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Analysis of induced seismicity recorded in the period 1 September 2007 through 15 November 2010 for Z1. Panel 3a shows the monthly average injection rate in millions of gallons per day (mgd) for the entire Geysers field. Panel 3b shows the minimum magnitude of completeness M c as function of time, including uncertainties. Panel 3c shows the b-values and the uncertainties as functions of time using a one month time window. Panel 3d shows the weekly seismicity rate (gray lines) and the monthly seismicity rate (black lines). The squares identify the dates of the selected observation periods during which seismic-hazard analysis was performed.
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The growing installation of industrial facilities for subsurface exploration worldwide requires continuous refinements in understanding both the mechanisms by which seismicity is induced by field operations and the related seismic hazard. Particularly in proximity of densely populated areas, induced low-to-moderate magnitude seismicity characterize...
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... studies dealing with induced seismicity, one of the most debated issues concerns the selection of the maximum magnitude value M max that could be induced by field opera- tions. As an example, Shapiro et al. (2007) proposed a tech- nique for estimating M max from an analysis of injection duration, the strength of the injection source, and rock prop- erties such as hydraulic diffusivity. Recently, Shapiro et al. (2010) also proposed the seismogenic index to quantify the seismotectonic state at an injection location. The seismo- genic index depends only on the tectonic features and is in- dependent of injection time or other injection characteristics, whose value correlates with the probability of a significant magnitude event. However, particularly for the period of interest analyzed in the present paper, detailed data about injection rate and extraction rate are not freely available. As a consequence, we follow Van Eck et al. (2006) and estimate M max for each source zone by using the technique of Makropoulos and Burton (1983), although it is based on a stationary assumption. The technique assumes that the total energy that may be accumulated and released in a seismo- genic volume is fairly constant in the considered time win- dow. This hypothesis can be considered valid if the whole duration of the analyzed dataset is taken into account. Then, when the cumulative energy is plotted as function of time (Fig. 2), the distance between the two parallel lines envelop- ing the released energy correlates with the upper limit of the energy that would be observed in the region, if the accumu- lated energy during the time was released by a single earth- quake. From the analyzed dataset we obtain M max values of 4.5 for Z1 and 3.8 for Z2, respectively, as shown in Figure 2. These estimated values are coherent with the observations reported in Table 2 that confirm that the magnitude M 4.5 has never been exceeded in the analyzed period. Table 1 Regression Coefficients and Relative Uncertainty of Equation (4). Figures 3d and 4d indicate the weekly seismicity rates that provide a detailed picture of the activity rate. However, for the purposes of performing a stable PSHA, a monthly observation time is considered. A one month time window permits a statistically significant data sample for computing both the b-value and seismicity rate. The same time window has been selected by EberhartPhillips and Oppenheimer (1984) and Majer and Peterson (2007) as it enables capture of the main features of the tem- poral seismicity evolution, such as the difference between win- ter and summer months. Thus, assuming a non-homogenous Poisson model for earthquake occurrence, we integrated the seismicity rate α t in each time interval of one month length, and the resulting α values are plotted as black lines in Figure 3d for Z1 and Figure 4d for Z2. As a general consid- eration, it is evident that the monthly seismicity rate level in Z1 is on average three times that in Z2. Particularly for Z1, some interesting insights can be gained from the analysis of the plots. At a large timescale, three main peaks in the seismicity rate can be observed corresponding to March 2008, January 2009, and November 2009, while the rate is quite constant between January 2010 and July 2010. In each year, a seasonal variation of the seismicity rate can be noted, Figure 2. Results of the maximum-magnitude estimate obtained using the technique proposed by Makropoulos and Burton (1983). The left panel refers to zone Z1 and the right panel refers to zone Z2. In each panel the black line corresponds to the cumulative energy in the respective zone, the dashed-gray line corresponds to the average trend, and continuous-gray lines correspond to the upper and lower limit of the cumulative energy. The two crosses indicate the maximum and minimum cumulative energy, respectively, whose difference provides the estimated maximum magnitude. which is related to changes in the amount of water injection throughout the year as shown in Figures 3a and 4a. A similar observation has been made by Majer and Peterson (2007) at The Geysers for the period from 2000 to mid-2006. However, these observations must be accompanied with the analysis of M c , which shows several fluctuations, particularly for Z2. This means that the catalog has variable minimum-magnitude of completeness, which reflects several effects, such as seis- mic-network malfunctioning or improvements. Looking at the M c value variation as function of time in our present application, we choose to use only events with magnitude lar- ger than 1.2 for subsequent ...
