Arrival Of Seismic Waves Research Articles

Fast and full characterization of large earthquakes from prompt elastogravity signals

Prompt ElastoGravity Signals are light-speed gravity-induced signals recorded before the arrival of seismic waves. They have raised interest for early warning applications but their weak amplitudes close to the background seismic noise have questioned their actual potential for operational use. A deep-learning model has recently demonstrated its ability to mitigate this noise limitation and to provide in near real-time the earthquake magnitude (Mw). However, this approach was efficient only for large earthquakes (Mw ≥ 8.3) of known focal mechanism. Here we show unprecedented performance in full earthquake characterization using the dense broadband seismic network deployed in Alaska and Western Canada. Our deep-learning model provides accurate magnitude and focal mechanism estimates of Mw ≥ 7.8 earthquakes, 2 minutes after origin time (hence the tsunamigenic potential). Our results represent a major step towards the routine use of prompt elastogravity signals in operational warning systems, and demonstrate its potential for tsunami warning in densely-instrumented areas.

Developing and evaluating a risk-informed decision support system for earthquake early warning at a railway bridge

Earthquake early warning (EEW) systems provide timely information on the arrival of strong seismic waves at a site. Such information can help mitigate the consequences of earthquakes on the operation of infrastructure assets. EEW-informed mitigation actions should stem from risk-based decision-making protocols, and EEW benefits should be evaluated for the range of possible rupture scenarios that affect an asset of interest. This paper addresses these challenges specifically for railway bridges by: (1) developing a risk-informed EEW decision support system (DSS) for these assets; and (2) quantifying the effectiveness of the proposed EEW-DSS in mitigating seismic risks for railway bridges across relevant rupture scenarios. The proposed EEW-DSS combines information on site-specific seismic hazard, time-dependent EEW algorithm outputs, probabilistic seismic demand modeling, damage/derailment fragilities, and seismic loss models. These modeling components compose a multi-criteria decision-making framework. Value of information theory is then proposed to estimate the loss-mitigation benefits of the EEW-DSS, accounting for varied stakeholder risk priorities as well as dynamic lead-time/accuracy tradeoffs related to EEW performance. A railway bridge with multiple piers is adopted for the case study, which demonstrates the importance of risk-based, uncertainty-informed decision-making to the overall effectiveness of EEW in potentially reducing seismic losses.

Seismic‐electromagnetic signals from two monitoring stations in Southern Italy: Electromagnetic time series release

AbstractThe seismic‐electromagnetic phenomenon entails the generation of transient electromagnetic signals, which can be observed both simultaneously (co‐seismic) and preceding (pre‐seismic) a seismic wave arrival. Following the most accredited hypothesis, these signals are mainly due to electrokinetic effects, generated on microscopic scale in porous media containing electrolytic fluids. Thus, the seismic‐electromagnetic signals are expected to be suitable for the detection and tracking of crustal fluids. Despite the growing interest in this phenomenon, there is a lack of freely available observational database of earthquake‐related electromagnetic signals recorded at co‐located seismic and magnetotelluric stations. To fill this gap, we set up two multicomponent monitoring stations in two seismically active areas of Southern Italy: the Gargano Promontory and the High Agri Valley. This work is both aimed to systematically analyse earthquake‐generated seismic‐electromagnetic recordings and to make the collected database accessible to the scientific community.

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.

High-Resolution Shallow Structure along the Anninghe Fault Zone, Sichuan, China, Constrained by Active Source Tomography

