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Article

Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic

by
Alexei D. Gvishiani
1,2,
Inessa A. Vorobieva
1,3,
Peter N. Shebalin
1,3,
Boris A. Dzeboev
1,*,
Boris V. Dzeranov
1 and
Anna A. Skorkina
1,2,3
1
Geophysical Center of the Russian Academy of Sciences (GC RAS), 119296 Moscow, Russia
2
Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences (IPE RAS), 119296 Moscow, Russia
3
Institute of Earthquake Prediction Theory and Mathematical Geophysics of the Russian Academy of Sciences (IEPT RAS), 117997 Moscow, Russia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(10), 5010; https://doi.org/10.3390/app12105010
Submission received: 21 March 2022 / Revised: 11 May 2022 / Accepted: 13 May 2022 / Published: 16 May 2022
(This article belongs to the Collection Geoinformatics and Data Mining in Earth Sciences)

Abstract

:
The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The article describes the catalog compilation algorithm, as well as formalized procedures for removing duplicates and choosing the optimal magnitude scale. Due to different network configurations and record processing methods, different agencies may register/miss different events. This results in the absence of some events in different earthquake catalogs. Therefore, merging the data of various seismological agencies will provide the most complete catalog for the studied region. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) necessarily arises. An additional difficulty arises when distinguishing between aftershocks and duplicates since both are events that are close in space and time. To solve this problem, we used a modified nearest neighbor method developed earlier by the authors. The modified version, which is focused on identifying duplicates and distinguishing between duplicates and aftershocks, uses a probabilistic metric in the network error space to determine the epicenters and times of seismic events. In the present paper, a comparison and regression analysis of the different magnitude types of the integrated catalog is carried out, and based on the obtained ratios, the magnitude estimates are unified.

1. Introduction

As is known, the Eastern Sector of the Arctic zone of the Russian Federation (AZRF) is a seismically active region [1,2,3,4]. Rather strong earthquakes can occur within its limits. For example, only in the last two decades, events with magnitudes of M ≥ 6 have occurred there: the Olyutorsk earthquake, with M = 7.6, on 20 April 2006; the Ilin-Tas (Abyi) earthquake, with M = 6.6, on February 14 2013; the earthquake near the border of Kamchatka and Chukotka, with M = 6.4, on 9 January 2020; and others [5,6,7,8,9,10]. The analysis of the seismic regime of the Arctic territories of Russia and the construction of seismic hazard maps [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] are topical problems today. The solution to these problems is impossible without the creation of a representative instrumental earthquake catalog [26]. The importance of these problems for the study and development of the Arctic is emphasized by the increasing level of industrial development in the region.
Earthquake studies in the Eastern part of the AZRF started not so long ago. The authors of the New Catalog of Strong Earthquakes in the U.S.S.R. from ancient times through 1977 [27], Nikolay Shebalin and Nadezhda Kondorskaya, made the first step in these studies in the late 1970s. Intensive earthquake catalog projects concerning the AZRF were implemented in the early 2000s by V.I. Ulomov [22], V.S. Imaev, and L.P. Imaeva, B.M. Koz’min, et al. [5,6]. The seismic zonation map of the AZRF (as a part of the general seismic zoning map of Russian Federation) was created in recent years.
At the same time, there still remains a need in the AZRF representative Eastern Sector catalog which combines data from available Russian and international sources with the magnitude reduction to a uniform scale. This paper describes the results of the study on such catalog creation.
Nowadays, the seismic monitoring of the Russian Arctic is carried out by regional branches of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) (http://www.gsras.ru/new/eng/catalog/, (accessed on 20 March 2022)). In the Eastern part of the Russian Arctic, this work is carried out by the Yakutsk, Magadan, and, partially, the Kamchatka Branches of the GS RAS. In addition, detailed information on earthquakes can be found in the global catalog of the International Seismological Center (ISC) (http://www.isc.ac.uk/isc-ehb/search/catalogue/, (accessed on 20 March 2022)), which combines the data from several global and national seismological networks. It has to be noted that a comparative analysis of the catalogs showed that for the Eastern Sector of the Russian Arctic, the ISC catalog does not contain many events that are presented in the regional catalogs of the GS RAS. This is explained, among other things, by the fact that the survey reports bulletins to the ISC for events starting at a certain magnitude threshold. For this reason, the information on low-magnitude seismicity is mainly contained in regional catalogs.
It should be also noted that due to the different configurations of seismic networks and methods for processing records, some agencies may skip earthquakes recorded by other networks. Thus, merging earthquake catalogs is a method for improving the completeness and representativeness of seismic events in the final catalog [28,29,30,31,32].
When merging catalogs, the problem of identifying duplicates arises. The main difficulty is discrimination [33] between aftershocks and duplicates since both of them are similar events in space and time. This problem is analogous to the discrimination between aftershocks and independent seismic events. In [34], an algorithm for merging two earthquake catalogs was developed, the main task of which was to identify the resulting duplicates and separate them from the aftershocks. The algorithm is based on the author’s modification of the nearest neighbor method [35,36] for duplicate identification. It is based on the fact that, unlike aftershocks, duplicates do not have a causal relationship. The algorithm establishes a correspondence between the events from two catalogs, after which the classification of earthquakes into unique and duplicates is performed using the Euclidean metric. The sequential application of the algorithm automates the integration of any number of earthquake catalogs. The developed algorithm efficiency was demonstrated in [34] using the example of merging the ComCat Advanced National Seismic System and the Japan Meteorological Agency catalogs for the aftershock sequence of the 2011 Tohoku earthquake.
In this paper, a unified earthquake catalog is created for the Eastern Sector of the Arctic zone of the Russian Federation. For this purpose, the following main issues are solved:
  • The sequential merging of three regional catalogs of the GS RAS and the ISC catalog, which implies the identification of duplicate events in the border areas of responsibility of the different networks; and
  • The unification of magnitude estimates in the integrated catalog by constructing regression relationships for the different types of magnitude/energy class due to the exact association of data from the different catalogs related to the same event.

