京都大学防災研究所年報 第 62 号 A
DPRI Annuals, No. 62 A, 2019

Long-term Research Visits (Project No.: 30L-03)
Project name: Studying non-earthquake signals recorded at seafloor OBS stations, as related to natural
hazards and natural resources
Principal Investigator: Emmy T.-Y. CHANG
Affiliation: Institute of Oceanography, National Taiwan University
Name of DPRI collaborative researcher: James Mori
Research period: January 10, 2019 ~ February 28, 2019
Research location: DPRI, Uji
Number of participants in the collaborative research: 2
1 DPRI (faculty), Dr. James Mori
1 non-DPRI (researcher), Dr. TakashiTonegawa- Number of graduate students: (provide numbers for Masters and Doctoral students)
none*- Participation role of graduate students []* During the period of this project, two Taiwanese research assistants work with PI in National
Taiwan University. These two assistants have already obtained their master degrees. Both tend toaccess PhD programs in the next few years. One of them has already requested the PhD admission
in the Institute of Oceanography (National Taiwan University) in April, who will follow the T-wave
study initializing in this project.
Anticipated impact for research and education
This project aims to advance the study of seafloor geological activity which is known to play an importantrole in the offshore natural hazards and resources. A few subjects extending from this project can enhance
the knowledge of the related domains and lead a few graduate students to the further scientificexplorations.
Research report(1) Purpose
At the present, Ocean bottom seismometer (OBS) is common to receive seismic signals that is located at ornear the seabed. Comparing to the in-land seismic stations, the seismic waves recorded at the seafloor stations
can propagate not only through the solid earth but also the water column and the water-sediment interface. The
recent studies reveal that the unconventional non-earthquake signals recorded at OBS stations can be a proxy
to study seafloor geological processes (e.g. Stehly et al., 2006; Géli et al., 2008; Tary et al 2012; Chang et al.
2016), which link to important issues of natural resources and seismic hazards in offshore areas. In this project,
our focus is lay on two distinct signals recorded at the OBS stations: the bubble signals and the T waves.
In terms of seafloor geological processes, natural gas emissions from the seafloor are a common phenomenon
that occurs worldwide. Along the OBS seismogram, the waveforms of gas emission signals exhibit a highfrequency resonant vibration alike the bubble bursting at the free surface of a non-Newtonian fluid. The typicalbubble signals are the very local energy onsets which are detected only at one OBS station which is very close
to the venue of gas emission. A single bubble waveform can be roughly described as a cosine wave chain rapidly
attenuating in a period less than 2 seconds. However, the efficiency of the mathematical matching is very limited.
Considering the bubble waveforms can be easily identified by a trained seismologist, the Machine Learning(ML) skill will be a good alternative approach in this task.
On the other hand, T waves propagate in the SOFAR channel of minimum sound velocity acting as a wave
guide for acoustic energy in the world's oceans. They can be excited by sources in the solid Earth such as
earthquakes through conversion of seismic energy into acoustic waves at the solid-liquid interfaces. Within the― 155 ―OBS data acquired from the northern South China Sea, The T-wave events can be located at the deep-sea
seamounts or the feature zone of the spreading system, which are far below the SOFAR axis minimum. Actually,
With the recent increase of OBS deployments worldwide, T wave detection in deep ocean regions below theSOFAR axis minimum has become abundant. The mode theory which describe coupling between elastic andacoustic modes under scattering by structural heterogeneities located at the ocean bottom, and which arebecoming increasingly successful at modeling the wave shapes of abyssal T phases is adopted in this study. Inpractice, the numerical code SPECFEM2D based on a spectral element method is used to model the generationand propagation of the T waves. Furthermore, the measurements combining the amplitude and duration of Twaves can yield information on source rupture, and more specifically detect the presence of source mechanism.
The OBS data used in this project are acquired from the northern South China Sea (mobile short-period OBSarrays), offshore Japan (mobile OBS arrays and the Donet network), and also the Lesser Antilles subduction
zones (mobile short-period OBS arrays). Besides the geological environment of convergent plate boundaries(such as Taiwan and Japan), these data are also applied to consider the offshore seismic hazards and kinetic
energy dissipation in the ocean.
(2) Summary of research progress
We specifically study the bubble signals and the T waves which embed within the OBS seismograms. Below
are the main approaches.
I. The bubble signals
In terms of seafloor geological processes, natural gas emissions from the seafloor are a common
phenomenon that occurs worldwide, e.g. in coastal deposition environments, delta fan deposits, hydrocarbonbearing sedimentary basins and accretionary prisms (Judd and Hovland, 2007). The gases emitted at seafloor
are principally composed of methane and fluid seeps can include hydrocarbons. The importance of theseemissions for a number of societal (e.g. the assessment of the contribution of submarine methane sources in gas
global budgets) and environmental issues (e.g. hydrocarbon leak detection) conducting to economic ones, hasfostered the interest of the scientific community for understanding the natural degassing processes from theseafloor.
The recent years we collaborate with the French Ifremer group in studying the seafloor bubble signals.
Based on the Ifremer OBS experiment on the Marmara seafloor, along the North Anatolian Fault zone, a single
bubble can be characterized by durations of less than 0.8-s, by frequencies ranging between 4 and 30 Hz, byhighly variable amplitudes and by one single-wave train, with no identified P- nor S-wave arrivals (Geli et al.,2008). The presence of gas in superficial sediments, together with analogies with laboratory experiments, has
led Tary et al (2012) to suggest that gas migration followed by the collapse of fluid filled cavities or conduits
could be the source of the observed microevents. Acoustic data from a seabottom bubble detector (Bayrakci etal, 2014) and geochemical data from a methane sensor (Embriaco et al, 2013) provide additional evidence
supporting the hypothesis that the bubble emergence could be related to degassing processes from the seafloor
Quantitative methods have been developed as to characterize gas releasing from the seafloor into thewater column (e.g. Wheeler and Gardiner, 1989; Sills et al., 1991) or provide the in-situ observations combining
the use of gas flux-meters and pore-pressure (e.g. Boles et al., 2001; Leifer and Boles, 2005; Kopf et al., 2010).
Remote, water column acoustic studies are also carried out with the use of a towed or hull-mounted sonar
systems (e.g. Merewether et al., 1985; Dupré et al., 2010; Greinert, 2008). In this study, we tried to develop thecomputer algorithm detect the bubble signals by both mathematic fitting and Machine Learning (ML) skill.
The typical bubble signals are the very local energy onsets which are detected only at one OBS station
which is very close to the venue of gas emission. A single bubble waveform exhibits a high-frequency resonant
wave chain and a bursting at the free surface of a non-Newtonian fluid. Each single bubble vibrates generally― 156 ―

