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Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity ("ERAS") stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the "non‐Poisson" power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity. Plain Language Summary: Earthquake nowcasting was proposed as a means of tracking the change in the potential for large earthquakes in a seismically active area, using the record of small earthquakes. The method was developed using observed seismic data, in which probabilities of future large earthquakes can be computed using machine learning methods that were originally developed with the advent of radar in the 1940s. These methods are now being used in the development of machine learning and artificial intelligence models in a variety of applications. In recent times, methods to simulate earthquakes using the observed statistical laws of earthquake seismicity have been developed. One of the advantages of these stochastic models is that it can be used to analyze the various assumptions that are inherent in the analysis of seismic catalogs of earthquakes. In this paper, we analyze the importance of the space‐time clustering that is often observed in earthquake seismicity. We find that the clustering is the origin of information that makes the earthquake nowcasting methods possible. We also find that a common practice of "aftershock de‐clustering", often used in the analysis of these catalogs, removes information about future large earthquakes. Key Points: Earthquake nowcasting tracks the change in the potential for large earthquakes, using information contained in seismic catalogsWe analyze the information contained in the space‐time clustering that is observed in earthquake seismicityWe find that "aftershock de‐clustering" of catalogs removes information about future large earthquakes that the nowcasting method uses [ABSTRACT FROM AUTHOR]

Yes Plain Language Summary: The question of whether earthquake occurrence is random in time, or perhaps chaotic with order hidden in the chaos, is of major importance to the determination of risk from these events. It was shown many years ago that if aftershocks are removed from the earthquake catalogs, what remains are apparently events that occur at random time intervals, and therefore not predictable in time. In the present work, we enlist machine learning methods using Receiver Operating Characteristic analysis. With these methods, probabilities of large events and their associated information value can be computed. Here information value is defined using Shannon entropy, shown by Claude Shannon to define the surprise value of a communication such as a string of computer bits. Random messages can be shown to have high entropy, surprise value, or uncertainty, whereas low entropy is associated with reduced uncertainty and high reliability. An earthquake nowcast probability associated with reduced uncertainty and greater reliability is most desirable. Examples of the latter could be the statements that there is a 90% probability of a major earthquake within 3 years, or a 5% chance of a major earthquake within 1 year. Despite the random intervals between major earthquakes, we find that it is possible to make low uncertainty, high reliability statements on current hazard by the use of machine learning methods using catalog data from 1970‐present. Key Points: Interval statistics have been used to conclude that major earthquakes are random events in time and cannot be anticipated or predictedMachine learning is a powerful new technique that enhances our ability to understand the information content of earthquake catalogsUsing small earthquake rates, we show that catalogs contain significant information on predictability of future large earthquakes [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way. Plain Language Summary: Earthquake nowcasting refers to the determination of hazard for major earthquakes at the present time, the recent past, and the near future. Nowcasting is an idea borrowed from economics, markets, and meteorology, where it has been frequently used. In this paper, we show that there is order hidden within chaotic earthquake seismicity using a very simple transformation of the data. Small earthquakes appear to transition from unstable stick‐slip events that produce seismic waves, to stable sliding where no seismic waves are produced. Our hypothesis is that this transition is due to a material phenomenon called strain‐hardening, that is frequently observed in laboratory rock mechanics experiments. The result is a state variable time series, computed over the last 51 years in California, that strongly resembles the long‐anticipated cycle of stress accumulation and release. Using supervised machine learning techniques, we can optimize the two‐parameter model. From that optimized model, we can rigorously calculate the probability of current hazard from major earthquakes. Extending these methods, we can also compute spatial hazard as well. The result is a new method for assessing earthquake hazard that may be useful for a variety of applications. Key Points: "Chaotic" seismicity contains hidden structure in the form of state variable time seriesStandard data science methods can be used to convert the time series to probabilitiesBoth temporal and spatial probabilities can be computed [ABSTRACT FROM AUTHOR]

The earthquake cycle of stress accumulation and release is associated with the elastic rebound hypothesis proposed by H.F. Reid following the M7.9 San Francisco earthquake of 1906. However, observing details of the actual values of time- and space-dependent tectonic stress is not possible at the present time. In two previous papers, we have proposed methods to image the earthquake cycle in California by means of proxy variables. These variables are based on correlations in patterns of small earthquakes that occur nearly continuously in time. The purpose of the present paper is to compare these two methods by evaluating their information content using decision thresholds and Receiver Operating Characteristic methods together with Shannon information entropy. Using seismic data from 1940 to present in California, we find that both methods provide nearly equivalent information on the rise and fall of earthquake correlations associated with major earthquakes in the region. We conclude that the resulting timeseries can be viewed as proxies for the cycle of stress accumulation and release associated with major tectonic activity. Article Highlights: The current state of the earthquake cycle of tectonic stress accumulation and release is unobservable We review two methods for visualizing the current state of the earthquake cycle from correlation in small earthquake patterns Machine learning techniques indicate that signals in a correlation time series corresponding to future large earthquakes can be detected [ABSTRACT FROM AUTHOR]

