Introduction

Earthquakes are one of the disastrous natural phenomena like landslides and tsunamis. An earthquake is the disturbance of the surface of the earth originate from a sudden release of energy when the rupture of the bedrock takes place. The fluctuating behavior of earthquakes has generated interest among scientists for several years and continues to challenge people to catch a viable solution. Recent earthquakes, such as the India-Bangladesh earthquake of magnitude (MW) 5.7 in 2017, the Sikkim Earthquake of magnitude (MW) 6.9 in 2011, and Bhuj earthquake of magnitude (MW) 7.7 in 2001, are enhanced the importance of seismic hazard analysis for every region in India. Earthquake hazard can be defined as an estimation of the mean probability (over space and time) of the occurrence of an earthquake event with a certain magnitude within a given time interval. The earthquake-induced hazard is uneven in space and has obvious directionality, i.e., after the occurrence of the earthquake, the degree of disaster may vary considerably at the same distance but in different directions (Ma et al. 2019a, b). These aspects make the seismic hazard assessment one of the challenging problems of geotechnical earthquake engineering. The first step of the earthquake hazard analysis is the determination of the seismicity parameters and development of seismotectonic map.

Amaravati is a city in the Guntur district of the Indian state of Andhra Pradesh, on the southern bank of the Krishna river. The city is the proposed legislative capital of Andhra Pradesh with 217 km2 and has a population of about 0.1 million (Census, 2011). The population of Amaravati is expected to reach about 3.58 million by 2050 because of its industrial growth and urbanization (Goodess et al. 2019). Amaravati has been recognized by the Bureau of Indian Standards (IS 1893 Part-I 2016) under seismic zone III. This zone can be defined as a moderate risk zone of damage subject to VII severity on the Medvedev–Sponheuer–Karnik (MSK) scale with a zone factor of 0.16 g (PGA). In addition, the study area includes dams such as Nagarjuna Sagar dam, Srisailam dam, which have an engineering significance. The role of characterizing and assessing the risk potential to make Amaravathi capital city immune to seismic activity is extremely important because of its socio-economic significance. Seismicity maps play a vital role in seismic hazard analysis of any region. Several researchers have developed fault, seismicity maps for different regions in India in the past (Kumar and Suman 2020; Baruwal et al. 2020; Joshi et al. 2020; Mehta and Thaker 2020; Sandhu et al. 2020; Khan et al. 2020; Naik and Choudhury 2015; Kataria et al. 2013: Thaker et al. 2012; Kolathayar et al. 2012) Shanker and Sharma (1998) attempted to evaluate seismic hazard parameters for the Himalayas and the surrounding region by considering boundary lies between 20° N–36° N and 69° E–100° E for the catalog data of a period 1900–1990. The author has divided the entire Himalayan region into six seismogenic zones depending on its seismotectonic style. The reported b value for the six zones varied between 0.58 and 1.52. However, the authors have not attempted catalog completeness analysis. Iyenger and Ghosh (2004) conducted a microzonation study for the greater Delhi by considering a 300 km radius with the center as India Gate. The author has conducted Probabilistic Seismic Hazard Analysis (PSHA) and reported a Peak Ground Acceleration (PGA) of 0.2 g. This study has not used the areal source models and volume source models in the analysis.

Anbazghan et al. (2017) developed a seismotectonic map for Kanpur city, India. The linear seismogenic sources within a radius of 500 km are considered in the analysis. The author has divided Kanpur city into two zones. The Gutenberg-Richter relation is used and the b values are reported as 0.87 and 0.97. The author has conducted ground response analysis for Kanpur city. Puri and Jain (2016) attempted to evaluate the seismicity parameters of Haryana state for the catalog data of January 1505 to June 2013. A seismotectonic map for the Haryana state was developed by considering the seismogenic sources within a radius of 300 km. The authors have conducted deterministic seismic hazard analysis (DSHA) for Haryana state. The maximum Peak Ground Acceleration obtained for the Haryana state is in the range of 0.023 to 0.514 g. Only a single attenuation equation was used and the logic tree approach was not adopted. Anbazghan et al. (2019) developed a seismotectonic map for Patna district (which is in the Bihar state of India) by considering the linear seismogenic sources within a boundary of 500 km around Patna district. Also, the seismicity parameters were estimated. Chandra et al. (2018) attempted to estimate seismicity parameters of Kashmir valley by choosing the catalog data of a period 1902 to 2017. Patil et al. (2018). compiled an earthquake catalog for the study area of Vijayapura (Part of South India), considering Gol Gumbaz (archeological site) as a center, and developed a seismotectonic map for Vijayapura. Ramakrishnan et al. (2019) evaluated seismicity parameters for Mangalore city (Karnataka state in India) by considering catalog data for 1828–2008. To date, no study had presented the seismotectonic map for Amaravati and determined seismicity parameters. For the new capital city like Amarvati, seismic hazard studies are essential for proper urban planning and infrastructure that minimize earthquake risk. The initial step in the seismic hazard assessment is to prepare the seismotectonic map and estimate the seismicity parameters. Hence, in the present study, an effort has been made to develop the seismotectonic map of the Amaravati region and evaluate seismicity parameters for the Amaravati region, which are essential for seismic hazard assessment.

