Earthquake Arrival Association with Backprojection and Graph Theory

The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P- or S-waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so we use a pattern detection metric based on the principle of backprojection to reveal candidate sources, followed by graph-theory-based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern Chile. For the synthetic tests we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of > 95%. For the real data we develop a new catalog between January 1, 2010 - December 31, 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day, and a magnitude-of-completion of ~M1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock activity, > 600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone

Fracture network characterization in enhanced geothermal systems by induced seismicity analysis

Subject of the doctoral project is to study induced seismicity in enhanced geothermal systems to characterize the underground fracture network. The first part of this work focuses on the case study of the Rittershoffen deep geothermal reservoir. It is demonstrated how the integration of advanced processing techniques can lead to a deeper insight into the structure of the fault system and its reaction to repeated fluid injection. In the second part of this work, a new method is proposed to highlight the fracture network in seismic clouds that do not form apparent planar structures. With this method, the likelihood of having a fracture at a given location is computed from the distribution of seismic events and their source parameters

Earthquake Nucleation Processes Across Different Scales and Settings

Extended nucleation phases of earthquakes have been regularly observed, yet the underlying mechanisms governing the initiation phase of rupture are yet to be understood in detail. Currently two end member models exist to explain earthquake nucleation: one model claiming that the nucleation phase of a small earthquake is indistinguishable from that of a large one, while the other proposes fundamental differences in the underlying process. Previous studies have been using the same seismological observations to argue for either model, leaving the need of further investigations into the nucleation behavior of earthquakes across scales and different settings. The thesis at hand contributes to the current discussion on earthquake nucleation by providing additional observational evidence for extended nucleation phases, complex rupture interaction and growth across a number of different scales and settings. Here, earthquake nucleation is investigated for three different scenarios, each with varying degrees of complexity: 1) the controlled case of induced seismicity in hydraulic stimulations of geothermal reservoirs, where rupture growth is assumed to be primarily governed by anthropogenic activity, 2) the partly-controlled setting of a geothermal field with a long history of fluid injection and production, and 3) the uncontrolled case of natural seismicity in the central Sea of Marmara, where earthquake nucleation is purely governed by the regional tectonics. First, the temporal evolution of seismicity and the growth of observed moment magnitudes for a range of past and present hydraulic stimulation projects associated with the creation of enhanced geothermal systems are analyzed. They reveal a clear linear relation between injected fluid volume/hydraulic energy and cumulative seismic moments. For most projects studied, the observations are in good agreement with existing physical models that predict a relation between injected fluid volume and maximum seismic moment of induced events. This suggests that seismicity results from a stable, pressure controlled rupture process at least for an extended injection period. Overall evolution of seismicity is independent of tectonic stress regime and is most likely governed by reservoir specific parameters, such as the preexisting structural inventory. In contrast, a few stimulations reveal unbound increase in seismic moment suggesting that for these cases evolution of seismicity is mainly controlled by stress field, the size of tectonic faults and fault connectivity. The uncertainty over whether or not a transition between behavior is likely to occur at any point during the injection is what motivates the need for a next generation monitoring and traffic-light system accounting for the possibility of unstable rupture propagation from the very beginning of injection by observing the entire seismicity evolution at high resolution for an immediate reaction in injection strategy. Furthermore, the majority of pressure-controlled stimulations shows the potential of actively controlling the size of induced earthquakes, if an injection protocol is chosen based on continuous feedback from a near-real-time seismic monitoring system. Second, moderate sized earthquakes at The Geysers geothermal field (California), where years of injection and production across hundreds of wells have led to a unique physical environment, are studied. While overall seismicity at The Geysers is generally governed by anthropogenic activities, contributions of individual wells or injection activities are hard to distinguish, thus making detailed managing of occurring magnitudes challenging. New high-resolution seismicity catalogs framing the occurrence of 20 ML > 2.5 earthquakes were created. The seismicity catalogs were developed using a matched filter algorithm, including automatic determination of P and S phase onsets and their inversion for absolute hypocenter locations with corresponding uncertainties. The selected 20 sequences sample different hypocentral depths and hydraulic conditions within the field. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b values, while the southeastern part is dominated by decreased seismic activity and higher b values. Periods of high injection coincide with high b values and vice versa. These observations potentially reflect varying differential and mean stresses and damage of the reservoir rocks across the field. Additionally, a systematic search for seismicity localization using a multi-step cross-correlation analysis was performed. No evidence for increased correlation between the occurring seismicity and the mainshock for any of the 20 sequences could be seen, indicating that each main nucleation spot was seismically silent prior to the main rupture. However, a number of highly inter-correlated earthquakes for sequences below the reservoir and during high injection activity is observed. Under these conditions, the seismicity surrounding the future mainshock source region is more concentrated and might be evidence for a cascading nucleation process. About 50% of analyzed sequences exhibit no change in seismicity rate in response to the large main event. However, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events, consistent with a cascading type nucleation. Third, the spatiotemporal evolution of seismicity during a sequence of moderate (MW4.7 and MW5.8) earthquakes occurring in September 2019 at the transition between a creeping and a locked segment of the North Anatolian Fault in the central Sea of Marmara (Turkey) was analyzed. A matched filter technique was applied to continuous waveforms from the regional network, substantially reducing the magnitude threshold for detection. Sequences of foreshocks preceding the two mainshocks are clearly seen, exhibiting different behaviors: a migration of the seismicity along the entire fault segment on the long-term and a concentration around the epicenters of the large events on the short-term. Suggesting that both seismic and aseismic slip during the foreshock sequences change the stress state on the fault, bringing it closer to failure. Furthermore, the observations also suggest that the MW4.7 event contributed to weaken the fault as part of the preparation process of the MW5.8 earthquake. Combining the results obtained from different settings, it becomes apparent that, regardless of the tectonic setting and degree of anthropogenic control over the seismicity, there is a wide range of complex nucleation behaviours not yet explained by any of the current models of earthquake nucleation. A simplistic view of earthquake nucleation as either a deterministic or a stochastic process seems inconsistent with the obtained results and fails to account for a more complex nucleation behaviour. Observations from The Geysers and the western Sea of Marmara earthquake sequence, suggest that both cascade triggering and aseismic slip can play major roles in the nucleation of moderate sized earthquakes. Both mechanisms seem to jointly contribute to fault initiation, even within the same rock volume. A separation of the two mechanisms can potentially be thought of at The Geysers, where cascade triggering seems to dominate in highly damaged parts of the reservoir, suggesting that the anthropogenic activity can at least partially influence the nucleation behavior of the occurring seismicity. This would be in agreement with the results obtained from analysis of hydraulic stimulations, where during the pressure-controlled phase of injection rupture growth is controlled by the injected fluid