We propose a new machine learning‐based method for nowcasting earthquakes to image the time‐dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross‐correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020ea001097), we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near‐future seismic hazards. Plain Language Summary: Major earthquakes on fault systems in a tectonically active region are thought to occur in approximately repetitive cycles as a result of the buildup and release of tectonic forces (stress). Nowcasting is a technique adopted from weather, finance, and other fields that use readily observable proxy data to represent the unobservable stress accumulation process of interest. This paper presents a method that computes a timeseries representing the weighted correlation of small earthquake activity in the California region from 1950 to 2020. Prior to major magnitude M > 7 earthquakes, the timeseries trends toward lower values. Just after the earthquake occurs, the timeseries increases suddenly in association with the earthquake, before resuming its gradual trend toward lower values. Plotting the timeseries on an inverted scale, one sees a cyclic behavior that strongly resembles the hypothesized earthquake cycle. In principle, we can therefore use this timeseries for nowcasting, as a proxy for stress accumulation and release. Using methods of signal detection first developed for radar by the British in the 1940's, we find that the timeseries contain information about future large earthquakes that can be used for hazard assessment. Key Points: The current state of the earthquake cycle of tectonic stress accumulation and release is unobservable with existing methodsWe show that readily observable small earthquake correlations can be used to nowcast the current state of the earthquake cycleMachine learning techniques indicate that signals corresponding to future large earthquakes can be detected in a correlation time series [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) interferograms contain valuable information about the fault systems hidden beneath the surface of the Earth. In a new approach, we aim to fit InSAR ground deformation data using a distribution of multiple seismic point sources whose parameters are found by a genetic algorithm. The resulting source distribution could provide another useful tool in solving the difficult problem of accurately mapping earthquake faults. We apply the algorithm to an ALOS‐2 InSAR interferogram and perform a multifractal analysis on the resulting distribution, finding that it exhibits multifractal properties. We report first results and discuss advantages and disadvantages of this approach. Key Points: Inversion for seismic moment of point source distributionMultifractal analysis of seismic moment distribution is performedSeismic moment distribution exhibits multifractal properties [ABSTRACT FROM AUTHOR]

Charles Richter's observation that 'only fools and charlatans predict earthquakes,' reflects the fact that despite more than 100 years of effort, seismologists remain unable to do so with reliable and accurate results. Meaningful prediction involves specifying the location, time, and size of an earthquake before it occurs to greater precision than expected purely by chance from the known statistics of earthquakes in an area. In this context, 'forecasting' implies a prediction with a specification of a probability of the time, location, and magnitude. Two general approaches have been used. In one, the rate of motion accumulating across faults and the amount of slip in past earthquakes is used to infer where and when future earthquakes will occur and the shaking that would be expected. Because the intervals between earthquakes are highly variable, these long-term forecasts are accurate to no better than a hundred years. They are thus valuable for earthquake hazard mitigation, given the long lives of structures, but have clear limitations. The second approach is to identify potentially observable changes in the Earth that precede earthquakes. Various precursors have been suggested, and may have been real in certain cases, but none have yet proved to be a general feature preceding all earthquakes or to stand out convincingly from the normal variability of the Earth's behavior. However, new types of data, models, and computational power may provide avenues for progress using machine learning that were not previously available. At present, it is unclear whether deterministic earthquake prediction is possible. The frustrations of this search have led to the observation that (echoing Yogi Berra) 'it is difficult to predict earthquakes, especially before they happen.' However, because success would be of enormous societal benefit, the search for methods of earthquake prediction and forecasting will likely continue. In this review, we note that the focus is on anticipating the earthquake rupture before it occurs, rather than characterizing it rapidly just after it occurs. The latter is the domain of earthquake early warning, which we do not treat in detail here, although we include a short discussion in the machine learning section at the end. [ABSTRACT FROM AUTHOR]

Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration (RG), allows us to connect the bursts to the temporal occurrence of the largest M ≥ 7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute RG for each cluster then filter them to identify those bursts with large numbers of events closely clustered in space, which we call "compact" bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the RG of these bursts systematically decreases prior to large earthquakes, in a process that we might term "radial localization." The RG then rapidly increases during an aftershock sequence, and a new cycle of "radial localization" then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region. Plain Language Summary: Earthquake nowcasting is a method to evaluate the current state of seismic hazard from large earthquakes. In this paper, we connect the temporal occurrence of the largest and most potentially destructive earthquakes in California since 1984 with a readily observable property of small earthquake seismicity in the region. Our method involves the calculation of the time history of the average radius (horizontal size or extent) of "bursts" of small earthquakes, in the time leading up to and following major earthquakes in the region. We observe that the radius systematically and gradually decreases leading up to major earthquakes, increasing suddenly and discontinuously following the event. This observable pattern resembles the long‐hypothesized cycle of regional tectonic stress buildup and release, or elastic rebound, associated with large destructive earthquakes. We propose that the radius of these bursts might be considered to be a proxy variable for the changing state of regional stress in Southern California. Key Points: A new nowcasting method using earthquake swarms and aftershocks reveals the hazard from large M ≥ 7 earthquakes in Southern CaliforniaMachine learning defines a time series of bursts using an exponential moving average filtered and optimized to reveal temporal structureCycles of recharge and discharge associated with M ≥ 7 earthquakes are revealed, reminiscent of stress accumulation and release [ABSTRACT FROM AUTHOR]

