Analysis of Seismic Noise of Broadband Seismological Stations installed along the Western Ghats

Analysis of Seismic Noise of Broadband Seismological Stations installed along the Western Ghats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Seismic Noise of Broadband Seismological Stations installed along the Western Ghats Krishna Jha, Padma Rao B, Sribin C, Silpa S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1869555/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2023 Read the published version in Journal of Seismology → Version 1 posted 7 You are reading this latest preprint version Abstract The Western Ghats (WG) is one of the great escarpments that extend ~ 1500 km parallel to the west coast of India in the NNW-SSE. We deployed a network of seven broadband seismological stations along the WG to decipher it’s evolution. In the present study, we investigate the characteristics of different kinds of noises at the stations by utilizing the power spectral density measurements. Further, the results are compared with the global standard noise models to assess the data quality. The PSD results reveal that the short period (cultural) noise is more prominent at stations AGMB, SDPR, MGLI when compared to the other stations, especially during day hours since these sites were in the proximity of roads. The seasonal variations are observed especially in the microseismic period range and noise levels are more prominent in the months of July to August since the western part of India experiences peak monsoon during this period. These variations are observed especially at PCH and KNUR stations as their locations were near the coastline. Further, the results indicate that the noise levels are more prominent in vertical component than that of horizontal components in the microseismic period range whereas it is reversed in the short and long period ranges. Later, the results indicate that the noise levels at all the localities of stations are within the global standard noise models, which suggest that our installation of broadband seismological stations have been successful and has good data quality. Western Ghats Seismic Noise PSD PDF Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction A seismogram contains signals generated by the earth's vibrations, including those from natural (e.g., earthquakes) and/or anthropogenic sources. Seismic noise is a continuous vibration of ground owing to a multitude of causes, it varies according to the frequencies of the vibration and manifests the temporal and spatial variance. Seasonal weather changes, day-night variations and local conditions of the area affect the spectral patterns of the seismic data. The noise hinders the uncovering of useful information hidden in a signal. The ambient noise is a composition of various frequency surface waves and can be natural and/or anthropogenic. The most common natural sources are wind, rain, rivers, ocean microseisms and earthquakes (Withers et al., 1996 ; Webb, 2002 ; McNamara and Buland, 2004 ). Moreover, anthropogenic noise sources include human activity, industrial sources, vehicular traffic, etc. The noise baseline is determined with respect to the geographical location since it may vary from site to site, type of sensor and installation stage. A signal is considered to be optimal if the noise content is minimal, which leads to better detection of useful information like earthquake detection. The sampling rate and the type of sensor installed are the two key factors determining the recording’s minimum and maximum observable periods. Noise sources in the local neighbourhood determine the noise level at each period (Webb, 2002 ) and its analysis can help to recognize site conditions (de la Torre and Sheehan, 2005 ; Abd el Aal, 2013). The noise spectrum is classified into three frequency bands as per McNamara et al. ( 2009 ). The long period consists of noise within 0.01 Hz to 0.1 Hz. The frequency band of 0.1 Hz to 1 Hz fall in the microseismic period range and that of 1 Hz to 10 Hz or higher is classified as short period noise. The long period noise is mainly caused by atmospheric effects like wind, storms, tilt and pressure. The noise in this range usually affects the horizontal components of the seismometer rather than the vertical component. Further, the seismic noise is more predominant in the microseismic period range. The noise levels in this range both in the primary and secondary microseismic range are could be due to the interaction of ocean waves with the coast. The peak in the primary microseism is generated by the superposition of ocean waves, it is equivalent to 0.2 Hz. Whereas the secondary microseism is generated by the crashing of waves on the shores, it is in between 0.05 Hz and 0.1 Hz (McNamara and Buland 2004 ; Bormann 2002 ). The microseism majorly affect the coastal sites than the continental sites, which are affected majorly by cultural noise. Also, the local weather conditions affect the noise of over 1 Hz (Peterson 1993 ; Webb 1988). The noise generated by anthropogenic sources (man-made) is usually referred to as cultural noises, it falls within the short period range (> 1 Hz). It’s characteristics include propagation as high-frequency surface waves, attenuation in the small distance range and less depth. There is a significant difference in day and night noise levels in this category and it has characteristic frequencies depending on the source of the disturbance. Another type of locally generated noise is by the wind and swinging of towers or masts (Young et al., 1996 ; Withers et al., 1996 ). Noise generated by objects moved by wind falls in the high-frequency range, while those by swinging objects generate low-frequency signals. These are different from cultural noise as the latter is generally periodic with diurnal variability, thus allows it to be isolated from wind noise (Ringdal and Bungum, 1977 ; McNamara and Buland, 2004 ). Further, the running water, surf, volcanic activity can also contribute to the seismic noise and the temperature effects can also produce the noise in the seismometer recordings. The ground fluctuations generated by the heating during the day and cooling during the night time can induce the tilt and long-period noise (0.01 to 0.05 Hz) in horizontal components (Stutzmann et al., 2000 ; McNamara and Buland, 2004 ). A comparison of horizontal and vertical noise models can decipher these kinds of noises. In addition, seismometer recordings can also contain the noise generated by electrical and mechanical instrument noise (Bormann, 2002 ; Wielandt, 2012 ). As a part of the study to decipher the Western Ghats (WG) evolution, National Centre for Earth Science Studies (NCESS) deployed a network of seven broadband seismological stations along the WG, details are shown in Table 1 and Fig. 1 . Most of the stations are in remote locations and close to the coast. The stations are equipped with RefTek 151B Observer sensor with RefTek DAS except for the PCH station, which is equipped with Trillium 240 instrument. All the stations are recording the continuous data at 100 samples per second. The primary objective of this experiment is to decipher the lithospheric structure and mantle deformation along the WG to shed light on it’s evolution. The WG is one of the great escarpments that extend ~ 1500 km parallel to the west coast of India in the NNW-SSE direction with an elevation of ~ 1.2 km. The linear extent of these Ghats begins from Gujarat in the north, south of the Narmada rift and ends at Kanyakumari in the south. The low altitude coastal plains banked by the Arabian sea is on the west side of the WG and the elevated plateau of WG comprising of Southern Granulite Terrain (SGT) in the south, Western Dharwar craton (WDC) at the centre and Deccan Volcanic Province (DVP) in the northern part. Out of our seven stations, two stations were installed in the region of SGT and four were installed in the WDC region and one station is in the region of DVP. To achieve our primary objective in this experiment, it is essential to run the stations with proper functionality. Thus, in the present study, we analysed the station performance by analysing noise levels at all the stations (Table 1 and Fig. 1 ) and compared them to the standard noise models. Table 1 List of stations installed along the Western Ghats and the corresponding details. S. No. Station Code Latitude (°N) Longitude (°E) Altitude (m) Sensor Type Data Availability 01 PCH 10.530 76.340 60 Trillium 240 2000 - Till Date 02 KNUR 11.872 75.588 44 RefTek 151B-120 Observer 2018 - Till Date 03 SBMN 12.660 75.618 134 RefTek 151B-120 Observer 2019 - Till Date 04 AGMB 13.507 75.095 659 RefTek 151B-120 Observer 2018 - Till Date 05 SDPR 14.333 74.760 535 RefTek 151B-120 Observer 2018 - Till Date 06 JODA 15.176 74.490 588 RefTek 151B-120 Observer 2018 - Till Date 07 MGLI 16.172 74.126 900 RefTek 151B-120 Observer 2018 - Till Date 2. Estimation Of Power Spectral Density To analyse the seismic noise, recorded at broadband seismological stations established along the WG, we utilized the power spectral density (PSD) and the probability density function (PDF) of each category of background noise. This is the standard method for quantifying seismic background noise. The PSD can be obtained by means of averaging as seismograph recordings are stochastic signals. In the present study, we utilized an open-source software PQLX, which was developed by USGS for evaluating the seismic station performance and data quality. This software requires the input waveform data and instrument response files to compute the PSD and PDFs. These measurements are computed based on the algorithm by McNamara and Buland ( 2004 ). The advantage of using this tool is that it accepts the raw data and removes the instrument response using the input response files. There is no need to remove earthquakes, system glitches or general data traces since these are low-probability occurrences that do not contaminate high-probability ambient seismic noise. This method uses the probability density function to calculate the power spectral density, which consider system transients into the low background and ambient noise into high probability. Earthquakes are observed in the PDFs as low probability signals at