Volcan de Fuego: A Machine Learning Approach in Understanding the Eruptive Cycles Using Precursory Tilt Signals

Volcan de Fuego is an active stratovolcano located in the Central Guatemalan segment of the 1100 m long Central America Volcanic Arc System (CAVAS). Fuego-Acatenango massif consists of at least four major vents of which the Fuego summit vent is the most active and the youngest member. The volcano exhibits primarily Strombolian and Vulcanian behavior along with occasional paroxysms and pyroclastic flows. Historically, Fuego has produced basaltic-andesitic rocks with more recent eruptions progressively trending towards maficity. Several studies have used short-term deployments of broadband seismometers, infrasound, and long-term remote sensing techniques to characterize the mechanism of Fuego. In our study, we analyze the tilt derived from transient broadband seismometers and tiltmeter stationed over several days during 2009, 2012, and 2015 near the summit crater using unsupervised learning. Unsupervised learning has the potential to play a significant role in monitoring volcanoes dominated by large, unlabeled datasets. In our study, we make use of dynamic time warping distance measure along with unsupervised classification methods to identify precursory tilt signals. The unsupervised classification revealed two types of tilt signals with opposite polarity, one of which confirms features identified in previous studies while the other signal has been previously unknown. Template matching implemented with the known signal identified 268 events between October 1, 2015, and January 13, 2016, the duration of which varied between 7 and 39 minutes. The temporal distribution of these events as well as the maximum amplitude of inflation showed clustering activity accompanied by intra-cluster waxing and waning. We created subsets of temporal clusters and calculated repose times between successive events. Auto-correlation functions were calculated for each subset and probability density functions were fitted which support survival/failure processes. The long-term tilt records provided a useful tool to characterize the activity and revealed a near-continuous cyclicity

Source Mechanism of Small Long-Period Events at Mount St. Helens in July 2005 Using Template Matching, Phase-Weighted Stacking, and Full-Waveform Inversion