Study region and tectonic setting

Amaravati region, Andhra Pradesh, is considered as a study area in the present research. Figure 1 represents the layout of the study area digitized using the (ArcGIS 10.7.1 2020) software. Amaravati is one of the most important geological heritage sites of India. Amaravati is having the oldest Precambrian rocks, such as Khondalites and Charnockites (3000 million years old), trending north-east and south-west directions followed by Proterozoic Kadapa rocks (600 million years old) in the south (Ramaswamy and Murty 1973). Some of the significant earthquake epicenters close to the present study area are Jaggayyapeta of magnitude (MW) 4.4 in 2020, Ongole earthquake of magnitude (MW) 4.6 in 1987, and Bhadrachalam earthquake of magnitude (MW) 5.7 in 1969 has sent alarm signals that need a seismic hazard assessment in the region. For seismic hazard analysis of any region, a seismic influence zone should be considered a minimum of 350 km radius as per the United States Nuclear Regulatory Guide (USNRG 1997). Andhra Pradesh High Court as a center bearing latitude and longitude of 16.5195° N and 80.4856° E with a 400 km radius is considered as a seismic influence zone for the present study. The seismic events that fall under this seismic influence zone were considered for the preparation of the catalog. In the current study, various types of faults were identified within the influence zone such as neotectinic faults, subsurface fault, strike-slip fault, and fault involving with basement and cover, etc. are listed in Table 1.

Fig. 1
figure 1

Layout of study area, Amaravati, Andhra Pradesh, India

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Table 1 Important faults surrounding Amaravati area, Andhra Pradesh
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Methodology

Past earthquake data in a control region of 400 km has been compiled to estimate the seismicity parameters for the current study area. The collected catalog data is converted to well established moment magnitude (MW) scale. The catalog contains clusters such as aftershocks, foreshocks which can be removed using different approaches, which were discussed in the “Identification of main shocks” section. To estimate seismic hazard parameters, a detailed completeness analysis of catalog data is required. The completeness analysis for the present catalog data was discussed in the “Data completeness” section. The seismic sources within the influence zone were identified, georeferenced, and digitized to develop a fault map. For seismotectonic map of current study, epicenters of magnitude of MW ≥ 3.5 events were superimposed on the fault map. The detailed method of analysis is discussed in the following subsections. The methodology detailed below related to the determination of seismicity parameters is also shown as a simple flowchart in Fig. 2(a). Also, the methodology explained below corresponding to the development of a seismotectonic map is presented as a simple flowchart in Fig. 2(b).

Fig. 2
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a Methodology for the determination of seismicity parameters. b Methodology for the development of seismotectonic map

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Data sources

Until date, there is no specific catalog data available for Amaravati, a new capital city of Andhra Pradesh. The earthquake catalog was prepared by incorporating the catalog data from different sources such as the National Earthquake Information Centre National Earthquake Information Centre (NEIC), United States Geological Survey (USGS), Advanced National Seismic System (ANSS), International Seismological Centre (ISC) NEIC, USGS, ISC (2011), and from other research works in literature on seismic hazard assessments of India (Kolathayar et al. 2012; Jaiswal and Sinha 2007;Schulte and Mooney 2005; Rao and Rao 1984; Chandra 1977; Oldham 1883). A total of 919 earthquake events with all ranges of magnitudes are reported from 1801–2021 (220 years) for Amaravati region. Earthquakes of magnitude greater than or equal to 4.0 have considerable engineering significance, and minor tremors may not have much engineering significance. In the present study, earthquake events with a magnitude greater than or equal to 3.5 are considered for seismicity analysis.

