The city of Auckland, New Zealand (Tāmaki Makaurau, Aotearoa), sits on top of
an active volcanic field. Seismic stations in and around the city monitor
activity of the Auckland Volcanic Field (AVF) and provide data to image its
subsurface. The seismic sensors – some positioned at the surface and others
in boreholes – are generally noisier during the day than during nighttime. For most
stations, weekdays are noisier than weekends, proving human activity
contributes to recordings of seismic noise, even on seismographs as deep as
384 m below the surface and as far as 15 km from Auckland's
Central Business District. Lockdown measures in New Zealand to battle the
spread of COVID-19 allow us to separate sources of seismic energy and evaluate
both the quality of the monitoring network and the level of local
seismicity. A matched-filtering scheme based on template matching with known
earthquakes improved the existing catalogue of five known local earthquakes to 35
for the period between 1 November 2019 and 15 June 2020. However, the Level-4
lockdown from 25 March to 27 April – with its drop in anthropogenic seismic
noise above 1 Hz – did not mark an enhanced detection
level. Nevertheless, it may be that wind and ocean swell mask the presence of
weak local seismicity, particularly near surface-mounted seismographs in the
Hauraki Gulf that show much higher levels of noise than the rest of the local
network.
Introduction
The Auckland Volcanic Field (AVF) is an active intra-plate volcanic field
consisting of 53 known volcanoes . The last eruption ∼600 years ago is responsible for the formation of Rangitoto Island, a prominent geologic
feature in the Hauraki Gulf, ∼10km from the Auckland Central
Business District (CBD). Given the risk involved for the 1.5 million
inhabitants of a city built on top of the AVF, a seismic network (Auckland Volcanic Seismic Network – AVSN,
Fig. and Table ) monitors for early warning signs
of an impending eruption . However, seismic recordings in
urban environments suffer from contamination by anthropogenic noise. To
minimise the recording of anthropogenic noise in Auckland, 7 of 10
stations of the AVSN are installed in boreholes. This measure also reduces the
recording of seismic signal from wind and ocean waves see
Table and. The seismic data from the AVSN are hosted by GeoNet
(https://www.geonet.org.nz/, last access: 30 January 2021)
and are publicly available in near-real time.
* MKAZ is part of the national network of seismometers.
New Zealand entered a Level-4 lockdown to combat the spread of COVID-19 at
23:59 on 25 March 2020 (LT). Schools were closed, work was
halted or moved to home, and travel reduced to trips to the doctor and the
supermarket. A limited workforce continued to work and commute if their
profession was deemed “essential”. On 27 April, New Zealand lowered
this lockdown to Level 3, which meant mobility of Aucklanders increased, and
construction work, for example, resumed. The following results present the
impact of the lockdown on seismic recordings of the AVSN. Recent studies have
shown impact in the 1–10 Hz and 4–14 Hz ranges. The AVF is an active volcanic field with ongoing
efforts to image the subsurface with
seismic data that span the entire seismic data spectrum. Therefore, our
analysis includes the impact of the lockdown on seismic data from 0.1 to
50 Hz. In addition, we use this uniquely quiet period of Auckland in
lockdown to (1) evaluate the sensitivity of the AVSN and (2) explore the
seismic character of the Auckland Volcanic Field by increasing the earthquake
catalogue with a matched-filtering technique with known
template earthquakes.
Results
Figure displays 24 h of the vertical component of
ground velocity measured on station HBAZ, a short-period seismometer installed
in a borehole, 380 m underground in Herne Bay, Auckland. The dominant
feature in this seismogram is a signal at 10:06 UTC from a M6.4
earthquake in the Kermadec Islands region (event ID us60008fl8). In general,
however, the seismogram is less noisy during the (local) nighttime than during
the daytime. As a result, signals associated with the smallest earthquakes
detectable at night could be masked by noise during the day. An example is the
small event around 17:23 UTC (in green), which may have been obscured in the case of daytime noise levels.