Nowcasting refers to the use of proxy data to estimate the current dynamic state of driven complex systems such as earthquakes, neural networks, or the financial markets. In previous papers, methods to nowcast earthquakes have been presented based on the natural time count of small earthquakes after the last large earthquake in a defined, seismically active geographic region. In this method, a large geographic region is identified in which a local region of interest is embedded. The primary assumption in the method is that the frequency-magnitude statistics of the local region are the same as the frequency-magnitude relation of the large region. The nowcasting technique relies on seismic catalogs that are complete in the sense that all events whose magnitude is larger than a completeness threshold have been detected. For this reason, the previous papers have been limited to nowcasting large earthquakes with magnitudes of approximately M7.5. In this article, we extend the nowcasting method to great global earthquakes of magnitudes as large as M9 by defining the surrounding large region as the entire earth. We then analyze the current hazard of a number of selected "local" regions and rank the regions in terms of current risk of great earthquakes. These great events also present significant hazards for generating mega-tsunamis as well as local damage due to intense shaking. We also perform a sensitivity analysis to establish the reliability of the nowcasts. One of our main results is that the eastern Aleutian Islands, site of the M8.6 earthquake of 1 April 1946, is currently the region most at current risk of a great earthquake larger than M8.0. We finish by presenting comments on the applicability of our methods for anticipating the occurrence of great destructive earthquakes and tsunamis. [ABSTRACT FROM AUTHOR]

The Nonlinear Geophysics Technical Committee was formed in 1998 by a small group of AGU members to meet a growing interest in the analysis, modeling, and prediction of nonlinear behavior in geophysical processes. This paper provides a history as the technical committee, evolved into a focus group, and into a section, including officers, awards, prizes, named lectures, standing committees, and books written or edited by members on the subject of nonlinear geophysical patterns and processes. Key Point: This manuscript provides a history of the Nonlinear Section of AGU [ABSTRACT FROM AUTHOR]

A standard approach to quantifying the seismic hazard is the relative intensity (RI) method. It is assumed that the rate of seismicity is constant in time and the rate of occurrence of small earthquakes is extrapolated to large earthquakes using Gutenberg- Richter scaling. We introduce nowcasting to extend RI forecasting to time-dependent seismicity, for example, during an aftershock sequence. Nowcasting uses 'natural time'; in seismicity natural time is the event count of small earthquakes. The event count for small earthquakes is extrapolated to larger earthquakes using Gutenberg-Richter scaling. We first review the concepts of natural time and nowcasting and then illustrate seismic nowcasting with three examples. We first consider the aftershock sequence of the 2004 Parkfield earthquake on the San Andreas fault in California. Some earthquakes have higher rates of aftershock activity than other earthquakes of the same magnitude. Our approach allows the determination of the rate in real time during the aftershock sequence. We also consider two examples of induced earthquakes. Large injections of waste water from petroleum extraction have generated high rates of induced seismicity in Oklahoma. The extraction of natural gas from the Groningen gas field in The Netherlands has also generated very damaging earthquakes. In order to reduce the seismic activity, rates of injection and withdrawal have been reduced in these two cases. We show how nowcasting can be used to assess the success of these efforts. [ABSTRACT FROM AUTHOR]

A standard approach to quantifying the seismic hazard is the relative intensity (RI) method. It is assumed that the rate of seismicity is constant in time and the rate of occurrence of small earthquakes is extrapolated to large earthquakes using Gutenberg- Richter scaling. We introduce nowcasting to extend RI forecasting to time-dependent seismicity, for example, during an aftershock sequence. Nowcasting uses 'natural time'; in seismicity natural time is the event count of small earthquakes. The event count for small earthquakes is extrapolated to larger earthquakes using Gutenberg-Richter scaling. We first review the concepts of natural time and nowcasting and then illustrate seismic nowcasting with three examples. We first consider the aftershock sequence of the 2004 Parkfield earthquake on the San Andreas fault in California. Some earthquakes have higher rates of aftershock activity than other earthquakes of the same magnitude. Our approach allows the determination of the rate in real time during the aftershock sequence. We also consider two examples of induced earthquakes. Large injections of waste water from petroleum extraction have generated high rates of induced seismicity in Oklahoma. The extraction of natural gas from the Groningen gas field in The Netherlands has also generated very damaging earthquakes. In order to reduce the seismic activity, rates of injection and withdrawal have been reduced in these two cases. We show how nowcasting can be used to assess the success of these efforts. [ABSTRACT FROM AUTHOR]

