earthquakes inan aftershock sequence left by a big event, areused to study the distribution of the fault system,total rupture area, and the evolution of the stressconfiguration. This information, in combination toadditional geological studies, allow assessing thedestructive potential of that given fault.

The detection problem of local earthquakeevents consists of estimating the arrival times ofthe primary (P) and secondary (S) waves to arecording seismic station. These onset timesfeed subsequent processing analyses to estimatehypocenter location, focal source mechanismand important spectral properties, being thisinformation recorded in earthquake catalogsafterward. Detection and processing analyses areroutinely performed by a human analyst, who mustestimate arrival times based on his own knowledgeand experience. This estimation requires asignificant amount of pattern recognition, and istied to the identification problem of individual P andS arrivals from their amplitude, propagation speed,and induced signal polarization. Nowadays, theneed of automatic processing tools grows rapidlywith the amount of seismic data delivered byexpanding monitoring networks, so detection andidentification of P and S waves can be performedin a faster, robust and objective way.

The automation of seismic phase detection andidentification must deal with characteristic featuresof seismic records. As commonly used for dataanalysis, signals are processed on time windows.In these cases, those windows may contain a lotof noise and high redundancy, so true P and Swaves can be placed anywhere in the time spanand polluted by alternative signals. For instance,wave conversions that depend on undergroundstructures and site conditions, or even surfacewaves, may be also present on long time windows.Also, these windows may contain P and S wavestriggered by simultaneous or overlapping seismicevents. In many cases, seismic stations recorddata in several channels, allowing either a separateor combined processing of horizontal, vertical andtransverse components of soil kinematics.

All these features make difficult and laborious therecognition of similar patterns on seismic signals,

and the P and S detection and identification fromone or various events. Filtering, polarization anal-ysis, and additional signal processing techniqueshave been of primary help for automation.

Artificial Neural Network (ANN) represent anatural implementation framework for P andS wave detection and identification becauseof the huge amount of pattern recognitioninvolved. The pioneer applications in [16] and[17] have been followed by multiple and diversenetworks, using either assisted, or semisupervisedor completely unsupervised learning. In thesupervised family, we find several generic andconvolutional networks, and even some scattercases of recurrent and residual networks. In theunassisted family, there are applications basedon generative adversarial networks and othersexploiting autoencoders.

Only few cases are built upon ensemblelearning, but their potentials are worthy ofmentioning. Now, all these ANN applicationsmust deal with the processing difficulties of timewindows of seismic records, mentioned above.These difficulties translate into more complexheuristic rules to estimate the number of neuronsper each network level, and the size of the learningset, if it is required. This paper reviews all theseimplementation aspects, as well as the detectionand identification performance achieved by ANN.

Alternative machine learning (ML) techniquescan also offer advanced pattern-matching abil-ities and well serve as automated earthquakemonitoring algorithms. Moreover, the expandingML applications to seismology have reachedareas such as earthquake early warnings, groundmotion estimations, and seismic tomography andinversion, and we here also briefly comment onprominent works, for the sake of completeness.However, we refer the reader to the followingcomplementary survey papers [18, 23, 29],Kong2019, for additional insights.

2 Picking of Seismic Phases by aTrained Analyst

Modern data acquisition systems use sample ratesof more than 100 Hz, and this limits the estimationaccuracy of P and S arrival times, among


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other factors such as signal-to-noise conditions atmonitoring stations [11]. In addition, hand-pickeddata are human dependent and errors are difficultto estimate, since this activity is performed byseveral analysts. Different trained analysts maygive different onset times for the same signalphase, and sometimes the same analyst maygive different interpretations after some time haspassed.

For events at epicentral distances not largerthan 10◦ (∼ 1000 Km), recorded seismic waveshave propagated to the crust and along the Mohodiscontinuity. At those stations, these waves arecalled crustal waves, being triggered by local andregional earthquakes. On the contrary, teleseismicevents refer to earthquakes that occur at distanceslarger than 10◦ from the measurement site. Thenext description follows [32] and it is focused oncrustal waves.