short and long periods. This provides the advantage of representing the true ambient noise levels rather than a simple minimum. In this tool, hour-long, continuous data can be processed without removing earthquakes and other data glitches. Further, the instrument response is removed by utilizing the input response files to produce the ground acceleration. The hour-long time series data is divided into 15 minutes segments with 75% overlap. The overlap time series data segments are used to reduce the variance in the PSD estimate (Cooley and Tukey, 1965 ). These are processed by removing mean, long-period trend and a 10% of sine function tapering is applied along with the Fast Fourier Transform (FFT). The tapering helps to smoothen the FFT and minimize the discontinuity effect between the beginning and end of the time series data. Further, the long period trend is removed to eliminate large scale distortions in spectral processing. The averaged value for these segments provides the one-hour time series PSD after deconvolving the seismometer instrument response. The smoothed PSD estimate is converted into decibels (dB) with respect to acceleration (m/s 2 ) 2 /Hz, unit of intensity of the random vibration signal vs frequency. Later, to compute the PDF, the raw frequency distribution is constructed from the individual PSDs by binning the periods into 1/8 octave intervals and binding the power in 1 dB intervals. The process reduces the number of frequencies by a factor of 169 (McNamara and Boaz, 2006 ). The power is averaged between a short period (high frequency) corner (Ts) and a long period (low frequency) corner Tl = 2*Ts, with a centre period Tc = √(Ts*Tl) is the geometric mean period within the octave. The averaged power for that octave, period ranging from Ts to Tl, is stored with the centre period of the octave, Tc. Ts is incremented by 1/8 octave such that Ts = Ts*2 0.125 , to compute the average power for the next period bin. Powers are averaged within the next period range i.e., recomputed Ts to Tl and the process continues until it reach the longest resolvable period of a given time series data. This process is repeated for every 1-hour PSD estimate. Then these raw frequency bins are normalized by the total number of PSDs to estimate the PDF. The probability of a given power occurrence at a particular period is compared with the Peterson High Noise model and Low Noise model (Peterson, 1993 ). 3. Results And Discussion We computed the PSD and PDFs for seven stations installed along the WG (Fig. 1 and Table 1 ). Results reveal that the noise levels are within the limits of the New Low Noise Model (NLNM) and New High Noise Model (NHNM) (Peterson, 1993 ) with seasonal variations (Fig. 2 ). The short period noise is more prominent at stations AGMB, SDPR, MGLI when compared to the other stations PCH, KNUR, SBMN, JODA (Fig. 2 ). The primary reason could be due to the station locations i.e., the stations AGMB, SDPR, MGLI were bit close to the road, which is having significant vehicular traffic as compared to the other stations. Low power in a short period refers the less cultural noise. We observed more noise levels in the microseismic period at stations PCH, KNUR as they were very closer to the shoreline. Since the data is not filtered to remove earthquake signals, the body and surface waves noise can be seen in PSD/PDF data. For example, the frequency higher than 1 Hz or period lower than 1 s, low probability high power events can be attributed to the body waves of earthquakes. Similarly, for frequency less than 0.1 or a period greater than 10 s, can be attributed to earthquake surface waves. On average, the noise levels at stations PCH, KNUR, SDPR, JODA indicate in the middle of NLNM and NHNM. However, the noise levels at stations AGMB, MGLI have more towards the NHNM in the low period cultural noise band. The noise levels at station SBMN have towards the NLNM, which indicates less human-induced noise in the recordings (Fig. 2 ). The predominance of cultural noise at stations AGMB and MGLI can be attributed to its proximity to relatively busy roads. The 90% of noise at AGMB lies close to NHNM, especially at the period range of 0.5 to 0.9 s, similar things were observed at MGLI also. The difference between the 90th percentile and 10th percentile of the noise level (vertical component) in the month of April 2019 at SBMN is ~ 7 dB in the cultural noise range and similar values were observed at AGMB station. The corresponding values at stations PCH, KNUR, SDPR, JODA, MGLI are 4 dB, 5 dB, 6 dB, 7 dB, 6 dB respectively. At all the stations, the horizontal components are noisier than the vertical component in the short period cultural noise, however, the difference between these two components is quite small. Moreover, the noise levels at all stations in the long period range are low when compared to the short period noise. In this period band, the noise levels are closer to NLNM and the difference between 10th and 90th percentile in vertical component lies between 7 dB and 20 dB in the month of April 2019. The microseismic noise also dominates at all the stations, owing to the proximity to the coast of the Arabian sea. The noise in primary microseism (4 to 10 s) is more dominant than the secondary microseism. At PCH stations, the double and single frequency peaks are observed more prominently as compared to SBMN where it is less owing to being bit farther from the coast. The effect of microseismic noise in the period range of 1 to 20 s is observed at all the stations with stability except for the seasonal variations. 3.1. Diurnal Variations We observed the variations in the noise levels over day and night at all the stations, especially in short period cultural noise band (Figs. 3 ). The noise levels at the MGLI station is high during the daytime (~ 06:00 to 18:00 hrs IST) and then decreases till 03:00 hrs (Fig. 3 ). This could be due to the human activity and passages of vehicles from the nearby roads. Though the difference between the day and night noise levels at the JODA station is very small, the noise levels at day time are bit high. This could be justified with the location of the station, i.e., which is located in the forest area. At SDPR station, owing to its location nearby a local road and state highway, the noise is higher in the daytime and decreases after 20:00 hrs. The noise levels are quite strong at AGMB station over day time and continue to till midnight and decreasing for a few hours from midnight to the early morning hours. This could be due to the fact that this station is located near to the state highway, which is busy with vehicles and public transport throughout the day and even till midnight. The station SBMN is located in a habitat region and surrounded by forest, thus the noise levels are quite low and even the diurnal variations are also significantly low at this station. The location of the KNUR station is in a small village and there is no much disturbance of heavy vehicles except the human activities. Therefore, we observed low noise levels even over the daytime, however, the daytime noise levels are a bit high when compared to the nighttime noise levels. A similar pattern in noise levels is observed at the PCH station, which is also located in a similar locality. High cultural noise levels were observed at AGMB and MGLI stations and out of which the noise levels are more prominent at AGMB station even during the several hours of the night. All the stations indicate stable noise levels with very minor diurnal variations in the period range of microseism. However, noise levels are bit higher side at PCH and KNUR stations since these stations were very close to the shoreline. Even in the long period also, no significant variations in the noise levels over day and night. The Fig. 3 shows the diurnal variations of noise PSD for three components at seven stations, which is calculated by using the four months of data (of the year 2019) at each station and the diurnal variations in a short period (cultural noise) range is shown in Fig. 4 . We observed that the short period noise is much stronger in the vertical component and the day-night variations seem to be smaller than that in the horizontal components, especially at AGMB. In the long period range, the horizontal components seem to be having higher noise levels than the vertical components. This could be due to the effect of thermal and tilt since long period noises are sensitive to temperature changes and tilt of the sensor. The long period noise is low in general if the sensor is insulated from the surrounding environment by using an insulating cover. The variations induced by tilt are mostly seen in the horizontal components when compared to the vertical component, corroborating with the observations from this study. 3.2. Seasonal Variations The significant seasonal variations were observed in the period range of microseism rather than the short and long period ranges (Figs. 5 and S1). The noise levels at the PCH station indicate higher values in the monsoon period when compared to the other seasons. Moreover, not much variation in the difference between 90th and 10th percentile over the four seasons in both horizontal and vertical components especially in short and microseismic period ranges, while this difference is more in long period range. However, the mode values of PSD lie closer to the NLNM in the long period range. Similar patterns of seasonal variations were observed at KNUR station and there is an increase in noise levels in the season of monsoon especially in the microseismic period range. The difference in 90th and 10th percentile across all the stations in the monsoon season is 8 dB for the short period range and it is 12 dB for the microseismic period range. The mode values are closer to the NHNM in the short period range and to the NLNM in the long period range. The PSD values at the SBMN station also show an increase in noise levels in the season of monsoon when compared to other seasons. At AGMB station, it indicates almost stable noise levels in all seasons except for monsoon. In addition, there is an increase in noise levels in both microseismic and short period range at this station. The noise level increase in monsoon season is observed at microseismic period range at SDPR, JODA and MGLI as well. The variations of noise PSD at KNUR station is shown in Fig. 5 and at other stations is shown in Figure S1 over the four seasons i.e., spring, summer, monsoon and winter for the year 2019 and the variations of PSD mode over four seasons at all the stations are shown in Fig. 6 . 