Seismic nowcasting uses counts of small earthquakes as proxy data to estimate the current dynamical state of an earthquake fault system. The result is an earthquake potential score that characterizes the current state of progress of a defined geographic region through its nominal earthquake "cycle." The count of small earthquakes since the last large earthquake is the natural time that has elapsed since the last large earthquake (Varotsos et al., 2006, https://doi.org/10.1103/PhysRevE.74.021123). In addition to natural time, earthquake sequences can also be analyzed using Shannon information entropy ("information"), an idea that was pioneered by Shannon (1948, https://doi.org/10.1002/j.1538‐7305.1948.tb01338.x). As a first step to add seismic information entropy into the nowcasting method, we incorporate magnitude information into the natural time counts by using event self‐information. We find in this first application of seismic information entropy that the earthquake potential score values are similar to the values using only natural time. However, other characteristics of earthquake sequences, including the interevent time intervals, or the departure of higher magnitude events from the magnitude‐frequency scaling line, may contain additional information. Plain Language Summary: Earthquake nowcasting has been developed to determine the current level of earthquake risk for seismically active areas. Historically, statistical communication theory has been developed to analyze the information content of signals involved in telecommunications and computer systems. We combine these techniques to analyze the information contained in earthquake magnitudes for great earthquakes and mega‐tsunamis. Key Points: Nowcasting was previously used to analyze earthquake data to determine current seismic risk to global megacities for magnitudes M ≥ 6We extend the method to computing hazard from great earthquake and mega‐tsunami sources arising from earthquakes with magnitudes M > 7.9We also develop nowcasting methods using the information entropy of earthquake sequences [ABSTRACT FROM AUTHOR]

The Groningen gas field in the Netherlands has a significant level of induced seismicity; this seismicity is attributed to fluid withdrawal not fluid injection. This is one of the most productive gas fields in Europe. Production began in 1963 and the first induced earthquake occurred in 1991. Increasing levels of seismicity led to the installation of a sensitive seismic network in 1995 with a minimum magnitude completeness of M ≥ 1.5. The largest induced earthquake was a magnitude 3.6 in 2012, which caused thousands of damage claims and led to a reduction in gas production in 2014 January. In this paper, we show that the induced earthquakes satisfy Gutenberg–Richter (GR) frequency–magnitude statistics over the range 1.5 ≤ M ≤ 3.0. We utilize truncated GR scaling to introduce a possible maximum earthquake with M = 3.85. We also demonstrate an almost complete absence of aftershocks following the largest induced earthquakes. To quantify the temporal evolution of the induced seismicity, we perform our nowcasting method. Nowcasting uses the catalogue of seismicity in the region. Two earthquake magnitudes are selected, one large say M λ ≥ 2.5, and one small say M σ ≥ 1.5. The method utilizes the number of small earthquakes that occur between pairs of large earthquakes. A major advantage of nowcasting is that it relies on 'natural time', earthquake counts, between events rather than clock time. Thus, the results are applicable to induced seismicity that varies in time. We count the rate of occurrence of small induced earthquakes to nowcast the probability of occurrence of larger earthquakes. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the temporal clustering of large global earthquakes with respect to natural time, or interevent count, as opposed to regular clock time. To do this, we use two techniques: (1) nowcasting, a new method of statistically classifying seismicity and seismic risk, and (2) time series analysis of interevent counts. We chose the sequences of Mλ≥7.0 and Mλ≥8.0 earthquakes from the global centroid moment tensor (CMT) catalog from 2004 to 2016 for analysis. A significant number of these earthquakes will be aftershocks of the largest events, but no satisfactory method of declustering the aftershocks in clock time is available. A major advantage of using natural time is that it eliminates the need for declustering aftershocks. The event count we utilize is the number of small earthquakes that occur between large earthquakes. The small earthquake magnitude is chosen to be as small as possible, such that the catalog is still complete based on the Gutenberg-Richter statistics. For the CMT catalog, starting in 2004, we found the completeness magnitude to be Mσ≥5.1. For the nowcasting method, the cumulative probability distribution of these interevent counts is obtained. We quantify the distribution using the exponent, β, of the best fitting Weibull distribution; β=1 for a random (exponential) distribution. We considered 197 earthquakes with Mλ≥7.0 and found β=0.83±0.08. We considered 15 earthquakes with Mλ≥8.0, but this number was considered too small to generate a meaningful distribution. For comparison, we generated synthetic catalogs of earthquakes that occur randomly with the Gutenberg–Richter frequency–magnitude statistics. We considered a synthetic catalog of 1.97×105Mλ≥7.0 earthquakes and found β=0.99±0.01. The random catalog converted to natural time was also random. We then generated 1.5×104 synthetic catalogs with 197 Mλ≥7.0 in each catalog and found the statistical range of β values. The observed value of β=0.83 for the CMT catalog corresponds to a p value of p=0.004 leading us to conclude that the interevent natural times in the CMT catalog are not random. For the time series analysis, we calculated the autocorrelation function for the sequence of natural time intervals between large global earthquakes and again compared with data from 1.5×104 synthetic catalogs of random data. In this case, the spread of autocorrelation values was much larger, so we concluded that this approach is insensitive to deviations from random behavior. [ABSTRACT FROM AUTHOR]