Abstract The Anninghe fault (ANHF), located in southwest China, was a major block boundary that hosted M 7.5 earthquakes historically. For seismic hazard assessment, it is critical to investigate fault properties before future earthquakes. To investigate the fault structure, we deployed three linear dense arrays with an aperture of ∼8–9 km across different segments of the ANHF from October 2019 to March 2020. More importantly, we detonated a new methane source to generate seismic waves, which is environmentally friendly and can be used in different regions such as mountainous and urban areas. After data acquisition, we first removed the noise to accurately pick up the first arrivals of seismic waves. Then, we conducted the first-arrival seismic tomography, a method commonly used in the petroleum industry, to obtain the high-resolution P-wave velocity structure. The tomographic results showed distinct low-velocity zones (LVZs) of ∼1000–1500 m in width and ∼300–400 m in depth along the fault, well consistent with the lateral distribution of site amplification that was derived from regional earthquake waveforms. These LVZs may have formed as a combined result of the fault damage zone and ANHF-controlled sediments. As the Anning River Valley is densely populated, our newly identified LVZs shed lights on earthquake hazard in the region. In addition, we demonstrate that using a combination of methane detonation sources, linear dense arrays, and active source tomography can effectively determine the shallow P-wave velocity model in complex environments (i.e., mountains and urban areas).

Hierarchical Bayesian calibration of regional moment magnitude predictive models

Onsite Earthquake Early Warning (EEW) applications require robust predictive models that can relate the early part of the recorded seismic waves to seismicity and/or ground shaking characteristics, the latter representing the variables used to make decisions about raising or not an alarm. The use of limited information to establish these models within a real-time estimation setting, typically corresponding to the first few seconds after the identification of the seismic wave arrival, introduces significant uncertainty (variability) in the predictions. To increase the number of historical observations available for the model calibration, data from multiple seismic stations within the same geographic region are utilized for the latter task. Unfortunately, this introduces additional sources of variability in the predictive model development, originating from local geographical effects and site conditions that affect differently the underlying physics at each station. This paper introduces a hierarchical Bayesian model updating framework to address this challenge. To better focus the research effort, the calibration of earthquake magnitude nonlinear regression models based on the seismic maximum predominant period is investigated, though underlying principles can be extended to other type of decision variables and intensity measures used in EEW applications. Within the hierarchical updating framework, the predictive models for each station are separately calibrated (first level), while sharing information across the stations by selecting common calibration hyper-parameters (second level). This facilitates a reduction of the variability of the estimates originating from regional wave propagation effects while simultaneously providing sufficient amount of data for calibrating the predictive models even for stations with scarce available records. To support computational efficiency in the hierarchical calibration, conjugate priors are utilized, while for accommodating nonlinear regression relationships samples from the posterior distribution, representing the model calibration, are obtained using Metropolis-within-Gibbs sampling. As illustrative case study, the application to the Sichuan region of Southwestern China is examined, with extensive comparisons performed for the accuracy and robustness of the resultant probabilistic predictive models.

Seismo-Hydrogeodynamic Effects in Groundwater Pressure Changes: A Case Study of the YuZ-5 Well on the Kamchatka Peninsula

Seismo-hydrogeodynamic effects (SHGEs) in groundwater level (pressure) variations in a range of periods from minutes to hours and days during local and teleseismic earthquakes were considered based on the data of precision observations in a deep piezometric well located in a seismically active region. With the use of the tidal analysis and frequency dependence of the barometric response of the water level, a static confined response of groundwater pressure in a range of periods from hours to the first tens of days was established. The annual water level trend was characterized by the seasonal function of a hydrostatic head change in the well. In the groundwater pressure, changes were detected due to several types of seismo-hydrogeodynamic effects: 1—the coseismic fluctuations during the first tens of seconds and minutes after the arrival of seismic waves from the earthquakes with magnitudes of 5.3–9.1 at epicentral distances of 80–700 km; 2—the supposed hydrogeodynamic precursors of the two strongest events; 3—the four types of variations under the vibration impact of seismic waves from Mw = 6.8–9.1 earthquakes at epicentral distances of 80–14,600 km. The dependence of the distinguished types of SHGEs on the earthquake parameters, the intensity of the seismic impact in the well area and the amplitude-frequency composition of seismic waves were considered.

