WIHG studied this disaster using seismological data, satellite imagery
and numerical modelling and estimated that \(27\times 10^{3}\ m^{3}\) of rock and glacier-ice collapsed from the steep north face of the Ronti
Peak (Shugar et al., 2021; Tiwari A., et al., 2022). The main rock-fall
followed by a noteworthy sequence of small events recorded by nearby
seismic stations. The main event appears to have been initiated by
precursory signals for nearly 2:30 h. The seismic data of three nearby
stations within 50 km distance also distinguished debris flow and
hitting obstacles from other seismic sources. The proximal high-quality
seismic data allowed estimation of debris-flow speed and reconstruction
of the complete chronological sequence, from the initiation of the
nucleation phase to the occurrence of the rock-fall.
8.2 Earthquake Precursory
Studies
Vijaya Kumar et al. (2020) recognized pre- and co-seismic signatures in
MT data for the Mw 4.6 earthquake that occurred on 2007-11-24 in the
Koyna-Warna region. Wavelet analysis of the MT time series data shows
significant enhancement at 3–6 Hz frequency band in the scalogram
during the earthquake in comparison with pre- and post-time. The
spectral polarization ratio technique was implemented on these events to
identify the precursory signatures. A few days before the earthquake, a
significant anomaly was identified for most of the earthquakes using
this technique. Akilan et al. (2021) studied changes in
the zenith total delay (ZTD) and
total electron content (TEC)
associated with the 2015 Nepal earthquake (Mw 7.8). They analyzed the
ZTD derived from GPS data received at HYDE, LCK3 International GNSS
Service (IGS) stations and MSUN operated by CSIR-NGRI. The decrease in
ZTD and TEC suggests hydration in the atmosphere by the joining of ions
in the atmosphere during earthquakes. Almost the same results regarding
the changes in ZTD and TEC were obtained for another large event (the
1999 Chamoli earthquake of Mw 6.8) in the Himalayan region, although
they differ slightly in intensity depending on the observation distance.
Probabilistic analysis was performed on the seismic data of 100 years
(1918–2018) for forecast of probable future earthquakes above Mw \(\ge\) 5.0
in NE India (20°–30°N and 86°–98°E) and its vicinity to ascertain mean
occurrence period E(t) for earthquakes of Mw \(\ge\) 5 (Chetia et al., 2019a).
Here, Kolmogorov–Smirnov statistics constrained by Weibull distribution
has been utilized to achieve the best fit on the dataset. E(t) is found
to be \(\sim\) 74 days with 50% probability. Similarly,
cumulative probability function indicates a time of 140 days with 80%
probability, while 400–500 days of recurrence time period is embedded
with 90–100% probability for an earthquake of Mw \(\ge\) 5.0 to recur
following the occurrence of the last earthquake.
Mechanical deformations from within the earthquake preparation zones are
believed to cause
seismo-electromagnetic (SEM)
emission in ultra-low frequency
(ULF) band, i.e. between 0.001 and 10 Hz. The 3-component ULF induction
coil magnetometer
data from
Multi-parametric Geophysical
Observatory (MPGO), Tezpur were used to study SEM emissions employing
both polarization ratio analysis and fractal analysis during the
campaign period of Apr.20 – Sep.3, 2019 (Dey et al., 2021). The
findings show candidate SEM emissions, in the form of enhancements in
SZ/SH, associated with all the seven credible events, even as nine
enhancements could not be attributed to immediately adjacent credible
events.
WIHG operates an MPGO at Ghuttu (Tehri, Uttarakhand) for earthquake
precursory studies. Inert gas (soil Radon) and magnetic field changes
before the occurrence of small-to-moderate magnitude earthquakes provide
evidence for short-term (months-to-days, hours) earthquake precursors.
However, this remote site in the Higher-Himalaya, away from human
generated noise, indicates that there are different background natural
noises to make the data very complex. There is a very high hydrological
effect in the gravity and radon emanation (Shukla et al., 2020; Chauhan
et al., 2021). After removing the background noises, anomalous changes
are reported in case of 19-20 moderate magnitude local earthquakes.
Earlier, this observatory has also reported precursory changes during
the Mw 7.8 Nepal earthquake of 2015.
ISR has established three MPGOs in Gujarat for precursory studies. Soil
radon (Rn-222) data of the Badargadh station were used to identify
precursory signal of two earthquakes of M3.7 and M4.2 which occurred on
March 26, 2011, and May 17, 2011, respectively through advanced
processing of the time series (Sahoo et al., 2020, 2021). The ultra-low
frequency geomagnetic variations were observed before the Dholavira
earthquake (M 5.1) of 2012-06-20 in the Kachchh region (Joshi and Rao.,
2021).
The apparent resistivity imaging at MPGO, Tezpur, operated by
CSIR-NEIST, was carried out since 2016-08-31 for fixed interval time of
3 days to investigate precursory signatures prior to earthquake events.
