A threshold-based earthquake early warning using dense accelerometer networks

Most earthquake early warning systems (EEWS) developed so far are conceived as either ‘regional’ (network-based) or ‘on-site’ (stand-alone) systems. The recent implementation of nationwide, high dynamic range, dense accelerometer arrays makes now available, potentially in real time, unsaturated waveforms of moderate-to-large magnitude earthquakes recorded at very short epicentral distances (<10–20 km). This would allow for a drastic increase of the early warning lead-time, for example, the time between the alert notification and the arrival time of potentially destructive waves at a given target site. By analysing strong motion data from modern accelerograph networks in Japan, Taiwan and Italy, we propose an integrated regional/on-site early warning method, which can be used in the very first seconds after a moderate-to-large earthquake to map the most probable damaged zones. The method is based on the real-time measurement of the period (τ_c) and peak displacement (Pd) parameters at stations located at increasing distances from the earthquake epicentre. The recorded values of early warning parameters are compared to threshold values, which are set for a minimum magnitude 6 and instrumental intensity VII, according to the empirical regression analyses of strong motion data. At each recording site the alert level is assigned based on a decisional table with four alert levels defined upon critical values of the parameters Pd and τ_c, which are set according to the error bounds estimated on the derived prediction equations. Given a real time, evolutionary estimation of earthquake location from first P arrivals, the method furnishes an estimation of the extent of potential damage zone as inferred from continuously updated averages of the period parameter and from mapping of the alert levels determined at the near-source accelerometer stations. The off-line application of the method to strong motion records of the M_w 6.3, 2009 Central Italy earthquake shows a very consistent match between the rapidly predicted (within a few seconds from the first recorded P wave) and observed damage zone, the latter being mapped from detailed macroseismic surveys a few days after the event. The proposed approach is suitable for Italy, where, during the last two decades, a dense network of wide dynamic-range accelerometer arrays has been deployed by the Department of Civil Protection (DPC), the Istituto Nazionale di Geofisica e Vulcanologia (INGV) and other regional research agencies

High Resolution Attenuation Images From Active Seismic Data: The Case Study of Solfatara Volcano (Southern Italy)

&lt;p&gt;The anelastic attenuation of rocks strongly depends on the contained fluid physical state and saturation. Furthermore, it is more sensitive than elastic parameters to changes in the physical state of materials. In a geologically complex&amp;#160; volcanic context, where fluids play a very important role, anelastic imaging of the subsoil is therefore a very powerful tool for a better understanding of its dynamics.&lt;/p&gt;&lt;p&gt;In this study we present a robust workflow aimed at retrieve accurate 1-D and 3-D anelastic models from the processing of active seismic data, in terms of lateral and depth variations of P-wave quality factors Q&lt;sub&gt;P&lt;/sub&gt;. This methodology has been applied to data collected during a high resolution active seismic experiment in a very small-scale volcanic volume, the Solfatara crater, within Campi Flegri caldera, Southern Italy. The presented methodology is developed in three distinct steps: 1) the active seismic data have been properly processed and analyzed for measuring the t* attenuation parameter for all possible source-receivers couples. First, the source contribution has been removed by cross-correlating the recorded signal with the sweep function of the Vibroseis, which was the adopted active seismic source. Then, the spectral decay method has been applied in order to compute the t* values. 2) A reference 1-D attenuation model has been retrieved by means of a grid search procedure aiming at finding the 1-D Qp structure that minimizes the residual between the average observed t* and the theoretical t* distributions. The obtained starting reference model allowed to build a preliminary map of t* residuals through which the retrieved t* dataset has been validated. 3) The 15,296 t* measurements have been inverted by means of a linearized, perturbative approach, in a 160 x 160 x 45 m&lt;sup&gt;3 &lt;/sup&gt;tomographic grid.&lt;/p&gt;&lt;p&gt;The retrieved 3-D attenuation model describes the first 30 m depths of Solfatara volcano as composed of very high attenuating materials, with Qp values ranging between 5 and 40. The very low Qp values, correlated with low Vp values retrieved by a previous tomographic work carried out in the area, indicate the low consolidation degree of very superficial volcanic materials of Solfatara volcano. Finally, in the NE part of the crater, lower attenuating bodies have been imaged: it is a further hint for characterizing this area of the volcano as the shallow release of the CO&lt;sub&gt;2 &lt;/sub&gt;plume through the main fumaroles of the crater.&lt;/p&gt