In general, a seismic phase is identified by twomain features. A signal amplitude increase thatexceeds the background noise in the case of Pwaves, or exceeds the coda of earlier phases inthe case of S and later waves. A change ofthe dominant frequency that is often much moredifficult to visualize and quantify [21]. Once thephase is identified, we have to determine theprecise wavelet onset or arrival time, and theprecise phase type. That is, a Pg phase or a directP wave, a Pn that is the refracted P wave along theuppermost mantle, a Sg phase or a direct S wave,among other possibilities. The visual examinationof amplitude and frequency changes can be rathersubjective and would depend on the chosenwidth of time windows. As illustrative examples,figures 1 and 2 show the P and S phasesidentified at the CUMV and CACV stations of theVenezuelan Funvisis seismic network (2019/01/1613:42, 4.0 Mw). In figure 1, both P and S phasesare identified through amplitude changes of therecorded seismogram. In figure 2, identificationof the P wave is straightforward from a clearchange of the frequency content after its arrival,with respect to the background noise.

Signal filtering can help to identify the phasearrivals by improving the signal-to-noise ratio.However, filtering can also introduce shapechanges on the waveform, and even a phase

shift. This time shift could modify the phasearrivals in the order of tenth of seconds [21].Figure 3 shows the vertical component of groundacceleration recorded at the BAUV station of theFunvisis network, in Cojedes, Venezuela. Thisstation is located 198 km away from the epicenterof the event (2018/04/02 16:24, 2.0 Mw). Later,this seismogram is passband filtered (5-10 Hz) andresulting signal is depicted in figure 4. Here, wecan see how the P wave is enhanced, and it canbe easily identified by the first notorious change inamplitude.

There are software available to assist thehuman analyst that could even be used forautomatic processing, most of them beingcreated by university groups or seismologicalinstitutions. Some packages widely usedare SHM, SEISAN, SeisComp3, Earthwormand ObsPy. These processing software weredeveloped and published under a free, orpartially free license. Software libraries forroutinely analysis and additional researchwork, can be downloaded from web sites ofthe United State Geological Survey (USGS:https://earthquake.usgs.gov/research/software/),and of the IncorporatedResearch Institutes for Seismology(https://www.iris.edu/hq/data and software/).

3 Machine Learning Algorithms andDeep Neural Networks

The fundamentals and pivotal developments onautomatic pattern recognition and ML techniquescan be reviewed on reference textbooks, suchas [10], [41], and [25], as well as on somecomprehensive online courses, as for instance,the ”Neural Networks for Machine Learning” fromG. Hinton1 and the ”Machine Learning” from A.Ng2. This section highly lacks of such generality,and only presents a basic ML classification basedon the learning strategy, that later serves tointroduce ML and ANN applications to geophysicaldata, in next sections. Thus, we recommend

1http : //www.cs.toronto.edu/ hinton/coursera slides.html2https : //www.coursera.org/learn/machine− learning


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Fig. 1. Seismogram recorded at CUMV station in Sucre, Venezuela (2019/01/16 13:42 4.1 Mw), and used for P and Swave identification based on the change of signal amplitude

Fig. 2. P-wave identification based on the change of the frequency content for the same event in Figure 1, but recordedat CACV station in Bolivar, Venezuela

aforementioned books and materials for a broaderpresentation.

The rich variety of ML algorithms can be groupedaccording to the type of data used during training,a stage where optimization strategies allow tuningthe algorithm parameters usually by minimizing acost function. To this purpose, the training datasetcan be fully or partly labelled, and even it maypresent no labels at all. Coarsely speaking, thisdataset property frames the learning algorithm andalso defines its potential applications. Supervisedlearning employs labelled datasets and developsmodels used for either quantitative predictionor categorical classification. In this case, thetraining and evaluation stages are time consumingbecause of the labelling and data preparation.Alternatively, unsupervised learning works onunlabelled datasets aiming at clustering the inputdata into groups based on similarity measures, orat reducing the input data dimensions. Becauseof labelling is not required, the preparation ofthe training and evaluation datasets takes less

effort, which allows using bigger amount of data.Finally, semisupervised learning uses a hybriddataset for training and evaluation, typically witha small fraction of labelled data, compared to alarger amount of unlabelled data. These differentmachine learning algorithms are shown in thecategorization figure 5 along with their typicalapplications that vary from data classification,quantitative regression, grouping or clustering, andreduction of data dimensionality. An additionalapplication pursuing data grouping by followingimplicit association rules to discover relationsamong variables in large databases, is alsoconsidered in this figure. The specific algorithmsreferred in the technical literature are also shownfor each category at the bottom.

