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'.

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.

Investigation of the correlation of successive earthquakes preceding main shocks in the Greek territory.

The Canonical Correlation Analysis (CCA) estimates the correlation between two vector variables by maximizing the correlation of linear combinations of their respective components. Here, the CCA is used to find correlation patterns in the last five successive, per pairs, earthquakes ( M ≥ 4.0 ) preceding 271 main shocks ( M ≥ 5.5 ) that occurred in the Greek territory during 1964-2018. The vector variables have two components, the earthquake magnitude and interevent time. The statistical significance of CCA is determined by the standard parametric test along with two proposed randomization tests, one using random shuffling of each paired dataset and one using randomly selected pairs of successive earthquakes. Simulations were designed on synthetic data from vector variables having the statistical characteristics of the real observations. The results on uncorrelated variables showed the correct size for the two randomization tests but larger type I error for the parametric significance test for small sample size. For correlated variables, the test power was equally high for both test types. The application of CCA and the significance tests to the Greek seismicity evidence the significant correlation among the last five successive preshocks, proving to be a promising tool in an a posteriori short-term earthquake forecasting.

On the Use of High-Resolution and Deep-Learning Seismic Catalogs for Short-Term Earthquake Forecasts: Potential Benefits and Current Limitations.

Enhanced earthquake catalogs provide detailed images of evolving seismic sequences. Currently, these data sets take some time to be released but will soon become available in real time. Here, we explore whether and how enhanced seismic catalogs feeding into established short-term earthquake forecasting protocols may result in higher predictive skill. We consider three enhanced catalogs for the 2016-2017 Central Italy sequence, featuring a bulk completeness lower by at least two magnitude units compared to the real-time catalog and an improved hypocentral resolution. We use them to inform a set of physical Coulomb Rate-and-State (CRS) and statistical Epidemic-Type Aftershock Sequence (ETAS) models to forecast the space-time occurrence of M3+ events during the first 6 months of the sequence. We track model performance using standard likelihood-based metrics and compare their skill against the best-performing CRS and ETAS models among those developed with the real-time catalog. We find that while the incorporation of the triggering contributions from new small magnitude detections of the enhanced catalogs is beneficial for both types of forecasts, these models do not significantly outperform their respective near real-time benchmarks. To explore the reasons behind this result, we perform targeted sensitivity tests that show how (a) the typical spatial discretizations of forecast experiments ( ≥ 2 km) hamper the ability of models to capture highly localized secondary triggering patterns and (b) differences in earthquake parameters (i.e., magnitude and hypocenters) reported in different catalogs can affect forecast evaluation. These findings will contribute toward improving forecast model design and evaluation strategies for next-generation seismic catalogs.

Towards advancing the earthquake forecasting by machine learning of satellite data.

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.

How will induced seismicity in Oklahoma respond to decreased saltwater injection rates?

In response to the marked number of injection-induced earthquakes in north-central Oklahoma, regulators recently called for a 40% reduction in the volume of saltwater being injected in the seismically active areas. We present a calibrated statistical model that predicts that widely felt M ≥ 3 earthquakes in the affected areas, as well as the probability of potentially damaging larger events, should significantly decrease by the end of 2016 and approach historic levels within a few years. Aftershock sequences associated with relatively large magnitude earthquakes that occurred in the Fairview, Cherokee, and Pawnee areas in north-central Oklahoma in late 2015 and 2016 will delay the rate of seismicity decrease in those areas.