The SHIELD’21 deep seismic experiment

The wide-angle reflection and refraction (WARR) SHIELD’21 profile carried out in 2021 crosses from SW to NE the main tectonic of Ukraine. The SHIELD’21 targeted the structure of the Earth’s crust and upper mantle of the southwestern margin of the East European Craton with overlying Neogene Carpathian Foredeep and Vendian-Paleozoic Volyn-Podolian Monocline, Archaean and Paleoproterozoic segments of Ukrainian Shield and Late Paleozoic Dnieper-Donets Basin. The ~650 km long profile is an extension of realized in 2014 RomUkrSeis line in Romania and Ukraine from Apuseni Mountains to southwestern Ukrainian Shield. The field work performed in 2021, included the deployment of autonomous seismic stations and drilling-explosive works. A total of 264 seismic receivers were deployed (160 DATA-CUBE and 104 TEXAN stations) with the average spacing between the observation points about 2.65 km. The sampling interval for all stations was 0.01 s. Seismic energy was generated by 10 shot points with ~50 km of distance between them and total charge in all wells 5775 kg. The SHIELD’21 experiment using TEXAN and DATA-CUBE short-period seismic stations provided high-quality seismic records. The main recorded seismic waves are the refractions of P- and S-waves in sediments, basement, crust and upper-most mantle, and reflections from crustal boundaries, Moho interface and boundaries in the uppermost mantle. Correlation of travel time arrivals of seismic waves provides calculation of the velocity model for both P- and S-waves. The main objective of the SHIELD’21 project is to get new seismic data that increase our knowledge on the lithosphere structure as well as geodynamics of the oil-and-gas-bearing and ore regions of Ukraine.

Numerical modeling of the co-seismic electromagnetic signals observed during the 2014 Mw 4.9 Hammam Melouane earthquake, Northern Algeria

The seismic activity in northern Algeria provides the opportunity to observe earthquake-related electromagnetic signals through magnetotelluric (MT) measurements. Through an MT station, we measured the two telluric and three orthogonal magnetic field components. On December 23rd, 2014, a moderate earthquake (Mw = 4.9) occurred in the village of Hammam Melouane (Algeria), located 36 km northeast of the MT site. This event allowed the observation of a co-seismic electromagnetic signal in the MT time series. The analysis of the electromagnetic time series using Fourier transform revealed the spectral characteristics of these co-seismic electromagnetic signals with a high spectral amplitude that started at the frequency of 0.3 Hz. Then, we simulate the electric and magnetic responses of the Hammam Melouane earthquake based on the electrokinetic mechanism. We conducted the simulations using a point source model with a realistic layered earth model and compared the synthetic signals to the observed data in the frequency band 0.3–6 Hz. The result shows that the synthetic electric signals match the measured data in both amplitude and waveform for the parts of the recordings after the arrival of the S wave with a phase shift. Similarly, the synthetic magnetic field agrees with the observed data in the waveforms with a phase shift, but their amplitudes are smaller than the observed data. In the latter part of the records (>18 s), a poor disagreement can be seen between the synthetic and observed electromagnetic field signals in their shape and amplitudes. We investigated the rotation-induced magnetic field effect as a possible mechanism for the generation of additional co-seismic magnetic signals. The synthetic magnetic fields resulting from the magnetometer rotation modeling are in agreement with the observed magnetic data in terms of amplitudes after the arrival of seismic waves. The main conclusion of this study reveals that the electrokinetic effect is responsible for the generation of co-seismic electric fields while the rotation of magnetic sensors is likely the main cause of co-seismic magnetic fields.

Восстановление данных о разности времен прихода продольной и поперечной сейсмических волн на сейсмических датчиках

We developed a method for seismic sensor data recovery and simulated seismic sensor operation using artificial neural networks. We selected two parameters to train the artificial neural network on: the time between seismograph recordings of longitudinal (primary) and transverse (secondary) seismic waves, as well as the time between primary seismic wave recordings by two seismographs located at a certain distance from each other. We used data on 2636 earthquakes that occurred in 2020 in the Republic of Dagestan for our seismograph data recovery. The existing 19 seismic stations recorded less than 60 % of the total number of earthquakes. To recover seismic data, we trained the neural network twice for each seismic sensor, the first time involving zero time differences regarding seismic wave arrival at seismographs that did not record the time, and the second time involving time differences recovered from the results of training the neural network for the first time. Times between seismic wave recordings with known data were used as inputs to train the artificial neural network, while time differences to be determined were designated as outputs. The trained neural network displays a correlation coefficient related to real time intervals between seismograph recordings of seismic waves that exceeds 0.99919. The paper provides root-mean-square error plots of the neural network operation by epochs of its training, plots demonstrating how training results calculated by the neural network matchthe initial data, and histograms of neural network operation errors