2. Materials and Methods

The studied region represents a geographical area limited by the following coordinates: 60° N, 100° E; 77° N, 100° E; 77° N, 165° W; 57.5° N, 165° W; 57.5° N, 138° E; and 60° N, 138° E (dashed line in Figure 1). All of the following four existing earthquake catalogs for the period 1962–2020 were considered as the initial data (Table 1 and Table 2):
  • The regional catalog of Yakutia from the annual journals Earthquakes in the USSR (1962–1991), Earthquakes in Northern Eurasia (1992–2014), and Earthquakes in Russia (2015–2019) (GS RAS) (hereinafter YAK);
  • The regional catalog of the northeast of Russia from the annual journals Earthquakes in the USSR (1968–1991), Earthquakes in Northern Eurasia (1992–2014), and Earthquakes of Russia (2015–2019) (GS RAS) (hereinafter NER);
  • The regional catalog of earthquakes in Kamchatka of the Kamchatka Branch of the GS RAS, 1962–2019 (hereinafter KAM); and
  • The ISC 1962–2020 catalog, which is a composite catalog containing data from many world and Russian agencies.
It has to be noted that the technique developed in [34] allows the pairwise merging of earthquake catalogs. Thus, any two chosen catalogs are merged in the first step. Then, another catalog is merged with them, and so on. At the same time, we emphasize that the list of events in the integrated catalog weakly depend on the sequence of pairwise merging. It was shown in [34] that the merging procedure is symmetric. In other words, when two catalogs are merged, the same events are selected as duplicates, regardless of the catalogs’ merging sequence. The only difference will be which version of the earthquake record (from which input catalog) falls into the merged catalog.
We believe that earthquake identification based on global network data is the most reliable. A subset of these events from the ISC catalog is the core—the main catalog to which other catalogs will be added. The core only contains information about strong and moderate earthquakes in the region because weak earthquakes are not registered by global networks. Further, it is logical to add data from local networks, which provide information about weak earthquakes in the region. In the final merge step, we use the data from the ISC catalog that was not included in the core. Thus, to merge catalogs (Table 1 and Table 2), the following sequence for sources of the initial data was chosen:
  • Earthquakes from the ISC global catalog (the abbreviation of the ISC and GCMT agency in Table 2) with the magnitudes MWGCMT and/or mbISC are the core (hereinafter CORE) (1393 events);
  • Earthquakes from Russian catalogs with local estimates for the magnitude of weak events. In the intersection zones, preference is given to the data from the catalog of Yakutia (Table 1);
  • Other earthquakes from the ISC (abbreviation of the ISC agency in Table 2, without the magnitude data MWGCMT or mbISC), as well as data from other agencies in the ISC catalog (16,642 events). This selection from the ISC catalog will be further denoted by ISC_Other.
To discriminate and remove duplicates resulting from the merging of catalogs, we apply the modified nearest neighbor method and the Euclidean metric in the space of the variance in the definitions of seismic event parameters by different networks [34]. We apply a basic three-parameter model that takes into account the differences in time and the coordinates of the epicenter, the effectiveness of which was shown in [34]. We do not analyze the difference in depth because for a significant number of events, information on the hypocentral depth is not presented in the original catalogs or a standard value of 10 km is given. The magnitude is also excluded from consideration because different catalogs use different types of magnitude.
At the input, there are two catalogs: main Catalog 1 and additional Catalog 2. We believe that neither Catalog 1 nor Catalog 2 contain duplicates within themselves, since the modern automatic processing of seismic records almost completely eliminates technical errors. The problem is to find records in Catalogs 1 and 2 that will correspond to the same seismic events (duplicates) and divide Catalog 2 into events that have duplicates in Catalog 1 and unique events.
A modification of the nearest neighbor method is based on the assumption that duplicates form pairs in which the events must belong to different source catalogs. As a result of applying the modified nearest neighbor method, a set of pairs of potential duplicates is formed. We consider the events of the additional Catalog 2, with the value of the neighborhood function as less than the threshold one, as duplicates. The rest of the Catalog 2 events are declared unique and added to Catalog 1. Further, any number of catalogs can be sequentially added.
The choice of the proximity function is based on a probabilistic model. We assume that the difference in earthquake detection by different networks is a random variable with a normal distribution and zero mean for each of the parameters:
f ( D T ) = 1 σ T 2 π exp ( D T 2 2 σ T 2 ) ,
f ( D X ) = 1 σ X 2 π exp ( D X 2 2 σ X 2 ) ,
f ( D Y ) = 1 σ Y 2 π exp ( D Y 2 2 σ Y 2 ) .
Here DT, DX, and DY are the differences in time, longitude, and latitude, respectively, between different determinations of a seismic event, and σ T , σ X , and σ Y are the corresponding standard deviations. If we assume that all errors are independent, then the duplicate probability density will be the product of the error probabilities for all parameters. This will be the multivariate normal distribution, as follows:
f ( D T , D X , D Y ) = 1 σ T σ X σ Y ( 2 π ) 3 2   · exp ( ( D T 2 2 σ T 2 + D X 2 2 σ X 2 + D Y 2 2 σ Y 2 ) ) .
Thus, we naturally arrive at the Euclidean metric:
R o = D T 2 σ T 2 + D X 2 σ X 2 + D Y 2 σ Y 2
The preliminary identification of duplicates is done with the standard metric parameters in (1): σ 0 T = 0.05 min and σ 0 X = σ 0 Y = 15 km. The initial values of the parameters have little effect on the identification of duplicates; however, they significantly affect the value of the duplicate probability and the estimate of the percentage of errors. At this stage, we check that each of the parameters follows a normal distribution, and we refine the values of the standard deviations σ T , σ X , and σ Y . After that, the final identification of duplicates is performed. The choice of the optimal metric threshold for identifying duplicates and the estimation of the percentage of errors will be explained in detail below.
Before proceeding with the merging process of the four catalogs, we checked each of them for internal duplicates. For this reason, we built the distribution of metric (1) for the nearest events within each catalog (Figure 2). The analysis was performed with the metric parameters σ T = 0.05 min and σ X = σ Y = 15 km. As a result of the analysis, no events with the same time and epicenter coordinates were found in any of the four catalogs. For such events, Ro = 0, and, thereafter, we will call them absolute duplicates. Statistical analysis also did not reveal anomalous groups of nearest events. The duplicates are characterized by the value Ro < 10. The value Ro = 10 corresponds to a distance of 150 km or a time interval of 0.5 min. From our experience, we know that duplicates have smaller differences in instrumental catalogs, and the number of such nearest events within each of the catalogs is very small. These are mainly the early aftershocks of the Olyutorsk earthquake, with M = 7.6, on 20 April 2006, and the Ilin-Tas earthquake, with M = 6.6, on 14 February 2013. There is no reason to consider such events as duplicates since the early aftershocks can occur at very small distances and time intervals. Thus, the necessary condition for applying the method in [34] is met.

3. Results

3.1. Merging Catalogs

At the initial stage, the regional data from the catalogs of the Russian agencies YAK, NER, and KAM were merged and then combined with ISC data. The earthquakes with unknown magnitudes/classes were not included in the merging. Below is a sequence of the stages of merging the catalogs.

3.1.1. Stage 1. Merging YAK and NER

YAK was considered as the main catalog and NER as the additional one. The preliminary analysis of duplicates was performed with the distribution parameters σ T = 0.05 min and σ X = σ Y = 15 km. As a result, 1834 absolute duplicates and about 370 potential duplicates (events with a small metric) were identified. The preliminary threshold was determined by the minimum distribution of the metric. Absolute duplicates were not used to determine the dispersions (Figure 3). It was verified that each of the parameters followed a normal distribution and that the mean was small compared to the standard deviation for all three parameters (DT, DX, and DY). It was also verified that the variance was almost independent of the event magnitude and time (Figure 3).
The final duplicate analysis was performed with the parameters σ T = 0.041 min, σ X = 17.4 km, and σ Y = 16.3 km. The metric values between the nearest events of the YAK catalog were also calculated. This made it possible to estimate the probability that the duplicate was chosen incorrectly due to the high density of earthquakes. Figure 4a shows the distributions of metric (1) for the YAK/NER pairs and the same metric for the YAK/YAK earthquakes (the algorithm for calculating the metric is the same as for two different catalogs, but only the comparison of the earthquake with itself is excluded). A group of anomalously close YAK/NER events is identified well. The optimization of the threshold value of the metric is illustrated in Figure 4b. The red line is the probability of missing a duplicate in the 3D normal distribution model (error of the first kind) and the blue line is the probability of a false duplicate (error of the second kind), which is defined as the ratio of the number of YAK/YAK pairs for a given value of the metric Ro to the number of events in the YAK catalog.
An equal number of errors of the first and second kind is achieved at Ro = 5.8. In the NER catalog, only 15 earthquakes have a distance to the nearest neighbor of Ro < 5.8. This made it possible to estimate the probability that the duplicate was chosen incorrectly due to the high density of earthquakes. The upper estimate of the probability of false duplicates P = 15/6600 = 0.0023 is approximately 0.25%. At Ro = 7.9, the number of such earthquakes increases to 26, which corresponds to a probability of 0.4% (see the blue line in Figure 4b).
The choice of the metric threshold for identifying duplicates depends on the objective of further research of the integrated catalog. If it is important to ensure that duplicates are removed, then a higher Ro threshold is preferable. If it is important to keep the integral characteristics of the catalog, then the Ro threshold that ensures the equality of errors of the first and second kind is preferable.
We chose the threshold Ro = 5.8. In this case, in addition to 1834 absolute duplicates, 319 more duplicates were identified. In total, 5515 unique events were identified in the NER catalog in the study area. These events were added to the YAK catalog, and thus a merged YAK_NER catalog containing 12,115 events was obtained. Figure 5 shows the spatio–temporal structure of the YAK/NER duplicates and the naturally grouped events in the YAK catalog. The metric values for earthquakes in the YAK catalog are significantly larger than those for the YAK/NER duplicates. The metric (1) level lines provide a close-to-optimal separation of duplicates and naturally grouped events (the lower cluster of black dots).