Figure 5 places ANN into the classificationhierarchy of supervised learning algorithms, de-lineating also their possible applications whenprocessing earthquake data. In fact, specificANN implementations on earthquake detection arementioned in next sections, along with interesting



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Fig. 3. P wave onset on the vertical acceleration seismogram recorded at the Funvisis BAUV station in Cojedes,Venezuela, during a local earthquake (2018/04/02 16:24 Mw=2,0)

Fig. 4. The new signal after applying a Butterworth filter in the band 510 Hz to the seismogram in figure 3.

additional works based on autoencoder algorithms,whose applications also facilitate this interpretationtask.

To emphasize the focus of our study on theseML algorithms, we use grey boxes in figure5. Most of those ANN applications are basedon deep learning, as a way to achieve highaccuracy by the incorporation of several neuronlayers, and therefore heuristic rules. The ANNprediction accuracy would partly depend on aprevious good training, but also on the number ofnetwork layers that allow a parallel and multi-leveldetection of important data features (also referredas abstraction levels).

The presence of two or more neuron layersis defined as deep learning, while very deeplearning is referred to using more than tenlayers. Nowadays, applications based on very

deep learning are scarce (because of complexityof the network structure), as also those using ashallow ANN with only one hidden layer (becauseof the limited learning capability).

It is worthy of mentioning that deep architecturescould first use unsupervised learning, and thenappeal to fine-tuning in a final stage of supervisedtraining, aiming at a superior performance [49].

4 Machine Learning Applications toEarthquake Data

The physics of earthquakes is very complex andseismologists rely on processing and interpretingmassive data sets in search for insights. ANN,among other ML strategies, have the potential tofind unseen patterns and new features, both with



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Fig. 5. Supervised, unsupervised and semisupervised algorithms for ML

physical consistency, in available datasets. Thus,seismologists and computer scientists have beenactively developing ML tools to process earthquakedata and assist interpretation. Numerousapplications have focused on earthquake detectionand phase picking, exploiting the ML proficiency ofdata classification and regression when processingreal seismic traces.

Recent and interesting ML applications toearthquake detection are the classification Markovmodel proposed in [6], the image-based searchfor waveform matching presented in [56] and theseismic arrival picking based on fuzzy clusteringdeveloped in [14]. In next section, we reviewadditional and prominent works in this area, butfocus on contributions based on ANN. However,we before comment on three main additionalML applications to seismology, which are undercurrent expansion, and relate to earthquake earlywarnings, ground motion estimations, and seismictomography and inversion.

Earthquake early-warning systems intend toidentify a seismic event onset at remote stations,and exploits the faster propagation of P wavescompared to more destructive S and surfacewaves, to trigger alerts at local sites, secondsor few minutes before the strongest shakinghappens. Earthquake early warning require tools

for earthquake detection, and some works basedon ANN can be found in [26, 13, 30]. Alternatively,similar applications have been also addressed bythe ML approaches in [15, 12, 30].

During an earthquake, ground motion experi-enced at a given surface location would dependon several parameters of the seismic source,material properties along wave propagation pathsand local site conditions. The statistical predictionof ground motion is based on regression modelsthat combines these parameters, and strives forreducing uncertainties by increasing observationaldata and model complexity. Ground motionestimates are key inputs to probabilistic analysesof seismic hazard, that are sensitive to theseprediction uncertainties [4].

Alternatively, Authors in [19, 20, 28] employ ANNfor ground motion estimation using as input, keyparameters of the seismic source and the velocitymodel. In particular, results in [28] are moreaccurate than those provided by the Central andEastern North America attenuation model (CENA)developed in [8]. Alternative ML approacheshave been also used in similar applications, asdiscussed in [45, 48].