Vertical component of the seismic wavefield at borehole
station HBAZ for 14 March 2020. The signal with the largest
amplitudes is from an earthquake in the Kermadec Islands region
(event ID us60008fl8).
Standard deviation over 30 min intervals of the ground
acceleration of short-period borehole station HBAZ. Times are in
local NZT, and weekdays have a green background. Note the lower
nighttime noise level compared to daytime, and quieter
weekends. The origin time of the Kermadec Islands region event ID
us60008fl8 is annotated with a star.
To study seismic signal levels over longer time periods, we compute the
standard deviation in 30 min time windows, after an instrument
correction to acceleration, as defined in the volcano monitoring technique
called real-time seismic amplitude measurement
RSAM;. We filter the data between 0.1 and
50 Hz to both cover the range of frequencies of interest in volcano
monitoring and in seismic tomography. Figure displays the
standard deviation from 8 to 15 March 2020 on station HBAZ in local
time. From here on, all timescales are in the local time zone and weekdays
are marked by a light green background. In addition to a difference between
day- and nighttime noise, there is a clear distinction between weekdays and
weekends: especially data on Sundays appear less noisy. RSAM for HBAZ varies
between 6 and 12 µms-2 during the day on weekdays, while the nighttime RSAM values are
less than 2 µms-2. The narrow spike late on 14 March is
due to the previously mentioned earthquake in the Kermadec Islands region.
Seismic noise levels during a Level-4 lockdown
Restrictions during a Level-4 lockdown to combat the COVID-19 pandemic in New
Zealand resulted in a reduction of weekday daytime RSAM values on HBAZ
(Fig. ). Before the lockdown, indicated by the vertical
dashed red line, the periodicity of RSAM follows the familiar day–night and
weekend–weekday pattern from Fig. , but after the Level-4
lockdown ended (at the vertical dash-dotted green line) weekday daytime RSAM
levels resemble those of a typical Sunday. For comparison, the nearest
broadband station south of Auckland (MKAZ) generally has lower RSAM levels and
appears unaffected by the New Zealand lockdown.
RSAM at short-period borehole seismometer HBAZ and the
nearest broadband station (MKAZ). In addition to the contrasts in
RSAM values for days, nights, weekends and weekdays, the Level-4
lockdown period (its start marked by a dashed red line and the
transition to Level 3 is annotated by a dash-dotted green line) is
marked by lower RSAM values for station HBAZ.
RSAM levels for three other borehole stations of the AVSN
closest to the Auckland CBD are reduced during the lockdown.
RSAM levels at three AVSN stations southwest of Auckland's
CBD. Only RSAM levels on station WTAZ dropped during the
lockdown.
RSAM values for stations east of Auckland CBD, on the
Hauraki Gulf. Only the borehole RSAM data from MBAZ, artificially
multiplied by 25 for visual purposes, correlate with the Level-4
lockdown in New Zealand.
The lockdown measures affect AVSN data in different ways. Stations EPAZ, RVAZ
and ETAZ are – as station HBAZ – in a borehole near the CBD. And similarly
to station HBAZ, their RSAM values, captured in the top panel of
Fig. , were reduced at the start of the Level-4 lockdown
and increased to pre-lockdown levels after the severest restrictions were
lifted in Level 3. In addition, station EPAZ, located under Eden Park Stadium,
suffers from a continuous source of high-frequency (∼50Hz)
noise, which results in elevated RSAM values at all times. Figure
contains RSAM values for stations to the southwest of the Auckland CBD. WTAZ
data are less noisy during the lockdown, despite not showing a significant
weekday–weekend signature. Conversely, KBAZ has quieter weekends but no
reduction in RSAM during the lockdown. Furthermore, the data for KBAZ are
marked by extended periods of larger RSAM values in the beginning of January
and mid-May. Even though KBAZ and WTAZ are borehole stations, the least noisy
station of these three is station AWAZ, located in a borehole on the Āwhitu Peninsula.
