DESCRIPTION:

This event occurred on 2 March 2016 at 12:49:46 UTC 800 km off the west coast of southern Sumatra, Indonesia with magnitude 7.9.The hypocentre is located offshore in an area that is not well monitored. The completeness magnitude is high even for inland earthquake (Mc≥4.5) and due to the limited number of earthquakes cannot be estimated for earthquakes occurred near the epicentre.

 

Figure 1: Location of the EQ epicenter and Dobrovolsky area.

This event is located 600 km to the southwest of the major subduction zone that defines the plate boundary between the India/Australia and Sunda plate offshore Sumatra. At this location, the India/Australia plates move north-northeast relative to the Sunda plate at a velocity of about 55 mm/yr.

 

 

ANALYSES:

 

  1. SEISMOLOGY
    1. Accelerated Moment Release (AMR) & Revised-AMR

The analysis of the USGS seismic catalogue extended back in time of 10 years before the EQ. In the reduced cumulative strain behaviour, the occurrence of large events inside DbA is evident from the sudden jumps, but their overall effect did not affect the fault of the main rupture impressing an acceleration to the seismicity and, in fact, R-AMR analysis confirm the findings that no acceleration preceded the main rupture.

 

Figure 2: Reduced Cumulative strain for 10-year data. It confirms that no acceleration preceded the mainshock.

 

 

 

  1. SWARM – GEOMAGNETISM

Two different approaches were developed to search EQ precursor using geomagnetic Swarm data: MASS method and Wavelet method. Both analyses are based on Level 1B MAGxLR Swarm product.

 

  1. MASS algorithm (Magnetic Swarm anomaly detection by Spline analysis)

The algorithm MASS (Magnetic Swarm anomaly detection by Spline analysis) was applied to Sumatra 2016 earthquake with different thresholds (kt=2.0, 2.5 and 3.0), while the moving window was fixed at 3.0°. The algorithm analyses all tracks in DbA one month before and one after the EQ. The tracks are marked as “anomaly” only if the centre of the moving window is in DbA and if geomagnetic conditions are quiet. Figure 3 shows an example of anomalous track detected by MASS method for Sumatra EQ.

 

Figure3:Example of anomalous track in Y magnetic component (Satellite C- February 14, 2016).

The cumulative number of anomaly tracks detected by MASS one month before and after the Sumatra EQ (threshold  kt= 2.75) is shown in Figure 4. The cumulative number of Y magnetic field anomaly tracks is compatible with a linear fit. The number of the anomalies after the EQ (18) is greater than before EQ (15), so nEQ is bad. However, the C factor is very good, because the number of anomalies detected by Bravo is smaller than that detected by Alpha or Charlie, so it is a good indication for a possible internal source of these anomalies.

 

Figure 4: Cumulative number of anomaly detected by MASS one month before and one month after the M7.9 Sumatra EQ. Threshold is kt = 2.75  and the anomalies are selected only with geomagnetic quiet time (|Dst| ≤ 20 nT and ap≤ 10 nT).

 

 

  1. Wavelet Analysis

 

The Wavelet spectral analysis has evidenced the existence of different anomalous families, each characterized by some particular featuresthat altogether do not clarify whether they are linked to LAIC or not.

Some different types of anomalies appear during the time of observation before and after the mainshock. This event happened close to the magnetic equator so that we may expect some features of the anomalies emerging from data can repeat equals as in other region of the world at the same latitude. Indeed, the sequence of Figure 5 shows a couple of anomaly already encountered in other two cases.

 

 

Figure 5:This figure shows a family of anomaly already encountered in the same (magnetic) latitudes (i.e. for Chile 2014, M8.2; Ecuador 2016, M7.8). It appears as a couple of wave packets symmetric with respect to the magnetic equator.

The discrimination of the source of emerging signals from the background by applying this method alone may need the adoption of some other more complex scheme.

 

  1. SWARM – IONOSPHERE from Satellite

Satellite-based data for the ionospheric characterization of the EQ-related events are mainly those referred to the LP (Langmuir Probe) instrument aboard the SWARM satellites. The electron density Ne is the relevant parameter used for the ionospheric characterization in the frame of SAFE project. Two different methods were developed to analyze Swarm ionospheric data: NeSTAD and NeLOG.

