In this paper, we take the Fengjie-Hongtu borehole strain gauge observation data from January 1, 2010 as a benchmark to December 31, 2022 data (Fig. 3) to analyze the information it contains. Based on the four-component borehole strain observation data, it mainly contains high-frequency information, cycle information (solid tides are dominant), crustal strain information and noise information.
Usually the 4-component borehole strain gauge data processing is taken to first analyze the surface strain and observation data correlation; then in order to meet the theoretical formula:, use the data correction to improve the correlation between the two kinds of data, so that the shape of the basic overlap[2].
In this paper, we consider that the observed values of the four components (S1, S2, S3, S4) are the relative observed changes, and in the actual observation process, due to the sensitivity of the instrument, the slight difference of crustal deformation and other reasons, the specific observed values must have some differences. In the actual processing should be based on the real record data and respectively, compared to analyze the morphological differences between the two.
Therefore, this paper takes a new idea to calculate and analyze. Firstly, the surface strain sum is obtained by calculating the observed values of four components (S1, S2, S3, S4), and then the surface strain sum is normalized:
$${S}_{1+3}=({S}_{1}+{S}_{3})/\text{m}\text{a}\text{x}\left(abs\left({S}_{1}+{S}_{3}\right)\right)$$
$${S}_{2+4}=({S}_{2}+{S}_{4})/\text{m}\text{a}\text{x}\left(abs\left({S}_{2}+{S}_{4}\right)\right)$$
Finally, to ensure the accuracy of extracting the surface strain trend sequence, two methods, EMD(Empirical Mode Decomposition) and STL(Seasonal-Trend decomposition using Loess), were used to decompose the surface strain components, remove high-frequency information, periodic information, and noise information, and obtain two types of surface strain trend change information.
From the statistics in Fig. 4 and Table 1, it can be seen that: the surface strain had a turning point in 2012 and 2013, which gradually increased and then gradually decreased, and the Lushan M7.0 earthquake occurred soon after; the surface strain gradually increased in the middle of 2014 and then gradually decreased in the middle of 2018, and the Changning M6.0 earthquake occurred after the turning point appeared; and from 2020 onwards, the surface strain gradually The turnaround occurred when the Lushan M6.1 earthquake occurred; from 2020 onwards, the surface strain gradually increased and then gradually decreased in early 2022.
Table 1
Surface strain changes and the moment of earthquake occurrence
base period
|
face strain
(1 + 3)
|
face strain
(2 + 4)
|
seismic moment
|
2010.01-2011.12
|
build up
|
build up
|
2013.4.20
Lushan M7.0
|
2012.01-2013.03
|
build up
|
fade
|
2013.03-2014.06
|
fade
|
fade
|
2014.06-2018.01
|
build up
|
build up
|
2019.6.17
Changning M6.0
|
2018.01-2019.11
|
fade
|
fade
|
2020.01-2021.11
2022.01-2022.12
|
build up
fade
|
build up
fade
|
2022.6.1
Lushan M6.1
|