Travel-time tomography and full-wavefrom inver-sion are subsurface imaging procedures basedon the minimization of certain discrepancies



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between seismic data and simulations. Given areference model, the former seeks the reductionof source-receiver travel times differences byrelocating material interfaces, while the latter up-dates medium properties to reduce mismatchingsbetween data and synthetic waveforms. Theconvergence of both methods to a realistic struc-tural model is time consuming, while tomographymay require continuous intervention of a humanexpert, full-wavefrom inversion is computationallydemanding and requires a good initial model.

Recent ANN applications to subsurface imagingclaim to overcome these deficiencies, usingseismic data as input to identify importantstructures. In particular, [39] uses earthquakedata to accurately predict 1-D velocity models,and applications in [5, 34, 27, 53] employ datacollected in seismic surveys for structural modelbuilding with interest in hydrocarbon exploration.In addition, the study in [37] applies a ANN toinfer the prior distribution of acoustic properties ofa geological model, that is later improved by fullwaveform inversion. This work includes promisingapplications to a synthetic reservoir-scale datasetof channel bodies. Lastly, we would like to noteon alternative ML approaches that use data-drivendictionaries to adaptively capture complex profilesof geophysical parameters, and used to improvethe convergence of seismic tomography [9] orinversion [58].

5 Neural Networks for EarthquakeDetection

According to the figure 5, the identification of Pand S waves on seismic traces is a classificationproblem, while the estimation of onset arrival times,as required for complete detection, corresponds toa regression problem. Either processing objectivewill be highlighted below when reviewing ANNsolution approaches developed during the lastthirty years, and this discussion will also includea ANN feature suitable to achieving it. Thenetwork input data can be the direct (probablyprocessed) seismograms, or some previouslyextracted (primarily statistical) features from thesesignals.

Full traces provide to the ANN with the wholeinformation carried by the seismic signal, butdue to its high dimensionality, the mapping oflearning examples to the feature space mightbe highly sparse, making the ANN prone towrong convergences at the production stage.Alternatively, a more compact feature space resultsfrom a network learning process using onlyrelevant signal features as input data, that willtranslate into high operational accuracy. However,if some important features are omitted, the ANNmight performs poorly. In the following, weonly focus on earthquake detection ANN undersupervised and unsupervised learning given theirvery active development, and enough contributionsto motivate this survey paper.

5.1 Supervised Networks

Supervised ANN fall into four main categories:Feed forward (FFNN), recurrent (RcNN), convolu-tional (CNN) and residual (RsNN). FFNN present asimple structure and are commonly used for dataclassification. RcNN are usually applied to timeseries analysis to identify sequence patterns, whileCNN are typically employed for image recognition.Finally, among all supervised ANN, RsNN presenta more complex structure with skipped connectionsor shortcuts, that enable a fast learning and givebetter performance than a plain network.

5.1.1 FFNN

For picking seismic phases on local earthquakedata, deep FFNN using the direct full waveformsas input are employed in [16, 17, 22, 57]. Networkin [16] uses the amplitude of the three-componentinput seismograms, and achieves successfulresults on more of the 90% of the testing datafor both P and S phases. Same Authors in [17]employ a single component of the input data,and attain P and S detection performances mostlyabove 80% in tested cases, with strong variationsaccording to the chosen signal component, giventhe influences of the ray path and source position.The input traces to the three-layer ANN in[22] were band-pass filtered for a better arrivaltime prediction of P waves. The detectionperformance is assessed in terms of different



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noise levels, that consistently affects predictionaccuracy as graphically shown by Authors. Thetechnique in [57] behaves successfully for thephase identification of P waves on 95% of a testingset, that comprises 1254 seismograms of the IRISnetwork. The estimated onset times present anerror less than 0.5 sec in 80% of the cases.

The parallel application in [51] for P wavedetection implements two FFNN, which employtrace features as input data. The first oneuses an extension of the standard STA/LTAtriggering algorithm, developed in [2, 3], whereinput are given by the ratios of short- andlong-term averages (STA and LTA, respectively)of normalized trace windows. The rate of correctphase identification was nearly 92%. The secondnetwork operates with spectrograms of movingtrace windows, which typically present a differentbehavior of those from non-earthquake signals.For the same testing data set, 98% of correctdetection is observed. Same Authors in [50],develop a S-phase identification and picking FFNNthat includes as input attributes, autoregressivemodel coefficients and measures of the signalpower and polarization angles. On testing results,86% correct rate of phase identification wasachieved, and 74% of them were picked with onsettime errors less than 0.1 sec.