less than 2 seconds. Because this signal coming from the gas emission from the ground, the geophonecomponents can better record its waveform than the hydrophone one. Based on the character of mono-toneresonant vibration and quick attenuation, we have roughly described the bubble waveform as an attenuatedcosine wave chain, which is,
F(t)=Acos(2 f t) exp(at)F(t) is the time series of bubble waveform; A, f and a are the maximum amplitude, specific mono-tone vibration
frequency, and attenuation rate of each bubble. This mathematical matching can help to identify the bubble
waveforms from the continuous OBS seismogram. By means of mathematical matching, we can have 459
bubbles. By these found bubble waveforms, the resonant frequency and attenuation rate are determined as
5.2~5.8 Hz and -1.8 ~-1.0 count/sec, respectively.
In fact, the OBS seismogram is commonly full of noise and unknow signals, which disturbs themathematical matching. The efficiency of the mathematical matching is therefore very limited. Considering thebubble waveforms can be easily identified by a trained seismologist, the ML skill will be a good alternative
approach in this task. In this project, we establish a convolutional neural network (CNN) structure to classify
the OBS amplitudes into the bubble signals from the others. By means of the ML algorithm, this subject may
be carried out quickly and efficiently.
A key issue in developing a ML model is to train model with a large dataset. By means of the resonant
frequency and attenuation rate retrieved from the previous process, we create over 40,000 synthetic bubbles anduse them to compose a well-trained ML model. At the moment, we try to insert various scenarios for thesynthetic waveforms (for instance, different noise levels, bubble-alike waveforms, etc) and refine our training.
The detection efficiency of the ML model will be discussed soon.
II. T waves
T waves propagate in the SOFAR channel of minimum sound velocity acting as a wave guide for acoustic
energy in the world's oceans. They can be excited by sources in the solid Earth such as earthquakes throughconversion of seismic energy into acoustic waves at the solid-liquid interfaces. Actually, with the recent increaseof ocean-bottom seismometer (OBS) deployments worldwide, T wave detection in deep ocean regions below
the SOFAR axis minimum has become abundant (Butler and Lomnitz, 2002; Okal, 2008; Ito et al., 2012). A
theoretical explanation may be given by Park et al.’s (2001) mode-coupling model, which states that the higher
modes of acoustic oscillation couple with bathymetry, through this process T wave energy can reach the seafloor.
In this project, we model the generation and propagation of such acoustic waves, using the numerical code
SPECFEM2D based on a spectral element method (Tromp et al., 2008). The simulations of an explosion sourcein the western Japan Sea is set to make the first trial in this study. We aim to reproduce the T waves provoking
by the 2017 North Korean nuclear test. Synthetic signals will be then studied at different spreading distancesand compared to the associating T-wave signals recorded at the in-land F-net seismic stations.