Natural Time (“NT”) refers to the concept of using small earthquake counts, for example of M > 3 events, to mark the intervals between large earthquakes, for example M > 6 events. The term was first used by Varotsos et al. (2005) and later by Holliday et al. (2006) in their studies of earthquakes. In this paper, we discuss ideas and applications arising from the use of NT to understand earthquake dynamics, in particular by use of the idea of nowcasting. Nowcasting differs from forecasting, in that the goal of nowcasting is to estimate the current state of the system, rather than the probability of a future event. Rather than focus on an individual earthquake faults, we focus on a defined local geographic region surrounding a particular location. This local region is considered to be embedded in a larger regional setting from which we accumulate the relevant statistics. We apply the nowcasting idea to the practical development of methods to estimate the current state of risk for dozens of the world’s seismically exposed megacities, defined as cities having populations of over 1 million persons. We compute a ranking of these cities based on their current nowcast value, and discuss the advantages and limitations of this approach. We note explicitly that the nowcast method is not a model, in that there are no free parameters to be fit to data. Rather, the method is simply a presentation of statistical data, which the user can interpret. Among other results, we find, for example, that the current nowcast ranking of the Los Angeles region is comparable to its ranking just prior to the January 17, 1994 Northridge earthquake. [ABSTRACT FROM AUTHOR]

Nowcasting is a new method of statistically classifying seismicity and seismic risk (Rundle et al. 2016). In this paper, the method is applied to the induced seismicity at the Geysers geothermal region in California and the induced seismicity due to fluid injection in Oklahoma. Nowcasting utilizes the catalogs of seismicity in these regions. Two earthquake magnitudes are selected, one large say $$M_{\lambda } \ge 4$$ , and one small say $$M_{\sigma } \ge 2$$ . The method utilizes the number of small earthquakes that occurs between pairs of large earthquakes. The cumulative probability distribution of these values is obtained. The earthquake potential score (EPS) is defined by the number of small earthquakes that has occurred since the last large earthquake, the point where this number falls on the cumulative probability distribution of interevent counts defines the EPS. A major advantage of nowcasting is that it utilizes 'natural time', earthquake counts, between events rather than clock time. Thus, it is not necessary to decluster aftershocks and the results are applicable if the level of induced seismicity varies in time. The application of natural time to the accumulation of the seismic hazard depends on the applicability of Gutenberg-Richter (GR) scaling. The increasing number of small earthquakes that occur after a large earthquake can be scaled to give the risk of a large earthquake occurring. To illustrate our approach, we utilize the number of $$M_{\sigma } \ge 2.75$$ earthquakes in Oklahoma to nowcast the number of $$M_{\lambda } \ge 4.0$$ earthquakes in Oklahoma. The applicability of the scaling is illustrated during the rapid build-up of injection-induced seismicity between 2012 and 2016, and the subsequent reduction in seismicity associated with a reduction in fluid injections. The same method is applied to the geothermal-induced seismicity at the Geysers, California, for comparison. [ABSTRACT FROM AUTHOR]

SUMMARY We propose a time-dependent slider-block model which incorporates a time-to-failure function for each block dependent on the stress. We associate this new time-to-failure mechanism with the property of stress fatigue. We test two failure time functions including a power law and an exponential. Failure times are assigned to 'damaged' blocks with stress above a damage threshold, σ W and below a static failure threshold, σ F. If the stress of a block is below the damage threshold the failure time is infinite. During the aftershock sequence the loader-plate remains fixed and all aftershocks are triggered by stress transfer from previous events. This differs from standard slider-block models which initiate each event by moving the loader-plate. We show the resulting behaviour of the model produces both the Gutenberg-Richter scaling law for event sizes and the Omori's scaling law for the rate of aftershocks when we use the power-law failure time function. The exponential function has limited success in producing Omori's law for the rate of aftershocks. We conclude the shape of the failure time function is key to producing Omori's law. [ABSTRACT FROM AUTHOR]

Many driven threshold systems display a spectrum of avalanche event sizes, often characterized by power-law scaling. An important problem is to compute probabilities of the largest events ("Black Swans"). We develop a data-driven approach to the problem by transforming to the event index frame, and relating this to Shannon information. For earthquakes, we find the 12-month probability for magnitude m > 6 earthquakes in California increases from about 30% after the last event, to 40%-50% prior to the next one. [ABSTRACT FROM AUTHOR]