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Citations
... The framework has been used by the United States Geological Survey (USGS) to produce one-year hazard maps in areas with induced seismicity [e.g. Petersen et al., 2017], and by Convertito et al. [2012] and Bourne et al. [2014 to assess the time-dependent seismic hazard due to geothermal operations and fluid extraction, respectively. A number of studies have gone one step further performing seismic hazard and risk assessment of varying complexity for the Groningen gas field van Elk et al., 2019], for an Enhanced Geothermal System in Basel [Mignan et al., 2015], for a HF sequence in the UK [Edwards et al., 2021] ...
... As for the possibility of real-time applications of the proposed technique, the results of the warning strategy suggest that a system based on three levels (No warning, Warning, and Alert) can be implemented, which can issue an alert hours before the occurrence of a significant earthquake in the framework of the induced seismicity. This may be of great help to avoid adverse consequences -social and economic -during field operations and reduce seismic hazard related to induced seismicity [55][56][57][58] . In fact, the computational times for the warning strategy are quite short, less than half a second to process three whole series, i.e. 4600 observations. ...
Precursory phenomena to earthquakes have always attracted researchers’ attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.
... In this context, the present study aims to develop a nonparametric and robust ANN model to investigate groundmotions from induced seismicity, recorded at The Geysers geothermal region. Similar to other exploited areas for which induced earthquakes have been shown to represent a threat due to their shallow depths and relatively high frequency content (e.g., Van Eck et al., 2006;Bachmann et al., 2011;Bommer et al., 2016), several studies demonstrate that The Geysers-induced earthquakes represent a hazard for population in surrounding areas and on structures (e.g., Majer and Petersen, 2007;Convertito et al., 2012). Studies such as Convertito et al. (2012) show that observed peak ground acceleration (PGA) in The Geysers geothermal area has exceeded 120 cm/s 2 (around 12% of g; g being the acceleration of gravity). ...
... Similar to other exploited areas for which induced earthquakes have been shown to represent a threat due to their shallow depths and relatively high frequency content (e.g., Van Eck et al., 2006;Bachmann et al., 2011;Bommer et al., 2016), several studies demonstrate that The Geysers-induced earthquakes represent a hazard for population in surrounding areas and on structures (e.g., Majer and Petersen, 2007;Convertito et al., 2012). Studies such as Convertito et al. (2012) show that observed peak ground acceleration (PGA) in The Geysers geothermal area has exceeded 120 cm/s 2 (around 12% of g; g being the acceleration of gravity). According to the Modified Mercalli Intensity (MMI) scale, this value corresponds to light-to-moderate shaking level, which can be annoying for people living close to the field. ...
Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed by Sharma et al. (2013) for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both the R 2 statistic and the total standard deviation, together with inter-event and intra-event components.
... However, in the case of the real catalogue data, we do not know one 'true' value of b, even though Geffers et al. (2022) have previously suggested that of The Geysers catalogue is likely to have a b-value close to 1.0. This falls within the b-values estimated in prior literature, ranging from b ∼ 0.8 to 1.3 (Henderson et al., 1999;Convertito et al., 2012;Kwiatek et al., 2015;Leptokaropoulos et al., 2018). Similarly, b ∼ 1.0 for Southern California (Kamer & Hiemer, 2015). ...