3.1.2. Stage 2. Merging YAK_NER and KAM into the RUS Catalog

The catalog YAK_NER, obtained in the previous step, was taken as the main one, with KAM as an additional catalog. The preliminary analysis of the duplicates was performed with the distribution parameters determined for the NER and YAK catalogs: σ T = 0.041 min, σ X = 17.4 km, and σ Y = 16.3 km. Twenty-eight potential duplicates were identified (Figure 6), which is not enough to determine the variances. For this reason, the metric parameters defined for the YAK and NER catalogs were used. With Ro = 5.8, the KAM catalog contains 26 duplicates and 4472 unique events that have been added to the YAK_NER catalog. The merged RUS catalog obtained in this way contains 16,587 events.

3.1.3. Stage 3. Merging RUS and Data from the ISC_Other Catalog

The ISC catalog contains a large amount of data from Russian agencies (Table 2). Accordingly, at this stage of the merging procedure, a large number of duplicates, including absolute ones, are expected.
When merging, the catalog RUS obtained at Stage 2 was taken as the main one and ISC_Other was taken as the additional. The resulting catalog will be designated RUS_ISC. The preliminary analysis of duplicates was performed with the standard distribution parameters σ T = 0.05 min, σ X = 15 km, and σ Y = 15 km (Figure 7).
More than 10,000 potential duplicates have been identified, about 5000 of which have the same times and/or epicenters. Such pairs represent the same registration of events by the networks of the GS RAS, which are included in the Russian catalogs and the ISC catalog. They were excluded to determine the variances. We have verified that each of the parameters follows a normal distribution and that the mean is small compared to the standard deviation for all three parameters (DT, DX, and DY) (Figure 7). It was also verified that the variance is almost independent of the event magnitude and time. The final analysis of duplicates was performed with the parameters σ T = 0.032 min, σ X = 12.3 km, and σ Y = 12.0 km. We calculated the metric values between events of the RUS catalog. This made it possible to estimate the probability that the duplicate was determined incorrectly due to the high density of earthquakes.
We chose the threshold Ro = 6.0 (Figure 8). In this case, in addition to 4802 absolute duplicates, another 5706 potential duplicates are identified. Many pairs of earthquakes have the same time or the same coordinates of the epicenter, and 73 of such pairs have large metric values of Ro > 6. An analysis of these pairs indicates that the records in the RUS and ISC catalogs differ in one digit. Most likely, these are technical errors of the era of manual information entry, which were corrected when compiling the catalog Earthquakes of Northern Eurasia. These events are considered to be duplicates and they are not included in the integrated catalog, despite the large values of the metric.
Figure 9 shows the spatio–temporal structure of the duplicates in RUS/ISC_Other and the naturally grouped events in the RUS catalog. The metric values for the earthquakes in the RUS catalog are significantly larger than for the RUS/ISC_Other duplicates. The metric level lines Ro = 6 and Ro = 7.6 provide close-to-optimal separation of the duplicates and naturally grouped events (the lower cluster of black dots). In total, for the studied territory there are 6411 unique events in the ISC_Other catalog. These events have been added to the RUS catalog. The merged catalog RUS_ISC contains 22,998 events.

3.1.4. Stage 4. Merging RUS_ISC and CORE

As the main catalog selected, CORE includes events from the ISC catalog with the magnitudes MWGCMT or mbISC. As an additional catalog, we consider RUS_ISC, obtained at the previous stage. The preliminary analysis of the duplicates was performed with the standard catalog distribution parameters σ T = 0.05 min, σ X = 15 km, and σ Y = 15 km. Approximately 1000 duplicates were identified and used to determine the variances. It was verified that each of the parameters follows a normal distribution and that the mean is small compared to the standard deviation for all three parameters (DT, DX, and DY). It was also verified that the variance is almost independent of the event magnitude and time (Figure 10).
The final analysis of the duplicates was performed with the parameters σ T = 0.044 min and σ X = σ Y = 18.3 km. The metric values between the events of the CORE catalog were also calculated. This made it possible to estimate the probability that the duplicate was chosen incorrectly due to the high density of earthquakes.
The value Ro = 5.9 was chosen as a threshold (Figure 11). In this case, 1011 duplicates are detected. In total for the studied territory, there are 21,987 unique events in the RUS_ISC catalog. These events were added to the CORE catalog, and a combined ARCTIC catalog containing 23,370 events was obtained. Figure 12 shows the space–time structure of the CORE/RUS_ISC duplicates and the naturally grouped events in the CORE catalog. The metric level lines Ro = 5.9 and Ro = 8.4 provide close-to-optimal separation of the duplicates and naturally grouped events (the lower cluster of black dots).

3.1.5. Stage 5. Exclusion of Explosions

At the final stage, we check the catalog for any type of explosion. The information about the 12 explosions in the ISC catalog is given in the ISC bulletins. In addition, 104 events are labeled “exp” or “exp?” in the NER and YAK catalogs from the annual journal Earthquakes in Northern Eurasia. In the annual journal Earthquakes in Russia, explosions are excluded by the authors. We merged all explosions into the EXP catalog and performed duplicate analysis.
We choose EXP as the main catalog and ARCTIC, which was obtained as a result of combining ISC and Russian data, as the additional one. The duplicate analysis was performed with the standard distribution parameters σ T = 0.05 min and σ X = σ Y = 15 km. All 116 events of the EXP catalog are absolute duplicates of the events from the ARCTIC catalog. After the explosions were removed, the final integrated catalog of the Eastern Sector of the Arctic zone of the Russian Federation, E_ARCTIC, contains 23,254 events. The assembly scheme, statistics, and parameters for excluding duplicates are presented in Table 3.

