Analysis of Radon Measurements in Relation to Daily Seismic Activity Rates in the Vrancea Region, Romania

Many previous research studies have shown how local and even regional earthquakes can significantly affect the release of radon in the soil. The aim of this work is to investigate the relationship between radon measurements and the daily seismic activity rate and develop a methodology that allows estimating the seismic activity rate using only radon measurements. To carry out this study, the earthquake catalogue of the Vrancea region (Romania) has been used to estimate the daily seismic activity rate during a given time period, in which radon measurements were also recorded, from January 2016 to September 2020. The Vrancea zone represents the most active seismic zone in Europe and is located on the eastern edge of the strongly bent Carpathian arc. In the case of the radon measurements, seasonal behaviours and linear trends due to non-seismic factors have been identified and subsequently removed. The discrete wavelet transform has been used to analyse the radon signal at two different scales: long and short periods. From the analysis carried out on a long-period scale, an approximate linear relationship has been obtained between the radon series and the daily seismic activity rate, which provides insights into the behaviour of the seismic activity in the study region with only the radon information. In addition, the study reveals certain characteristics that could be used as precursors of earthquakes at different scales: weeks in the case of the estimated daily seismic activity rate, and days in the case of the short-period signal obtained by the wavelet analysis. The results obtained for this region allow us to hope that the analysis of the radon time series can become an effective complement to the conventional seismic analysis used in operational earthquake forecasting.This study was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 821046, the Spanish Government through research projects CGL2016-77688-R and RTI2018-099052-B-I00, and Research Group VIGROB-116 (University of Alicante)

Wavelet analysis applied on temporal data sets in order to reveal possible pre-seismic radio anomalies and comparison with the trend of the raw data

Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10-50 kHz) and LF (150-300 kHz) radio precursors of earthquakes. Precursors can be related to “anomalies” in the night-time behavior of VLF signals. A suitable method of analysis is the use of the Wavelet spectra. Using the “Morlet function”, the Wavelet transform of a time signal is a complex series that can be usefully represented by its square amplitude, i.e. considering the so-called Wavelet power spectrum. The power spectrum is a 2D diagram that, once properly normalized with respect to the power of the white noise, gives information on the strength and precise time of occurrence of the various Fourier components, which are present in the original time series. The main difference between the Wavelet power spectra and the Fourier power spectra for the time series is that the former identifies the frequency content along the operational time, which cannot be done with the latter. Anomalies are identified as regions of the Wavelet spectrogram characterized by a sudden increase in the power strength. On January 30, 2020 an earthquake with Mw= 6.0 occurred in Dodecanese Islands. The results of the Wavelet analysis carried out on data collected some INFREP receivers is compared with the trends of the raw data. The time series from January 24, 2020 till January 31, 2000 was analyzed. The Wavelet spectrogram shows a peak corresponding to a period of 1 day on the days before January 30. This anomaly was found for signals transmitted at the frequencies 19,58 kHz, 20, 27 kHz, 23,40 kHz with an energy in the peak increasing from 19,58 kHz to 23,40 kHz. In particular, the signal at the frequency 19,58 kHz, shows a peak on January 29, while the frequencies 20,27 kHz and 23,40 kHz are characterized by a peak starting on January 28 and continuing to January 29. The results presented in this work shows the perspective use of the Wavelet spectrum analysis as an operational tool for the detection of anomalies in VLF and LF signal potentially related to EQ precursors

Study of VLF/LF wave propagations above seismic areas

Abstract: We report on radio transmitter signals recorded in Europe by INFREP network which is mainly devoted to search for earthquakes electromagnetic precursors (Biagi et al., 2011). We consider in this analysis the detection of transmitter signals recorded by INFREP receivers located in different regions of Europe, i.e. Romania, Italy, Greece and Austria. The aim is the investigation of the electromagnetic environment above earthquakes regions. We selected seismic events which occurred in the year 2016 and characterized by a moment magnitude (Mw) above 5.0 and a depth of less than 50 km. A common method is applied to all events and which involves the analysis of the VLF/LF signal detection taking into consideration the following parameters: (a) the distance transmitters-receivers, (b) the signal to noise ratio during the diurnal and night observations, (c) the daily and night averaged amplitude and (d) the sunset and sunrise termination times. This leads us to specify the key factors which can be considered as criteria to distinguish and to identify earthquakes precursors. We discuss in this contribution the radio wave propagation in the D- and E-layers and their impacts on the VLF/LF amplitude signal. We show that the 'seismic anomaly' requests a more precise analysis of the 'quiet' and 'disturbed' ionospheric conditions and their corresponding spectral traces on the VLF/LF transmitter signals

Prospective Neural Network Model for Seismic Precursory Signal Detection in Geomagnetic Field Records