3.3. Monthly Variations To understand the monthly variations of noise levels, we analysed the PSDs of monthly data (Figs. 7 and S2), results reveal that no significant variations in the short (cultural noise) period range over the 12 months at all the stations. However, the noise levels in the microseismic period range were quite strong in the months of June to September. The western part of India experiences monsoon during these months with onset in the month of June. Among them, July - August seems to be the noisiest in this period range. This could be due to the strongest period of monsoon along the western parts of India. The noise levels decay back to that of the pre-monsoon period. Majorly, the short period microseism and primary microseismic period ranges show significant noise variations. This could be explained by the stormy conditions in the sea causing violent crashing of waves on shores. The long period noise remains stable over all the months though the high temperature in the months of summer and wind conditions causing movement of trees and poles, which gets transferred to ground as long period vibrations, which may cause the long period noise. The monthly variations at station KNUR is shown in Fig. 7 and at other stations is shown in figure S2. Further, the variation of PSD median values for all the 12 months at station KNUR is shown in Fig. 8 and at other stations is shown in figure S3. We evaluated the monthly change of noise peak mode value in the period ranges of short (1–4 s), primary (4–10 s) and secondary (10–16 s) microseism (Figs. 9 and S4) for a better understanding of noise level variations over the 12 months. Results reveal that the noise levels are high in the months of July and August, especially in the primary microseismic period range and falls down starts in the month of October i.e., the post-monsoon period. Interestingly, we observed the shifting of peaks from June-July at the southern stations to July-August at the northern stations. This could be due to the hitting of monsoon i.e., monsoon hits first in the southern side of WG and it travels towards the northern side. The monthly peak PSD values and their corresponding periods in the short period microseism shows that noise levels shift towards higher side with the advent of monsoon and gradually declines as monsoon recedes. This variation is very less in primary microseism and lies mostly in 4 to 5 s range. In secondary microseism, monthly PSD peaks were at 10.2 s (Figs. 9 and S4). 3.4. Dominant Period vs Peak Noise We analysed the monthly data to decipher the dominant period in all the noise period ranges. The observed results reveal that the dominant noise period in short and primary microseismic period ranges, shifts towards higher values of PSD during the months of monsoon. Moreover, the peak period in the short (cultural noise) period ranges remains nearly the same. The dominant periods in different period ranges at KNUR station during the February, May, July and December months of 2019 with their respective PSD values is shown in Fig. 10 and at other stations is shown in Figure S5. These months falls in the spring, summer, monsoon and winter seasons, respectively. The curve in the part (b) of figure illustrates the PSD vs probability of occurrence at peak period over the months in the short (cultural noise) period ranges. This reveals that during the months experiencing the monsoon (july), the probability of occurrence is scattered in PSD values, while during the other months, it is nearly focussed around − 140 to -150 dB. Further, we also evaluated the monthly peak values and their corresponding periods. We observed that in the short period and primary microseismic range, the periods corresponding to peak values shifted towards the higher side of PSD values in monsoon (Fig. 10 ). The similar trend has observed at SDPR station as well. In the three components, the period corresponding to peak PSD shifts to higher side during monsoon in a short period and primary microseism noise ranges The noise levels in the cultural noise band remains in the same power limits, with the noise around 0.7 to 1 s dominating. At AGMB, peak noise period shifts to higher side in monsoon and winter. AGMB experiences heavy rain, especially in monsoon, while winter noise seems to because of traffic as a result of tourist influx and vehicle movement in the highway nearby. In summer, the curve narrows, which indicates only a particular period having more probability. This may be due to lesser vehicle movement from tourism due to extreme heat. At station JODA, results show no much variations across all seasons, except for minor increase in noise levels in monsoon. It shows similarity with AGMB in summer in terms of probability of peak noise period. Similar levels observed at MGLI station with slightly on the higher side, as a consequence of daily vehicular movement along the road nearby. Results at PCH station show peak noise period in primary and secondary microseism shifted to higher noise levels, while no significant variations being observed in the other noise ranges. PCH also shows less scattering of peak periods, which may indicate the dominance of a particular noise levels, particularly those of the microseismic, owing to proximity to the sea. 3.5. Vertical vs Horizontal Noise To understand the differences in the noise levels in vertical and horizontal components of the seismological data, we analysed the difference in seasonal noise PSD mode of vertical and horizontal components. The difference in PSD mode for two consecutive seasons for the vertical and horizontal components is shown in Fig. 11 . The negative values (< 0) indicates that the noise PSD in the horizontal component is more than that of vertical component and vice versa. Results reveal that the horizontal component noise PSD level is bit higher when compared to the vertical component in the short period (cultural) range. Moreover, the vertical component has higher PSD in the period range of microseism. Also the long period noise is dominant in horizontal components, similar to the short period ranges. This could be due to the tilt and/or thermal effect or long period sources such as wind, swinging of poles or trees, etc. Interestingly, the noise levels in the long period range is slightly more in summer, this could be due to the thermal effect. The maximum difference which can be seen is slightly above 40 dB. Results are corroborating with the observations of Webb ( 2002 ), indicate that the horizontal component long period noise is ~ 10–30 dB higher than the vertical component at the surface installation of stations. The highest difference is observed at the JODA station, could be due to the noise generated by the swinging of trees and local temperature conditions. The positive difference is observed at PCH and KNUR stations up to 24 s in the long period range, which may be due to the effect of the ocean. While difference between at KNUR is higher in summer and lower in monsoon, at PCH shows very little difference in PSD values in both the seasons. At station AGMB, noise in vertical component is greater in cultural noise band initially, however, eventually becomes lesser than horizontal with increasing period. Moreover in the short period microseism and primary and secondary microseism, vertical component noise is higher than that in horizontal components. Horizontal noise remains higher than vertical throughout secondary microseism as well as in long period noise range. Results at JODA and SDPR exhibit similar trend in summer, while in monsoon its horizontal components show bit more PSD values in the primary microseism. Results at MGLI station show more noise in horizontal component than in vertical. Results at SBMN shows different behaviour as compared to other stations. It shows positive Vertical vs Horizontal differences for short period, primary as well as secondary microseim. Moreover in the long period range, it also shows horizontal component noise to be dominating with smaller difference, similar to KNUR. 4. Conclusions The importance of noise analysis is to check the quality of the data and understand the variations of noise levels at different localities of the stations. Results reveal that the cultural noise at AGMB, SDPR, MGLI stations is more prominent during the day hours. This could be due to the station locations i.e., these three stations are in the proximity of the road, having significant vehicular traffic as compared to other stations. Further, the seasonal variations were observed at almost all the stations, especially in the period range of microseism, however, noise levels are more significant at PCH and KNUR stations as they were very close to the shoreline. Interestingly, we observed the shifting of noise peaks from June-July at the southern stations to July-August at the northern stations. This could be due to the hitting of monsoon i.e., monsoon hits first in the southern side of WG and it travels towards the northern side. Further, the results indicate that the noise levels are bit high in horizontal components than that of vertical components especially in the period ranges of short and long, whereas in the microseismic period range, the noise levels are more prominent in vertical component. Overall, the noise levels at all the stations are within the global standard models (NHNM and NLNM), which yields the workable data quality at the stations installed along the WG. Declarations Statements & Declarations Ethics approval and consent to participate I, on the behalf of all the authors, give my consent to publish the manuscript Consent for publication I, on the behalf of all the authors, give my consent to publish the manuscript Availability of data and material No data/material is available from this manuscript Competing Interests The authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the Ministry of Earth Sciences, Government of India under the core program of National Centre for Earth Science Studies (NCESS). Authors’ Contributions Krishna Jha: Data curation, Formal analysis, Investigation, Writing- Original draft preparation. B. Padma Rao: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing- Original draft preparation, Writing - Review & Editing. Sribin C: Data curation Silpa S: Data curation Acknowledgements We sincerely acknowledge the Ministry of Earth Sciences, Government of India for supporting this project under the core program of the National Centre for Earth Science Studies (NCESS). References Abd el Aal, A. e.