3.2. Magnitudes in the Integrated Catalog of the Eastern Sector of the Russian Arctic

The integrated catalog of the Eastern Sector of the Russian Arctic contains 23,254 events that have different types of magnitude estimates determined by different agencies (Table 4). It is necessary to unify them (bring them to the reference scale of magnitudes).
At present, the only physical magnitude scale is the seismic moment-based magnitude MW, which is preferable when analyzing estimates of different magnitude scales [37,38]. However, when moving from large to small magnitudes (from global estimates to regional ones), discrepancies in MW estimates are observed everywhere at M < 5.0 [39,40]. It should be noted that the Eastern Arctic region was not considered in [39].
In the present work, we use only global estimates of the MWGCMT magnitude. If the global estimate of moment magnitude is unknown, we prefer the magnitude mbISC, which is used by ISC in its practice to obtain “quasi-MW” estimates in the range M < 5.0 [41].
A feature of the catalogs used is the presence of estimates of energy classes, and not magnitudes. Theoretically, the energy class estimate proposed by T.G. Rautian [42] was assumed to be the same physical parameter as the radiated seismic energy, or moment magnitude, which was presented later [43]. However, it was shown in [40] that, in practice, the Rautian energy classes are rather a magnitude characteristic (with its saturation) than a physical one. It should also be noted that there are two approaches for index in the energy class abbreviation. It can be “KR”, “KF”, or “KS” for Rautian, Fedotov, or Solov`ev, respectively, or “KP”, “KS”, or “KPS” for the wave type, which were used for calculation. In this paper, we stand for the second approach to emphasize the difference in energy class scales. Therefore, the estimates expressed in energy classes were converted to MW using regression relations. In the studied territory, the number of earthquakes with the known magnitude MWGCMT is small, so regressions with the magnitude mbISC are built, which is well aligned with MWGCMT [41].
It is necessary to notice that about 7% of the events in the integrated catalog have other types of magnitudes. If an event has several magnitudes, then preference was given to those for which it is possible to construct a correlation with magnitude mbISC. In few cases, when there were no pairs to determine direct correlations to mbISC, we used indirect correlations with other magnitudes, and we consider these correlations unreliable (indicated in the “Note” column of Table 4). The 95% confidence intervals are constructed by the Grapher Golden Software built-in tool (https://www.goldensoftware.com/products/grapher, (accessed on 20 March 2022)).
Thus, we adhere to the following priority when choosing the optimal magnitude estimate:
  • MWGCMT or MSISC for strong earthquakes before 1976;
  • mbISC;
  • Magnitude by energy class; and
  • Other magnitudes.
Statistics on magnitudes in the integrated catalog are given in Table 4.
Figure 13a shows the correlation between the MWGCMT and mbISC magnitudes in the studied territory (mb = 0.99MW + 0.03). Earthquakes with an MW of <6.0 were used to construct the best linear approximation. For stronger earthquakes, the magnitude mb saturates and becomes smaller than MW. The magnitude MSMW is used for earthquakes with an MW of ≥6.0. For weaker earthquakes, MS < MW (Figure 13b) is used, which generally agrees with previously obtained correlations [41].
There are 105 events in the integrated catalog with the magnitude MWGCMT. During the considered period, 15 earthquakes with an M of >6.0 occurred. Two strong earthquakes occurred in 1969 (mb = 6.4 and MS = 7.5) and 1971 (mb = 6.0 and MS = 7.0). For these earthquakes, the MS estimate is preferred (Figure 13b, Table 5). For the remaining 13 strong earthquakes, the magnitude MWGCMT is known.
To determine the magnitude by energy class, the correlation with the magnitude mbISC was used (Figure 14). The Yakutsk and Northeastern branches of the GS RAS estimate the Rautian KPS class [42], while the Kamchatka Branch estimates the Fedotov KS class [44]. Earthquakes in Kamchatka were selected only in the studied territory, north of latitude 57.5°.
The magnitude ratio of mbISC and KPS is the same in Yakutia and in the northeast. The ratio for KS in Kamchatka is noticeably different since in the formula of energy classes according to Fedotov [43,44], KS = 2lgApeak + f(r), while in the classes according to Rautian [42,43], KPS = 1.8lgApeak + f(r), where Apeak is a peak amplitude of an S-wave or the sum of peak amplitudes of P- and S-waves, and f(r) is an attenuation function.
In the unified catalog, 1551 earthquakes have other types of magnitudes. We try to give estimates in MW, which is an absolute scale in the first approximation. Therefore, a shift-type transformation M = M + constant was used, corresponding to the approach of [45], which assumed that one day, relative logarithmic magnitude estimates would be converted to absolute energy estimates by adding a constant (“Since the scale is logarithmic, any future reduction to an absolute scale can be accomplished by adding a constant to the scale numbers”). Taking into account that such ratios were obtained for limited ranges of magnitudes, and, in particular, magnitude ranges within M < 5, we consider that the assumption of the absence of nonlinear effects can be applied.
The number of earthquakes with the magnitude MWGCMT is small; therefore, to construct correlations, we used all earthquakes from the ISC catalog for which the magnitude mbISC and the studied magnitude are known. Reliable correlations with mbISC are determined for 617 earthquakes. For 609 events, unreliable correlations are determined. This is due either to a small number of events or to the use of an indirect correlation with other magnitudes.
No correlations are defined for 349 events, and 324 of these are reported by the WASN agency (Table 4). Correlations of different magnitudes are shown in Figure 15, Figure 16, Figure 17, Figure 18 and Figure 19.

4. Conclusions

Based on the generalization and integration of data from the various networks that serve the Eastern Sector of the Arctic zone of the Russian Federation, the most complete and representative earthquake catalog has been compiled. The catalog contains information on 23,254 seismic events for the period 1962–2020, of which 7781 events are from ISC and 15,473 events are from the Russian catalogs of GS RAS. Such a detailed and universal catalog for the whole Eastern part of the AZRF never existed before. Before 1968, the catalog contained quite a small number of events. In 2020, the catalog contained the events only from the ISC because the GS RAS catalog was not completed yet. The integrated catalog is to be updated accordingly once the former catalog is available.
The correlation of the magnitude types in the catalog was analyzed for various seismic networks. Based on the relations obtained, the unification of the magnitude estimates was carried out. For the most earthquakes, the quasi-MW magnitude is calculated by converting the energy class using the original regression relationships (Table 4). The distribution of event magnitudes over time and magnitude–frequency graphs are shown in Figure 20. The integrated catalog completeness is quite heterogeneous. A detailed analysis of the changes in the level of registration in space and time is a big work that goes beyond the scope of the present study. We plan to conduct this work in the future.
The creation of the unified magnitude-based integrated earthquake catalog realized in this paper opens new prospects in earthquake studies in the Arctic region. Further development of the Russian Arctic seismic zonation and systems analysis of strong earthquake-prone areas are among them.
The map of earthquake epicenters of the integrated catalog is shown in Figure 21. The catalog developed in this article is made available to the public on the website of the World Data Center for Solid Earth Physics, Moscow, at http://www.wdcb.ru/arctic_antarctic/arctic_seism.html, (accessed on 20 March 2022).

Author Contributions

Conceptualization, A.D.G., I.A.V. and P.N.S.; data curation, P.N.S., B.A.D. and A.A.S.; formal analysis, I.A.V.; investigation, P.N.S., B.A.D., B.V.D. and A.A.S.; methodology, I.A.V.; resources, B.A.D.; software, I.A.V.; validation, I.A.V. and A.A.S.; visualization, I.A.V. and B.V.D.; writing—original draft, A.D.G., I.A.V., P.N.S., B.A.D., B.V.D. and A.A.S.; writing—review and editing, A.D.G., I.A.V., P.N.S., B.A.D., B.V.D. and A.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

