Growth and stabilization of induced seismicity rates during long-term, low-pressure fluid injection.

We examine the temporal evolution of sequences of induced seismicity caused by long-term fluid injection using a compilation of over 20 case studies where moderate magnitude (M > 3.0) induced events have been recorded. We compare rates of seismicity with injection rates via the seismogenic index and seismic efficiency parameters, computing both cumulative and time-windowed values. We find that cumulative values tend to accelerate steeply as each seismicity sequence initiates-most cases reach a value that is within 0.5 units of their maximum value within 1-3 years. Time-windowed values tend to increase to maximum values within 25%-35% of the overall sequence, before decreasing as levels of seismicity stabilize. We interpret these observations with respect to the pore pressure changes that will be generated in highly porous, high permeability reservoirs. In such situations, the rate of pore pressure change is highest during the early phases of injection and decreases with time. If induced seismicity scales with the rate of deformation, which in turn is controlled by the rate of pore pressure change, then it is to be expected that induced seismicity is highest during the early phases of injection, and then decreases with time. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.

Connection between Variations of the Probability Distribution of the Recurrence Time and Phases of the Seismic Activity.

The probability distribution of the interevent time between two successive earthquakes has been the subject of numerous studies for its key role in seismic hazard assessment. In recent decades, many distributions have been considered, and there has been a long debate about the possible universality of the shape of this distribution when the interevent times are properly rescaled. In this work, we aim to discover if there is a link between the different phases of a seismic cycle and the variations in the distribution that best fits the interevent times. To do this, we consider the seismic activity related to the Mw 6.1 L'Aquila earthquake that occurred on 6 April 2009 in central Italy by analyzing the sequence of events recorded from April 2005 to July 2009, and then the seismic activity linked to the sequence of the Amatrice-Norcia earthquakes of Mw 6 and 6.5, respectively, and recorded in the period from January 2009 to June 2018. We take into account some of the most studied distributions in the literature: q-exponential, q-generalized gamma, gamma and exponential distributions and, according to the Bayesian paradigm, we compare the value of their posterior marginal likelihood in shifting time windows with a fixed number of data. The results suggest that the distribution providing the best performance changes over time and its variations may be associated with different phases of the seismic crisis.

The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data.

'Every Earthquake a Precursor According to Scale' (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.

Self-Optimized Computational Method Calculating Robust b Values for Earthquake Catalogs Containing Small Number of Events.

In the Gutenberg-Richter relation that describes the frequency-magnitude distribution of earthquakes, the b value represents the distribution's slope. Since b values can be used for mapping the dynamic response of earthquake source, methodologies for calculating robust b values are of great importance. Although nowadays software which is meant for statistical analysis of earthquake data can determine b values with high accuracy, in occasions where catalogs that contain small number of earthquake events, the produced results are not satisfactory. In this paper we present a new self-optimized algorithm for a more efficient calculation of the b value. The algorithm's results are compared with two widely known software for statistical analysis of earthquake data, showing a better performance in evaluating b values for earthquake catalogs containing small number of events.

Probabilistic estimation of the source component of seismic hazard in North-Eastern Brazil.

Stable continental regions pose unique challenges for conducting Probabilistic Seismic Hazard Analysis because the earthquake activity driving mechanisms are poorly understood. For instance, the lower seismicity (hence the paucity of data) and the absence of well-defined active fault systems complicate accurately determining seismic source parameters. Northeastern Brazil is a stable continental region exhibiting moderate-size events recorded with significant seismic intensities and provoking the collapse of poorly constructed buildings in the last century. Thus, assessing the seismic hazard is critical for seismic risk mitigation. The seismic hazard depends on three components: source, path, and site, and here, we present the probabilistic seismic hazard analysis of the source component for NE Brazil. Spatial aggregation of earthquake sources outlined four areal seismic zones. A goodness-of-fit test rejected the Gutenberg-Richter model of magnitude frequency distribution in one of the studied seismic zones. For this reason, we estimated the magnitude probability distribution function in that zone using a nonparametric adaptive kernel estimator. In other zones the Gutenberg-Richter magnitude frequency model was applied. In either way of the magnitude probability distribution modelling we considered the upper bound for magnitude equal to 6.6 mR, based on the upper bound of a 95 % confidence interval for the standard normal distribution of palaeoearthquake sizes. Our findings suggests that potentially damaging events are likely to occur, and we cannot neglect chances for the occurrence of earthquakes exceeding 5.2 mR. The calculated mean return periods indicate significantly shorter intervals between consecutive large events than palaeoseismic records.