-A. K(2013). Very broadband seismic background noise analysis of permanent good vaulted seismic stations. Journal of Seismology, 17 , 223–237, https://doi.org/10.1007/s10950-012-9308-5 . Bormann, P. (2002). 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A method to establish seismic noise baselines for automated station assessment, Seismological Research Letters. 80, 628–637. Peterson, J. (1993). Observations and modelling of seismic background noise. US Geological Survey, Open-File Report: 93–322. Ringdal, F., and Bungum, H. (1977). Noise level variation at NORSAR and its effect on detectability. Bulletin of the Seismological Society of America, 67(2), 479–492. Stutzmann, E., Roult, G., and Astiz, L. (2000). GEOSCOPE station noise levels. Bulletin of the Seismological Society of America, 90(3), 690–701. Webb, S. C. (1998). Broadband seismology and noise under the ocean. Reviews of Geophysics, 36(1), 105–142. Webb, S. C., & Lee, W. H. K. (2002). Seismic noise on land and on the seafloor. International Geophysics Series, 81 (A), 305–318. Wielandt, E. (2012). Seismic sensors and their calibration. In New Manual of Seismological Observatory Practice 2 (NMSOP-2), 1–51, Deutsches GeoForschungsZentrum GFZ. Withers, M. M., Aster, R. C., Young, C. J., and Chael, E. P. (1996). High-frequency analysis of seismic background noise as a function of wind speed and shallow depth. Bulletin of the Seismological Society of America, 86(5), 1507–1515. Young, C. J., Chael, E. P., Withers, M. M., and Aster, R. C. (1996). A comparison of the high-frequency (> 1 Hz) surface and subsurface noise environment at three sites in the United States. Bulletin of the Seismological Society of America, 86(5), 1516–1528. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2023 Read the published version in Journal of Seismology → Version 1 posted Editorial decision: Major revision 24 Oct, 2022 Reviews received at journal 06 Sep, 2022 Reviewers agreed at journal 25 Aug, 2022 Reviewers invited by journal 25 Aug, 2022 Editor assigned by journal 25 Aug, 2022 Submission checks completed at journal 23 Aug, 2022 First submitted to journal 18 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1869555","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":131202126,"identity":"4b6b8c4c-504c-4a6b-b581-655a3136c894","order_by":0,"name":"Krishna Jha","email":"","orcid":"","institution":"National Centre for Earth Science Studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"","lastName":"Jha","suffix":""},{"id":131202128,"identity":"89c7ba44-a85a-44d3-b8fa-c2ee95022c1d","order_by":1,"name":"Padma Rao B","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYHACAwjFzMAgkcBgA2QxNh4gRUsaSEsDkVqAQIKB4TCYgVeLOfvhrRt+7rGTY2DnPXjjQc15u7Xth4G21NhE49Ji2ZNWdrPnWbIxAzNfskXCsdvJ284kArUcS8ttwOWqAzlmN3gOMCc2MPOYSSSw3U42OwDUwthwGLeW82/Mbv45UA/V8u9cstn5hwS03Mgxu81z4DBES2LbATuzGwRssZzxrOy2zIHjxmzMPMYWiX3JCWY3gLYk4PGLOX/ytptvDlTL8fOfMbz545udvdn59IcPPtTY4HYYjMEGpRPBKhNwKEfRAgP2eBSPglEwCkbBCAUAoShie7IWZqgAAAAASUVORK5CYII=","orcid":"","institution":"National Centre for Earth Science Studies","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Padma","middleName":"Rao","lastName":"B","suffix":""},{"id":131202129,"identity":"09279e18-9bc4-4118-b6d7-5746866c607a","order_by":2,"name":"Sribin C","email":"","orcid":"","institution":"National Centre for Earth Science Studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sribin","middleName":"","lastName":"C","suffix":""},{"id":131202130,"identity":"b7996bfe-cd47-49fd-bbb5-6eed448b301c","order_by":3,"name":"Silpa S","email":"","orcid":"","institution":"National Centre for Earth Science Studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Silpa","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2022-07-18 10:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1869555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1869555/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10950-023-10138-8","type":"published","date":"2023-02-27T19:03:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25725347,"identity":"8e5e890b-75c6-41b0-a0d5-7c57d7fce287","added_by":"auto","created_at":"2022-08-26 17:49:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":415409,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the installed broadband seismometers (red inverted triangles) along the Western Ghats. Tectonic units: SGT – Southern Granulite Terrain, WDC – Western Dharwar Craton, DVP – Deccan Volcanic Province, CG – Closepet Granite, EDC – Eastern Dharwar Craton , CB – Cuddapah Basin, EGMB – Eastern Ghats Mobile Belt and WG – Western Ghats.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/7f76a1028a56fdd3672a8fe8.png"},{"id":25725346,"identity":"c131ff3f-fb13-4042-aff0-ba0c8b17c137","added_by":"auto","created_at":"2022-08-26 17:49:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1962254,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of PSD estimated at seven stations, using one year data. The period range of data utilized to estimate the PSD is mentioned at the top of each plot. Solid gray lines indicate the NHNM, NLNM models, solid black line indicates mode, dashed lines indicate 90\u003csup\u003eth\u003c/sup\u003e and 10\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/d66cf9ff4668babdc94accab.png"},{"id":25725355,"identity":"f664d2ff-cc73-4b75-a545-043e018ce05d","added_by":"auto","created_at":"2022-08-26 17:49:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":811252,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal variations of noise PSD at seven stations. The plots are placed according to the station locations i.e., from north to south. The anomalous signal in red box at ~1 s indicates the calibration parameter induced PSD. Left Panel: East-West component; Middle Panel: North-South component; Right Panel: Vertical component.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/aba1f11dcb0b7569eb64dae4.png"},{"id":25725345,"identity":"016c71b1-dcfc-4f55-8499-1a71e07104cb","added_by":"auto","created_at":"2022-08-26 17:49:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":892281,"visible":true,"origin":"","legend":"\u003cp\u003eShort period diurnal variations of PSD (vertical component) at seven stations. The anomalous signal in red box at ~1 s indicates the calibration parameter induced PSD.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/a8aa62ae9a461db1e0842a32.png"},{"id":25725349,"identity":"a1818927-1ab4-4140-b1c4-c903d60a2044","added_by":"auto","created_at":"2022-08-26 17:49:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1156336,"visible":true,"origin":"","legend":"\u003cp\u003eSeason wise PSD plots (spring, summer, monsoon, winter) at KNUR station.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure5KNUR.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/9b93462b0882946dcc054f63.png"},{"id":25725350,"identity":"68083aaa-c357-45b2-9363-119266e740be","added_by":"auto","created_at":"2022-08-26 17:49:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":889126,"visible":true,"origin":"","legend":"\u003cp\u003ePSD mode plots at KNUR and AGMB stations. Seasonal wise variations are represented with colour code: Spring – Red; Summer – Green; Monsoon – Violet; Winter – Blue.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure6Z.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/ea86b9c7ec429f74f12b41f5.png"},{"id":25725839,"identity":"fa909dc8-1213-4f72-9314-81cdd0199313","added_by":"auto","created_at":"2022-08-26 17:54:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2299926,"visible":true,"origin":"","legend":"\u003cp\u003ePSD plots over 12 months of the year 2019 at KNUR station.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure7KNURZ.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/431607002b7b02612fa4f8de.png"},{"id":25725838,"identity":"4d52c82c-577c-46f6-986e-e13416f6dc7c","added_by":"auto","created_at":"2022-08-26 17:54:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":408306,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly PSD median values at KNUR station. The different period bands are highlighted with different colour shades. The months are represented with different colour code, indicated in the legend.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure8KNUR.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/801529e52b0ac197f2bb4ccd.png"},{"id":25726091,"identity":"61ba473b-dedd-431e-9fb9-f4ac97413872","added_by":"auto","created_at":"2022-08-26 17:59:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":143045,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly noise peak value variations at KNUR station in short-period (1-4 s), primary (4-10 s) and secondary microseism bands (10-16 s) along with their corresponding period plot.\u003c/p\u003e","description":"","filename":"Figure9KNUR.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/d83e7977f389824706a40ab2.png"},{"id":25725841,"identity":"bc6fb923-6caf-4b9b-a6c6-4fc32f69e88e","added_by":"auto","created_at":"2022-08-26 17:54:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":423463,"visible":true,"origin":"","legend":"\u003cp\u003ePeak noise period vs PSD probability and the corresponding noise peak period at KNUR station. The top part (a) of the figure illustrates period vs probability of occurrence for noise PSD. The solid line histograms are the peak period in their respective noise bands. (b) The probability percent of PSD for the corresponding period of peak mode values. The three graphs (c) on the bottom left illustrates the probability vs PSD at each period band peak period, while those on bottom right (d) illustrates the probability vs PSD for cultural noise vs corresponding periods.