The data from this station do not show the weekend–weekday
signature nor a lockdown reduction in RSAM values.
The influence of wind
Figure presents the RSAM values for three seismic stations in
the Hauraki Gulf. The two seismic surface stations (ABAZ and WIAZ) are
approximately 25 times noisier than the other stations in the AVSN. To compare
the features in the RSAM data for all three stations, the MBAZ signal is
multiplied by a factor of 25. Station MBAZ is in a borehole on Motutapu Island
in the Hauraki Gulf – a seismically quiet location. There are no distinctions
between weekdays and weekends, but still a small reduction in noise levels
during the lockdown is evident in the data. Figure includes
the running average of wind speed over a 72 h window from Auckland's
Sky Tower in the CBD. Correlation between the noisiest periods and the wind is
strongest for the surface stations, for example, during high-wind times in the
middle of February and April, as well as in early May.
Mobility data
Figure contains the same RSAM values for HBAZ as in
Fig. but filtered between 1 and 14 Hz. This frequency
band has previously proved to be most sensitive to human activity
but is also in the range of frequencies of interest for
seismic monitoring of volcanic unrest. Figure shows that RSAM
values drop for even higher frequencies, possibly as high as the Nyquist
frequency in the data (50 Hz). The right vertical axis is for Google's
public mobility data for Auckland . The mobility data are
broken down into different human activities. Prior to the lockdown, the
strongest correlation between mobility data and seismic noise levels is seen
in the work places category; in both cases, the weekend leads to a drop in
noise and work-place activity. Not surprisingly, all but the residential
activity (movement inside the home) dropped significantly during the lockdown,
but the correlation between seismic noise on HBAZ and mobility associated with
“grocery and pharmacy” is particularly strong. Because the mobility data are
presented as a change in activity, the workplace activities dropped overall,
but work deemed essential continued at the weekends, resulting in a temporary
increase in weekend workplace activity during the lockdown period.
RSAM values for station HBAZ, compared to Google's mobility
data for Auckland.
Earthquake detection
To test whether the reduction in noise during lockdown affects the
detectability of local seismicity, we employed a network matched-filter
detector to construct a catalogue of the AVF. The
matched-filter method complements standard energy-based detection methods that
rely on variations in waveform amplitude and are therefore strongly controlled
by background noise levels. Matched filters commonly provide robust earthquake
detections at lower amplitudes (and therefore magnitudes) than standard
detectors, often generating catalogues with one magnitude unit lower
completeness around 10 times more earthquake
detections. All codes to generate the
following results can be retrieved from .
We made earthquake templates from the GeoNet catalogue with 59 events between
1 January 2011 and 15 April 2020 that had picks on at least five stations of
the AVSN. Templates are constructed by re-sampling the seismic data to
50 Hz and filtered between 2 and 15 Hz using a fourth-order
Butterworth band-pass filter. Template waveforms were cut to 6 s
length starting 0.5 s before P-wave picks on vertical channels and
0.5 s before S-wave picks on horizontal channels. We required a
minimum signal-to-noise ratio of 5 to retain waveforms in the templates. All
59 templates were correlated with data processed with the same parameters
between 1 November 2019 and 15 June 2020 using EQcorrscan
. Detections are made when the network sum of the
normalised cross-correlations exceeds 9 times the median absolute deviation
(MAD) of the cross-correlation sum for that day . If
more than one detection occurs within 2 s of another detection, the
detection with the highest average correlation value is retained.
Matched filtering with templates built from
GeoNet-catalogued earthquakes results in 40 events between 1 October 2019 and 15 June 2020. Of these 40 events, 35 are
identified as local earthquakes, including the five earthquakes in
the GeoNet catalogue for the same period.