Magnitude conversion

The earthquake data collected from different sources are in different magnitude scales such as body wave magnitude (mb), surface-wave magnitude (Ms), local magnitude (ML), moment magnitude (MW), and earthquake intensity scale (I). A complete and homogenized earthquake catalog is needed for the seismic hazard analysis. Unluckily, saturation restricts the use of certain magnitude scales for major earthquakes with mb > 6.0, ML > 6.5, and MS > 8.0. Moment magnitude (MW) does not saturate, and it is based on a seismic moment, which represents reliable earthquake magnitude (Scordilis 2006). Before the instrumental era, the shaking was measured based on damage caused by earthquakes in terms of Modified Mercalli Intensity scale (I). Based on 23 Peninsular India (PI) earthquakes from 1969 to 2001 with independent measures of Modified Mercalli Intensity scale (I) and magnitude (MW) from different sources, a magnitude-intensity relationship was proposed by Lai et al. (2009). In the present study, MW is determined using the following expression proposed by Lai et al. (2009).

$${M}_{W}=0.45I+2.38$$
(1)

Also, the earthquake magnitudes are converted to moment wave magnitude by the following regression equations proposed by Kolathayar and Sitharam (2018).

$${M}_{W}=1.08\left(\pm 0.0152\right){m}_{b}-0.325(\pm 0.081)4\le {m}_{b}\le 7.2$$
(2)
$${M}_{W}=0.815\left(\pm 0.04\right){M}_{L}-0.767\left(\pm 0.174\right)3.3\le {M}_{L}\le 7$$
(3)
$${M}_{W}=0693\left(\pm 0.006\right){M}_{S}+1.922\left(\pm 0.035\right)3.7\le {M}_{S}\le 8.8$$
(4)

Identification of main shocks

Minor tremors before seismic network development are unnoticeable. Incomplete reporting of the earthquake catalog leads to the recurrence rate of large earthquakes being overestimated and the recurrence rate of smaller earthquakes being underestimated. It is difficult to fit the Gutenberg Richter (G-R) equation for incomplete earthquake catalog data. Hence, one must check the completeness of the catalog before the calculation of G-R parameters. De clustering is a process of removing foreshocks and aftershocks from the raw catalog. There are several approaches proposed by various researchers available declustering catalog such as Gardner and Knopoff (1974), Reasenberg (1985). Gardner and Knopoff (1974) developed a declustering algorithm that claims that foreshocks and aftershocks are dependent on the size of the main event. Therefore, the time and distance window parameters vary depending on the magnitude of the main shock. In the current study, declustering was done using an algorithm developed by Gardner and Knopoff (1974) and modified by Uhrhammer (1986). Total earthquake events of all ranges of magnitude with latitude, longitude, and time are uploaded in the text format into a ZMAP tool. By giving proper input details such as the catalog data’s beginning and ending year, and bin length ZMAP for Amaravti capital city was established. After that, the decluster algorithm must be specified. The decluster algorithm used in the present study was proposed by Gardner and Knopoff (1974) which is based on the following relation.

$$\begin{array}{cc}D={e}^{-1.024+0.804M}& t={e}^{-2.87+1.235*M}\end{array}$$
(5)

where D = distance in kilometers, t = time in days.

After removing the foreshocks and aftershocks, 753 earthquake events are present in the study area.

Data completeness

Data completeness with respect to magnitude (M c )

Minimum magnitude (Mth) should be considered for a well-constrained catalog in order to prevent errors caused by earthquakes of low magnitude. It is common practice to use the minimum magnitude of completeness as a threshold for any study that uses a well-constrained earthquake catalog, as this prevents error due to the presence of low magnitude earthquakes in the region. The magnitude of completeness (Mc) is the lowest magnitude above which all magnitude of earthquakes are detected by network stations (Rydelek and Sacks 1989). Magnitude of completeness plays a significant role in the estimation of seismicity parameters. If the assumed threshold magnitude (Mth) is less than the magnitude of completeness of a catalog, estimated seismicity parameters may be erroneous. Hence, the events below this magnitude are skipped by the network because they are too small to be identified by enough stations.

To estimate the magnitude of completeness of the catalog, the following two methods are popular in the literature.