Many natural hazards exhibit inverse power-law scaling of frequency and event size, or an exponential scaling of event magnitude (m) on a logarithmic scale, e.g. the Gutenberg-Richter law for earthquakes, with probability density function p(m) ∼ 10−bm. We derive an analytic expression for the bias that arises in the maximum likelihood estimate of b as a function of the dynamic range r. The theory predicts the observed evolution of the modal value of mean magnitude in multiple random samples of synthetic catalogues at different r, including the bias to high b at low r and the observed trend to an asymptotic limit with no bias. The situation is more complicated for a single sample in real catalogues due to their heterogeneity, magnitude uncertainty and the true b-value being unknown. The results explain why the likelihood of large events and the associated hazard is often underestimated in small catalogues with low dynamic range, for example in some studies of volcanic and induced seismicity.
... Most probabilistic models (Shapiro et al. 2007(Shapiro et al. , 2010(Shapiro et al. , 2013Convertito et al. 2012;Barth et al. 2013) have focused on estimating the occurrence probability of earthquakes with a certain magnitude due to a GPP's operation. To estimate the regional seismic risk of buildings from an engineering perspective, other earthquake features, such as magnitude, focal depth, and epicenter-to-GPP distance, need to be simulated as well. ...
This study statistically evaluated the characteristics
of induced earthquakes by geothermal power plants (GPPs)
and generated a probabilistic model for simulating stochastic seismic events. Four well-known power plant zones
were selected worldwide from the United States, Germany,
France, and New Zealand. The operational condition information, as well as the corresponding earthquake catalogs
recorded in the vicinity of GPPs, were gathered from their
commencement date. The statistical properties of events
were studied elaborately. By using this proposed database,
a probabilistic model was developed capable of generating
the number of induced seismic events per month, their magnitude, focal depth, and distance from the epicenter to the
power plant, randomly. All of these parameters are simulated
as a function of power plant injection rate. Generally speaking, the model, introduced in this study, is a tool for engineers and scientists interested in the seismic risk assessment
of built environments prone to induced seismicity produced
by GPPs operation.
... Therefore, motivated by these discussed facts, in the present study, we decided to develop the ground-motion prediction equations (GMPEs) because there are no specific ones available for the St. Gallen area. Studies such as timedependent analysis of GMPEs and seismic hazards might prove helpful in monitoring and controlling the effects of the seismicity rate levels on inhabitants living in surrounding areas and on structures as well (Convertito et al. 2012;Convertito et al. 2021). ...
Ground shaking, whether it is due to natural or induced earthquakes, has always been a matter of concern since it correlates with structural/non-structural damage and can culminate in human anxiety. Industrial activities such as water injection, gas sequestration and waste fluid disposals, promote induced seismicity and consequent ground shaking that can hinder ongoing activities. Therefore, keeping in mind the importance of timely evaluation of a seismic hazard and its mitigation for societal benefits, the present study proposes specifically designed ground-motion prediction equations (GMPEs) from induced earthquakes in the St. Gallen geothermal area, Switzerland. The data analysed in this study consist of 343 earthquakes with magnitude −1.17 ≤ ML, corr ≤ 3.5 and hypocentral distance between 4 and 15 km. The proposed study is one of the first to incorporate ground motions from negative magnitude earthquakes for the development of GMPEs. The GMPEs are inferred with a two-phase approach. In the first phase, a reference model is obtained by considering the effect of source and medium properties on the ground motion. In the second phase the final model is obtained by including a site/station effect. The comparison between the GMPEs obtained in the present study with GMPEs developed for the other induced seismicity environments highlights a mismatch that is ascribed to differences in regional seismic environment and local site conditions of the respective regions. This suggests that, when dealing with induced earthquakes, GMPEs specific for the study should be inferred and used for both monitoring purposes and seismic hazard analyses.
... When dealing with induced seismicity, however, standard approaches, such as Probabilistic Seismic Hazard Analysis (PSHA) e.g., [1,2] cannot be applied as they have been originally conceived. They need to be modified to consider the specific features of the induced seismicity e.g., [3][4][5]. The main differences are related to both temporal and spatial distribution of the induced seismicity compared to natural seismicity. ...
... Indeed, induced seismicity tends to cluster in limited volumes near the wells where field operations (e.g., fluids injection, extraction, fracking, etc.) are performed. From a temporal point of view, earthquake occurrence, in some cases, may be not stationary over small time-windows such as the extent of the geoengineering project (e.g., [5][6][7][8]). Hence, the homogeneous Poisson recurrence model can be used under some assumptions that can impact the final hazard estimates. ...