The reported study was funded by the Russian Science Foundation, project number 21-77-30010, System analysis of geophysical process dynamics in the Russian Arctic and their impact on the development and operation of the railway infrastructure.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work employed data provided by the Shared Research Facility, the Analytical Geomagnetic Data Center of the Geophysical Center of RAS (http://ckp.gcras.ru/ accessed on: 24 February 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Imaeva, L.P.; Imaev, V.S.; Koz’min, B.M. Dynamics of the Zones of Strong Earthquake Epicenters in the Arctic–Asian Seismic Belt. Geosciences 2019, 9, 168. [Google Scholar] [CrossRef] [Green Version]
  2. Daragan-Sushchova, L.A.; Petrov, O.V.; Sobolev, N.N.; Daragan-Sushchov, Y.I.; Grin’ko, L.R.; Petrovskaya, N.A. Geology and tectonics of the northeast Russian Arctic region, based on seismic data. Geotectonics 2015, 49, 469–484. [Google Scholar] [CrossRef]
  3. Kanao, M.; Suvorov, V.; Toda, S.; Tsuboi, S. Seismicity, structure and tectonics in the Arctic region. Geosci. Front. 2015, 6, 665–677. [Google Scholar] [CrossRef] [Green Version]
  4. Skorkina, A.A. Scaling of two corner frequencies of source spectra for earthquakes of the Bering fault. Russ. J. Earth Sci. 2020, 20, ES2001. [Google Scholar] [CrossRef]
  5. Imaev, V.S.; Imaeva, L.P.; Koz’min, B.M. Strong Ulakhan-Chistay earthquake (Ms = 5.7) January 20, 2013 in the zone of influence Ulakhan fault system in North East Russia. Vestn. St. Petersburg Univer. Earth Sci. 2020, 65, 740–759. (In Russian) [Google Scholar] [CrossRef]
  6. Shibaev, S.V.; Kozmin, B.M.; Imaev, V.S.; Imaeva, L.P.; Petrov, A.F.; Starkova, N.N. The February 14, 2013 Ilin-Tas (Abyi) earthquake (Mw = 6.7), Northeast Yakutia. Russ. J. Seismol. 2020, 2, 92–102. [Google Scholar] [CrossRef]
  7. Chebrov, V.N. The Olyutorskii earthquake of April 20, 2006: Organizing surveys, observations, problems, and results. J. Volcanol. Seismol. 2010, 4, 75–78. [Google Scholar] [CrossRef]
  8. Rogozhin, E.A.; Ovsyuchenko, A.N.; Marakhanov, A.V.; Novikov, S.S. A geological study of the epicentral area of the April 20(21), 2006 Olyutorskii earthquake. J. Volcanol. Seismol. 2010, 4, 79–86. [Google Scholar] [CrossRef]
  9. Lander, A.V.; Levina, V.I.; Ivanova, E.I. The earthquake history of the Koryak Upland and the aftershock process of the MW7.6 April 20(21), 2006 Olyutorskii earthquake. J. Volcanol. Seismol. 2010, 4, 87–100. [Google Scholar] [CrossRef]
  10. Dzeboev, B.A.; Agayan, S.M.; Zharkikh, Y.I.; Krasnoperov, R.I.; Barykina, Y.V. Strongest Earthquake-Prone Areas in Kamchatka. Izv. Phys. Solid Earth 2018, 54, 284–291. [Google Scholar] [CrossRef]
  11. Dzeboev, B.A.; Gvishiani, A.D.; Belov, I.O.; Agayan, S.M.; Tatarinov, V.N.; Barykina, Y.V. Strong Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai-Sayan-Baikal Region, M ≥ 6.0. Izv. Phys. Solid Earth 2019, 55, 563–575. [Google Scholar] [CrossRef]
  12. Dzeboev, B.A.; Karapetyan, J.K.; Aronov, G.A.; Dzeranov, B.V.; Kudin, D.V.; Karapetyan, R.K.; Vavilin, E.V. FCAZ-recognition based on declustered earthquake catalogs. Russ. J. Earth Sci. 2020, 20, ES6010. [Google Scholar] [CrossRef]
  13. Gorshkov, A.I.; Soloviev, A.A. Recognition of earthquake-prone areas in the Altai-Sayan-Baikal region based on the morphostructural zoning. Russ. J. Earth Sci. 2021, 21, ES1005. [Google Scholar] [CrossRef]
  14. Gvishiani, A.; Dobrovolsky, M.; Agayan, S.; Dzeboev, B. Fuzzy-based clustering of epicenters and strong earthquake-prone areas. Environ. Eng. Manag. J. 2013, 12, 1–10. [Google Scholar]
  15. Gvishiani, A.D.; Dzeboev, B.A.; Agayan, S.M. FCAZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian Mountain belts. Izv. Phys. Solid Earth 2016, 52, 461–491. [Google Scholar] [CrossRef]
  16. Gvishiani, A.D.; Dzeboev, B.A.; Sergeyeva, N.A.; Belov, I.O.; Rybkina, A.I. Significant Earthquake-Prone Areas in the Altai–Sayan Region. Izv. Phys. Solid Earth 2018, 54, 406–414. [Google Scholar] [CrossRef]
  17. Gvishiani, A.D.; Soloviev, A.A.; Dzeboev, B.A. Problem of Recognition of Strong-Earthquake-Prone Areas: A State-of-the-Art Review. Izv. Phys. Solid Earth 2020, 56, 1–23. [Google Scholar] [CrossRef]
  18. Levin, B.W.; Kim, C.U.; Solovjev, V.N. A seismic hazard assessment and the results of detailed seismic zoning for urban territories of Sakhalin Island. Russ. J. Pac. Geol. 2013, 7, 455–464. [Google Scholar] [CrossRef]
  19. Lunina, O.V.; Gladkov, A.S.; Gladkov, A.A. Systematization of active faults for the assessment of the seismic hazard. Russ. J. Pac. Geol. 2012, 6, 42–51. [Google Scholar] [CrossRef]
  20. Magrin, A.; Peresan, A.; Kronrod, T.; Vaccari, F.; Panza, G.F. Neo-deterministic seismic hazard assessment and earthquake occurrence rate. Eng. Geol. 2017, 229, 95–109. [Google Scholar] [CrossRef]
  21. Nekrasova, A.; Kossobokov, V.G.; Parvez, I.A.; Tao, X. Seismic hazard and risk assessment based on the unified scaling law for earthquakes. Acta Geod. Et Geophys. 2015, 50, 21–37. [Google Scholar] [CrossRef] [Green Version]
  22. Ulomov, V.I. Seismic hazard of Northern Eurasia. Ann. Di Geofis. 1999, 42, 1023–1038. [Google Scholar] [CrossRef]
  23. Zaalishvili, V.; Chernov, Y.K. Methodology of detailed assessment of the seismic hazard of the Republic of North Ossetia-Alania. Open Constr. Build. Technol. J. 2018, 12, 309–318. [Google Scholar] [CrossRef]
  24. Zaalishvili, V.B.; Rogozhin, E.A. Assessment of seismic hazard of territory on basis of modern methods of detailed zoning and seismic microzonation. Open Constr. Build. Technol. J. 2011, 5, 30–40. [Google Scholar] [CrossRef] [Green Version]
  25. Zavyalov, A.D.; Peretokin, S.A.; Danilova, T.I.; Medvedeva, N.S.; Akatova, K.N. General Seismic Zoning: From Maps GSZ-97 to GSZ-2016 and New-Generation Maps in the Parameters of Physical Characteristics. Seism. Instr. 2019, 55, 445–463. [Google Scholar] [CrossRef]
  26. Beauval, C.; Yepes, H.; Palacios, P.; Segovia, M.; Alvarado, A.; Font, Y.; Aguilar, J.; Troncoso, L.; Vaca, S. An Earthquake Catalog for Seismic Hazard Assessment in Ecuador. Bull. Seismol. Soc. Am. 2013, 103, 773–786. [Google Scholar] [CrossRef]
  27. Kondorskaya, N.V.; Shebalin, N.V. (Eds.) New Catalog of Strong Earthquakes in the USSR from Ancient Times through 1977; Report SE-31; World Data Center A for Solid Earth Geophysics: Boulder, CO, USA, 1982; 608p. [Google Scholar]
  28. Shebalin, P.N. Compilation of earthquake catalogs as a task of clustering analysis with learning. Dokl. Akad. Nauk. SSSR 1987, 292, 1083–1086. [Google Scholar]
  29. Markušić, S.; Gülerce, Z.; Kuka, N.; Duni, L.; Ivančić, I.; Radovanović, S.; Glavatović, B.; Milutinović, Z.; Akkar, S.; Kovačević, S.; et al. An updated and unified earthquake catalogue for the Western Balkan Region. Bull. Earthq. Eng. 2016, 14, 321–343. [Google Scholar] [CrossRef]
  30. Sawires, R.; Santoyo, M.A.; Peláez, J.A.; Corona Fernández, R.D. An updated and unified earthquake catalog from 1787 to 2018 for seismic hazard assessment studies in Mexico. Sci. Data 2019, 6, 241. [Google Scholar] [CrossRef] [Green Version]
  31. Braclawska, A.; Idziak, A. Unification of data from various seismic catalogues to study seismic activity in the Carpathians Mountain arc. Open Geosci. 2019, 11, 837–842. [Google Scholar] [CrossRef]
