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A new methodological approach to the identification of potential earthquake source zones (PESZ) in the Earth’s crust, tested on the territory of the Republic of the Union of Myanmar, is proposed. It involves processing morphometric terrain parameters related to seismicity in the best possible way by the method of equivalent gradations or using the apparatus of fuzzy logic. The integral morphometric index (neotectonic activity index) calculated this way is compared to the relative stress values estimated from computer modeling data. These two parameters form the basis for the algorithm of the PESZ identification, which may be used for seismic zoning, primarily of remote and poorly studied territories, since it is based on publicly available source data and is largely formalized.
INTRODUCTION
This work considers the problem of improving the methodology for the identification of potential shallow (crustal) earthquake source zones (PESZ) by the new possibilities of geomorphological methods and computer geodynamic modeling. In our opinion, they are insufficiently used in solving this urgent applied research task, although for several regions a high degree of correlation is established between morphometric terrain parameters and the earthquake epicenter localization; the informativeness of computer modeling for delineating seismically active areas is proved [1, 2]. We note that the urgency of the mentioned problem is emphasized by the necessity to formalize the algorithm for PESZ identification, to refine the configuration of their boundaries and seismic regime parameters with respect to recent data and capabilities of processing thereof. The methodological approach we proposed was tested by the example of the central part of the Republic of the Union of Myanmar, where a catastrophic Mw 7.7 earthquake occurred near the city of Mandalay on March 28, 2025.
METHODS
Information on modern crustal seismicity was obtained from the USGS Catalog. Various magnitude types recorded therein were converted to moment magnitude () by the formulas [3, 4], and a recurrence plot was constructed for the earthquakes that occurred at depths ≤35 km. This depth corresponds to the crustal thickness in the study region [5] and was chosen because geomorphological methods are usually uninformative for understanding geodynamic processes at great depths. Thus, the sample we analyzed consists of 960 earthquakes, including 122 seismic events with .
For the geomorphological study, we used the FABDEM V 1–2 digital surface model [6] with horizontal resolution of 1 arcsecond (~30 m at the equator) and a mean elevation error of 0.33 m [7]. The territory of Myanmar was divided along the Sagaing transform fault into two regions that differ significantly in geological and geomorphological terms, as they lie within different lithospheric plates: the western region belongs to the Indian Plate, the eastern one, to the Eurasian Plate. Morphometric parameters of the terrain that are related to seismicity in the best way were selected for each region. For the western part, they include height asymmetry and stream density [8], calculated taking into account their order determined by the Strahler–Filosofov method [9, 10]; for the eastern part, they include residual terrain heights obtained by subtracting the base surface of order 2 from the hypsometric surface, as well as differences between the base surfaces of orders 1–2 and 3–4. The degree of relationships with seismicity is quantified by calculating the proportion of earthquake epicenters located within positive anomalies of each of the mentioned morphometric parameters. Such anomalies are delineated using the values exceeding the median () or the third quartile (). In the first case, they account for at least half of the earthquake epicenters, in the second case, at least a quarter. A similar calculation was performed for the epicenters of strong earthquakes with Mw ≥ 5.5. The choice of morphometric terrain parameters was determined not only by the results of their quantitative correlation with seismicity but also by the fact that they reflect a character of neotectonic movements to a certain extent. For example, the difference between the base surfaces allows estimating the amplitude and direction of the most recent movements at the stage of terrain development corresponding to the timing of formation of valleys of certain orders [11]. The pattern of residual terrain shows what volume of rocks occurs above the base surface and is subjected to further denudation. In a general case, high positive values of this parameter mark the actively uplifting areas [12]. Density of streams serves as a measure of the horizontal dissection of the terrain. For several regions, its increased values mark the most actively developing structural forms, primarily the recent uplifts [13]. Height asymmetry (A) has no analogs in “traditional” morphometry [14]; however, depending on the sign of asymmetry, either complicating positive (A > 0) or negative (A < 0) forms predominate on a subhorizontal surface. The first ones may be associated with the growth of recent uplifts, while the second ones correspond to the subsidence of troughs and basins.