To improve the quality of the resulting catalogue, we computed
cross-correlation-derived pick corrections for each detection using the method
with a minimum normalised cross-correlation value of
0.4 required for each pick. We retained detections that were picked on at
least three stations, resulting in a final catalogue of 40 events. One of
these events is a quarry blast, and a further four events are not related to
visible phase arrivals. In the same time period in which we detect 35
earthquakes, the GeoNet catalogue contains five earthquakes (which are a
subset of the events in Fig. ).
Discussion
A total of 7 of the 10 stations of the AVSN are installed in boreholes, reducing the
impact of environmental and anthropogenic noise sources. For example, station
AWAZ at 371 m below the surface is in line with the runways of the
Auckland airport and appears insensitive to airplane noise, compared to
surface station WTAZ. Nevertheless, borehole stations closest to the CBD
remain sensitive to anthropogenic noise, which was reduced during the Level-4
lockdown.
Correlations of RSAM values during the lockdown at station HBAZ are strongest
with the “grocery and pharmacy” category of Google mobility data. This
station is 380 m underground, but the local grocery and medical centre
are less than 2 km away.
Surface stations on Waiheke Island (WIAZ) and on the Whangaparaoa Peninsula
(ABAZ) are the noisiest stations, and RSAM values correlate with wind
speed. In fact, the wind data are the result of a moving average of
72 h of data during which winds vary significantly. Therefore, we
believe the correlation between seismic noise and this averaged wind speed
indicates the correlation is more related to the ocean swells associated with
storms. In fact, we believe all the seismic stations of the AVSN are sensitive
to ocean swell, indicated by variations in RSAM amplitudes between day and
night. Installing WIAZ and ABAZ in boreholes would reduce their noise floor by
1 or 2 orders of magnitude, based on RSAM values on nearby borehole
station MBAZ and others, improving sensitivity of the AVSN network for both
volcano monitoring and seismic tomography.
Large amplitudes in RSAM for station KBAZ in January and May 2020 cannot be
linked to storms or lockdown effects and may be the result of local farming
activity. Additional variations in weekday–weekend RSAM signals are robust
even in the presence of the lockdown. We attribute this to the proximity of
one of the main highways that carry transport of (essential) services into and
out of Auckland.
The reduction in anthropogenic noise is noted in the frequency band from
1 Hz and up, possibly all the way to the Nyquist frequency in the
data, 50 Hz (see Fig. in
Appendix ). Anthropogenic noise in this frequency band affects
both volcano monitoring with RSAM and its ability to detect weak and
local seismicity. Nevertheless, the reduction in seismic noise throughout New
Zealand's Level-4 lockdown does not appear to have affected our ability to
detect earthquakes in this seismically quiet part of New Zealand. By employing
a matched-filter approach, we limit the sensitivity of our detector to
variations in noise amplitude. Analysis of the cross-correlation sums for the
full period studied shows little variation in this detection statistic (see
Fig. in Appendix ), resulting (combined
with low seismicity rates) in little change in detection rate. In contrast,
classic earthquake detectors dependent on ratios of seismic amplitudes would
be sensitive to this variation in noise. This highlights the efficacy of
matched-filter detectors for consistent detection capability during periods of
variable noise.
Conclusions
During the Level-4 lockdown to combat the COVID-19 pandemic in New Zealand,
6 of the 10 seismometers that monitor the Auckland Volcanic Field display
reduced seismic noise levels above 1 Hz. One seismic station has low
levels of noise all the time (AWAZ), and one station (KBAZ) appears to be
affected by local farming and a motorway supporting essential services. Two
other (surface) stations (WIAZ and ABAZ) in the Hauraki Gulf have real-time
seismic amplitude measurement (RSAM) values that are 1–2 orders of
magnitude greater than the rest of the AVSN and show the strongest
correlation with ocean swell.