  32. Long, F.; Jiang, C.; Qi, Y.; Liu, Z.; Fu, Y. A joint probabilistic approach for merging earthquake catalogs of two neighboring seismic networks: An example of the 2014 Ludian sequence catalog. Acta Geophys. Sin. 2018, 61, 2815–2827. [Google Scholar] [CrossRef]
  33. Muller, S.; Garda, P.; Muller, J.-D.; Cansi, Y. Seismic events discrimination by neuro-fuzzy merging of signal and catalogue features. Phys. Chem. Earth Part. A Solid Earth Geod. 1999, 24, 201–206. [Google Scholar] [CrossRef]
  34. Vorobieva, I.; Gvishiani, A.; Dzeboev, B.; Dzeranov, B.; Barykina, Y.; Antipova, A. Nearest Neighbor Method for Discriminating Aftershocks and Duplicates When Merging Earthquake Catalogs. Front. Earth Sci. SI Nat. Clust. Earthq. Process. Var. Scales Lab. Exp. Large Earthq. 2022, 10, 820277. [Google Scholar] [CrossRef]
  35. Zaliapin, I.; Ben-Zion, Y. Earthquake clusters in southern California. I: Identification and stability. J. Geophys. Res. Solid Earth 2013, 118, 2847–2864. [Google Scholar] [CrossRef]
  36. Zaliapin, I.; Ben-Zion, Y. A global classification and characterization of earthquake clusters. Geophys. J. Int. 2016, 207, 608–634. [Google Scholar] [CrossRef] [Green Version]
  37. Kanamori, H. The Energy Release in Great Earthquakes. J. Geophys. Res. 1977, 82, 2981–2987. [Google Scholar] [CrossRef] [Green Version]
  38. Hanks, T.C.; Kanamori, H. A Moment Magnitude Scale. J. Geophys. Res. Solid Earth 1979, 84, 2348–2350. [Google Scholar] [CrossRef]
  39. Di Giacomo, D.; Harris, J.; Storchak, D.A. Complementing regional moment magnitudes to GCMT: A perspective from the rebuilt International Seismological Centre Bulletin. Earth Syst. Sci. Data 2021, 13, 1957–1985. [Google Scholar] [CrossRef]
  40. Abubakirov, I.R.; Gusev, A.A.; Guseva, E.M.; Pavlov, V.M.; Skorkina, A.A. Mass determination of moment magnitudes Mw and establishing the relationship between Mw and ML for moderate and small Kamchatka earthquakes. Izv. Phys. Solid Earth 2018, 54, 33–47. [Google Scholar] [CrossRef]
  41. Di Giacomo, D.; Bondár, I.; Storchak, D.A.; Engdahl, E.R.; Bormann, P.; Harris, J. ISC-GEM: Global Instrumental Earthquake Catalogue (1900–2009), III. Re-computed MS and mb, proxy MW, final magnitude composition and completeness assessment. Phys. Earth Planet. Inter. 2015, 239, 33–47. [Google Scholar] [CrossRef]
  42. Rautian, T.G. Energy of earthquakes. In Methods for the Detailed Study of Seismicity; The USSR Academy of Sciences: Moscow, Russia, 1960; pp. 75–114. (In Russian) [Google Scholar]
  43. Rautian, T.G.; Khalturin, V.I.; Fujita, K.; Mackey, K.G.; Kendall, A.D. Origins and methodology of the Russian energy K-class system and its relationship to magnitude scales. Seismol. Res. Lett. 2007, 78, 579–590. [Google Scholar] [CrossRef]
  44. Fedotov, S.A. Energy Classification of the Kuril-Kamchatka Earthquakes and the Problem of Magnitudes; Nauka: Moscow, Russia, 1972; p. 117. (In Russian) [Google Scholar]
  45. Richter, C.F. An instrumental earthquake magnitude scale. Bull. Seismol. Soc. Am. 1935, 25, 1–32. [Google Scholar] [CrossRef]
Figure 1. Studied region. The circles are the earthquake epicenters from the YAK (black), NER (red), and KAM (blue) catalogs.
Figure 1. Studied region. The circles are the earthquake epicenters from the YAK (black), NER (red), and KAM (blue) catalogs.
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Figure 2. Distribution of metric (1) for events within the source catalogs (Table 1 and Table 2). The catalogs are indicated on the histograms.
Figure 2. Distribution of metric (1) for events within the source catalogs (Table 1 and Table 2). The catalogs are indicated on the histograms.
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Figure 3. Distributions of DT, DX, and DY for the nearest events from the YAK and NER catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,     D X ¯ ,     and   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
Figure 3. Distributions of DT, DX, and DY for the nearest events from the YAK and NER catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,     D X ¯ ,     and   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
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Figure 4. (a) Comparison of the metric distribution for YAK/NER pairs (blue histogram) and the same metric for YAK/YAK earthquakes (red histogram). (b) Threshold optimization: the red line shows the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate (see text), the black line is the total probability of errors of the first and second kind, the dashed line Ro = 5.8 corresponds to an equal number of errors of the first and of the second kind (the number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.5%, and the gray bar shows the range of values for the metric Ro = 6.3 ÷ 7.9, minimizing the total number of errors (approximately 0.4%).
Figure 4. (a) Comparison of the metric distribution for YAK/NER pairs (blue histogram) and the same metric for YAK/YAK earthquakes (red histogram). (b) Threshold optimization: the red line shows the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate (see text), the black line is the total probability of errors of the first and second kind, the dashed line Ro = 5.8 corresponds to an equal number of errors of the first and of the second kind (the number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.5%, and the gray bar shows the range of values for the metric Ro = 6.3 ÷ 7.9, minimizing the total number of errors (approximately 0.4%).
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Figure 5. Distribution of normalized DT and DR and metric level lines (1). The colored dots are YAK/NER pairs and the black dots are the distances between YAK/YAK events in metric (1). The metric levels Ro = 5.8 and Ro = 7.9 are shown by black lines, and absolute duplicates are not shown.
Figure 5. Distribution of normalized DT and DR and metric level lines (1). The colored dots are YAK/NER pairs and the black dots are the distances between YAK/YAK events in metric (1). The metric levels Ro = 5.8 and Ro = 7.9 are shown by black lines, and absolute duplicates are not shown.
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Figure 6. Metric distributions for the YAK_NER and KAM pairs.
Figure 6. Metric distributions for the YAK_NER and KAM pairs.
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Figure 7. Distributions of DT, DX, and DY for the nearest events from the RUS and ISC_Other catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,   D X ¯ ,   a n d   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
Figure 7. Distributions of DT, DX, and DY for the nearest events from the RUS and ISC_Other catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,   D X ¯ ,   a n d   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
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Figure 8. (a) Comparison of the metric distribution for RUS/ISC_Other pairs (blue histogram) and the same metric for RUS/RUS earthquakes (red histogram). (b) Threshold optimization: the red line is the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate, the black line is the total probability of errors of the first and second kind, the dashed line Ro = 6.0 corresponds to an equal number of errors of the first and second kind (number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.3%, and the gray bar shows the range of values for the metric Ro = 6.7 ÷ 7.6, minimizing the total number of errors (approximately 0.2%).
