BATS and

Broadband Seismology in Taiwan

Honn KaoPacific Geoscience Centre, Geological Survey of Canada

“A decade+ of research on earthquake science at Taiwan Earthquake Research Center: Now and Beyond”

Taipei, Taiwan, September 19, 2019

Observation, Observation, Observation!

“Seismology is a science based on data called seismograms.” “The great progress made in seismology in the past hundred years has been stimulated principally by the availability of steadily improving data.

It all started in the 1992 IRIS/SSA meeting…

• Prof. Wang-Ping Chen, Francis T. Wu, and Ta-Liang Teng started the discussion of establishing a broadband seismograph network in Taiwan.

• Prof. Wang-Ping Chen took the main responsibility of preparing a document that was formally approved by the IES’s Advisory Committee Meeting in June 1992.

• Dr. Honn Kao joined IES in 1993, and headed a group of technicians to begin the construction of a broadband seismograph network in Taiwan, later known as the Broadband Array in Taiwan for Seismology (BATS).

High-Quality Site Condition and Construction

KMNB

YULBNACBTDCB

• Inside caves or abandoned tunnels• Rock sites• Low natural and cultural noise

High-Quality Instruments and Data

Streckheisen STS-2

3-comp STS-1 at TDCBStreckheisen STS-1

24-bit Quanterra Data Logger (Q630 and Q4120)

1 day

1996/5/02 – 1996/5/05at TDCB

High-Quality Data Service• BATS data distribution

service began in 2001 via the Data Management Center of IES (DMC-IES).

• BATS data service was later expanded to include other seismological products (e.g., BATS QCMT).

• With the establishment of TEC Data Center in 2006, seismological data service in Taiwan has become even more comprehensive and efficient.

http://tecdc.earth.sinica.edu.tw/http://bats.earth.sinica.edu.tw/

Regional Moment-Tensor Solutions and

Seismotectonics of Taiwan

Kao and Chen (2000) Kao et al. (2001)

Real-Time RMT Solutions

http://tecws1.earth.sinica.edu.tw/AutoBATS/Jian et al. (2018)

http://rcs.earth.sinica.edu.tw/Lee et al. (2014)

Earthquake Physics (1)

Coulomb Stress and local Seismicity Rate

(Ma and Chan, 2005)

Non-volcanic Tremors and Low-frequency Earthquakes(Peng and Chao, 2008; Tang et al., 2010, Sun et al., 2015)

Earthquake Physics (2)

Repeating Earthquakes(Chen et al., 2007, 2009)

Rupture Process(Lee et al., 2016)

Crustal and Lithospheric Structures

3D tomography(Wang et al., 2006; Huang et al., 2014)

Receiver function inversion and Moho depth(Wang et al., 2010)

Inference of a subducted slab beneath central Taiwan from waveform and travel

time anomalies (Chen et al., 2004)

Surface Wave Dispersion and Ambient Noise Tomography

Huang et al. (2012)You et al. (2010)Hwang and Yu (2005)

Seismic Anisotropy

Huang et al. (2015)SKS/SKKS splitting

(Kuo-Chen et al., 2009)SKS splitting (Huang et al., 2006)

Geohazard Monitoringfiner grids and time steps are adopted to obtain the optimal time-

location combination (4-km interval at 1-s time step in the second

stage and 1 km at 0.1 s in the third). Because the fine scanning is

limited to the vicinity of the best solution(s) obtained in the previous

stage, the required processing time can be greatly reduced to satisfy

the criterion of near-real-time monitoring.

Representative examples

The SSA analysis of the tremor-like waveforms recorded during the

passage of typhoon Morakot pinpoints the source locations to within

a few km from the places where large-scale LMDF are later identified

on satellite images (Figs. 1 and 5). The determined origin times of

these events are also consistent with the accounts of LMDF survivors,

as reported by the local news media. For all LMDF we have studied,

final solutions were obtained within 2 min from the start of SSA

calculation. Based on the determined source parameters and the

observed maximum amplitudes measured from individual seismo-

grams, the equivalent seismic magnitudes of the LMDF events were

estimated to range from ML of ∼2.2 to 2.9.