Networks developed in all aforementioned workshave been trained on and applied to local andregional earthquake data. FFNN’s introduced in[46] use a training data base comprising P-wavesignals of 193 teleseismic events, and operate oninput data consisting of STA/LTA values computedin seven frequency bands. After training, theseANN detectors found 25% more events of theofficial event bulletin, compared to the referenceMurdock-Hutt detector [40]. Appropriate ANNweight tuning led to a reduction of the false alarmrate. In addition, [46] explores the detectioncapabilities of Elman-Jordan RcNN, but found poorperformances.

An important contribution to the automatic onsettime picking of P and S waves is the FFNNproposed in [24], a neural tree called IUANT2,that presents a problem adaptive structure. Thebest structure of IUANT2 is inferred duringthe training phase, while allowing for noise

filtering. The three-component input seismogramsare preprocessed by removing trace mean andpass-band filtering, followed by the calculationof the vector magnitude of both horizontalcomponents.

The estimation of P and S onset times areperformed on statistical features, such variance,absolute skewness, kurtosis and combinations ofthese statistics, which are altogether computedon time windows of preprocessed traces. Thetesting dataset consists of more than 300 localearthquakes recorded by 23 different stations, andthe picking accuracy, quantified through the normaldistribution of time differences relative to manualpicks, have standard deviations of 0.06 sec and 0.1sec, for P and S waves, respectively. The accuracyof IUANT2 is then measured as the inverse of thesedeviation values, even though manual picks couldalso be erroneous.

5.1.2 RcNN

A RcNN for the real time detection of smallmagnitude (below 2.5 Mw) earthquakes is pre-sented in [52]. In this case, the distribution ofseismic stations in populated areas yields datawith significant levels of noise. This requires aspecial detection method capable of recognizingsmall events without using preprocessing basedon standard pass-band filtering. Instead, [52]applies a filter bank of STA/LTA ratios of thevertical component of seismic waveforms, with anelaborate design to keep all important frequenciesof the signal, including the highest range, wheredisturbances are less significant. Thus, this RcNNis low prone to false detection occurrences. Theinitial training set comprises about 170 eventsincluding regional and teleseismic earthquakes,but the full operatibility was achieved afterprogressive tuning for real time P and S phasedetection, on data from various monitoring stations.Testing was carried out during different periods oftime in 2009, and relative to the standard STA/LTAmethod, this RcNN misses a few percentage ofreal events, but behaves much less sensitive tofalse detections, as expected. Authors found thatthe number of triggering stations is an important



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parameter to adjust, so it is crucial for potentialapplications in new regions.

Recent applications of RcNN to earthquake dataare given in [26, 7] with the purpose of earthquakeprediction. The common architecture of a RcNNpresents a set of Long Short-Term Memory(LSTM) layers, combined to dropout layers to avoidoverfitting, and supply an efficient framework fordetection and inference of temporal patterns. Inboth works, a series of past earthquakes is usedfor network training, that is used to predict a nexttrend of known events, to allow testing. Resultsare encouraging, although earthquake forecastremains as a highly controversial subject.

5.1.3 CNN

Among the state-of-the-art earthquake detectionmethods, the most popular deep CNN areprobably ConvNetQuake [44], the generalizedphase detection (GPD) [42], and PhaseNet [59].However, we next also comment on a fewadditional and prominent works. Figure 6 illustratesthe schematic structure of an automatic detectionseismic system based on a fully connected ANNfor classification and regression.

The raw seismic traces are usually subjectedto waveform normalization (mean removal andamplitude scaling), band-pass filtering to isolateseismic phases, time splitting and windowing.Then, preprocessed data is used for networklearning and validation, probably preceded bydata labeling to allow supervised learning. Inmany cases, the network outputs reduce to classprobabilities that associate window traces withP waves, S waves, and simple noise. Theactual architecture of CNN mentioned below aresomewhat more complex, and some cases are alsocapable of locating earthquakes.