Besides analysing the sensitivity of the network, the lockdown period allowed
us to explore local earthquakes that are smaller in magnitude than normally would be
detectable. A first attempt at template matching resulted in the detection of
30 new local events in the time where the GeoNet catalogue contain only five
local earthquakes. However, the detection rate was not higher during the
lockdown period than in the periods before or after the lockdown. More
advanced matched-filtering detection efforts with existing data are underway,
but re-installing surface stations WIAZ and ABAZ in boreholes can improve
seismic monitoring and tomography of the AVF.
Spectrograms
Spectrograms for the seismic data of the Auckland Volcanic
Seismic Network. The vertical dashed lines indicate the start and
end dates of the COVID-19 lockdown in New Zealand.
Figure contains the spectrograms for every station of the AVSN,
plus the broadband station MKAZ of the national seismic monitoring
network. The spectrograms are built using the continuous seismic records. The
data are split in windows of 30 min duration that overlap by
50 %. The power spectral density (PSD) of each window is calculated using MSNoise
, which relies on the probabilistic power spectral density
(PPSD) implementation of ObsPy . The frequency binning is done
by 1.25 % of an octave (default 12.5 %, or one-eighth) and the
PSDs are smoothed over 2.5 % of an octave (default 100 %, or
1 octave) around the central frequency of each bin. The amplitudes of the PSDs
are binned in 0.25 dB bins.
All the stations have a distinct spectral signature near (but just above)
0.1 Hz for event ID us60008fl8. The period between the vertical
dashed black lines marks the extent of the lockdown in New Zealand. For most
stations, anthropogenic noise is reduced for frequencies from 1 Hz to
the Nyquist frequency of 50 Hz. However, the effect of the lockdown is
not equally clear in all stations: wind noise on stations on the Hauraki Gulf
(WIAZ and ABAZ) dominates over any anthropogenic noise, as previously seen in
the seismograms.
Matched-filtering details
(a) The median cross-correlation sum for the AVSN against a
template of one of the GeoNet-located earthquakes, showing no
reduction in this value for the lockdown period. (b) Weighted
multitaper spectrum with 5 % and 95 % confidence intervals of
the top panel time series. Spikes at 12 and 24 h confirm the
noise is dominated by the difference between night- and daytime noise
levels.
The lockdown period is short, especially when compared to seismogenesis in
this relatively low seismicity region. Because our template analysis focuses
on the efficacy of the matched-filter method, the detectability is affected by
the noise level in the cross-correlation sum. To demonstrate why we do not
expect a change in detection rate during lockdown, we computed and plotted the
network cross-correlation sum for one template (GeoNet public ID 3469372)
between 29 February 2020 and 8 May 2020, alongside the amplitude spectra for
this time series (Fig. ). Plotting the full sample-rate
correlation sum shows little power outside the 2–15 Hz range used;
however, computing the hourly mean correlation sum provides more useful
information regarding the variability in noise in the correlation sum. In this
hourly correlation sum, reductions should correspond to reduced noise in the
correlation sum and hence enhanced detectability. We find clear daily
variations (evidenced by a peak in the amplitude spectra at 24 h
periods); however, there is no clear reduction in background correlation values
during lockdown. It is based on this evidence that we can be confident that
there is no significant change in detectability during lockdown. Note also
that our detection threshold is based on the daily median absolute deviation
of the correlation sum, which further smooths the daily variability in the
correlation sum. The range of daily median absolute deviations, upon which our
threshold is based, is from 0.234 to 0.254, with the lowest values falling
outside the lockdown period.
Code availability
All codes for the event detections are available
from 10.17608/k6.auckland.12915551.v2.
Data availability
All seismic data for this study are available from the FDSN service run by GeoNet. For further information, please see the “Acknowledgements” section.
Author contributions
All listed authors wrote the manuscript. KvW did
the seismic noise analysis. The original idea to analyse seismic data
during the COVID-19 pandemic is from TL and KVN; EQcorrscan analysis was done by
by CJC.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Social seismology –
the effect of COVID-19 lockdown measures on seismology”. It is not associated with a conference.