  1. 1)

    Waveform based technique (Rydelk and Sacks 1989)

  2. 2)

    Catalog-based methods (Wiemer and Wyss 2000)

In the present study, catalog-based approach is considered for estimation of magnitude of completeness. These methods are developed based on the Gutenberg-Richter frequency magnitude curve. From the collected earthquake data, it is observed that small to medium range of magnitude events are occurred more compared to greater magnitude earthquakes. Also, in all the events, the earthquake magnitude is less than the moment magnitude of 7 (Fig. 3). Accordingly, the maximum possible earthquake for the present study area can be considered as 7. It can be noticed from Fig. 3 that more number of earthquakes are identified after the year 1964. The seismic network was well established after 1964; hence, recording events are more compared to before the 1960s. The magnitude completeness obtained by the b value stability approach is optimal in dealing with network-reported catalogs, whose detection is progressively enhanced with magnitude (Cao and Gao 2002). The best fit method and maximum curvature method underestimate the value of Mc (Wiemer and Wyss 2000). Stable and moderate Mc value is given by Entire Magnitude Spectrum system (EMR) (Woessner and Wiemer 2005). When the number of events is not too many and the missed events are in high number, the approach of EMR is recommended Yi lei et al. (2016). In the present study, Mc obtained by EMR method is considered to choose the catalog’s threshold magnitude. From Fig. 4, it is observed that the magnitude of completeness for the present catalog data is about 2.7 (MW). The moment magnitude of MW = 3.5 is considered threshold magnitude for the seismicity analysis of the present study area. The magnitude of completeness is varying with time because the number of seismograms recorded is also varying with time. These variations in Mc are shown in Fig. 5. The main catalog consisting of 162 events with MW greater than or equal to 3.5 is presented in Table 2.

Fig. 3
figure 3

Earthquake magnitude with time for the period 1801–2020

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Fig. 4
figure 4

Magnitude of completeness by EMR method

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Fig. 5
figure 5

Temporal variation of magnitude of completeness (Mc) for Amaravati region, India

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Table 2 Number of earthquakes per each decade
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Data completeness with respect to time

In the present study, a catalog completeness analysis was done by using the following two methods and the results obtained were compared.

  1. 1.

    Cumulative visual inspection method (Mulargia and Tinti 1985)

  2. 2.

    Stepp procedure (1972)

Cumulative visual inspection method

In the present study, all the earthquake events that occurred in the span of 220 years were subdivided into six magnitude classes viz. (3.5 ≤ MW < 4), (4.0 ≤ MW < 4.49), (5.0 ≤ MW < 5.49), (5.5 ≤ MW < 5.99), (MW≥ 6). For different magnitude classes, the number of earthquake events per decade is presented in Table 2. For a given magnitude class, the completeness period is considered to begin when a straight line approximates the slope of the fitting curve. It can be observed from Fig. 6(a) that for the magnitude range 3.5–3.99, the catalog is completed for the last 50 years (1971–2021). From Fig. 6(b), it can be noticed that for the magnitude range 4.0–4.49, the catalog is completed for the last 60 years (1961–2021); similarly from Fig. 6(c), (d), (e), and 6(f), for the magnitude ranges 4.5–4.99, 5.0–5.49, 5.5–5.99, and 6 and above, the catalog is completed for 60 years (1961–2021), 65 years (1956–2021), 220 years (1801–2021), and 220 years (1801–2021) respectively.

Fig. 6
figure 6figure 6figure 6

Catalog completeness analysis by CUVI method

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Stepp procedure (1972)

To estimate the completeness period for given catalog, the standard deviation for different magnitude classes was calculated. The logarithmic graph was plotted between standard deviation and time. Each class of plotted points represents a straight path as long as the dataset is complete in that magnitude interval. The unbiased mean rate per unit time interval can be calculated using the following equation

$$\lambda =\frac{1}{n}\sum_{i=1}^{n}{X}_{i}$$
(6)

where λ = mean rate per unit time interval,

X1, X2……Xn are number of earthquake events reported per unit time interval and.

n = T = number of unit time intervals or sample length.

Variance is given by \({\sigma }_{\lambda }^{2}=(\lambda /n)\), where “n” is the number of unit time intervals; for the present study, n = T = 10 years. When unit time interval is taken as 1 year, then the standard deviation becomes \({\sigma }_{\lambda }=\surd \lambda /\surd T\). As long as data is complete, the standard deviation is proportional to \(1/\surd T\). Figure 7 presents the standard deviation versus time plot for every range of magnitude class. It can be observed from Fig. 7 that for a specific region, all the range of magnitudes has the same slope of \(1/\surd T\). The period from the beginning to the point where the slope of magnitude class deviates from \(1/\surd T\) line is the completeness period for that specified class of magnitude. It is also noticed from Fig. 7 that the catalog completeness period for the different range of magnitudes such as 3.5–3.99, 4.0–4.49, 5.0–5.49, 5.5–6.0, and above magnitude 6 is 50 years, 50 years, 80 years, 160 years, and 220 years respectively. The catalog is completed for the completeness period by both the methods was reported in Table 3.