Long-period (LP, 0.5-5 Hz) seismicity, observed at volcanoes worldwide, is a recognized signature of unrest and eruption. Cyclic LP “drumbeating” was the characteristic seismicity accompanying the sustained dome-building phase of the 2004–2008 eruption of Mount St. Helens (MSH), WA. However, together with the LP drumbeating was a near-continuous, randomly occurring series of tiny LP seismic events (LP “subevents”), which may hold important additional information on the mechanism of seismogenesis at restless volcanoes. We employ template matching, phase-weighted stacking, and full-waveform inversion to image the source mechanism of one multiplet of these LP subevents at MSH in July 2005. The signal-to-noise ratios of the individual events are too low to produce reliable waveform inversion results, but the events are repetitive and can be stacked. We apply network-based template matching to 8 days of continuous velocity waveform data from 29 June to 7 July 2005 using a master event to detect 822 network triggers. We stack waveforms for 359 high-quality triggers at each station and component, using a combination of linear and phase-weighted stacking to produce clean stacks for use in waveform inversion. The derived source mechanism points to the volumetric oscillation (∼10 m3) of a subhorizontal crack located at shallow depth (∼30 m) in an area to the south of Crater Glacier in the southern portion of the breached MSH crater. A possible excitation mechanism is the sudden condensation of metastable steam from a shallow pressurized hydrothermal system as it encounters cool meteoric water in the outer parts of the edifice, perhaps supplied from snow melt

Development of a Semi-Automated Methodology for Discriminating Between Natural and Manmade Seismic Events Using the OIINK Seismic Array

Broadband waveforms from the Ozark, Indiana, Illinois, Kentucky temporary seismic array OIINK, a Flexible Array in the EarthScope project, were used to develop routines to identify and remove mine blast events from a database of local events and preserve the infrequent, small, natural earthquakes. The approach taken was to first create a database of all seismic events that were detected by the OIINK Seismic Array. False-detections, events detected from outside of the project area (approx. 302 thousand square km), and known (i.e. cataloged) local earthquake were also removed. The remaining in the database were local unknown events. During the Phase III of the OIINK project, from 2014 through 2015, the array focused on Kentucky. One month of Phase III waveforms was processed with the Antelope software package to develop a database of event locations and magnitudes. The discrimination routine was developed, primarily in Matlab. A cross-correlation routine was developed to identify mine blasts using waveform correlation coefficient (CC), allowing seismograms to be grouped into common waveform families. Identification of event families was more successful when events within particular spatial clusters were simultaneously examined. As a result, this research focused on six regions that included mining sites in the area. 244 events were semi-automatically identified as blasts from 351 events inside the selected areas, this amount represents 70 % of the events analyzed

Fluid injections in the subsurface: a multidisciplinary approach for better understanding their implications on induced seismicity and the environment.