We designed a convolutional neural network application to detect seismic precursors in geomagnetic field records. Earthquakes are among the most destructive natural hazards on Earth, yet their short-term forecasting has not been achieved. Stress loading in dry rocks can generate electric currents that cause short-term changes to the geomagnetic field, yielding theoretically detectable pre-earthquake electromagnetic emissions. We propose a CNN model that scans windows of geomagnetic data streams and self-updates using nearby earthquakes as labels, under strict detectability criteria. We show how this model can be applied in three key seismotectonic settings, where geomagnetic observatories are optimally located in high-seismicity-rate epicentral areas. CNNs require large datasets to be able to accurately label seismic precursors, so we expect the model to improve as more data become available with time. At present, there is no synthetic data generator for this kind of application, so artificial data augmentation is not yet possible. However, this deep learning model serves to illustrate its potential usage in earthquake forecasting in a systematic and unbiased way. Our method can be prospectively applied to any kind of three-component dataset that may be physically connected to seismogenic processes at a given depth

Prospective Neural Network Model for Seismic Precursory Signal Detection in Geomagnetic Field Records

We designed a convolutional neural network application to detect seismic precursors in geomagnetic field records. Earthquakes are among the most destructive natural hazards on Earth, yet their short-term forecasting has not been achieved. Stress loading in dry rocks can generate electric currents that cause short-term changes to the geomagnetic field, yielding theoretically detectable pre-earthquake electromagnetic emissions. We propose a CNN model that scans windows of geomagnetic data streams and self-updates using nearby earthquakes as labels, under strict detectability criteria. We show how this model can be applied in three key seismotectonic settings, where geomagnetic observatories are optimally located in high-seismicity-rate epicentral areas. CNNs require large datasets to be able to accurately label seismic precursors, so we expect the model to improve as more data become available with time. At present, there is no synthetic data generator for this kind of application, so artificial data augmentation is not yet possible. However, this deep learning model serves to illustrate its potential usage in earthquake forecasting in a systematic and unbiased way. Our method can be prospectively applied to any kind of three-component dataset that may be physically connected to seismogenic processes at a given depth

Correlation between Seismic Waves Velocity Changes and the Occurrence of Moderate Earthquakes at the Bending of the Eastern Carpathians (Vrancea)

Seismic velocity is the geophysical property that has a key role in characterizing dynamic processes and the state of the stress around the faults, providing valuable information regarding the change in the tectonic regime. The stress in the crust is an important indicator of the possible occurrence of a major earthquake, and the variation of seismic velocities, in time, can provide a clearer picture on the tectonic processes taking place in the region. In the crust, velocities change before, during, and after earthquakes through several mechanisms related to fault deformations, pore pressure, stress changes, and recovery processes. In this study, we investigate the possible correlation between the changes of seismic velocities (Vp/Vs) in time and the occurrence of moderate size crustal and intermediate depth earthquakes from the Vrancea region. Our findings show that there are no significant variations in Vp/Vs for the intermediate depth earthquakes, while crustal events have decreased seismic activity prior to the main earthquake and no high Vp/Vs anomalies. Our results indicate key aspects, and such analyses should be carried out in real-time to continuously explore any unusual pattern pointed out by the seismic velocity changes. Vp/Vs and their standard errors can also be used to describe seismic activity patterns that shape the tectonic evolution of the area

Exploring the Relationship between Geomagnetic Variations and Seismic Energy Release in Proximity to the Vrancea Seismic Zone

Understanding the seismo–ionospheric coupling mechanism requires a quiet geomagnetic condition, as this represents an ideal situation to detect abnormal variations in the geomagnetic field. In reality, continuous interactions between solar wind and Earth’s magnetosphere create many fluctuations in the geomagnetic field that are more related to sun–magnetosphere interactions than to seismotectonic causes. A triaxial magnetometer was installed at the Muntele Rosu Observatory near the Vrancea seismic zone in 1996 to measure the local magnetic field. Since 2002, the data have become more consistent, allowing for the representation of long time series. Since then, variations have been observed on the eastern component (By) of the magnetic field, which sometimes overlaps with significant earthquakes. Previous studies have shown that high decreases in amplitude recorded on the By component of the magnetic field measured at Muntele Rosu have been accompanied by higher seismicity, while small decreases have been accompanied by lower seismic energy release. This research analyzes the geomagnetic data collected between September 2002 and May 2008 from two geomagnetic observatories, one located in the proximity of the Vrancea seismic zone and another one situated 120 km away. For each geomagnetic anomaly identified, the daily seismic energy released was plotted logarithmically, along with seismicity and Kp indices. Additionally, the daily seismic energy released was also plotted logarithmically for all earthquakes with Mw ≥3. To identify variations in the By component, datasets recorded at Muntele Rosu (MLR) were compared with those recorded at Surlari National Geomagnetic Observatory (SUA), to discriminate between global magnetic variations associated with solar activity and possible seismo–electromagnetic variations. The standard deviation (SDBy) was calculated for each anomaly recorded on the By component of the magnetic field and compared with the cumulative seismic energy release. To determine if this type of variation was present in other components of the magnetic field, the following ratios were calculated for all data recorded at Muntele Rosu: Bz/Bx, Bz/By, and Bz/BH. The size of the anomalies resulting from the standard deviation measured on the By component (SDBy) partially validates the relationship between the size of the anomalies and the seismic energy release during the anomaly. The relationship between the released seismic energy and the anomaly magnitude is vaguely respected, but these variations seem to follow two patterns. One pattern is described by smooth decreases, and the other pattern involves decreases where the By component varies significantly over short periods, generating decreases/increases in steps. It was noticed that seismic activity is greater for the second pattern. Additionally, using standard deviation measured on the magnetic field represents a great tool to discriminate external magnetic field variations from local, possibly seismo–magnetic variations