ConvNetQuake is based on collection ofnonlinear filters, and operates on time windowsof single-station three-channel seismograms forP-wave detection and event location. Its mainapplication has been the detection of natural orhuman-induced (related to waste water injection)low-magnitude earthquakes in Central Oklahoma,USA. Data preprocessing splits traces into monthlystreams, applies mean subtraction and divides

each trace component by the absolute peakamplitude, to finally defines appropriate windows.For training, near to two thousand catalogedseismic events occurred during 2014-2016 wereemployed, and 209 were later used for testing.

Here, ConvNetQuake displays a precision of94.8%, measured as the fraction of detectedevents that are true events, with 74.5% of thembeing correctly located on a coarse Voronoigeographic partition of the study region. Also,the recall was 100%, defined as the fractionof true events correctly detected. On an inde-pendent testing set comprising 21 earthquakeswith magnitude below 4.1 Mw that occurredin Northern California, ConvNetQuake achieves100% detection, confirmed by autocorrelation, and74.6% location correctness.

In seismology, nonetheless, an earthquake’shypocenter corresponds to be the physical locationof the starting point of the rupture process,where stored strain energy in the rock is firstreleased. Commonly, hypocenter coordinates aregiven in terms of longitude, latitude and depthbelow surface, and those define the earthquakelocation. Mapping the hypocenters of foreshocks,main event, and aftershocks allows picturingthe three-dimensional movement of the faultsystem. Thus, the event association to a clusterof different geographical areas, as performedby ConvNetQuake, represents an incompleteinformation to seismologists. Recent CNN basedapproaches aim at actually solving the locationproblem.

The network in [31], handles three componentseismic records of multiple stations, and aftertraining the first convolutional layer becomessensitive to characteristic features of seismicwaveforms. Thus, this layer can behave as anevent detector by itself. Training employs anearthquake swarm of 2000 events recorded bynine local stations. Later, during testing, thisnetwork successfully locates 908 earthquakes withstandard deviations nearly of 56 m, 124 m and136 m, along east-west, north-south and verticaldirections, respectively. Alternatively, the networkin [54] operates on single-station waveformslocated in Oklahoma, USA, where earthquakes



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Fig. 6. Schematic structure of an automatic detection system of seismic phases based on a fully connected ANN forclassification and regression

have been induced by hydrocarbon (oil and gas)production.

A training dataset consists of 1,013 historicevents, and the output is a 3D probability volumeof location likelihood inside the Earth. Testing wascarried out using 194 earthquakes, and resultspresent errors of approximately 4.9 km to theepicenter and 1.0 km to the hypocentral depth, onaverage, based on data from 30 network stations.

GPD employs a feature extraction system fromseismic data, that are later used as input toa fully connected ANN, and finally outputs aclassification as P waves, S waves, or just noise.The feature extraction system is a collectionof preprocessing layers where seismic data issequentially convolved with a set of digital filters

for characteristic recognition, and then decimatedfor down-sampling and trace evaluation at differentlength scales.

Both processes combined to an activationfunction, allows the identification of seismic phasesanywhere in a seismogram, regardless theirduration and amplitude. The final stage of ANNclassification outputs probabilities of the likelihoodof each possible class (P, S or noise). Fortraining and validation, GDP employs 4.5 millionsof four second windows, where P and S wavescorrespond to events of magnitude below 5.7Mw, recorded by the Southern California SeismicNetwork. The validation precision is nearly 99%for both phases and various detection probabilitythresholds, and recall is somewhat lower, between



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96% and 99% for most threshold choices,suggesting a minor number of misclassificationsof seismic phases as noise. GDP detection hasbeen also validated on the 2016 Bombay Beach,California swarm of small and moderate (≤ 4.8Mw) earthquakes, and with data of the 2016 Mw7.0 Kumamoto earthquake recorded at multiplestations, within 100 km of the hypocenter. Highlysuccessful results were shown for both cases.

PhaseNet operates on three-component unfil-tered seismograms, and estimates P- and S-wavearrival times by generating probability time seriesthat quantify the likelihood of P- and S-waveonsets. Its exhaustive training and validationmake use of the extremely large dataset ofanalyst-labelled P and S arrival times fromthe Northern California Earthquake Data Center.Specifically, Authors employ stratified samplingbased on stations and divide data into a training,validation and test sets, with more than 600K, 77Kand 78K waveform samples, respectively.