To obtain a general evaluation of neotectonic activity extent based on the terrain analysis data, a dimensionless integral morphometric parameter termed the neotectonic activity index (I) was calculated by the method of equivalent gradations [15]. To do this, all morphometric characteristic values were converted into points: 1 point corresponds to the values (Z) that are less than the 1st quartile (Q1), 2 points refers to , 3 points to , and 4 points to Z ≥ Q3. The neotectonic activity index equals the ratio of the sum of points to the number of morphometric parameters used. The pattern of I calculated by the method of equivalent gradations was compared with the values of this parameter determined using the apparatus of fuzzy logic. The calculation procedure involved conversion of morphometric patterns into fuzzy sets by a linear algorithm, whose elements take on the values from 0 to 1, and their subsequent processing by a γ-operator of fuzzy logic at [1].
To improve the accuracy and reliability of delineating seismically active zones by geomorphological methods, we performed computer geodynamic modeling based on the use of the Coulomb–Mohr criterion. The modeling procedure was described in detail in [2] and consists in creating an elastic model based on elevation data, onto which a grid of active faults [16] is superposed. A certain type of external loading causes dynamic displacements along them. Based on the solutions of focal mechanisms of earthquake sources, we prescribed a shear stress field with the maximum compression axis oriented at an azimuth of 225°. The most significant result of the modeling was the relative magnitudes of horizontal stresses (F): the configuration of the areas where they were less than the median was compared to the localization of earthquake epicenters. Our previous modeling of recent geodynamics in other seismically active regions showed that in a shear stress regime where , the greatest concentration of strains is reached due to the combination of compressive and tensile stresses acting in the horizontal plane. In a compressional environment, it was informative to consider the areas where F receives the maximum values, while for the extensional environment, F takes on the smallest values.
The PESZs were identified by integrating the data of morphometric terrain analysis and computer modeling. They represent the regions where the I values range from 3 to 4. The PESZs also include the regions where and . Small areas that met the above criteria were either excluded from consideration or were generalized with respect to the fault configurations and the distribution of earthquake epicenters in the form of a linear-elongated zone. Based on the length of the largest fault, the maximum moment magnitude Based on the length of the largest fault, the maximum moment magnitude () of the expected earthquake was estimated for the largest PESZs using the formula [17]. It was compared with similar values calculated from the maximum recorded by the formula Mmax = [18].
RESULTS AND DISCUSSION
The recurrence plot of crustal earthquakes in the Republic of the Union of Myanmar is described by the equation = –0.71(±0.05)Mw + 3.79(±0.28). This regression model and its parameters are significant, since the values of Fisher test and Student test exceed the critical values for , and the Pearson correlation coefficient equals 0.986 (Fig. 1).
[See PDF for image]
Fig. 1.
Earthquake recurrence plot for the Republic of the Union of Myanmar: N is number of earthquakes, T is the time (in years), is the moment magnitude.
The western and eastern parts of Myanmar display a sufficiently high degree of confinement of the earthquake epicenters to the regions of increased morphometric terrain parameters. Based on the proportions of the earthquake epicenters within the limits of positive anomalies of I, the neotectonic activity index is more informative for delineation of seismically active regions than each of the mentioned morphometric parameters. For the entire study territory, the region where accounts for 59% of all earthquake epicenters and 71% of epicenters with . The regions where make up 30 and 47%, respectively (Table 1). The regions with decreased (less than the median) values of F, calculated by computer modeling, can also be considered as seismically active zones, since they occupy a half of the study territory and contain 51% of all earthquake epicenters and 65% of epicenters with (Fig. 2).