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure10KNUR.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/dcb74fb1432fcdcd8f202dcd.png"},{"id":25726093,"identity":"d6703efc-4c60-40f8-b321-5a124f8b37bb","added_by":"auto","created_at":"2022-08-26 17:59:12","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":701950,"visible":true,"origin":"","legend":"\u003cp\u003eFigure shows the difference of Vertical and Horizontal noise levels for two consecutive seasons (summer and monsoon). The different period bands are highlighted with different colour bands. Lines with different symbols represent the different stations.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/5bc132fe7e864ca98b619514.png"},{"id":44720664,"identity":"a9bbb8ce-c31e-4a12-a6f7-7dbb2868dd88","added_by":"auto","created_at":"2023-10-16 19:12:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3809183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/cec96fd1-ce2b-46cb-83df-ce7f2a535d1e.pdf"},{"id":25725356,"identity":"14cc2c84-cbb3-419c-b7e9-fc79f015d79c","added_by":"auto","created_at":"2022-08-26 17:49:12","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":10478135,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1869555/v1/e63e3f252d90d69a55b7e06a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Seismic Noise of Broadband Seismological Stations installed along the Western Ghats","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA seismogram contains signals generated by the earth's vibrations, including those from natural (e.g., earthquakes) and/or anthropogenic sources. Seismic noise is a continuous vibration of ground owing to a multitude of causes, it varies according to the frequencies of the vibration and manifests the temporal and spatial variance. Seasonal weather changes, day-night variations and local conditions of the area affect the spectral patterns of the seismic data. The noise hinders the uncovering of useful information hidden in a signal. The ambient noise is a composition of various frequency surface waves and can be natural and/or anthropogenic. The most common natural sources are wind, rain, rivers, ocean microseisms and earthquakes (Withers et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Webb, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; McNamara and Buland, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, anthropogenic noise sources include human activity, industrial sources, vehicular traffic, etc. The noise baseline is determined with respect to the geographical location since it may vary from site to site, type of sensor and installation stage. A signal is considered to be optimal if the noise content is minimal, which leads to better detection of useful information like earthquake detection. The sampling rate and the type of sensor installed are the two key factors determining the recording\u0026rsquo;s minimum and maximum observable periods. Noise sources in the local neighbourhood determine the noise level at each period (Webb, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and its analysis can help to recognize site conditions (de la Torre and Sheehan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Abd el Aal, 2013).\u003c/p\u003e \u003cp\u003eThe noise spectrum is classified into three frequency bands as per McNamara et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The long period consists of noise within 0.01 Hz to 0.1 Hz. The frequency band of 0.1 Hz to 1 Hz fall in the microseismic period range and that of 1 Hz to 10 Hz or higher is classified as short period noise. The long period noise is mainly caused by atmospheric effects like wind, storms, tilt and pressure. The noise in this range usually affects the horizontal components of the seismometer rather than the vertical component. Further, the seismic noise is more predominant in the microseismic period range. The noise levels in this range both in the primary and secondary microseismic range are could be due to the interaction of ocean waves with the coast. The peak in the primary microseism is generated by the superposition of ocean waves, it is equivalent to 0.2 Hz. Whereas the secondary microseism is generated by the crashing of waves on the shores, it is in between 0.05 Hz and 0.1 Hz (McNamara and Buland \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bormann \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The microseism majorly affect the coastal sites than the continental sites, which are affected majorly by cultural noise. Also, the local weather conditions affect the noise of over 1 Hz (Peterson \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Webb 1988). The noise generated by anthropogenic sources (man-made) is usually referred to as cultural noises, it falls within the short period range (\u0026gt;\u0026thinsp;1 Hz). It\u0026rsquo;s characteristics include propagation as high-frequency surface waves, attenuation in the small distance range and less depth. There is a significant difference in day and night noise levels in this category and it has characteristic frequencies depending on the source of the disturbance. Another type of locally generated noise is by the wind and swinging of towers or masts (Young et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Withers et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Noise generated by objects moved by wind falls in the high-frequency range, while those by swinging objects generate low-frequency signals. These are different from cultural noise as the latter is generally periodic with diurnal variability, thus allows it to be isolated from wind noise (Ringdal and Bungum, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; McNamara and Buland, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Further, the running water, surf, volcanic activity can also contribute to the seismic noise and the temperature effects can also produce the noise in the seismometer recordings. The ground fluctuations generated by the heating during the day and cooling during the night time can induce the tilt and long-period noise (0.01 to 0.05 Hz) in horizontal components (Stutzmann et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; McNamara and Buland, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A comparison of horizontal and vertical noise models can decipher these kinds of noises. In addition, seismometer recordings can also contain the noise generated by electrical and mechanical instrument noise (Bormann, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wielandt, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a part of the study to decipher the Western Ghats (WG) evolution, National Centre for Earth Science Studies (NCESS) deployed a network of seven broadband seismological stations along the WG, details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Most of the stations are in remote locations and close to the coast. The stations are equipped with RefTek 151B Observer sensor with RefTek DAS except for the PCH station, which is equipped with Trillium 240 instrument. All the stations are recording the continuous data at 100 samples per second. The primary objective of this experiment is to decipher the lithospheric structure and mantle deformation along the WG to shed light on it\u0026rsquo;s evolution. The WG is one of the great escarpments that extend\u0026thinsp;~\u0026thinsp;1500 km parallel to the west coast of India in the NNW-SSE direction with an elevation of ~\u0026thinsp;1.2 km. The linear extent of these Ghats begins from Gujarat in the north, south of the Narmada rift and ends at Kanyakumari in the south. The low altitude coastal plains banked by the Arabian sea is on the west side of the WG and the elevated plateau of WG comprising of Southern Granulite Terrain (SGT) in the south, Western Dharwar craton (WDC) at the centre and Deccan Volcanic Province (DVP) in the northern part. Out of our seven stations, two stations were installed in the region of SGT and four were installed in the WDC region and one station is in the region of DVP. To achieve our primary objective in this experiment, it is essential to run the stations with proper functionality. Thus, in the present study, we analysed the station performance by analysing noise levels at all the stations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and compared them to the standard noise models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of stations installed along the Western Ghats and the corresponding details.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAltitude (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensor Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eData Availability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrillium 240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2000 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKNUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJODA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRefTek 151B-120 Observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2018 - Till Date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Estimation Of Power Spectral Density","content":"\u003cp\u003eTo analyse the seismic noise, recorded at broadband seismological stations established along the WG, we utilized the power spectral density (PSD) and the probability density function (PDF) of each category of background noise. This is the standard method for quantifying seismic background noise. The PSD can be obtained by means of averaging as seismograph recordings are stochastic signals. In the present study, we utilized an open-source software PQLX, which was developed by USGS for evaluating the seismic station performance and data quality. This software requires the input waveform data and instrument response files to compute the PSD and PDFs. These measurements are computed based on the algorithm by McNamara and Buland (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The advantage of using this tool is that it accepts the raw data and removes the instrument response using the input response files. There is no need to remove earthquakes, system glitches or general data traces since these are low-probability occurrences that do