Fig. 7
figure 7

Catalog completeness analysis by Stepp (1972) procedure

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Table 3 Catalog completeness period by CUVI and Stepp (1972) methods
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Earthquake recurrence

Several researchers are focused on estimation of seismicity parameters for different regions in India from both historical as well as instrumental catalog data (Joshi et al. 2020; Anbazhagan and Abraham 2020; Keshri et al. 2020; Khan and Kumar 2020; Mehta and Thaker 2020; Naik and Choudhury 2015; Shukla et al. 2015; Desai and Choudhury 2014). In the present study, the seismicity parameters of Amaravati region are calculated by least square method of regression analysis. The commonly used expression for earthquake size distribution of a region is the Gutenberg-Richter earthquake recurrence law (Gutenberg and Richter 1944) i.e.,

$${Log}_{10}N=a-b{M}_{W}$$
(7)

where N is the number of earthquake events in a year, which are greater than or equal to the specified magnitude. The parameter a represents the logarithm of total number of earthquakes with magnitude greater than or equal to threshold magnitude and b represents a particular region’s seismicity rate or the average size distribution of the earthquakes. The higher b value indicates that the smaller magnitude of earthquakes dominates in the region; similarly, the lower b value means the dominance of greater magnitude of earthquakes in that specified region. It is to be noted that the value of b depends upon the space, time, faulting style, and magnitude (Gulia and Wiemer, 2019). However, the present study did not consider these aspects in the analysis, which is a limitation of the study.

The seismicity parameters are estimated by using Gutenberg-Richter recurrence law for completed catalog data obtained from cumulative visual inspection (CUVI) method and Stepp (1972) method (Table 4). The frequency magnitude distribution curves for Amaravathi region using CUVI and Stepp approach are presented in Fig. 8(a) and Fig. 8(b) respectively. The seismicity parameters for Amaravati region are also estimated based on maximum likelihood method (Aki 1965). The estimation of b value through maximum likelihood method is carried out using the seismic tool ZMAP (Wiemer 2001).

Table 4 Seismicity parameters by least square and maximum likelihood method
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Fig. 8
figure 8

a Frequency magnitude distribution curve for Amaravati region using CUVI method. b Frequency magnitude distribution curve for Amaravati region using the approach of Stepp (1972)

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Figure 9 presents the calculation of b-values using the maximum likelihood method. In Fig. 9, the cumulative number of earthquake events is represented by square and noncumulative events are represented by triangles. The slope of the linear fitting curve will give the b value for the given catalog data.

Fig. 9
figure 9

Estimation of b value by Maximum likelihood estimation method

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Maximum earthquake magnitude (M max) estimation

The proper estimation of maximum magnitude (Mmax) of a seismic source is crucial for seismic hazard analysis. There are several methods available in the literature to estimate the maximum magnitude such as Wells and Coppersmith (1994), Bonilla et al. (1984), Nowroozi (1985), Slemmons et al. (1989), and Guptha (2002).

In the present study, Mmax value is calculated by using Guptha (2002) method. It is a popular conventional method of applying an increment of 0.5 units to the maximum observed magnitude of a specified source. The estimated maximum magnitude values for different sources in the Amaravati region are presented in Table 5.

Table 5 Estimation of maximum magnitude for different sources using the Gupta (2002) method
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Development of seismotectonic map

To quantify seismic hazard and seismic risk of any region, one must have knowledge of the active faults, lineaments, and any other active seismogenic sources present in the study area. In the present study, a fault map was developed by considering the faults with in the radius of 400 km with Andhra Pradesh High Court, Thulluru, Amaravati as a center. The fault map of Amaravati was developed by digitizing the eight sheets which are relevant to our study area from Seismotectonic atlas – 2000 (Geological Survey of India). A total of 49 faults, four major lineaments, and nine shear zones were identified with in the study area. In identificating seismogenic sources, the sources which are extended beyond the influence zone are also taken into consideration for preparation of seismotectonic map. The fault map of Amaravati area is presented in Fig. 10. The color of the fault represents the type of faults which are present in the influence zone of current study area.