Fluid injections in the subsurface are common operations in underground industrial activities such as oil and gas exploitation, geothermal energy development, and carbon capture and storage (CCS). In recent years, it became a focal point as new drilling technologies (e.g., hydraulic fracturing) enable the extraction of oil and gas in unconventional reservoirs and the development of CCS injection techniques became a key research topic in the context of the low-carbon energy transition. Fluid injections have drawn the attention also in the general public because of their main potential implications such as the induced seismicity phenomenon (Rubinstein and Mahani, 2015) and the environmental pollution (Burton et al., 2016, Pitchel et al., 2016). Considering the strong socioeconomic impact of fluid injection operations (National Research Council, 2013; Ellsworth, 2013; Grigoli et al., 2017) the current research in this field needs the integration of multidisciplinary studies, involving knowledge on geology, seismology, source physics, hydrogeology, fluid geochemistry, rocks geomechanics for a complete understanding of the phenomenon and to set-up the most effective and “best practice” protocols for the monitoring of areas where injection operation are performed. On this basis, this work applied a multidisciplinary approach integrating seismological methods, geochemical studies, and machine learning techniques. Two key-study areas characterized by high fluid-rock interaction and fluid-injection in the subsurface were analyzed: i) the High Agri Valley (hereinafter HAV), hosting the largest onshore oil field in West Europe, in which wastewater disposal operations have been carried out since 2006 at the Costa Molina 2 injection well and where both natural and induced seismicity clusters were recognized; ii) the Mefite d’Ansanto, the largest natural emission of CO2-rich gases with mantle-derived fluids (from non‐volcanic environment) ever measured on the Earth (Carcausi et al., 2013; Caracausi and Paternoster, 2015; Chiodini et al, 2010). Regarding the HAV study area, we reconstructed the preliminary catalogue of seismicity through accurate absolute locations in a 3D-velocity model (Serlenga and Stabile, 2019) of earthquakes detected from the local seismic INSIEME network managed by the CNR-IMAA. A total of 852 between local tectonic and induced earthquakes occurred in the HAV between September 2016 and March 2019. We tested the potential of the unsupervised machine-learning approach as an automated tool to make faster dataset exploratory analysis, founding the density-based approach (DBSCAN algorithm-Density-Based Spatial Clustering of Applications with Noise, Ester et al., 1996) particularly suitable for the fast identification of clusters in the catalogue resulting from both injection-induced events and tectonic local earthquake swarms. Moreover, we proposed a semi-automated workflow for earthquake detection and location with the aim to improve the current standard procedures, quite time-consuming and strictly related to human operators. The workflow, integrating manual, semi-automatic and automatic detection and location methods enabled us to characterize a low magnitude natural seismic sequence occurred in August 2020 in the southwestern area of the HAV (Castelsaraceno sequence) in a relatively short time with respect to the application of standard techniques, thus representing a starting point for the improvement of the efficiency of seismic monitoring techniques of both anthropogenic and natural seismicity in the HAV. Our multidisciplinary approach involved the geochemical study of the HAV groundwaters with the aim to: (1) determine the geochemical processes controlling the chemical composition; (2) define a geochemical conceptual model regarding fluid origin (deep vs shallow) and mixing processes by means isotopic data; (3) establish a geochemical baseline for the long-term environmental monitoring of the area. A total of 39 water samples were collected from springs and wells located at the main hydro-structures bordering the valley to determine chemical (major, minor and trace elements) and isotopic composition (e.g., dD, d18O, d13C-TDIC and noble gas). All investigated water samples have a meteoric origin, although some springs show long and deep flow than the other ones, and a bicarbonate alkaline-earth composition, thus suggesting the carbonate hydrolysis as the main water-rock interaction process. Our results demonstrated that HAV groundwater is chemically suitable for drinking use showing no criticalities for potentially toxic metals reported by the Italian and European legislation guidelines. Particular attention was given on thermal water of Tramutola well, built by Agip S.p.a. for oil & gas exploration, with the occurrence of bubbling gases. The geochemical study highlighted a substantial difference of these CH4-dominated thermal fluids with the rest of the dataset. Helium isotope (3He/4He) indicate a prevalent radiogenic component with a contribution of mantle-derived helium (~20%) and the average δ13C-CO2 value is of – 4.6 ‰ VPDB, consistent with a mantle origin. Methane isotope composition indicates a likely microbial isotopic signature (δ13C-CH4 =−63.1‰, −62.4‰, δD-CH4=−196‰, −212‰), probably due to biodegradation processes of thermogenic hydrocarbons. The methane output at the well, evaluated by means of anemometric measurement of the volume flow (m3/h) is of ~156 t/y, that represent about 1.5% of total national anthropogenic sources related to fossil fuel industry (Etiope et al., 2007). Our work highlighted that Tramutola well may represent a key natural laboratory to better understand the complex coupling effects between mechanical and fluid-dynamic processes in earthquake generation. Moreover, the integration of seismic and geochemical data in this work allowed us to identify the most suitable locations for the future installation of multiparametric stations for the long-term monitoring of the area and development of integrated research in the HAV. Regarding the Mefite d’Ansanto, we analyzed the background seismicity in the emission area recorded by a dense temporary seismic network deployed at the site between 30-10-2019 and 02-11-2019. First, we implemented and tested an automated detection algorithm based on non-parametric statistics of the recorded amplitudes at each station, collecting a total dataset of 8561 events. Then, both unsupervised (DBSCAN) and supervised (KNN-k-nearest neighbors classification, Fix & Hodges, 1951) machine learning techniques were applied, based on specific parameters (duration, RMS-amplitude and arrival slope) of the detected events. DBSCAN algorithm allowed to determine characteristic bivariate correlations among tremors parameters: a high linear correlation (r~0.6-0.7) between duration and RMS-amplitude and a lower one (r~0.5-0.6) between amplitude and arrival slope (first arrival parametrization). These relationships let us to define training samples for the KNN algorithm, which allowed to classify tremor signals at each station and to automatically discriminate between tremors and accidentally detected anthropogenic noise. Results allowed to extract new information on seismic tremor at Mefite d’Ansanto, previously poorly quantitively analyzed, and its discrimination, thus providing a starting workflow for monitoring the non-volcanic emission. Isotopic geochemistry (3He/4He, 4 He/20Ne, δ13CCO2) indicated a mixing of mantle (30%-40%) and crust-derived fluids. The source location of the emission related tremor would represent a step forward in its characterization, and for setting up more advanced automated detection and machine learning classification techniques to exploit the information provided by seismic tremor for an improved automatic monitoring of non-volcanic, CO2 -gas emissions