Test of a Threshold‐Based Earthquake Early‐Warning Method Using Japanese Data

Most of existing earthquake early‐warning systems are regional or on‐site systems. A new concept is the integration of these approaches for the definition of alert levels and the estimation of the earthquake potential damage zone (PDZ). The key element of the method is the real‐time, simultaneous measurement of initial peak displacement (P_d) and period parameter (τ_c) in a 3‐s window after the first P‐wave arrival time at accelerometer stations located at increasing distances from the epicenter. As for the on‐site approach, the recorded values of P_d and τ_c are compared to threshold values, which are set for a minimum magnitude M 6 and instrumental intensity I_MM VII, according to empirical regression analysis of strong‐motion data from different seismic regions. At each recording site the alert level is assigned based on a decisional table with four entries defined by threshold values of the parameters P_d and τ_c. A regional network of stations provides the event location and transmits the information about the alert levels recorded at near‐source stations to more distant sites, before the arrival of the most destructive phase. We present the results of performance tests of this method using ten M>6 Japanese earthquakes that occurred in the period 2000–2009 and propose a very robust methodology for mapping the PDZ in the first seconds after a moderate‐to‐large earthquake. The studied cases displayed a very good matching between the rapidly predicted earthquake PDZ inferred from initial P‐peak displacement amplitudes and the instrumental intensity map, the latter being mapped after the event, using peak ground velocity and/or acceleration, or from field macroseismic surveys

CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas

First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology

Three dimensional seismic imaging and earthquake locations in a complex, segmented fault region in Southern Apennines (Italy)

The southern Apennines of Italy have been experienced several destructive earthquakes both in historic and recent times. The present day seismicity, characterized by small-to-moderate magnitude earthquakes, was used like a probe to obatin a deeper knowledge of the fault structures where the largest earthquakes occurred in the past. With the aim to infer a three dimensional seismic image both the problem of data quality and the selection of a reliable and robust tomographic inversion strategy have been faced. The data quality has been obtained to develop optimized procedures for the measurements of P- and S-wave arrival times, through the use of polarization filtering and to the application of a refined re-picking technique based on cross-correlation of waveforms. A technique of iterative tomographic inversion, linearized, damped combined with a strategy of multiscale inversion type has been adopted. The retrieved P-wave velocity model indicates the presence of a strong velocity variation along a direction orthogonal to the Apenninic chain. This variation defines two domains which are characterized by a relatively low and high velocity values. From the comparison between the inferred P-wave velocity model with a portion of a structural section available in literature, the high velocity body was correlated with the Apulia carbonatic platforms whereas the low velocity bodies was associated to the basinal deposits. The deduced Vp/Vs ratio shows that the ratio is lower than 1.8 in the shallower part of the model, while for depths ranging between 5 km and 12 km the ratio increases up to 2.1 in correspondence to the area of higher seismicity. This confirms that areas characterized by higher values are more prone to generate earthquakes as a response to the presence of fluids and higher pore-pressures

An application of automatic event detection based on neural network at St Gallen (Switzerland) deep geothermal field

In seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences.The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters. In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components.The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (MLcorr) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well.We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time.This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research

Ground motion prediction equations as a proxy for medium properties variation due to geothermal resources exploitation

Sub surface operations for energy production such as gas storage, fluid injection or hydraulic fracking modify the physical properties of the crust, in particular seismic velocity and anelastic attenuation. Continuously measuring these properties may be crucial to monitor the status of the reservoir. Here we propose a not usual use of the empirical ground-motion prediction equations (GMPEs) to monitor large-scale medium properties variations in a reservoir during fluid injection experiments. In practice, peak-ground velocities recorded during field operations are used to update the coefficients of a reference GMPE whose variation can be physically interpreted in terms of anelastic attenuation and seismic velocity. We apply the technique to earthquakes recorded at The Geysers geothermal field in Southern California and events occurred in the St. Gallen (Switzerland) geothermal field. Our results suggest that the GMPEs can be effectively used as a proxy for some reservoir properties variation by using induced earthquakes recorded at relatively dense networks

Seismic Activity in the Central Adriatic Offshore of Italy: A Review of the 1987 ML 5 Porto San Giorgio Earthquake

On 3 July 1987, a seismic sequence, with a mainshock of M(L )5, took place in the offshore Adriatic, close to the coast of Porto San Giorgio (PSG), Italy. We present an accurate relocation of the PSG seismic sequence using a nonlinear probabilistic approach (Lomax et al, 2000). The trade-off between the hypo-central location and the velocity model was exhaustively explored using six different velocity models available for the area provided by previous studies. Through numerous tests performed by relocating the mainshock, we selected the two best velocity models providing two different depths (2.0 and 18.0 km). To resolve this intrinsic ambiguity, we developed a technique that uses the macroseismic intensity field data based on a grid search of the magnitude-depth space. The results show that the mainshock has a depth of 5.7 km and a magnitude (M-L) equal to 5; moreover, the relocated seismic sequence (similar to 30 events) developed in the upper portion of the crust (at a depth less than 15 km), thus activating thrust faults, which is typical of the main geological features that characterize the outer Apennines thrust belt and the Adriatic foreland folds. Because the Adriatic Sea hosts several hydrocarbon (mainly gas) production fields located near active faults, with some of them in the area of this study, analyzing the instrumental seismicity is necessary to better understand the seismicity generated by these seismogenic faults and improve the assessment of the area's seismic hazards