not contaminate high-probability ambient seismic noise. This method uses the probability density function to calculate the power spectral density, which consider system transients into the low background and ambient noise into high probability. Earthquakes are observed in the PDFs as low probability signals at short and long periods. This provides the advantage of representing the true ambient noise levels rather than a simple minimum. In this tool, hour-long, continuous data can be processed without removing earthquakes and other data glitches. Further, the instrument response is removed by utilizing the input response files to produce the ground acceleration. The hour-long time series data is divided into 15 minutes segments with 75% overlap. The overlap time series data segments are used to reduce the variance in the PSD estimate (Cooley and Tukey, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). These are processed by removing mean, long-period trend and a 10% of sine function tapering is applied along with the Fast Fourier Transform (FFT). The tapering helps to smoothen the FFT and minimize the discontinuity effect between the beginning and end of the time series data. Further, the long period trend is removed to eliminate large scale distortions in spectral processing. The averaged value for these segments provides the one-hour time series PSD after deconvolving the seismometer instrument response. The smoothed PSD estimate is converted into decibels (dB) with respect to acceleration (m/s\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e2\u003c/sup\u003e/Hz, unit of intensity of the random vibration signal vs frequency. Later, to compute the PDF, the raw frequency distribution is constructed from the individual PSDs by binning the periods into 1/8 octave intervals and binding the power in 1 dB intervals. The process reduces the number of frequencies by a factor of 169 (McNamara and Boaz, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The power is averaged between a short period (high frequency) corner (Ts) and a long period (low frequency) corner Tl\u0026thinsp;=\u0026thinsp;2*Ts, with a centre period Tc = \u0026radic;(Ts*Tl) is the geometric mean period within the octave. The averaged power for that octave, period ranging from Ts to Tl, is stored with the centre period of the octave, Tc. Ts is incremented by 1/8 octave such that Ts\u0026thinsp;=\u0026thinsp;Ts*2\u003csup\u003e0.125\u003c/sup\u003e, to compute the average power for the next period bin. Powers are averaged within the next period range i.e., recomputed Ts to Tl and the process continues until it reach the longest resolvable period of a given time series data. This process is repeated for every 1-hour PSD estimate. Then these raw frequency bins are normalized by the total number of PSDs to estimate the PDF. The probability of a given power occurrence at a particular period is compared with the Peterson High Noise model and Low Noise model (Peterson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results And Discussion","content":"\u003cp\u003eWe computed the PSD and PDFs for seven stations installed along the WG (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Results reveal that the noise levels are within the limits of the New Low Noise Model (NLNM) and New High Noise Model (NHNM) (Peterson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) with seasonal variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The short period noise is more prominent at stations AGMB, SDPR, MGLI when compared to the other stations PCH, KNUR, SBMN, JODA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The primary reason could be due to the station locations i.e., the stations AGMB, SDPR, MGLI were bit close to the road, which is having significant vehicular traffic as compared to the other stations. Low power in a short period refers the less cultural noise. We observed more noise levels in the microseismic period at stations PCH, KNUR as they were very closer to the shoreline. Since the data is not filtered to remove earthquake signals, the body and surface waves noise can be seen in PSD/PDF data. For example, the frequency higher than 1 Hz or period lower than 1 s, low probability high power events can be attributed to the body waves of earthquakes. Similarly, for frequency less than 0.1 or a period greater than 10 s, can be attributed to earthquake surface waves. On average, the noise levels at stations PCH, KNUR, SDPR, JODA indicate in the middle of NLNM and NHNM. However, the noise levels at stations AGMB, MGLI have more towards the NHNM in the low period cultural noise band. The noise levels at station SBMN have towards the NLNM, which indicates less human-induced noise in the recordings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predominance of cultural noise at stations AGMB and MGLI can be attributed to its proximity to relatively busy roads. The 90% of noise at AGMB lies close to NHNM, especially at the period range of 0.5 to 0.9 s, similar things were observed at MGLI also. The difference between the 90th percentile and 10th percentile of the noise level (vertical component) in the month of April 2019 at SBMN is ~\u0026thinsp;7 dB in the cultural noise range and similar values were observed at AGMB station. The corresponding values at stations PCH, KNUR, SDPR, JODA, MGLI are 4 dB, 5 dB, 6 dB, 7 dB, 6 dB respectively. At all the stations, the horizontal components are noisier than the vertical component in the short period cultural noise, however, the difference between these two components is quite small. Moreover, the noise levels at all stations in the long period range are low when compared to the short period noise. In this period band, the noise levels are closer to NLNM and the difference between 10th and 90th percentile in vertical component lies between 7 dB and 20 dB in the month of April 2019. The microseismic noise also dominates at all the stations, owing to the proximity to the coast of the Arabian sea. The noise in primary microseism (4 to 10 s) is more dominant than the secondary microseism. At PCH stations, the double and single frequency peaks are observed more prominently as compared to SBMN where it is less owing to being bit farther from the coast. The effect of microseismic noise in the period range of 1 to 20 s is observed at all the stations with stability except for the seasonal variations.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Diurnal Variations\u003c/h2\u003e \u003cp\u003eWe observed the variations in the noise levels over day and night at all the stations, especially in short period cultural noise band (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The noise levels at the MGLI station is high during the daytime (~\u0026thinsp;06:00 to 18:00 hrs IST) and then decreases till 03:00 hrs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This could be due to the human activity and passages of vehicles from the nearby roads. Though the difference between the day and night noise levels at the JODA station is very small, the noise levels at day time are bit high. This could be justified with the location of the station, i.e., which is located in the forest area. At SDPR station, owing to its location nearby a local road and state highway, the noise is higher in the daytime and decreases after 20:00 hrs. The noise levels are quite strong at AGMB station over day time and continue to till midnight and decreasing for a few hours from midnight to the early morning hours. This could be due to the fact that this station is located near to the state highway, which is busy with vehicles and public transport throughout the day and even till midnight. The station SBMN is located in a habitat region and surrounded by forest, thus the noise levels are quite low and even the diurnal variations are also significantly low at this station. The location of the KNUR station is in a small village and there is no much disturbance of heavy vehicles except the human activities. Therefore, we observed low noise levels even over the daytime, however, the daytime noise levels are a bit high when compared to the nighttime noise levels. A similar pattern in noise levels is observed at the PCH station, which is also located in a similar locality. High cultural noise levels were observed at AGMB and MGLI stations and out of which the noise levels are more prominent at AGMB station even during the several hours of the night. All the stations indicate stable noise levels with very minor diurnal variations in the period range of microseism. However, noise levels are bit higher side at PCH and KNUR stations since these stations were very close to the shoreline. Even in the long period also, no significant variations in the noise levels over day and night. The Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the diurnal variations of noise PSD for three components at seven stations, which is calculated by using the four months of data (of the year 2019) at each station and the diurnal variations in a short period (cultural noise) range is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe observed that the short period noise is much stronger in the vertical component and the day-night variations seem to be smaller than that in the horizontal components, especially at AGMB. In the long period range, the horizontal components seem to be having higher noise levels than the vertical components. This could be due to the effect of thermal and tilt since long period noises are sensitive to temperature changes and tilt of the sensor. The long period noise is low in general if the sensor is insulated from the surrounding environment by using an insulating cover. The variations induced by tilt are mostly seen in the horizontal components when compared to the vertical component, corroborating with the observations from this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Seasonal Variations\u003c/h2\u003e \u003cp\u003eThe significant seasonal variations were observed in the period range of microseism rather than the short and long period ranges (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and S1). The noise levels at the PCH station indicate higher values in the monsoon period when compared to the other