Acknowledgements
Seismic data for the AVSN and MKAZ were made available through the GeoNet
project, sponsored by the New Zealand Government through its agencies:
Earthquake Commission (EQC), GNS Science and Land Information New Zealand
(LINZ). The wind speed data were made available by NIWA. Data processing was
done in ObsPy , and data visualisation was conducted with Matplotlib
and Cartopy in Python.
Review statement
This paper was edited by Stephen Hicks and reviewed by three anonymous referees.
References
Ashenden, C. L., Lindsay, J. M., Sherburn, S., Smith, I. E., Miller, C. A., and
Malin, P. E.: Some challenges of monitoring a potentially active volcanic
field in a large urban area: Auckland volcanic field, New Zealand, Nat.
Hazards, 59, 507–528, 2011.Boese, C. M., Wotherspoon, L., Alvarez, M., and Malin, P.: Analysis of
Anthropogenic and Natural Noise from Multilevel Borehole Seismometers in an
Urban Environment, Auckland, New Zealand, B. Seismol.
Soc. Am., 105, 285–299, 10.1785/0120130288, 2015.Chamberlain, C. and van Wijk, K.: EQcorrscan procedures for AVSN data over the
2020 lockdown period, 10.17608/k6.auckland.12915551.v2, 2020.
Chamberlain, C. J., Hopp, C. J., Boese, C. M., Warren-Smith, E., Chambers, D.,
Chu, S. X., Michailos, K., and Townend, J.: EQcorrscan: Repeating and
near-repeating earthquake detection and analysis in Python, Seismol.
Res. Lett., 89, 173–181, 2018.Dias, F. L., Assumpção, M., Peixoto, P. S., Bianchi, M. B., Collaço, B., and
Calhau, J.: Using Seismic Noise Levels to Monitor Social Isolation: An
Example From Rio de Janeiro, Brazil, Geophys. Res. Lett., 47,
e2020GL088748, 10.1029/2020GL088748, 2020.
Endo, E. T. and Murray, T.: Real-time seismic amplitude measurement (RSAM): a
volcano monitoring and prediction tool, B. Volcanol., 53,
533–545, 1991.
Ensing, J.: Multi-Component Ambient Seismic Noise Tomography of the Auckland
Volcanic Field, PhD thesis, The University of Auckland, 2020.Ensing, J. X. and van Wijk, K.: Estimating the Orientation of Borehole
Seismometers from Ambient Seismic Noise, B. Seismol. Soc. Am., 109, 424–432, 10.1785/0120180118, 2018.Ensing, J. X., van Wijk, K., and Spörli, K. B.: Probing the subsurface of
the Auckland Volcanic Field with ambient seismic noise, New Zeal. J. Geol. Geop., 60, 341–352, 10.1080/00288306.2017.1337643, 2017.