Figure 8. (a) Comparison of the metric distribution for RUS/ISC_Other pairs (blue histogram) and the same metric for RUS/RUS earthquakes (red histogram). (b) Threshold optimization: the red line is the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate, the black line is the total probability of errors of the first and second kind, the dashed line Ro = 6.0 corresponds to an equal number of errors of the first and second kind (number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.3%, and the gray bar shows the range of values for the metric Ro = 6.7 ÷ 7.6, minimizing the total number of errors (approximately 0.2%).
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Figure 9. Distribution of normalized DT and DR, and metric level lines (1). The colored dots are the RUS/ISC_Other pairs and the black dots are the distances between the RUS/RUS events in metric (1). The metric levels Ro = 6 and Ro = 7.6 are shown by the black lines, and absolute duplicates are not shown.
Figure 9. Distribution of normalized DT and DR, and metric level lines (1). The colored dots are the RUS/ISC_Other pairs and the black dots are the distances between the RUS/RUS events in metric (1). The metric levels Ro = 6 and Ro = 7.6 are shown by the black lines, and absolute duplicates are not shown.
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Figure 10. Distributions of DT, DX, and DY for the nearest events from the CORE/RUS_ISC catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,   D X ¯ ,   and   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
Figure 10. Distributions of DT, DX, and DY for the nearest events from the CORE/RUS_ISC catalogs, and the dependence of the standard deviations σ T , σ X , and σ Y and the mean values D T ¯ ,   D X ¯ ,   and   D Y ¯ on the time and magnitude of the events. The red dots and bars are the population mean values and standard deviations, respectively.
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Figure 11. (a) Comparison of the distribution of the metric for the CORE/RUS_ISC pairs (blue histogram) and the same metric for the CORE/CORE earthquakes (red histogram). (b) Threshold optimization: the red line is the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate, the black line is the total probability of errors of the first and second kind, the dashed line Ro = 5.9 corresponds to an equal number of errors of the first and second kind (number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.4%, and the gray bar shows the range of values of the metric Ro = 6.5 ÷ 8.4, minimizing the total number of errors (approximately 0.3%).
Figure 11. (a) Comparison of the distribution of the metric for the CORE/RUS_ISC pairs (blue histogram) and the same metric for the CORE/CORE earthquakes (red histogram). (b) Threshold optimization: the red line is the probability of missing a duplicate in the model with metric (1), the blue line is the probability of a false duplicate, the black line is the total probability of errors of the first and second kind, the dashed line Ro = 5.9 corresponds to an equal number of errors of the first and second kind (number of false duplicates is equal to the number of missed duplicates), the estimate of the total number of errors is approximately 0.4%, and the gray bar shows the range of values of the metric Ro = 6.5 ÷ 8.4, minimizing the total number of errors (approximately 0.3%).
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Figure 12. Distribution of normalized DT and DR and metric level lines (1). The colored dots are the CORE/RUS_ISC pairs and the black dots are the distances between the CORE/CORE events in metric (1). The metric levels Ro = 5.9 and Ro = 8.4 are shown by lines.
Figure 12. Distribution of normalized DT and DR and metric level lines (1). The colored dots are the CORE/RUS_ISC pairs and the black dots are the distances between the CORE/CORE events in metric (1). The metric levels Ro = 5.9 and Ro = 8.4 are shown by lines.
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Figure 13. Correlation ratios of GCMT and ISC magnitudes: (a) MWGCMT and mbISC for MW < 6.0 and (b) MWGCMT and MSISC for MW ≥ 6.0. The dashed lines show 95% confidence intervals. The events with an MW of ≥6.0 are highlighted in red.
Figure 13. Correlation ratios of GCMT and ISC magnitudes: (a) MWGCMT and mbISC for MW < 6.0 and (b) MWGCMT and MSISC for MW ≥ 6.0. The dashed lines show 95% confidence intervals. The events with an MW of ≥6.0 are highlighted in red.
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Figure 14. Correlation ratios of the energy classes KPS and KS and the magnitude mbISC. (a) Northeast, KPS; (b) Yakutia, KPS; and (c) Kamchatka, KS. The dashed lines show 95% confidence intervals.
Figure 14. Correlation ratios of the energy classes KPS and KS and the magnitude mbISC. (a) Northeast, KPS; (b) Yakutia, KPS; and (c) Kamchatka, KS. The dashed lines show 95% confidence intervals.
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Figure 15. (a) NEIC; (b) MOS; (c) EIDC; and (d) IDC. Correlations of the “shift” type are determined in the interval of the magnitudes indicated in Table 4. The dashed lines show 95% confidence intervals.
Figure 15. (a) NEIC; (b) MOS; (c) EIDC; and (d) IDC. Correlations of the “shift” type are determined in the interval of the magnitudes indicated in Table 4. The dashed lines show 95% confidence intervals.
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Figure 16. Correlation ratios “shift” of the magnitudes YARS and mbISC. (a) MLYARS. (b) MSVYARS. The dashed lines show 95% confidence intervals.
Figure 16. Correlation ratios “shift” of the magnitudes YARS and mbISC. (a) MLYARS. (b) MSVYARS. The dashed lines show 95% confidence intervals.
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Figure 17. Correlation ratios “shift” of (a) the MLAEIC and mbISC magnitudes; indirect relationships; (b) MLAEIC and MLNEIC; and (c) MLAEIC and mbLGNEIC. The dashed lines show 95% confidence intervals.
Figure 17. Correlation ratios “shift” of (a) the MLAEIC and mbISC magnitudes; indirect relationships; (b) MLAEIC and MLNEIC; and (c) MLAEIC and mbLGNEIC. The dashed lines show 95% confidence intervals.
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Figure 18. Correlation ratios of the magnitudes MSUGS, USCGS, and mbISC. (a). MMSUGS. (b) mbUSCGS. The dashed lines show 95% confidence intervals.
Figure 18. Correlation ratios of the magnitudes MSUGS, USCGS, and mbISC. (a). MMSUGS. (b) mbUSCGS. The dashed lines show 95% confidence intervals.
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Figure 19. Poorly defined “shift” correlations with the magnitude mbISC. (a) MLAO; (b) MZEMSU; and (c) MMOS. The dashed lines show 95% confidence intervals.
Figure 19. Poorly defined “shift” correlations with the magnitude mbISC. (a) MLAO; (b) MZEMSU; and (c) MMOS. The dashed lines show 95% confidence intervals.
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Figure 20. (a) Distribution of the magnitude of events in time. (b,c) Magnitude–frequency graphs before 1982 (b) and after 1982 (c). Before 1982, the energy classes were integers, and so the magnitudes are in increments of 0.5.
Figure 20. (a) Distribution of the magnitude of events in time. (b,c) Magnitude–frequency graphs before 1982 (b) and after 1982 (c). Before 1982, the energy classes were integers, and so the magnitudes are in increments of 0.5.