Fig. 10
figure 10

Fault map of Amaravati area, Andhra Pradesh, India

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Figure 11 presents the spatial distribution of earthquake events with in the influence zone of Amaravati region. The scale chosen for constructing these maps (Figs. 10 and 11) was 1 cm = 54.58 km. It is prepared by considering the events of magnitude MW ≥ 3.5 for a span of 220 years (1801–2021). The events are grouped in to three magnitude categories such as 3.5–4.2, 4.3–5.1, and 5.2–6.2 and are represented as circles in Fig. 11. The larger the size of the circle, the larger is the magnitude of earthquake. The seismotectonic map of Amaravati area is presented in Fig. 12. It is developed by superimposing both the fault data and seismic events using ArcGIS10.7.1 software.

Fig. 11
figure 11

Spatial distribution of earthquake events (MW ≥ 3.5) around Amaravati region, Andhra Pradesh India

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Fig. 12
figure 12

Seismotectonic map of Amaravati region, Andhra Pradesh, India

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Results and discussions

The prepared earthquake catalog of magnitude MW ≥ 3.5 for Amaravati region is presented in Appendix Table7. The magnitude of completeness was obtained as 2.7 by Entire Magnitude Range method. The completeness analysis by CUVI reveals that the data is complete for the last 50, 60, 60, 65, 220, and 220 years for magnitude classes (3.5–3.99), (4.0–4.49), (4.5–4.99), (5.0–5.49), (5.5–5.99), and (MW ≥ 6.0) respectively. The completeness analysis by Stepp (1972) procedure reveals that the data is complete for the last 50, 50, 80,160, and 220 years for different classes of magnitude (3.5–3.99), (4.0–4.49), (4.5–4.99), (5.0–5.49), (5.5–5.99), and (MW ≥ 6.0) respectively. The calculated b value for Amaravati region by least square method is varied from 0.70 to 0.85, whereas the b value calculated using the maximum likelihood method is found to be 0.88. The estimated b value for the Amaravati region in the present study is compared with the b values obtained in the earlier studies of Indian cities that are having similar seismotectonic setups (Table 6). It is observed that the obtained b value of 0.7–0.85 for the present study area compares well with the study of Khan et al. (2020). Also, the b value of 0.88 obtained by the maximum likelihood method is very close to the Karnataka region’s (which is also a part of Peninsular India) b value reported by Anbazhagan and Sitharam. (2008). Furthermore, the Working Committee Experts National Disaster Management Authority (WCE NDMA 2010) recommended a b value of 0.85 ± 0.09, specifically for the Godavari Graben region in which the present study area belongs.

Table 6 Comparison of b values with the existing literature
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The estimated maximum possible earthquake moment magnitude (MW) for Amaravati region is found to be 6.7. The seismotectonic map (Fig. 12) shows that the maximum number of earthquake epicenters is the nearest to the faults. This information is useful for estimating the maximum potential magnitude of faults. It is also observed that no earthquakes with MW ≥ 5.5 have their epicenters with in the geographical area of Amaravati region. It can also be seen that most of the subsurface faults and neotectonic faults are located at a distance of 10 km and 50 km to the Amaravati respectively. It can be noticed that most of the moderate earthquakes with MW lies between 4.2 and 5.2 are in Southern Amaravati region. The proposed map serves as a basis for seismic hazard analysis of Amaravati region, India.

Conclusions

An attempt has been made in the present study to quantify seismic hazard parameters for the area of Amaravati, Andhra Pradesh, India. In the present study, an earthquake catalog of magnitude MW ≥ 3.5 related to Amaravati region, for a period of 220 years, has been created by incorporating historically and instrumentally recorded events. The magnitude of completeness for the present catalog data is found to be moment magnitude of MW = 3.10. Catalog completeness analysis reveals that the data is complete for last 60 years from present for moderate magnitude of earthquakes. In the case of MW ≥ 5.5, the data is not complete even for the entire time period. The seismic hazard parameters for the Amaravati region were calculated using the least squares method and the maximum likelihood estimate method. The obtained a value varies from 2.6 to 3.5 and the obtained b value varies from 0.63 to 1.10. Seismotectonic map of Amaravati area is developed. The maximum possible earthquake of magnitude produced by any seismogenic source within the Amaravati influence zone is estimated as MW = 6.7.