SUMMARY We discuss the long-standing question of whether the probability for large earthquake occurrence (magnitudes m > 6.0) is highest during time periods of smaller event activation, or highest during time periods of smaller event quiescence. The physics of the activation model are based on an idea from the theory of nucleation, that a small magnitude earthquake has a finite probability of growing into a large earthquake. The physics of the quiescence model is based on the idea that the occurrence of smaller earthquakes (here considered as magnitudes m > 3.5) may be due to a mechanism such as critical slowing down, in which fluctuations in systems with long-range interactions tend to be suppressed prior to large nucleation events. To illuminate this question, we construct two end-member forecast models illustrating, respectively, activation and quiescence. The activation model assumes only that activation can occur, either via aftershock nucleation or triggering, but expresses no choice as to which mechanism is preferred. Both of these models are in fact a means of filtering the seismicity time-series to compute probabilities. Using 25 yr of data from the California-Nevada catalogue of earthquakes, we show that of the two models, activation and quiescence, the latter appears to be the better model, as judged by backtesting (by a slight but not significant margin). We then examine simulation data from a topologically realistic earthquake model for California seismicity, Virtual California. This model includes not only earthquakes produced from increases in stress on the fault system, but also background and off-fault seismicity produced by a BASS-ETAS driving mechanism. Applying the activation and quiescence forecast models to the simulated data, we come to the opposite conclusion. Here, the activation forecast model is preferred to the quiescence model, presumably due to the fact that the BASS component of the model is essentially a model for activated seismicity. These results lead to the (weak) conclusion that California seismicity may be characterized more by quiescence than by activation, and that BASS-ETAS models may not be robustly applicable to the real data. [ABSTRACT FROM AUTHOR]

Abstract: A variety of studies have modeled the physics of material deformation and damage as examples of generalized phase transitions, involving either critical phenomena or spinodal nucleation. Here we study a model for frictional sliding with long-range interactions and recurrent damage that is parameterized by a process of damage and partial healing during sliding. We introduce a failure threshold weakening parameter into the cellular automaton slider-block model which allows blocks to fail at a reduced failure threshold for all subsequent failures during an event. We show that a critical point is reached beyond which the probability of a system-wide event scales with this weakening parameter. We provide a mapping to the percolation transition, and show that the values of the scaling exponents approach the values for mean-field percolation (spinodal nucleation) as lattice size is increased for fixed . We also examine the effect of the weakening parameter on the frequency–magnitude scaling relationship and the ergodic behavior of the model. [Copyright &y& Elsevier]

In weather forecasting, current and past observational data are routinely assimilated into numerical simulations to produce ensemble forecasts of future events in a process termed “model steering”. Here we describe a similar approach that is motivated by analyses of previous forecasts of the Working Group on California Earthquake Probabilities (WGCEP). Our approach is adapted to the problem of earthquake forecasting using topologically realistic numerical simulations for the strike-slip fault system in California. By systematically comparing simulation data to observed paleoseismic data, a series of spatial probability density functions (PDFs) can be computed that describe the probable locations of future large earthquakes. We develop this approach and show examples of PDFs associated with magnitude M > 6.5 and M > 7.0 earthquakes in California. [ABSTRACT FROM AUTHOR]

Abstract: Observations indicate that earthquake faults occur in topologically complex, multi-scale networks driven by plate tectonic forces. We present realistic numerical simulations, involving data-mining, pattern recognition, theoretical analyses and ensemble forecasting techniques, to understand how the observable space–time earthquake patterns are related to the fundamentally inaccessible and unobservable dynamics. Numerical simulations can also help us to understand how the different scales involved in earthquake physics interact and influence the resulting dynamics. Our simulations indicate that elastic interactions (stress transfer) combined with the nonlinearity in the frictional failure threshold law lead to the self-organization of the statistical dynamics, producing 1) statistical distributions for magnitudes and frequencies of earthquakes that have characteristics similar to those possessed by the Gutenberg–Richter magnitude–frequency distributions observed in nature; and 2) clear examples of stress transfer among fault activity described by stress shadows, in which an earthquake on one group of faults reduces the Coulomb failure stress on other faults, thereby delaying activity on those faults. In this paper, we describe the current state of modeling and simulation efforts for Virtual California, a model for all the major active strike slip faults in California. Noting that the Working Group on California Earthquake Probabilities (WGCEP) uses statistical distributions to produce earthquake forecast probabilities, we demonstrate that Virtual California provides a powerful tool for testing the applicability and reliability of the WGCEP statistical methods. Furthermore, we show how the simulations can be used to develop statistical earthquake forecasting techniques that are complementary to the methods used by the WGCEP, but improve upon those methods in a number of important ways. In doing so, we distinguish between the “official” forecasts of the WGCEP, and the “research-quality” forecasts that we discuss here. Finally, we provide a brief discussion of future problems and issues related to the development of ensemble earthquake hazard estimation and forecasting techniques. [Copyright &y& Elsevier]