Gibbons, S. and Ringdal, F.: The detection of low magnitude seismic events
using array-based waveform correlation, Geophys. J. Int., 165, 149–166,
2006.Google: Community Mobility Reports, available at: https://www.google.com/covid19/mobility (last
access: June 2020), 2020.Hopkins, J. L., Smid, E. R., Eccles, J. D., Hayes, J. L., Hayward, B. W.,
McGee, L. E., van Wijk, K., Wilson, T. M., Cronin, S. J., Leonard, G. S.,
Lindsay, J. M., Németh, K., and Smith, I. E. M.: Auckland Volcanic Field
magmatism, volcanism, and hazard: a review, New Zeal. J. Geol. Geop., 10.1080/00288306.2020.1736102, online first, 2020.Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, 10.1109/MCSE.2007.55, 2007.Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C.,
and Wassermann, J.: ObsPy: a bridge for seismology into the scientific
Python ecosystem, Computational Science and Discovery, 8, 014003,
10.1088/1749-4699/8/1/014003, 2015.Lecocq, T., Caudron, C., and Brenguier, F.: MSNoise, a Python Package for
Monitoring Seismic Velocity Changes Using Ambient Seismic Noise,
Seismol. Res. Lett., 85, 715–726, 10.1785/0220130073, 2014.Lecocq, T., Hicks, S. P., Van Noten, K., van Wijk, K., Koelemeijer, P.,
De Plaen, R. S. M., Massin, F., Hillers, G., Anthony, R. E., Apoloner, M.-T.,
Arroyo-Solórzano, M., Assink, J. D., Büyükakpınar, P.,
Cannata, A., Cannavo, F., Carrasco, S., Caudron, C., Chaves, E. J., Cornwell, D. G., Craig, D., den Ouden, O. F. C., Diaz, J., Donner, S., Evangelidis, C. P., Evers, L., Fauville, B., Fernandez, G. A., Giannopoulos, D., Gibbons, S. J., Girona, T., Grecu, B., Grunberg, M., Hetényi, G., Horleston, A.,
Inza, A., Irving, J. C. E., Jamalreyhani, M., Kafka, A., Koymans, M. R.,
Labedz, C. R., Larose, E., Lindsey, N. J., McKinnon, M., Megies, T., Miller, M. S., Minarik, W., Moresi, L., Márquez-Ramírez, V. H.,
Möllhoff, M., Nesbitt, I. M., Niyogi, S., Ojeda, J., Oth, A., Proud, S.,
Pulli, J., Retailleau, L., Rintamäki, A. E., Satriano, C., Savage, M. K.,
Shani-Kadmiel, S., Sleeman, R., Sokos, E., Stammler, K., Stott, A. E.,
Subedi, S., Sørensen, M. B., Taira, T., Tapia, M., Turhan, F., van der
Pluijm, B., Vanstone, M., Vergne, J., Vuorinen, T. A. T., Warren, T.,
Wassermann, J., and Xiao, H.: Global quieting of high-frequency seismic noise
due to COVID-19 pandemic lockdown measures, Science, 369, 1338–1343,
10.1126/science.abd2438, 2020.Met Office: Cartopy: a cartographic python library with a Matplotlib
interface, Exeter, Devon, available at: https://scitools.org.uk/cartopy (last access: 30 January 2021),
2010–2015.Poli, P., Boaga, J., Molinari, I., Cascone, V., and Boschi, L.: The 2020
coronavirus lockdown and seismic monitoring of anthropic activities in
Northern Italy, Sci. Rep.-UK, 10, 9404,
10.1038/s41598-020-66368-0, 2020.
Ross, Z. E., Trugman, D. T., Hauksson, E., and Shearer, P. M.: Searching for
hidden earthquakes in Southern California, Science, 364, 767–771, 2019.Shelly, D. R. and Hardebeck, J. L.: Illuminating faulting complexity of the
2017 Yellowstone Maple Creek earthquake swarm, Geophys. Res. Lett.,
46, 2544–2552, 2019.
Sherburn, S., Scott, B. J., Olsen, J., and Miller, C.: Monitoring seismic
precursors to an eruption from the Auckland Volcanic Field, New Zealand, New
Zeal. J. Geol. Geop., 50, 1–11,
10.1080/00288300709509814, 2007.Warren-Smith, E., Chamberlain, C. J., Lamb, S., and Townend, J.: High-Precision Analysis of an Aftershock Sequence Using Matched-Filter
Detection: The 4 May 2015 ML 6 Wanaka Earthquake, Southern Alps, New
Zealand, Seismol. Res. Lett., 88, 1065–1077,
10.1785/0220170016, 2017.Warren-Smith, E., Fry, B., Kaneko, Y., and Chamberlain, C. J.: Foreshocks and
delayed triggering of the 2016 MW7.1 Te Araroa earthquake and dynamic
reinvigoration of its aftershock sequence by the MW7.8 Kaikōura earthquake,
New Zealand, Earth Planet. Sc. Lett., 482, 265–276,
10.1016/j.epsl.2017.11.020, 2018.