Three representative examples of locating LMDF using SSA are

shown in Fig. 5. For each event, a snapshot of the normalized

brightness function at the estimated origin time is plotted in the

lower panel to illuminate the source distribution. The brightest spot,

marked by a star, is deemed to be the most likely location (i.e., the

centroid) of each LMDF event because it best matches the arrivals of

large bursts of seismic energy at multiple stations (upper panel,

Fig. 5). It is important to realize that the observed multiple bursts

in LMDF waveforms may be associated with sources scattered over a

finite time and space. While theoretically it is possible to delineate a

complete spatiotemporal distribution of all sources (Kao and Shan

2007), in reality smaller sources can be difficult to identify, and their

solutions often have large uncertainties due to imperfect station

coverage and poor waveform quality (i.e., insufficient signal-to-noise

ratios). Our results indicate that the brightest spot of an SSA image

can reasonably represent the bulk location of a LMDF event where

subsequent rescue operations, if needed, should be given top

priority.

Discussion and conclusion

Since SSA is a mapping process that translates the observed seismic

amplitudes from the time-space domain at stations to the time-space

domain at the source (Kao and Shan 2004), each seismic burst can

correspond to an infinite number of sources as long as the observed

arrival time is consistent with the assumed combinations of origin

time and epicenter. Only the assumed origin time and epicenter that

correspond to the true source can, however, correctly predict the

arrival of large amplitudes at multiple stations. In other words, the

accuracy of SSA solutions can be rapidly improved in a near-real-

time system as additional waveforms from nearby stations are in-

crementally added to the analysis.

While the brightest spots on the SSA images (Fig. 5) are inter-

preted as the centroid locations of the corresponding LMDF, isolated

Fig. 5 Representative examples of LMDFepicenters located from real-time seismic observations using SSA: a one of the earliest induced LMDF, b that responsible for themost casualties, and c that with the largest equivalent seismic magnitude. Source scanning is done in three stages with increasing time and space resolution to maximizecalculation efficiency. Orange bars mark the predicted arrival times at selected stations from the determined origin times and epicenters. Snapshots of the normalizedbrightness functions at the corresponding origin times are shown in the lower panel. The most likely locations of LMDF sources are illuminated as the brightest spots(encircled). Triangles mark the locations of nearby broadband seismic stations

Technical Note

Landslides 9 &(2012)562

Author's personal copy

(presumably corresponding to primary waves, blue traces,

Fig. 2) and to the horizontal plane (presumably shear waves,

red traces, Fig. 2), respectively.

Our results suggest that the most characteristic feature of

LMDF waveforms is the frequent switch between high and low

dip angles of the particle motions (light blue and pink strips,

respectively; Fig. 2). This is in sharp contrast to the pattern of

an ordinary local earthquake whose P and S wave trains can

be easily recognized (Fig. 2c). In some cases, the LMDF wave-

forms appear to have equal amounts of P and S energies

(yellow stripes, Fig. 2), but most such occasions are observed

in the middle portion of LMDF signals where the amplitudes

are relatively large.

The intermitten t bursts of P and S phases are obviously

related to the dynamic history of individual LMDF events. As

an LMDF event is initiated, the downward-sliding mass interacts

Fig. 1 Map showing the path of typhoon Morakot across Taiwan in Aug 2009. Stars mark epicenters of typhoon-induced large-scale LMDFas determined by this study.Satellite images before and after Morakot for three representative cases (white stars) are shown with dashed white lines marking the approximate perimeters of thecorresponding LMDF. Thick lines with arrows indicate the general directions of mass flow. Large rectangles on the map correspond to the boundaries of sourceimages shown in Fig. 5. Open triangles mark the station locations of the BATS. Major cities in Taiwan are marked by white squares

Technical Note

Landslides 9 &(2012)558

Author's personal copy

Landslide modeling(Lin et al., 2010)

Near-real-time landslide monitoring

(Kao et al., 2012)Studies of the Tatun Volcano Group(Lin et al., 2005; Konstantinos et al., 2007)

Some Final Remarks• Innovation has nothing to do with how many R & D dollars you

have. When Apple came up with the Mac, IBM was spending at least 100 times more on R & D. It's not about money. It's about the people you have, how you're led, and how much you get it. -- Steve Jobs

• As scientists, we step on the shoulders of science, building on the work that has come before us - aiming to inspire a new generation of young scientists to continue once we are gone. -- Stephen Hawking

• Good luck is when opportunity meets preparation, while bad luck is when lack of preparation meets reality. -- Eliyahu Goldratt

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