Determination of seismic wave velocities on the ground using artificial neural networks

Objective. The purpose of the study is to determine the velocities of seismic waves on the ground.Method. To determine the velocities of seismic waves in different parts of the earth's crust, using a sample obtained by mathematical modeling, an artificial neural network was trained. Using at the input of the trained neural network, the real values of the differences in the times of arrival of seismic waves on the ground, we obtain at its output the values of the velocities of seismic waves on the ground.Result. A method for determining the velocities of seismic waves on the ground using artificial neural networks is proposed. Dependences of the root-mean-square error in determining the velocities of seismic waves on the ground on the training epoch, as well as the distribution of errors in determining the coordinates of the earthquake hypocenter using the obtained information about the structure of the earth, are obtained.Conclusion. The proposed method makes it possible to determine the structure of the earth's crust more accurately than previously proposed mathematical methods.

Instantaneous tracking of earthquake growth with elastogravity signals.

Rapid and reliable estimation of large earthquake magnitude (above 8) is key to mitigating the risks associated with strong shaking and tsunamis1. Standard early warning systems based on seismic waves fail to rapidly estimate the size of such large earthquakes2–5. Geodesy-based approaches provide better estimations, but are also subject to large uncertainties and latency associated with the slowness of seismic waves. Recently discovered speed-of-light prompt elastogravity signals (PEGS) have raised hopes that these limitations may be overcome6,7, but have not been tested for operational early warning. Here we show that PEGS can be used in real time to track earthquake growth instantaneously after the event reaches a certain magnitude. We develop a deep learning model that leverages the information carried by PEGS recorded by regional broadband seismometers in Japan before the arrival of seismic waves. After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks ‘true real-time’ access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning.

Audible acoustics from low-magnitude fluid-induced earthquakes in Finland

Earthquakes are frequently accompanied by public reports of audible low-frequency noises. In 2018, public reports of booms or thunder-like noises were linked to induced earthquakes during an Engineered Geothermal System project in the Helsinki Metropolitan area. In response, two microphone arrays were deployed to record and study these acoustic signals while stimulation at the drill site continued. During the 11 day deployment, we find 39 earthquakes accompanied by possible atmospheric acoustic signals. Moment magnitudes of these events ranged from -0.07 to 1.87 with located depths of 4.8–6.5 km. Analysis of the largest event revealed a broadband frequency content, including in the audible range, and high apparent velocities across the arrays. We conclude that the audible noises were generated by local ground reverberation during the arrival of seismic body waves. The inclusion of acoustic monitoring at future geothermal development projects will be beneficial for studying seismic-to-acoustic coupling during sequences of induced earthquakes.

Theoretical Investigation on Multiple Separation of Bridge under Near-Fault Vertical Ground Motion

In order to consider the effect of near-field vertical earthquake, a model of a two-span continuous beam bridge is established. On the basis of superposition of longitudinal and vertical seismic actions, the possible separation of vertical earthquake is considered, and the influence of separation on structural failure is calculated. The transient wave characteristic function method and the indirect mode superposition method are used to solve the response theory of the bridge structure during the earthquake. When the vertical seismic excitation period is close to the vertical natural vibration period of the bridge, structure may separate. For the high-pier-type bridge, the separation enlarges the deformation of the pier and even causes the pier failure. The external conditions such as the time difference of arrival of seismic wave and the stiffness of support will affect the deformation of bridge piers. In addition, the number of separations will also change the seismic response of the pier.