seasons. Moreover, not much variation in the difference between 90th and 10th percentile over the four seasons in both horizontal and vertical components especially in short and microseismic period ranges, while this difference is more in long period range. However, the mode values of PSD lie closer to the NLNM in the long period range. Similar patterns of seasonal variations were observed at KNUR station and there is an increase in noise levels in the season of monsoon especially in the microseismic period range. The difference in 90th and 10th percentile across all the stations in the monsoon season is 8 dB for the short period range and it is 12 dB for the microseismic period range. The mode values are closer to the NHNM in the short period range and to the NLNM in the long period range. The PSD values at the SBMN station also show an increase in noise levels in the season of monsoon when compared to other seasons. At AGMB station, it indicates almost stable noise levels in all seasons except for monsoon. In addition, there is an increase in noise levels in both microseismic and short period range at this station. The noise level increase in monsoon season is observed at microseismic period range at SDPR, JODA and MGLI as well. The variations of noise PSD at KNUR station is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and at other stations is shown in Figure S1 over the four seasons i.e., spring, summer, monsoon and winter for the year 2019 and the variations of PSD mode over four seasons at all the stations are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Monthly Variations\u003c/h2\u003e \u003cp\u003eTo understand the monthly variations of noise levels, we analysed the PSDs of monthly data (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and S2), results reveal that no significant variations in the short (cultural noise) period range over the 12 months at all the stations. However, the noise levels in the microseismic period range were quite strong in the months of June to September. The western part of India experiences monsoon during these months with onset in the month of June. Among them, July - August seems to be the noisiest in this period range. This could be due to the strongest period of monsoon along the western parts of India. The noise levels decay back to that of the pre-monsoon period. Majorly, the short period microseism and primary microseismic period ranges show significant noise variations. This could be explained by the stormy conditions in the sea causing violent crashing of waves on shores. The long period noise remains stable over all the months though the high temperature in the months of summer and wind conditions causing movement of trees and poles, which gets transferred to ground as long period vibrations, which may cause the long period noise. The monthly variations at station KNUR is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and at other stations is shown in figure S2. Further, the variation of PSD median values for all the 12 months at station KNUR is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and at other stations is shown in figure S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the monthly change of noise peak mode value in the period ranges of short (1\u0026ndash;4 s), primary (4\u0026ndash;10 s) and secondary (10\u0026ndash;16 s) microseism (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and S4) for a better understanding of noise level variations over the 12 months. Results reveal that the noise levels are high in the months of July and August, especially in the primary microseismic period range and falls down starts in the month of October i.e., the post-monsoon period. Interestingly, we observed the shifting of peaks from June-July at the southern stations to July-August at the northern stations. This could be due to the hitting of monsoon i.e., monsoon hits first in the southern side of WG and it travels towards the northern side. The monthly peak PSD values and their corresponding periods in the short period microseism shows that noise levels shift towards higher side with the advent of monsoon and gradually declines as monsoon recedes. This variation is very less in primary microseism and lies mostly in 4 to 5 s range. In secondary microseism, monthly PSD peaks were at 10.2 s (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Dominant Period vs Peak Noise\u003c/h2\u003e \u003cp\u003eWe analysed the monthly data to decipher the dominant period in all the noise period ranges. The observed results reveal that the dominant noise period in short and primary microseismic period ranges, shifts towards higher values of PSD during the months of monsoon. Moreover, the peak period in the short (cultural noise) period ranges remains nearly the same. The dominant periods in different period ranges at KNUR station during the February, May, July and December months of 2019 with their respective PSD values is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and at other stations is shown in Figure S5. These months falls in the spring, summer, monsoon and winter seasons, respectively. The curve in the part (b) of figure illustrates the PSD vs probability of occurrence at peak period over the months in the short (cultural noise) period ranges. This reveals that during the months experiencing the monsoon (july), the probability of occurrence is scattered in PSD values, while during the other months, it is nearly focussed around \u0026minus;\u0026thinsp;140 to -150 dB. Further, we also evaluated the monthly peak values and their corresponding periods. We observed that in the short period and primary microseismic range, the periods corresponding to peak values shifted towards the higher side of PSD values in monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The similar trend has observed at SDPR station as well. In the three components, the period corresponding to peak PSD shifts to higher side during monsoon in a short period and primary microseism noise ranges The noise levels in the cultural noise band remains in the same power limits, with the noise around 0.7 to 1 s dominating. At AGMB, peak noise period shifts to higher side in monsoon and winter. AGMB experiences heavy rain, especially in monsoon, while winter noise seems to because of traffic as a result of tourist influx and vehicle movement in the highway nearby. In summer, the curve narrows, which indicates only a particular period having more probability. This may be due to lesser vehicle movement from tourism due to extreme heat. At station JODA, results show no much variations across all seasons, except for minor increase in noise levels in monsoon. It shows similarity with AGMB in summer in terms of probability of peak noise period. Similar levels observed at MGLI station with slightly on the higher side, as a consequence of daily vehicular movement along the road nearby. Results at PCH station show peak noise period in primary and secondary microseism shifted to higher noise levels, while no significant variations being observed in the other noise ranges. PCH also shows less scattering of peak periods, which may indicate the dominance of a particular noise levels, particularly those of the microseismic, owing to proximity to the sea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Vertical vs Horizontal Noise\u003c/h2\u003e \u003cp\u003eTo understand the differences in the noise levels in vertical and horizontal components of the seismological data, we analysed the difference in seasonal noise PSD mode of vertical and horizontal components. The difference in PSD mode for two consecutive seasons for the vertical and horizontal components is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The negative values (\u0026lt;\u0026thinsp;0) indicates that the noise PSD in the horizontal component is more than that of vertical component and vice versa. Results reveal that the horizontal component noise PSD level is bit higher when compared to the vertical component in the short period (cultural) range. Moreover, the vertical component has higher PSD in the period range of microseism. Also the long period noise is dominant in horizontal components, similar to the short period ranges. This could be due to the tilt and/or thermal effect or long period sources such as wind, swinging of poles or trees, etc. Interestingly, the noise levels in the long period range is slightly more in summer, this could be due to the thermal effect. The maximum difference which can be seen is slightly above 40 dB. Results are corroborating with the observations of Webb (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), indicate that the horizontal component long period noise is ~\u0026thinsp;10\u0026ndash;30 dB higher than the vertical component at the surface installation of stations. The highest difference is observed at the JODA station, could be due to the noise generated by the swinging of trees and local temperature conditions. The positive difference is observed at PCH and KNUR stations up to 24 s in the long period range, which may be due to the effect of the ocean. While difference between at KNUR is higher in summer and lower in monsoon, at PCH shows very little difference in PSD values in both the seasons. At station AGMB, noise in vertical component is greater in cultural noise band initially, however, eventually becomes lesser than horizontal with increasing period. Moreover in the short period microseism and primary and secondary microseism, vertical component noise is higher than that in horizontal components. Horizontal noise remains higher than vertical throughout secondary microseism as well as in long period noise range. Results at JODA and SDPR exhibit similar trend in summer, while in monsoon its horizontal components show bit more PSD values in the primary microseism. Results at MGLI station show more noise in horizontal component than in vertical. Results at SBMN shows different behaviour as compared to other stations. It shows positive Vertical vs Horizontal differences for short period, primary as well as secondary microseim. Moreover in the long period range, it also shows horizontal component noise to be dominating with smaller difference, similar to KNUR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe importance of noise analysis is to check the quality of the data and understand the variations of noise levels at different localities of the stations. Results reveal that the cultural noise at AGMB, SDPR, MGLI stations is more prominent during the day hours. This could be due to the station locations i.e., these three stations are in the proximity of the road, having significant vehicular traffic as compared to other stations. Further, the seasonal variations were observed at almost all the stations, especially in the period range of microseism, however, noise levels are more significant at PCH and KNUR stations as they were very close to the shoreline. Interestingly, we observed the shifting of noise peaks from June-July at the southern stations to July-August at the northern stations. This could be due to the hitting of monsoon i.e., monsoon hits first in the southern side of WG and it travels towards the northern side. Further, the results indicate that the noise levels are bit high in horizontal components than that of vertical components especially in the period ranges of short and long, whereas in the microseismic period range, the noise levels are more prominent in vertical component. Overall, the noise levels at all the stations are within the global standard models (NHNM and NLNM), which yields the workable data quality at the stations installed along the WG.