Correlation between Seismic Waves Velocity Changes and the Occurrence of Moderate Earthquakes at the Bending of the Eastern Carpathians (Vrancea)

Seismic velocity is the geophysical property that has a key role in characterizing dynamic processes and the state of the stress around the faults, providing valuable information regarding the change in the tectonic regime. The stress in the crust is an important indicator of the possible occurrence of a major earthquake, and the variation of seismic velocities, in time, can provide a clearer picture on the tectonic processes taking place in the region. In the crust, velocities change before, during, and after earthquakes through several mechanisms related to fault deformations, pore pressure, stress changes, and recovery processes. In this study, we investigate the possible correlation between the changes of seismic velocities (Vp/Vs) in time and the occurrence of moderate size crustal and intermediate depth earthquakes from the Vrancea region. Our findings show that there are no significant variations in Vp/Vs for the intermediate depth earthquakes, while crustal events have decreased seismic activity prior to the main earthquake and no high Vp/Vs anomalies. Our results indicate key aspects, and such analyses should be carried out in real-time to continuously explore any unusual pattern pointed out by the seismic velocity changes. Vp/Vs and their standard errors can also be used to describe seismic activity patterns that shape the tectonic evolution of the area

Correlation of geomagnetic anomalies recorded at Muntele Rosu Seismic Observatory (Romania) with earthquake occurrence and solar magnetic storms

<p>The study presents a statistical cross-correlation between geomagnetic anomalies, earthquake occurrence and solar magnetic storms. The working data are from: (i) geomagnetic field records from Muntele Rosu (MLR) Observatory, and from Surlari (SUA) and/or Tihany (THY) INTERMAGNET Observatories; (ii) seismic data for the Vrancea source zone; and (iii) daily geomagnetic indices from the NOAA/Space Weather Prediction Center. All of the geomagnetic datasets were recorded from 1996 to the present, at MLR, SUA or THY, and they were automatically corrected using a LabVIEW program developed especially for this purpose, highlighting the missing or bad data. Missing data blocks were completed with the last good measured value. After correction of the data, there were a number of issues seen regarding previous interpretations of the geomagnetic anomalies. Some geomagnetic anomalies identified as precursory signals were found to be induced either by increased solar activity or by malfunction of the data acquisition system, which produced inconsistent data, with numerous gaps. The MLR geomagnetic data are compared with the data recorded at SUA/THY and correlated with seismicity and solar activity. These 15 years of investigations cover more than a complete solar cycle, during which time the solar-terrestrial perturbations have fluctuated from very low to very high values, providing the ideal medium to investigate the correlations between the geomagnetic field perturbations, the earthquakes and the solar activity. The largest intermediate depth earthquake produced in this interval had a moment magnitude Mw 6.0 (2004) and provided the opportunity to investigate possible connections between local geomagnetic field behavior and local intermediate seismicity.</p><p> </p&gt

FastICA Algorithm Applied on Black Sea Water-Level Ultrasound Measurements

The parameters influencing the sea level measured with ultrasonic devices that are analyzed in this paper are the air temperature, atmospheric pressure and wind speed. As these variations are independent to each other and to the sea level, they can be removed from the measured sea level by applying a filtering algorithm based on independent component analysis (FastICA), adapted and improved for this application. The sound speed increases with temperature, so an internal temperature sensor is required to compensate for the sound-speed variation. Though this may improve the measurement accuracy, it is not enough to achieve the best results because there is a discrepancy between the internal sensor and the actual environment temperature. For high accuracy measurements, an external temperature sensor is required. In our case, we imported temperature datasets from a weather station, along with other datasets regarding atmospheric pressure and wind speed. The use of these external datasets, along with an algorithm based on principal component analysis (PCA) for error removal and the filtering algorithm based on FastICA for environmental phenomena extraction, allows us to achieve more accurate values for the Black Sea level in Constanta (2017–2020), independent of external influences