The period of heightened volcanic and seismic activity at Kīlauea volcano on the island of Hawai'i, USA from late spring through summer 2018 included a remarkable quasi‐periodic sequence of caldera collapse events. From mid‐May to early‐August, 62 collapse events, each releasing the seismic energy equivalent of a Mw 5.0 ± 0.4 earthquake, occurred about every 1–2 days with over 300 M ≥ 2.5 earthquakes between sequential collapses. This region, experiencing very high rates of seismicity and frequent large magnitude events, is a good candidate to apply a regional seismic hazard assessment. Nowcasting is a type of statistical analysis that uses small magnitude events to estimate the occurrence of large magnitude events. This is done utilizing the concept of natural time in which time is counted by small magnitude event occurrences between large magnitude events, not in clock time (days passed). This method has produced a "nowcasted" set of large earthquakes that are in good agreement with the actual cataloged events in prior studies analyzing non‐volcanic regions. Previously applied to tectonic earthquakes and induced seismicity over longer time frames, this is the first test of nowcasting large caldera collapse events in volcanic associated seismicity and on a relatively short time scale. The technique produced limited "success" nowcasting 37 collapse events that agreed with the catalog of actual events. A temporal dependence of successful nowcasting during the sequence was found that may correlate to previously identified and analyzed physical changes in the volcanic system. Key Points: We applied earthquake nowcasting to 37 caldera collapse events, yielding close agreement with the actual recorded set of collapse eventsKīlauea volcano saw a temporal change in the natural time relationship that can be related to changes in the physical systemThis nowcasting method revealed limited success in a volcanic setting compared to prior tectonic earthquake applications [ABSTRACT FROM AUTHOR]

We analyze a new model for growing networks, the constrained Leath invasion percolation model. Cluster dynamics are characterized by bursts in space and time. The model quantitatively reproduces the observed frequency-magnitude scaling of earthquakes in the limit that the occupation probability approaches the critical bond percolation probability in d=2. The model may have application to other systems characterized by burst dynamics. [ABSTRACT FROM AUTHOR]

Focuses on the presence of self-organizing complexity in physical, biological and social sciences. Determination of the frequency-size distribution of earthquakes; Applicability of power-law scaling in biology; Symptoms associated with self-organizing complexity.

The spatial distribution of aftershocks following major earthquakes has received significant attention due to the shaking hazard these events pose for structures and populations in the affected region. Forecasting the spatial distribution of aftershock events is an important part of the estimation of future seismic hazard. A simple spatial shape for the zone of activity has often been assumed in the form of an ellipse having semimajor axis to semiminor axis ratio of 2.0. However, since an important application of these calculations is the estimation of ground shaking hazard, an effective criterion for forecasting future aftershock impacts is to use ground motion prediction equations (GMPEs) in addition to the more usual approach of using epicentral or hypocentral locations. Based on these ideas, we present an aftershock model that uses self-similarity and scaling relations to constrain parameters as an option for such hazard assessment. We fit the spatial aspect ratio to previous earthquake sequences in the studied regions, and demonstrate the effect of the fitting on the likelihood of post-disaster ground motion forecasts for eighteen recent large earthquakes. We find that the forecasts in most geographic regions studied benefit from this optimization technique, while some are better suited to the use of the a priori aspect ratio. [ABSTRACT FROM AUTHOR]

Injections of large volumes of water into tight shale reservoirs allow the extraction of oil and gas not previously accessible. This large volume “super” fracking induces damage that allows the oil and/or gas to flow to an extraction well. The purpose of this paper is to provide a model for understanding super fracking. We assume that water is injected from a small spherical cavity into a homogeneous elastic medium. The high pressure of the injected water generates hoop stresses that reactivate natural fractures in the tight shales. These fractures migrate outward as water is added creating a spherical shell of damaged rock. The porosity associated with these fractures is equal to the water volume injected. We obtain an analytic expression for this volume. We apply our model to a typical tight shale reservoir and show that the predicted water volumes are in good agreement with the volumes used in super fracking. [ABSTRACT FROM AUTHOR]

The article discusses the fracking technique for extracting oil and gas from shales in the U.S. Topics discussed include increase in the annual production of natural gas in the U.S. which led to lower prices of gas, consequence of the drop in natural gas prices has been the substitution of natural gas for coal in electric power generation plants and adds that oil and gas formation in black shales increase fluid pressure which results in hydraulic forces leading to fractures.

Recent developments in hydraulic fracturing (fracking) have enabled the recovery of large quantities of natural gas and oil from old, low-permeability shales. These developments include a change from low-volume, high-viscosity fluid injection to high-volume, low-viscosity injection. The injected fluid introduces distributed damage that provides fracture permeability for the extraction of the gas and oil. In order to model this process, we utilize a loopless nontrapping invasion percolation previously introduced to model optimal polymers in a strongly disordered medium and for determining minimum energy spanning trees on a lattice. We performed numerical simulations on a two-dimensional square lattice and find significant differences from other percolation models. Additionally, we find that the growing fracture network satisfies both Horton-Strahler and Tokunaga network statistics. As with other invasion percolation models, our model displays burst dynamics, in which the cluster extends rapidly into a connected region. We introduce an alternative definition of bursts to be a consecutive series of opened bonds whose strengths are all below a specified value. Using this definition of bursts, we find good agreement with a power-law frequency-area distribution. These results are generally consistent with the observed distribution of microseismicity observed during a high-volume frack. [ABSTRACT FROM AUTHOR]