Anomalies in apparent resistivity prior to earthquake events are
observed. These are not influenced by the rainfall activity in the
region during the investigation period. Weibull distribution technique
with observed apparent resistivity data is adopted to look for and
revalidate the observations. The Weibull parameters, i.e. K and m, are
estimated to be 0.26 and 1.04, respectively. No precise relation between
magnitude and earthquake precursory time is found. Weibull probability
distribution indicates that the probability of an earthquake occurrence
exceeds the measure up to 80% (9 days) after the precursory signal is
observed (Chetia et al., 2020). Similarly, temporal variability of the
soil radon emanations measured at the MPGO was scrutinized using
singular spectrum analysis (SSA)
(Chetia et al., 2019b). The study concludes that SSA eliminates diurnal
and semidiurnal components from time series of soil radon emanation for
better correlation of soil radon emanation with earthquakes.
Mukherjee et al. (2021) have proposed a novel approach for
Earthquake Early Warning (EEW)
System Design using deep learning Techniques. The method converts a
seismic signal into audio signals and then uses popular speech
recognition techniques. Both
Convolutional Neural Network
(CNN) and a Long Short-Term Memory (LSTM) network have been trained.
8.3 Ionospheric Seismology
Ionospheric seismology refers to the study of events of earthquakes and
tsunamis through co-seismic ionospheric waves produced by the dynamic
coupling between the Earth’s surface and the atmosphere. The ionosphere
is a highly dynamic ionized region of the Earth’s upper atmosphere
extending from \(\sim\) 60 km to \(\sim\) 1000 km. The
origin of any perturbations in the ionospheric electron density can be
traced to various sources either from above (e.g. solar, geomagnetic,
etc.) or below (e.g. lower atmospheric, earthquakes, tsunamis, volcano
eruptions, etc.) the ionosphere. In particular, short-period acoustic
waves and long-period gravity waves emitted by earthquakes and tsunamis
propagate upward in the region of exponentially decreasing atmospheric
neutral density, and thus, their amplitude increases with atmospheric
heights. On arrival at ionospheric heights, the waves redistribute
ionospheric electron density and produce electron density perturbations
known as co-seismic/co-tsunami ionospheric perturbations, respectively.
Recently, it has been suggested that ionospheric signals produced by
earthquakes and tsunamis can be inverted to infer the seismic source
characteristics of large earthquakes and to envisage the propagation
time and amplitudes of tsunami waves. It seems possible, but there are
difficulties in terms of non-tectonic forcing mechanisms that act upon
the ionospheric perturbation evolution at ionospheric altitudes (Bagiya
et al., 2019).
A simple and direct 3D model is developed to estimate the combined
effects of nontectonic forcing mechanisms of i) orientation between the
geomagnetic field and tectonically induced atmospheric wave
perturbations, ii) orientation between the GNSS satellite
line of sight (LOS) geometry and
coseismic atmospheric wave perturbations, and iii) ambient electron
density gradients on the manifestations of
Global Positioning System (GPS)
– Total Electron Content (TEC) measured near field co-seismic
ionospheric perturbations. This model can compute the nontectonic
effects at various ionospheric altitudes depending on the propagation
characteristics of seismo-acoustic rays (Fig.12). Further, this model is
tested on earthquakes occurring at different latitudes. It is presumed
that this model would induce and enhance a proper perception among the
researchers about the seismic source characteristics derived based on
the corresponding ionospheric manifestations (Bagiya et al., 2019).
Further, GPS-TEC observations provide adequate information on the
spatial and temporal characteristics of earthquake induced ionospheric
perturbations. However, one of the major limitations of this technique
is the lack of altitude information of the recorded ionospheric
perturbations. GPS derived TEC is an integrated quantity; hence it is
difficult to relate the detection of ionospheric perturbations in TEC to
a precise altitude. Using the modelled propagation of acoustic rays in
space and time and their interaction with satellite-station line of
sight (LOS) geometry, a novel method has been developed to infer the
detection altitude of ionospheric perturbations observed through
GPS-TEC. This modest method has been further upgraded to identify the
distinct seismic sources that evolved along an extended rupture varying
simultaneously in space and time akin to the seismic rupture of the Mw
9.0 2011-03-11 Tohoku-Oki earthquake (Bagiya et al., 2020).
In addition to the transient perturbations, prolonged ionospheric
oscillations following large earthquakes (Mw > 8.0) have
also been found to provide seismic source information from the
ionosphere. Such ionospheric oscillations related to the
earth-atmospheric resonance frequencies of \(\sim\) 3.7 and
\(\sim\) 4.4 mHz following the 2012-04-11 Sumatra doublet
(Fig.13) and 2011-03-11 Tohoku-Oki earthquakes were scrutinized (Nayak
et al., 2021, 2022). The Earth’s background free oscillations at
\(\sim\) 3.7 and \(\sim\) 4.4 mHz resonantly couple with
the atmospheric acoustic modes and thus energy cross-talk between the
earth-atmosphere system is maximum at these frequencies. Our studies
emphasized that resonant ionospheric signatures during the Sumatra
doublet event were related to the seismic source. Therefore, resonant
co-seismic ionospheric signatures could provide additional information
on the low frequency features of seismic ruptures.