A minimum preprocessing is applied to thetraining data, where each data component isnormalized by removing its mean and dividing itby the standard deviation. Testing results arecompared to those obtained by the standard ARpicker algorithm [1], and PhaseNet outperformsthis scheme with significant improvements on bothphases, particularly for the S waves.

A densely connected CNN to capture laboratoryslip events of different durations is given in [53].This network presents a cascaded architectureto generate multi-scale slip proposals and detectevents with various lengths. It also exploits atrousconvolutions with different dilation rates to enrichfeature extraction.

Training, validation and testing proceed usingacoustic data acquired at the Rock and SedimentMechanics Laboratory of Penn State University,that presents a thousand manually picked events,and 800 are taken for training. Relative to a wellestablished template matching algorithm, Authorsreport a detection accuracy significantly higher,especially for highly different subsequent events.

5.1.4 RsNN

Residual networks are usually a combination ofthe three previous types of supervised ANN,and have achieved higher accuracy when solvingsame problems already tackled by one of them,or even dealing with more complex problems.For earthquake detection, the RsNN proposedin [38] and named CRED, first employs a CNNfor feature extraction, that are later used by aRcNN for recognition of temporal patterns. Thenext and last stage of this network is a fullyconnected FFNN used for phase identification.Training and validation is based on a dataset of550K three component seismograms recorded by889 broadband and short-period stations in NorthCalifornia, and P-wave and S-wave arrivals havebeen labelled by manual picking. Traces of seismicnoise are also records from the same networkstations.

A first test on 50K waveform samples, yieldsdetection precision higher than 96% and a recallabove 99%, regardless the threshold choice onthe output probabilities. Further testing formicroseismic detection was undertaken in CentralArkansas, with a substantially different crustalstructure, and events of lower magnitude withshorter epicentral distances. Without additionaltraining, CRED finds 3 orders of magnitudemore events compared with STA/LTA results.Comparisons against the FAST algorithm [55],reveal that the CRED detection rate is muchlower, but it takes less than a hundredth of thecomputation time spent by FAST (non-parallelversion).

5.2 Unsupervised

In this final section, we discuss few applicationsof unsupervised ANN to seismic event detection.In general, ANN of this kind are classified asautoencoders (AE) and generative adversarialnetworks (GAN).



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5.2.1 AE

An autoencoder is a deep learning approachwith noticeable advantages over most other MLtechniques, especially when processing unlabelleddata. This unsupervised technique is able todirectly process the input raw data, with no needfor preprocessing and previous labeling. A mainapplication of AE is data compression, aimingat extracting representative features of the inputand removing redundancies, so the overall sizeof the data may be highly reduced [33], [36].This concept is called Dimensionality Reduction.Denoising AE (DAE) are also trained locally tomitigate noise on corrupted versions of the inputdata, with a consecutive step of reconstructiona cleaner version of the data, even under thepresence of high noise levels. These networks areknown as stacked DAE’s (SDAE) [49]. In [43], aSDAE is developed to mitigate background noiseon seismic traces, to later pick the P wave onsettimes on the cleaner waveforms. For evaluation,both synthetic and field seismic data are employed,and the SDAE algorithm accurately picks the onsettime of 94.1% for 407 of the field seismograms,with a standard deviation error of 0.10 s. Resultsalso indicates that this algorithm can make precisearrival time inferences on data with SNR as lowsas -14 dB.

A hybrid network, that combines a SAEand a deep-belief ANN, has been applied toclassify seven different classes of volcano-seismicevents in [47]. Classification results validatethe efficient capturing of complex relationshipson volcano-seismic data by this new network,and compared to those delivered by other MLalgorithms, the performance is higher under fasterconvergence.