Fractal analysis of seismoacoustic signals of near-surface sedimentary rocks in Kamchatka

We studied, by the mono- and multifractal detrended fluctuation analysis (DFA), time fluctuations in the dynamics of seismoacoustic data, recorded in Karymshina site, which is located in a seismic area of Kamchatka. We took a series of seismoacoustic responses to the regional seismic events with the mag-nitudes M > 4 for the period 2017–2018. The series was divided into three groups (high, medium and low) based on the amplitude of recorded seismoacoustic re-sponse. Background noise segments of the signals demonstrated monofractal behavior similar to white noise by almost constant values of generalized Hurst exponent Hq≈0.5 and very small width of the multifractal spectrum Δa ≈ 0.1. Analysis of the high amplitude seismoacoustic signals with clear P-, S- and coda waves showed that P- and S-waves demonstrate wider multifractal spectrum (ΔaP = 0.37, ΔaS = 0.35) and range of generalized Hurst exponents Hq in com-parison with coda wave, characterized by almost constant Hq and minimal width of multifractal spectrum (ΔaCODA = 0.13). We showed that the properties of the multifractal spectrum could be used in detection of seismic wave arrival, estima-tion its duration and separation of P-, S- and coda waves. Application of the monofractal DFA in a sliding window showed that the acoustic signal transits from monofractal and uncorrelated background noise (Hurst exponent equals to 0.5) into the long-range dependent state during seismic waves arrival, that is helpful in analysis of the signals, particularly in case of low amplitude acous-tic responses, usually demonstrating an unclear waveform. Difference in mul-tifractal spectrum width between the original low amplitude signal and its sur-rogates, obtained by random shuffling showed that the multifractality in the signal is dominantly due to long-range correlations.

Numerical wave propagation study of the unusual response of Nevados de Chillán volcano to two aftershocks of the 2010 MW = 8.8 Maule earthquake

In 2012, two large aftershocks of the MW = 8.8 Maule, Chile earthquake occurred near the Central Chilean coast. Both events, with magnitudes MW = 6.1 and MW = 7.1 were located about 200 km away from the volcanic/hydrothermal Nevados de Chillán (NdC) complex. The system responded with a significant increase in seismicity to the MW = 6.1 event, and with a reduction in seismicity following the latter, larger MW = 7.1 earthquake. To understand the physical changes induced by both earthquakes at the NdC, we set up a numerical wave propagation study. Our results show that these observations likely result from dynamic changes at the NdC complex during the passage of the seismic waves. In the case of the MW = 6.1 aftershock, the extensive stress transfer was large enough to induce changes at the hydrothermal system, which could have later led to an increase in fluid-driven seismicity. In the case of the MW = 7.1 earthquake, the oblique arrival of seismic waves to the main fault of the NdC due to the MW = 7.1 earthquake induced slight compressions at the reservoir and created openings in the main fault, promoting fluid migration, which can explain the reduction in seismicity beneath the main craters of the volcano. Results also show that the geometry of an underlying fault system can influence the volumetric response, suggesting that the role of local structures in earthquake-volcano interactions influence the behavior of triggered systems and should therefore not be disregarded.

Viability of long-short term memory neural networks for seismic refraction first break detection – a preliminary study

Seismic data processing and analysis focuses on identifying the arrival of seismic waves or ‘first-breaks’. The identification of the arrival of first breaks is complicated by the variance of recor...

Seismological Evidence for Lithospheric Low-Velocity Anomalies beneath the Eastern Mediterranean: Impact of Tectonics

Being a part of the eastern Mediterranean, northern Lebanon and northwestern Syria is a seismotectonically active region due to the juxtaposition of several plate boundaries, the existence of many active faults, and the intense seismicity generated along rifting, transform, and convergent plate boundaries. We applied a tomographic inversion on seismic body wave arrivals recorded at 12 seismic stations distributed in the northern part of Lebanon and northwestern Syria to study the P- and S-wave velocity (Vp, and Vs) structures and the seismogenic behavior of the upper portion of the lithosphere. The seismic data comprise 4855 and 2950 P- and S-wave arrivals, respectively, generated by 605 local crustal earthquakes which occurred mainly along the Yammouneh Fault and splays in the Eastern Mediterranean. The crustal structure is highly heterogeneous down to the upper mantle depths. Low-velocity (low-V) anomalies are widely distributed especially along the active faults delimiting the boundary between the Arabian and African plates and areas of thick sedimentary deposits and Cenozoic volcanics. Results of the checkerboard resolution test and hit count coverage indicate that the mapped velocity anomalies are reliable features. Furthermore, they are generally consistent with many geological and geophysical investigations which have been detected beneath the study area by other researchers including the emplacement of ophiolite rocks, Cenozoic volcanics, inefficient Sn propagation, low Lg Q values, and low seismic wave velocities.