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Figure 21. Map of earthquake epicenters of the integrated catalog of the Eastern Sector of the Arctic zone of the Russian Federation. The red and blue circles are the epicenters from the catalogs of GS RAS and ISC, respectively.
Figure 21. Map of earthquake epicenters of the integrated catalog of the Eastern Sector of the Arctic zone of the Russian Federation. The red and blue circles are the epicenters from the catalogs of GS RAS and ISC, respectively.
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Table 1. Data of Russian agencies.
Table 1. Data of Russian agencies.
CatalogPeriodNumber of Earthquakes with Energy Classes and/or MagnitudesNumber of Earthquakes with Unknown Energy Classes and Magnitudes
YAK1962, 1968–2019660046
NER1968–201976681
KAM1962–201944980
Table 2. ISC catalog statistics from 1962–2020.
Table 2. ISC catalog statistics from 1962–2020.
Agency AbbreviationAgencyNumber of Earthquakes with Energy Classes and/or Magnitudes *
AEICAlaska Earthquake Information Center, USA184
ANDREUSSR16
ANFUSArray Array Network Facility, USA2
BJIChina Earthquake Networks Center, China1
BYKLBaykal Regional Seismological Centre, GS SB RAS, Russia4
DNAGUSA13
EIDCExperimental (GSETT3) International Data Center, USA22
GCMTThe Global CMT Project, USA1
IDCInternational Data Centre, CTBTO, Austria123
ISCInternational Seismological Centre, United Kingdom1507
KRSCKamchatka Branch of the Geophysical Survey of the RAS, Russia2684
MATSSUSSR1400
MOSGeophysical Survey of Russian Academy of Sciences, Russia26
MSUGSMichigan State University, Department of Geological Sciences, USA2585
NEICNational Earthquake Information Center, USA192
NEISNational Earthquake Information Service, USA1
NERSNorth Eastern Regional Seismological Centre, GS RAS, Russia4688
NKSZUSSR8
SBDVUSSR107
SYKESSykes Catalogue of earthquakes 1950 onwards2
USCGSUnited States Coast and Geodetic Survey, NEIC, USA1
WASNUSA328
YARSYakutiya Regional Seismological Center, GS SB RAS, Russia2256
ZEMSUUSSR1884
Total:18,035
* The ISC catalog contains 6441 events with unknown energy classes and magnitudes.
Table 3. Scheme and compilation parameters of the integrated catalog.
Table 3. Scheme and compilation parameters of the integrated catalog.
StageMain CatalogAdditional Catalog Metric   Parameters   σ T   min ,   σ X   km ,   and   σ Y   km Threshold Value of the MetricEstimation of the Number of ErrorsNumber of DuplicatesMerged Catalog
1Catalog of Yakutia YAK 6600 eventsCatalog of the Northeast of Russia
NER 7668 events
0.041;
17.4;
16.3
5.80.5%2153YAK_NER
12,115 events
2YAK_NER
12,115 events
Earthquake catalog of the Kamchatka Branch of the GS RAS
KAM 4498 events
0.041;
17.4;
16.3
5.8 *26 *RUS
16,587 events
3RUS
16,587 events
ISC, events of various agencies
ISC_Other 16,642 events
0.032;
12.3;
12.0
6.00.3%10,231RUS_ISC
22,998 events
4CORE
1383 events
RUS_ISC
22,998 events
0.044;
18.3;
18.3
5.90.4%1011ARCTIC
23,370 events
5Exclusion of explosions
EXP 116 events
ARCTIC
23,370 events
0.05;
15.0;
15.0
0%116 **E_ARCTIC
23,254 events
* A small number of duplicates does not allow estimating the number of errors and optimizing the metric threshold. ** All duplicates are absolute.
Table 4. Magnitudes in the unified catalog of the Eastern Sector of the Russian Arctic.
Table 4. Magnitudes in the unified catalog of the Eastern Sector of the Russian Arctic.
AgencyType of MagnitudePriorityNumber of EventsFormula for Magnitude
in the Integrated Catalog
FigureMmin–Mmax. Initial Magnitude ScaleNote
GCMTMW1105M = MWGCMT 4.7–7.6
ISCmb21287M = mbISCFigure 13a3.0–5.9
ISCMS14M = MSISCFigure 13b5.7–7.5Strong events before 1976
YAK, NER, agencies of Russia and the USSR from ISCKPS316,301M = 0.5 KPS − 1.6Figure 14a,b0.6–14.0Information about energy classes is given in the ISC bulletins
KAM, KRSCKS34050M = 0.5KS − 0.75Figure 14c3.0–13.1
NEIC, NEISmb427M = mbNEIC − 0.2Figure 15a3.5–4.9
MOSmb416M = mbMOS − 0.2Figure 15b4.0–4.8
EIDCmb424M = mbEIDC + 0.2Figure 15c3.0–4.3
IDCmb4107M = mbIDC + 0.2Figure 15d2.9–4.4
YARSML4357M = MLYARS + 0.6Figure 16a0.5–3.0Unreliable correlation
YARSMSV495M = MSVYARS + 0.2Figure 16b0.0–2.1Unreliable correlation
AEICML4351M = MLAEICFigure 17a2.2–4.2
MSUGSM424M = MMSUGS + 0.1Figure 18a0.1–4.6
USCGSmb41M = mbUSCGSFigure 18b4.1Unreliable correlation
YARSM4104M = MYARS + 0.1 3.2–3.3Indirect correlation with energy class. The magnitude MYARS represents a conversion from the energy class KS according to the formula of Rautian MYARS = (KS − 4)/1.8.
For M[3.2–3.3] up to rounding, this is a shift of 0.1.
NERSM424M = MNERS + 0.2 2.3–2.5Indirect correlation with energy class. The magnitude MNERS represents a conversion from the energy class KPS according to the formula of Rautian MNERS = (KPS − 4)/1.8.
For M[2.3–2.5] up to rounding, this is a shift of 0.2.
NEICML413M = MLNEIC − 0.1Figure 17b2.5–4.2Unreliably used indirect correlation MLAEIC
NEICmbLg42M = mbLgNEIC + 0.1Figure 17c2.6–3.0Unreliably used indirect correlation MLAEIC
LAOM42M = MLAOFigure 19a4.0Very unreliable correlation
ZEMSUM42M = MZEMSUFigure 19b3.4–4.5Very unreliable correlation
MOSM41M = MMOS + 0.1Figure 19c5.0Very unreliable correlation
NEICM46M = MNEIC 2.5–4.9Very unreliable correlation. Only three events with two magnitudes were found, MNEIC = mbISC.
ANFML42M = MLANF − 1 4.2–4.3Very unreliable correlation. Found only two events with two magnitudes, MLANF>>mbISC.
DNAGM414M = MDNAG 2.5–4.4Correlation not established
WASNM4328M = MWASN 0.1–4.4Correlation not established
ZEMSUMPV41M = MPVZEMSU 4.5Correlation not established
YARSMU42M = MU_YARS 1.7–2.1Correlation not established
OTTML41M = MLOTT 3.9Correlation not established
PALM41M = MPAL 4.7Correlation not established
BJImb41M = mbBJI 4.8Correlation not established
EIDCML41M = MLEIDC 2.8Correlation not established
Total 23,254
Table 5. Strong earthquakes in the Eastern Arctic.
Table 5. Strong earthquakes in the Eastern Arctic.
DateTIMELatLonDepMag
122.11.196923:09:3857.67163.5125.67.5MSISC
218.05.197122:44:4163.93145.961.57.0MSISC
308.03.199111:36:3160.83167.0816.56.6MWGCMT
424.10.199619:31:5566.92−173.0422.26.0MWGCMT
520.04.200623:25:0260.88167.0523.97.6MWGCMT
621.04.20064:32:4460.45165.9614.66.1MWGCMT
721.04.200611:14:1661.30167.7522.86.0MWGCMT
829.04.200616:58:0660.45167.6210.96.6MWGCMT
922.05.200611:11:5960.73165.8113.96.6MWGCMT
1022.06.200823:56:3067.70141.3918.86.1MWGCMT
1130.04.201023:11:4360.46−177.9114.76.5MWGCMT
1230.04.201023:16:2960.48−177.6018.36.3MWGCMT
1324.06.20123:15:0157.50163.41166.0MWGCMT
1414.02.201313:13:5267.52142.708.96.7MWGCMT
1509.01.20208:38:0862.36171.06106.4MWGCMT
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Gvishiani, A.D.; Vorobieva, I.A.; Shebalin, P.N.; Dzeboev, B.A.; Dzeranov, B.V.; Skorkina, A.A. Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic. Appl. Sci. 2022, 12, 5010. https://doi.org/10.3390/app12105010

AMA Style

Gvishiani AD, Vorobieva IA, Shebalin PN, Dzeboev BA, Dzeranov BV, Skorkina AA. Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic. Applied Sciences. 2022; 12(10):5010. https://doi.org/10.3390/app12105010

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Gvishiani, Alexei D., Inessa A. Vorobieva, Peter N. Shebalin, Boris A. Dzeboev, Boris V. Dzeranov, and Anna A. Skorkina. 2022. "Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic" Applied Sciences 12, no. 10: 5010. https://doi.org/10.3390/app12105010

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