 

  1. Method I: NeSTAD

The NeSTAD analysis has been applied to the Swarm constellation data (LP and IBI data )available in the period from 2 February to 2 April have been used to derive the track anomaly parameters.The NeSTAD has been initialized with the mild outliers mode (k=1.5) and with an “excess area” parameter equal to 0.1.

Then, to tag the interesting track anomalies for this particular event, the following criteria have been applied:

  • R>Rthr=0.85 and standard deviation of the filtered track below σthr=0.1 or standard deviation of the filtered track above σthr=0.1 independently of R value.
  • Only morning tracks have been selected (02-06 LT), to remove the impact of the equatorial fountain during the day and to minimize the impact of the plasma bubble formation during night times
  • Quiet ionospheric conditions (absolute value of Dst in the considered day not exceeding 20 nT).

An example of tagged anomaly is provided in figure 6.

 

Figure 6: Identified anomaly with the NeSTAD algorithm and tagged as interesting for the Sumatra EQ event. Tagged anomaly refers to Swarm Alpha satellite on 21 February 2016.

 

 

Figure 7: - Cumulative number of anomalies identified through the NeSTAD algorithm for Swarm satellite Bravo.

 

Figure 7 shows the cumulative number of the tagged anomalies through the NeSTAD algorithm for Bravo satellite. The black dashed line indicates the day in which the Sumatra M7.9 EQ event occurred. The red boxes indicate the days in which disturbed geomagnetic conditions have been recorded and that are not included in the tagging process. The two blue dashed lines indicate the time interval during which the given Swarm satellite covered the DbA of the event at morningtime.

 

  1. Method II: NeLOG

The automatic search NeLOG of ionosphere electron density anomaly from Swarm data is applied to Sumatra M7.9 case study. A sample is classified as anomaly if the residual value exceeds a threshold kt times the RMS of the residual after the polynomial fit. A track is selected as an “anomaly” if it has more than 10 anomaly samples in DbA and if geomagnetic indices are |Dst|<20 nT and ap<10 nT. Tracks are selected within a mean local time between 22 and 6.

 

 

 

Figure 8: Anomaly detected by NeLOG (satellite Alpha, 29 February 2016) before the M7.9 Sumatra EQ.

Figure 8 shows an example of anomaly track 2 days before the mainshock, while Figure 9 reports the cumulative number of anomaly windows detected by NeLOG. For this event, the cumulative number of anomaly tracks is compatible with an S-shape behavior; this result is confirmed by the R2 = 0.944 value that underlines that the cumulate is not compatible simply with a linear trend.

 

Figure9:Cumulative number of anomaly samples detected by NeLOG one month before and one month after M7.9SumatraEQ. Threshold is kt = 4.0, the anomalies are selected only with geomagnetic quiet time (|Dst| ≤ 20 nT and ap ≤ 10 nT) and in night time (22 ≤ LT ≤ 6).

 

  1. IONOSPHERE Ground-based

Ionosondes&GNSS

For this event, no suitable Ionosonde and GNSS data were available.

 

 

CONCLUSIONS:

The M7.9 Sumatra EQ was analyzed using both Swarm geomagnetic and ionospheric data searching for earthquake-related anomalies in the frame of LAIC theory.

The cumulative number of anomalies derived by tagging procedure on the NeSTAD output on Swarm satellites allowed identifying 5 anomalies in total, 1 for Alpha and Charlie and 3 for Bravo. Swarm Alpha and Charlie satellites covered the DbA in the morning (02-06 LT) sector, as required by the tagging criteria, only for a very short time range at the beginning of considered period, not allowing catching a satisfying picture of the event from the data coverage point of view. This is not the case of Bravo, for which the space-time range of the cumulative covers from only beginning of the period, up to 10 days after the EQ. Moreover, according to the Dst criterion used by the NeSTAD algorithm, many days before the event have been found to be disturbed, but the days in the proximity of the EQ have found to be geomagnetically quiet.

Considering NeLOG method, the values of R2 and C resulting from the analysis seem to be good indication for a possible internal source of the anomalies.