... In the present study we propose a technique aimed at facing the above-mentioned modifications of the standard PSHA. Following the approach proposed by Convertito and Zollo [17] and modified by Convertito et al. [5], we use data-events' location and peakground velocities (PGVs)-collected during a specific phase of a project, to define a proper seismogenic volume, the maximum magnitude and, through the analysis of the interarrival times distribution, the recurrence model. This latter can then be used to perform a time-independent or a time-dependent seismic hazard analysis by considering specific probability density functions to compute the seismicity rate. ...
Reliable seismic hazard analyses are crucial to mitigate seismic risk. When dealing with induced seismicity the standard Probabilistic Seismic Hazard Analysis (PSHA) has to be modified because of the peculiar characteristics of the induced events. In particular, the relative shallow depths, small magnitude, a correlation with field operations, and eventually non-Poisson recurrence time. In addition to the well-known problem of estimating the maximum expected magnitude, it is im-portant to take into account how the industrial field operations affect the temporal and spatial distribution of the earthquakes. In fact, during specific stages of the project the seismicity may be hard to be modelled as a Poisson process—as usually done in the standard PSHA—and can cluster near the well or migrate toward hazardous known or—even worse—not known faults. Here we present a technique in which we modify the standard PSHA to compute time-dependent seismic hazard. The technique allows using non-Poisson models (BPT, Weibull, gamma and ETAS) whose parameters are fitted using the seismicity record during distinct stages of the field operations. As a test case, the procedure has been implemented by using data recorded at St. Gallen deep geo-thermal field, Switzerland, during fluid injection. The results suggest that seismic hazard analyses, using appropriate recurrence model, ground motion predictive equations, and maximum mag-nitude allow the expected ground-motion to be reliably predicted in the study area. The predic-tions can support site managers to decide how to proceed with the project avoiding adverse consequences.
... Many authors (Stark, 2003; have observed that this part of The Geysers field is more active seismically than the southeastern part (Zone 2), following the expansion of the field in that sector. Following and Convertito et al. (2012), the northwestern area includes all earthquakes with magnitude greater than 4, whereas the southeastern one is characterized by smaller magnitude events. Moreover, in Zone 1, earthquake hypocenters reach greater depths, reflecting the migration of fluids deeper into the reservoir (Johnson et al., 2016). ...
... Therefore, changes in the injection can induce changes in earthquake productivity (i.e., number of earthquakes), clustering properties, and known seismological parameters such as the b-value of the Gutenberg and Richter relation. Temporal variations in seismic activity at The Geysers geothermal field were studied by different authors (e.g., Eberhart-Phillips and Oppenheimer, 1984;Martínez-Garzón et al., 2013;Martínez-Garzón et al., 2014;Martínez-Garzón et al., 2018;Kwiatek et al., 2015;Johnson et al., 2016;Trugman et al., 2016), as well as changes in the b-value (Convertito et al., 2012;Martínez-Garzón et al., 2014;Kwiatek et al., 2015;Trugman et al., 2016). This section discusses how temporal fluctuations in fluid injection affect seismic activity and, consequently, the degree of memory in seismicity time series. ...
The present study aims at proving the existence of long memory (or long-range dependence) in the earthquake process through the analysis of time series of induced seismicity. Specifically, we apply alternative statistical techniques borrowed from econometrics to the seismic catalog of The Geysers geothermal field (California), the world’s largest geothermal field. The choice of the study area is essentially guided by the completeness of the seismic catalog at smaller magnitudes (a drawback of conventional catalogs of natural seismicity). Contrary to previous studies, where the long-memory property was examined by using non-parametric approaches (e.g., rescaled range analysis), we assume a fractional integration model for which the degree of memory is defined by a real parameter d, which is related to the best known Hurst exponent. In particular, long-memory behavior is observed for d > 0. We estimate and test the value of d (i.e., the hypothesis of long memory) by applying parametric, semi-parametric, and non-parametric approaches to time series describing the daily number of earthquakes and the logarithm of the (total) seismic moment released per day. Attention is also paid to examining the sensitivity of the results to the uncertainty in the completeness magnitude of the catalog, and to investigating to what extent temporal fluctuations in seismic activity induced by injection operations affect the value of d. Temporal variations in the values of d are analyzed together with those of the b-value of the Gutenberg and Richter law. Our results indicate strong evidence of long memory, with d mostly constrained between 0 and 0.5. We observe that the value of d tends to decrease with increasing the magnitude completeness threshold, and therefore appears to be influenced by the number of information in the chain of intervening related events. Moreover, we find a moderate but significant negative correlation between d and the b-value. A negative, albeit weaker correlation is found between d and the fluid injection, as well as between d and the annual number of earthquakes.