5.2.2 GANs

The GAN proposed in [35] is trained to learnthe characteristics of both earthquake first arrivalP waves and background noise, resulting ina discriminator that mitigates false triggering.Training employs 300K seismic records fromsouthern California and Japan to act as anautomatic feature extractor, and then a RandomForest classifier is subjected to a secondary

learning on a large set of earthquake signals andnoise waveforms. When tested, the recognitionperformance was about 99.2% for P waves,and 98.4% for signals of pure noise. Thisexcellent performance is accompanied by a verylow sensitivity to false triggers from local impulsivenoise.

6 Discussion and Conclusions

Detection of earthquake signals is fundamentalfor observational seismology. Main features of areliable detection algorithm of seismic waves mustinclude: high sensitivity to small magnitude eventswith variable trace patterns, low sensitivity to am-bient noise and non-earthquake signals, enoughflexibility to account for data from one or multipleseismic stations, and high computational efficiencyfor fast real time assistance or large datasetprocessing. These features are naturally fulfilled byArtificial Neural Networks (ANN). ANN, among fewother machine learning techniques, offer advancedpattern recognition abilities that allow matchingthe detection performance of experienced humananalysts, after being appropriate trained.

During the mid-late 90s, training of pioneer ANNproceeds on subsets of hundreds or few thousandsof seismic traces, but the paramount advancesof computational platforms and ANN implementa-tion frameworks afterwards, have allowed usingregional-wide earthquake catalogs with hundredsof thousands or even millions traces, during theANN learning over last years. Thus, training ofstate-of-the-art ANN really accounts for multiplestation information, seismic and noise signals withdifferent shapes, local and non local earthquakedata, and labeling (in case it is required) fromvarious human experts. Because of differentanalysts may pick seismic phases in a dissimilarway, such a modern well-trained ANN becomepractically free of any human bias or likely errors.

Inspired on the basic time and frequencyprocessing of seismic traces undertaken byhuman analysts, usually assisted by interpretationsoftware, the input data to most of detectingANN are not raw seismograms. A big varietyof ANN operate on preprocessed traces, aftera standard normalization (with optional filtering)



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and windowing, but other specimens only treatpreviously extracted statistical features, or evenmore specialized data representations such asspectrograms.

Developments before 2010 were mainly FFNN,mostly applied to local and regional earthquakedata, although few cases target teleseismic events.For historical reasons, this survey paper cited fewprominent works in that period, but the contributionin [24] pushed forward the feature-based traceprocessing, detection for single or multi stationinformation, and the performance assessment aswell. In current decade RcNN, with an inherentefficiency for recognition and inference of temporalpatterns, have been used for real-time small-eventmonitoring under poor signal-to-noise ratios, andeven have found successful applications in earlywarning systems and earthquake forecasts. Duringlast two years, CNN and RsNN have taken theautomatic seismic phase identification and onsetestimation to a new level of regional-wide trainingand operation, with few cases also capable ofseismological hypocentral location. With specialemphasis, we here discussed contributions in [38],[44], [42] and [59].

A performance assessment among differentalgorithms for automatic phase detection andpicking is rather difficult because of the specializedtarget application, the variety of accuracy metricsused, and the case-dependent testing datasetemployed by each algorithm. For a systematicevaluation of the accuracy in phase onset pickings,for instance in [24].

Authors use the standard deviation of the timedifferences between the automatic and manualpicks. This metric tends to be insensitive to thesize of the testing dataset. They also employthe metrics of recall and precision to measurethe ability of the system to detect the correctonset of seismic waves and to reject false alarms,considering the total number of reference picks.These metrics were rapidly adopted by othersstudies, and few others emerged. Because of thehighly active developments of ANN, among othersML and general algorithms, in this area, thereis a pressing need for establishing standardizedbenchmark datasets, of several sizes and SNR

levels, to facilitate full assessment with clear qualitymeasures.

Acknowledgements

This work is partially supported by the SpanishMinistry of Economy and Competitivity under con-tract TIN2015-65316-P, by the SGR programmes(2014-SGR-1051 and 2017-SGR- 962) of theCatalan Government and has received fundingfrom the European Union’s Horizon 2020 researchand innovation programme under the MarieSklodowska-Curie grant agreement No 777778(MATHROCKS).

We thank FUNVISIS for providing the seismicdata subject of our current studies.

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Article received on 15/10/2018; accepted on 10/02/2019.Corresponding author is Otilio Rojas.



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