Table 1. . Relationship between seismicity and morphometric terrain parameters of the Republic of the Union of Myanmar
Region | Morphometric terrain parameters | Proportion of earthquake epicenters | |||
|---|---|---|---|---|---|
of the entire range of Mw, | with Mw ≥ 5.5, | ||||
located in the areas where the values of morphometric terrain parameters exceed | |||||
Q2 | Q3 | Q2 | Q3 | ||
Western Myanmar | Height asymmetry | 0.51 | 0.28 | 0.62 | 0.36 |
Density of streams | 0.51 | 0.28 | 0.72 | 0.41 | |
Neotectonic activity index | 0.59 | 0.31 | 0.71 | 0.45 | |
Eastern Myanmar | Residual terrain heights | 0.54 | 0.30 | 0.65 | 0.43 |
Difference of base surfaces of orders 1–2 | 0.57 | 0.32 | 0.76 | 0.51 | |
Difference of base surfaces of orders 3–4 | 0.55 | 0.31 | 0.46 | 0.30 | |
Neotectonic activity index | 0.60 | 0.32 | 0.76 | 0.46 | |
Entire territory | Neotectonic activity index | 0.59 | 0.30 | 0.71 | 0.47 |
Q2 is the median and Q3 is 3rd quartile
[See PDF for image]
Fig. 2.
Map of the neotectonic activity index (I) for central Myanmar: 1–3, earthquake epicenters according to the USGS Catalog: 1, with ; 2, with ; 3, the epicenter of the Mandalay earthquake (March 28, 2025, ); 4, faults, after [16]; 5, the Sagaing transform fault [16]; 6, the regions of decreased (less than the median) relative values of horizontal stresses; 7–9, the regions where I varies within the intervals: 7, ; 8, ; 9, .
The PESZs we identified based on the results of the geomorphological and tectonophysical studies cover 27% of the study territory and account for 12% of all earthquake epicenters and 75% (91 out of 122) of epicenters with (Fig. 3). In most cases, the values estimated for the four largest PESZs using the formula [17] are close to or exceed the values calculated from the data on maximum recorded [18] (Table 2).
[See PDF for image]
Fig. 3.
Map of potential crustal earthquake source zones in central Myanmar: 1–3, earthquake epicenters according to the USGS Catalog: 1, with ; 2, with ; 3, epicenter of the Mandalay earthquake (March 28, 2025, ); 4, hydropower plants after [19]; 5, faults, after [16]; 6, PESZs and numbers of the largest zones.
Table 2. . Estimation of the maximum magnitude of expected earthquakes within the largest potential crustal earthquake source zones of the Republic of the Union of Myanmar
Potential crustal earthquake source zone no. | Mmax of expected earthquakes calculated by formula | |
|---|---|---|
[18] | [17] | |
I | 8.2 | 8.7 |
II | 8.7 | 7.5 |
III | 8.4 | 8.1 |
IV | 6.4 | 7.6 |
This suggests the possible occurrence of stronger seismic events compared to the events known during the instrumental observation period. An exception is PESZ No. II, where the strongest earthquake occurred at its marginal part on the western coast of Myanmar. This source zone is likely to extend in the Bay of Bengal waters; however, further studies are required to refine its configuration. We also note the high correlation (according to the Chaddock scale) between the values of I, calculated by the method of equivalent gradations and using fuzzy logic (the Pearson correlation coefficient is 0.88). This fact implies that the apparatus of fuzzy logic may be more useful compared to the method of equivalent gradations for delineating PESZs in other regions.
CONCLUSIONS
Thus, by the example of the territory of the Republic of the Union of Myanmar, the informativeness of the new methodological approach we proposed for delineating was substantiated. This approach involves selecting morphometric terrain parameters related to seismicity in the best possible way and their subsequent processing by the method of equivalent gradations or using fuzzy logic. Together with the estimates of relative stress values obtained by computer modeling, the calculated integral morphometric index (neotectonic activity index) served as the basis for the PESZ identification. This algorithm can be much-needed especially for seismic zoning of remote and poorly studied territories, since it is based on publicly available source data and is largely formalized. However, the results of its use need refinement based on a set of geological and geophysical studies, since some epicenters of strong earthquakes do not fall within the PESZs we delineated. In our opinion, this is explained by the relatively small volume of source data, as well as by the complexity and multifactorial nature of the seismogenic processes. Therefore, our work has an exploratory nature, and the methodological approach we proposed for PESZ delineation requires further testing and refinement.