Terrestrial single-station analog for constraining the martian core and deep interior: Implications for InSight

We used a terrestrial single-station seismometer to quantify the uncertainty of InSight (INterior explorations using Seismic Investigations, Geodesy and Heat Transport) data for determining Martian core size. To mimic Martian seismicity, we formed a catalog using 917 terrestrial earthquakes, from which we randomly selected events. We stacked ScS amplitudes on modeled arrival times and searched for where ScS produced coherent seismic amplitudes. A core detection was defined by a coherent peak with small offset between predicted and user-selected arrival times. Iterating the detection algorithm with varying signal-to-noise (SNR) ranges and quantity of events determined the selection frequency of each model and quantified core depth uncertainty. Increasing the quantity of events reduced core depth uncertainty while increasing the recovery rate, while increasing event SNR had little effect. Including ScS2 multiples increased the recovery rate and reduced core depth uncertainty when we used low quantities of events. The most-frequent core depths varied by back azimuth, suggesting our method is sensitive to the presence of mantle heterogeneities. When we added 1° in source distance errors, core depth uncertainty increased by up to 11 km and recovery rates decreased by <5%. Altering epicentral distances by 25% added ~35 km of uncertainty and reduced recovery rates to <50% in some cases. From these experiments, we estimate that if InSight can detect five events with high location precision (<10% epicentral distance errors), that there is at least an 88% chance of core depth recovery using ScS alone with uncertainty in core depth approaching 18 km and decreasing as more events are located. Plain language summaryWe used a seismometer on Earth to study how well the NASA mission, InSight (INterior explorations using Seismic Investigations, Geodesy and Heat Transport) could detect the Martian core. We created a catalog of earthquakes from which we randomly selected events to mimic the seismic events (marsquakes) InSight is expected to detect. Several models of Earth's interior were tested to see which best predicted the arrival of seismic waves reflecting off the core. We found utilizing more events allowed us to determine the core size better than using a limited number of events. Using an additional seismic phase also helped us recover a more accurate core depth if there were not a lot of events. We were also able to find variations in the mantle by using events within certain location ranges. Since the InSight team may not be able to locate marsquakes as accurately as we locate earthquakes, we changed the location of the earthquakes. When changes in location were small, we were still able to determine the size of the core. Larger changes in location prevented us from finding an accurate core depth. If InSight detects at least five events with high location accuracy, we will be able to determine the Martian core size.

ANOMALOUS ERRORS IN DETERMINING FOCUS COORDINATES EARTHQUAKES AND SUGGESTIONS FOR THEIR ELIMINATION

Objectives The aim of the study is to develop a method for estimating the speed of seismic waves in different directions of propagation and by taking into account the dimensions of the focus, reducing the error in determining the coordinates of the hypocenter. Method To find the hypocenter of the earthquake, the data of the seismic wave velocities, the differences in the times of arrival of seismic waves on seismic sensors and the error in determining the time difference are used. The data with an error determine the coordinates of the hypocenter using information from various combinations of seismic sensors. Processing the resulting array of coordinates, estimates the seismic wave velocities / or determines the spatial shape of the earthquake source and the coordinates of the hypocenter. According to the coordinates of the cinema center, the differences in the travel time of seismic waves are corrected and the distances to the seismic sensors are refined. Results After preliminary determination of the coordinates and shape of the earthquake source, if there are a large number of seismic sensors, it is possible to clarify the coordinates of the earthquake hypocenter taking into account the recommendations given in the works. Conclusion Using the proposed method implies the presence of a large number of sensors to determine the complex shape, the earthquake source.