... Recent studies focused on two approaches i.e. parametric and non-parametric approaches. The first approach is based on the use of the well-established Gutenberg-Richter law which was applied in many pieces of research on seismic hazard in mines [1,[4][5][6][7]. In the second approach, the non-parametric hazard assessment approach is evaluated under certain conditions [8,9]. ...
The spatiotemporal analysis of seismic zones characterised by the scattering and accumulation of strain energy in the roof-rocks of the excavated longwall panel where inelastic or elastic deformations occurred during hard coal seam mining is discussed. The studied longwall panel was designed to utilize the effect of partial stress relaxation caused by the earlier extraction of the coal seams located above. A full seismic moment tensor and spectral source parameter analyses were used to obtain information about the degree of inelastic and elastic coseismic deformations. This study also showed that these deformation changes correspond to variation in the Benioff strain release characteristics. Next, analyses of deformation zones were compared with the relationship between radiated energy and the excavated volume of rocks per month. The concept of balanced seismic energy release assumed the exponential increase of released seismic energy with the increase in the volume of excavated rock. Discrepancies between the observed and predicted radiated energies indicated that strain energy in selected zones in the rock mass was either scattered if the prediction was overestimated, or accumulated if underestimated. Moreover, the study showed that elastic deformation in one zone can lead to inelastic deformation in the same zone.
... The first approach is based on the use of the well-established Gutenberg-Richter law which was applied in many pieces of research on seismic hazard in mines (e.g. Lasocki 1994;Lasocki 1995;Kornowski 2011a, 2011b;Convertito et al. 2012;Mutke et al. 2015;Leptokaropoulos et al. 2017). The non-parametric hazard assessment approach is evaluated under certain conditions (e.g. ...
... By definition, if the value of m is lower than 1.0, the release curve is regarded as accelerating-like; if m is higher than 1.0, then the release curve is regarded as quiescence-like (Jiang and Wu 2005;Jiang and Wu 2006). The process of BSR for mining tremors has to fulfil two criteria for the sequence: (Brehm and Braile 1999) the magnitude of completeness (M c ) must be known, and the dataset should have a frequencymagnitude distribution (FMD) for an appropriate time interval preceding the main-shock; and (Convertito et al. 2012) there are no interfering events. An interfering event is considered to be any event greater than M L 0.5 less than the mainshock, located near the mainshock in both time and space. ...
Underground coal seam mining has been carried out in the Upper Silesian Coal Basin, Poland, for many years and with a simultaneous increase in exploitation depth. Frequently, coal seams are not fully extracted due to numerous reasons which lead to their edges and remnants remaining in the rock mass. Even in the case of the full extraction of a coal seam, mining usually ends at the border of a protecting pillar to protect underground or surface objects, sometimes at the border of the mining area, or some distance from the old goaf or high throw fault. Extraction of subsequent coal seams in an analogous range results in a cluster of coal seam edges remaining. In the vicinity of the mentioned remainders , the disrupted stress distribution is expected. The infraction of the aforementioned equilibrium repeatedly results in the occurrence of strong mining tremors. The observations from the studied coal seam no. 408's longwall panel indicated that mining works are able to disturb the present stress-strain equilibrium in the area of the edges of other coal seams, even if they are located at a greater vertical distance away. The seismo-logical parameters and distributions have been applied for this purpose.