(3) Summary of research findings
All the analyses are under processing. Below list the results obtained so far.
The bubble findings
Figure 1 demonstrates a typical seafloor bubble. In general, the bubble signals can be better recorded at
the geophone than hydrophone. It can be inferred that the bubble signals coming from the strata rather than thewater. By means of mathematical matching, we can have 459 bubbles. In Figure 2, the resonant frequency ofthe bubbles is about 5.2~5.8 Hz; the attenuation rate is -1.8 ~-1.0 count/sec.

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Figure 3 shows the examples of the synthetic waveforms. The accuracy of the ML model trained with thesynthetic waveforms can achieve 100% for the synthetic data. However the accuracy drops down very quickly
when the background noise level increases. We shall refine the ML model with more bubble waveforms ofvaried conditions.

Figure 1. The demonstration of a typicalbubble waveform at four channels. From top
to down: hydrophone, horizontal-EW,horizontal-NS, and vertical channels. Here therecording azimuths of two horizontal records
are not corrected.

(a)

(b)

Figure 2. The histograms of the mathematical coefficients of bubble waveforms: (a) attenuation rate, (b)resonant frequency. The Y axes are event numbers.
T-wave simulation
The objective is to set a physical model as close as possible to a real case to compare synthetic
seismograms with actual T waves recorded at seafloor and in-land seismic stations. In the first test, we check
the T waves propagating over the whole Japan Sea in an acoustic mode. Figure 4 illustrates the targeted sites
set in this simulation. The effect of geometric spreading is discussed with the different propagating distances.
Because our code is two-dimensional (2D), our simulation is done in the vertical planes passing through thesource and each of the targeted sites. Figure 5 shows the preliminary resultant waveforms, which are carriedout with a non-stratified water layer and the flat topography. The energy source is taken as a simple explosionwhich wavefield is radiated and reflects the pressure pulse in the far field. In Figure 5, the Groups A, B, and C
are the sites at the near shore of the western Japan, middle, and western Japan Sea, respectively. We can see asystematical change in T-wave amplitude varying with the spreading distance (Here the distance: Group C <Group B < Group A).

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In the next test, more physical properties of water, such as kinematic dissipation, attenuation, as well as
the seismic-wave conversion between solid earth and water shall be taken into account. By means of this study,
we can achieve a comprehensive understanding for the wave propagation within the shallow media of the earth.
It is important for the study of all seismic sources and, in particular, the physical interaction in-between thesolid earth and water.

Figure 3. The examples of the synthetic waveforms (the black lines in each subplot). The varied noise levels
are shown in blue.

Figure 4. The targeted sites for theT-wave simulation. Three groups,indicated as A, B, and C, aredesigned to see the effect ofgeometric spreading.

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Figure 5. The simulated T waves atthe targeted sites. Stations’ names
correspond to the indexes in Figure
4.

(4) Publications of research findings
The results of the related subjects are under arranging into journal papers.
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