FUNDING
This work was supported by the Ministry of Education and Science of the Russian Federation (Agreement no. 075-15-2024-642) and the contract for research project “Creation of Planetary Laser Interferometric Seismoacoustic Observatory.”
CONFLICT OF INTEREST
The authors of this work declare that they have no conflicts of interest.
Translated by L. Mukhortova
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REFERENCES
1 Sobisevich, A. L.; Steblov, G. M.; Agibalov, A. O.; Aleshin, I. M.; Balashov, G. R.; Kondratov, A. D.; Makeev, V. M.; Perederin, V. P.; Perederin, F. V.; Rozenberg, N. K.; Sentsov, A. A.; Kholodkov, K. I.; Fadeeva, K. V. J. Volcanol. Seismol.; 2024; 18, pp. 446-459. [DOI: https://dx.doi.org/10.1134/S0742046324700738]
2 Steblov, G. M.; Agibalov, A. O.; Makeev, V. M.; Perederin, V. P.; Perederin, F. V.; Sentsov, A. A. Vopr. Inzh. Seismol.; 2023; 50, pp. 25-35.
3 Flower, C. M. R. The Solid Earth: an Introduction to Global Geophysics; 2005; Cambridge, Cambridge Univ. Press:
4 Lolli, B.; Gasperini, P.; Vannucci, G. Geophys. J. Int.; 2014; 199, pp. 805-828. [DOI: https://dx.doi.org/10.1093/gji/ggu264]
5 Pavlenkova, N. I. Structural Features of Continental and Oceanic Lithosphere and Their Nature; 2019; Moscow, GEOS: [in Russian]
6 FABDEM V 1-2. https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn. Cited 28.04.2025.
7 Meadows, M.; Jones, S.; Reinke, K. Int. J. Digital Earth; 2024; 17, pp. 1-25. [DOI: https://dx.doi.org/10.1080/17538947.2024.2308734]
8 B. Lehner and G. Grill, Hydrol. Process., No. 27(15), 2171–2186 (2013).
9 Filosofov, V. P. A Brief Guide to Morphometric Methods in Search of Tectonic Structures; 1960; Saratov, Saratov Univ.: [in Russian]
10 Strahler, A. N. Geol. Soc. Am. Bull.; 1952; 63, pp. 1117-1142. [DOI: https://dx.doi.org/10.1130/0016-7606(1952)63[1117:HAAOET]2.0.CO;2]
11 Chernova, I. Yu.; Nugmanov, I. I.; Luneva, O. V.; Dautov, A. N. Uch. Zap. Kazan. Univ. Ser. Estestv. Nauki; 2011; 153, pp. 197-211.
12 Kulagina, I. R. Cadastral Register and Natural Resources Monitoring; 2015; Tula, Tula State Univ.: [in Russian]
13 Panina, L. V.; Zaitsev, V. A.; Sentsov, A. A.; Agibalov, A. O. Byull. Mosk. O-va. Ispyt. Prir. Otd. Geol.; 2016; 91, pp. 51-60.
14 A. I. Tregub and O. V. Zhavoronkin, Vestn. Voronezh. Univ., Ser. Geol., No. 3, 19–26 (2000).
15 Kopylov, I. S. Morphoneotectonic System for Assessing Geodynamical Activity; 2019; [in Russian]
16 The 1 : 5 000 000 Map of Fault Tectonics of Southern Asia, Ed. by A. Yu. Kuznetsov (Karpinsky Russ. Geol. Res. Inst., Leningrad, 1983).
17 Zav’yalov, A. D.; Zotov, O. D. J. Volcanol. Seismol.; 2021; 15, pp. 19-26. [DOI: https://dx.doi.org/10.1134/S0742046321010139]
18 Safety Norms for Atomic Energy Use No. RB-019-18: the Way to Estimate Initial Seismicity of the Area and Site of a Nuclear Facility during Engineering Survey and Researches. https://docs.cntd.ru/document/556827973. Cited January 17, 2025.
19 Saw, M. M. M.; Ji-Qing, L. Appl. Water Sci.; 2019; 9, pp. 1-7. [DOI: https://dx.doi.org/10.1007/s13201-019-0984-y]
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