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI, on the behalf of all the authors, give my consent to publish the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI, on the behalf of all the authors, give my consent to publish the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo data/material is available from this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Earth Sciences, Government of India under the core program of National Centre for Earth Science Studies (NCESS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKrishna Jha:\u0026nbsp;\u003c/strong\u003eData curation, Formal analysis, Investigation, Writing- Original draft preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Padma Rao:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing- Original draft preparation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSribin C:\u0026nbsp;\u003c/strong\u003eData curation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSilpa S:\u003c/strong\u003e Data curation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge the Ministry of Earth Sciences, Government of India for supporting this project under the core program of the National Centre for Earth Science Studies (NCESS).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbd el Aal, A. e.-A. K(2013). Very broadband seismic background noise analysis of permanent good vaulted seismic stations. Journal of Seismology, \u003cb\u003e17\u003c/b\u003e, 223\u0026ndash;237, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10950-012-9308-5\u003c/span\u003e\u003cspan address=\"10.1007/s10950-012-9308-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBormann, P. (2002). Seismic signal and noise, in the new manual of Seismological Observatory Practice, GeoForschungsZentrum, Potsdam, Germany, 33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooley, J., and Tukey, J. (1965). An algorithm for machine calculation of complex Fourier series. Mathematics of computing, reprinted 1972. Digital signal processing. IEEE Press, New York, NY, 223\u0026ndash;227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede la Torre, T. L., and Sheehan, A. F. (2005). Broadband seismic noise analysis of the Himalayan Nepal Tibet seismic experiment. Bulletin of the Seismological Society of America, 95(3), 1202\u0026ndash;1208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNamara, D., and Boaz, R. I. (2006). Seismic Noise Analysis system using power spectral density probability density functions: A stand-alone software package. US Geological Survey, Open-File Report.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNamara, D., Buland, R.P. (2004). Ambient noise levels in continental United States. Bulletin of Seismological Society of America, 94, 1517\u0026ndash;1527.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNamara, D., Hutt, C., Gee, L., Benz, H. M., Buland, R. (2009). A method to establish seismic noise baselines for automated station assessment, Seismological Research Letters. 80, 628\u0026ndash;637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, J. (1993). Observations and modelling of seismic background noise. US Geological Survey, Open-File Report: 93\u0026ndash;322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRingdal, F., and Bungum, H. (1977). Noise level variation at NORSAR and its effect on detectability. Bulletin of the Seismological Society of America, 67(2), 479\u0026ndash;492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStutzmann, E., Roult, G., and Astiz, L. (2000). GEOSCOPE station noise levels. Bulletin of the Seismological Society of America, 90(3), 690\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb, S. C. (1998). Broadband seismology and noise under the ocean. Reviews of Geophysics, 36(1), 105\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb, S. C., \u0026amp; Lee, W. H. K. (2002). Seismic noise on land and on the seafloor. International Geophysics Series, \u003cem\u003e81\u003c/em\u003e(A), 305\u0026ndash;318.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWielandt, E. (2012). Seismic sensors and their calibration. In New Manual of Seismological Observatory Practice 2 (NMSOP-2), 1\u0026ndash;51, Deutsches GeoForschungsZentrum GFZ.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWithers, M. M., Aster, R. C., Young, C. J., and Chael, E. P. (1996). High-frequency analysis of seismic background noise as a function of wind speed and shallow depth. Bulletin of the Seismological Society of America, 86(5), 1507\u0026ndash;1515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung, C. J., Chael, E. P., Withers, M. M., and Aster, R. C. (1996). A comparison of the high-frequency (\u0026gt; 1 Hz) surface and subsurface noise environment at three sites in the United States. Bulletin of the Seismological Society of America, 86(5), 1516\u0026ndash;1528.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-seismology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jose","sideBox":"Learn more about [Journal of Seismology](http://link.springer.com/journal/10950)","snPcode":"10950","submissionUrl":"https://submission.nature.com/new-submission/10950/3","title":"Journal of Seismology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Western Ghats, Seismic Noise, PSD, PDF ","lastPublishedDoi":"10.21203/rs.3.rs-1869555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1869555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Western Ghats (WG) is one of the great escarpments that extend\u0026thinsp;~\u0026thinsp;1500 km parallel to the west coast of India in the NNW-SSE. We deployed a network of seven broadband seismological stations along the WG to decipher it\u0026rsquo;s evolution. In the present study, we investigate the characteristics of different kinds of noises at the stations by utilizing the power spectral density measurements. Further, the results are compared with the global standard noise models to assess the data quality. The PSD results reveal that the short period (cultural) noise is more prominent at stations AGMB, SDPR, MGLI when compared to the other stations, especially during day hours since these sites were in the proximity of roads. The seasonal variations are observed especially in the microseismic period range and noise levels are more prominent in the months of July to August since the western part of India experiences peak monsoon during this period. These variations are observed especially at PCH and KNUR stations as their locations were near the coastline. Further, the results indicate that the noise levels are more prominent in vertical component than that of horizontal components in the microseismic period range whereas it is reversed in the short and long period ranges. Later, the results indicate that the noise levels at all the localities of stations are within the global standard noise models, which suggest that our installation of broadband seismological stations have been successful and has good data quality.\u003c/p\u003e","manuscriptTitle":"Analysis of Seismic Noise of Broadband Seismological Stations installed along the Western Ghats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-26 17:49:09","doi":"10.21203/rs.3.rs-1869555/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-24T16:13:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-06T13:24:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"722daadd-8085-4dce-ae54-0758b81abf7a","date":"2022-08-25T13:08:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-25T13:05:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-25T12:44:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-24T02:47:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Seismology","date":"2022-07-18T10:39:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-seismology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jose","sideBox":"Learn more about [Journal of Seismology](http://link.springer.com/journal/10950)","snPcode":"10950","submissionUrl":"https://submission.nature.com/new-submission/10950/3","title":"Journal of Seismology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"071253aa-d6d2-460b-a05d-1ee2ea389680","owner":[],"postedDate":"August 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:12:24+00:00","versionOfRecord":{"articleIdentity":"rs-1869555","link":"https://doi.org/10.1007/s10950-023-10138-8","journal":{"identity":"journal-of-seismology","isVorOnly":false,"title":"Journal of Seismology"},"publishedOn":"2023-02-27 19:03:58","publishedOnDateReadable":"February 27th, 2023"},"versionCreatedAt":"2022-08-26 17:49:09","video":"","vorDoi":"10.1007/s10950-023-10138-8","vorDoiUrl":"https://doi.org/10.1007/s10950-023-10138-8","workflowStages":[]},"version":"v1","identity":"rs-1869555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1869555","identity":"rs-1869555","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}