In this study, we used pattern informatics (PI) to visualize patterns in seismic migration for two large earthquakes in Taiwan. The 2D PI migration maps were constructed from the slope values of temporal variations of the distances between the sites and hotspot recognized from the PI map. We investigated the 2D PI migration pattern of the 1999 Chi-Chi and 2006 Pingtung earthquakes which originated from two types of seismotectonic setting. Our results show that the PI hotspots migrate toward the hypocenter only when the PI hotspots are generated from specific depth range which is associated with the seismotectonic setting. Therefore, we conclude that the spatiotemporal distribution of events prior to an impending earthquake is significantly affected by tectonics. [ABSTRACT FROM AUTHOR]

Terra Nova, 23, 369-374, 2011 Abstract Seismic precursors revealed by statistical physics methods are important for earthquake forecasting and helpful for understanding fault behaviour. One of the precursors, earthquake migration, has been observed in simulated models and rock experiments, as well as anecdotal observations for large earthquakes along major fault zones. However, there has been no systematic methodology for investigating earthquake migration in nature, and only a small number of studies have observed earthquake migration at large scales. In this work, we show that migration of small earthquakes towards the epicentre before large earthquakes can be visualized. We present a method for detecting small earthquake migration in the spatiotemporal domain, and retrospectively apply it to two recent examples of major earthquakes in Taiwan. We observe that anomalous seismicity occurs increasingly close to the epicentre as the occurrence time is approached. At that point, the bulk of the seismicity concentrates on the epicentre, accumulating stress on the rupture zone and leading to the full rupture. Our findings imply a new type of large-scale pre-seismic earthquake migration pattern that is complementary to the well-studied patterns of source-region activation and quiescence. [ABSTRACT FROM AUTHOR]

Observations suggest that contemporary wildfire suppression practices in the United States have contributed to conditions that facilitate large, destructive fires. We introduce a forest-fire model with natural fire resistance that supports this theory. Fire resistance is defined with respect to the size and shape of clusters; the model yields power-law frequency-size distributions of model fires that are consistent with field observations in the United States, Canada, and Australia. [ABSTRACT FROM AUTHOR]

We report the retrospective analyses of frequency-magnitude distributions (FMD) and pattern informatics (PI) for the Pingtung offshore doublet earthquakes having magnitudes 6.7 and 6.4. The frequency-magnitude distributions of earthquakes that occurred in the study area preceding the Pingtung doublets demonstrate the activation of moderate events since March 2004, about three years before the Pingtung doublets. In addition, in the calculated PI map, the hypocentral area of the doublets exhibits the signatures of anomalous activity associated with the precursory seismic activation. Our study therefore suggests that, by inspecting temporal variations in the frequency-magnitude distributions of earthquakes, together with the hotspot locations with intense seismicity changes in the PI map, it is possible to shed light on the relevant preparation processes of the forthcoming large earthquakes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a branching aftershock sequence (BASS) model for seismicity. We suggest that the BASS model is a preferred alternative to the widely studied epidemic type aftershock sequence (ETAS) model. In the BASS model an initial, or seed, earthquake is specified. The subsequent earthquakes are obtained from the statistical distributions of magnitude, time, and location. The magnitude scaling is based on a combination of the Gutenberg–Richter scaling relation and the modified Båth’s law for the scaling relation of aftershocks relative to the magnitude of the seed earthquake. Omori’s law specifies the distribution of earthquake times, and a modified form of Omori’s law specifies the distribution of earthquake locations. Since the BASS model is specified by the four scaling relations, it is fully self-similar. This is not the case for ETAS. We also give a deterministic version of BASS and show that it satisfies Tokunaga side-branching statistics in a similar way to diffusion-limited aggregation (DLA). [Copyright &y& Elsevier]

Abstract: Correlations in space and time play a fundamental role in earthquake processes. One direct manifestation of the effects of correlations is the occurrence of aftershocks due to the stress transfer in the vicinity of a main shock. Less obvious and more speculative changes in correlations may occur in the background seismicity before large earthquakes. Using statistical physics it is possible to introduce a measure of spatial correlations through a correlation length. This quantity characterizes how local fluctuations can influence the occurrence of earthquakes over distances comparable with the correlation length. In this work, the physical basis of spatial correlations of earthquakes is discussed in the context of critical phenomena and the percolation problem. The method of two-point correlation function is applied to the seismicity of California. Well defined variations in time of the correlation length are found for aftershock sequences and background seismicity. The scaling properties of our obtained distributions are analyzed with respect to changes in several scaling parameters such as lower magnitude cutoff of earthquakes, the maximum time interval between earthquakes, and the spatial size of the area considered. This scaling behavior can be described in a unified manner by utilizing the multifractal fit. Utilizing the percolation approach the time evolution of clusters of earthquakes is studied with the correlation length defined in terms of the radius of gyration of clusters. This method is applied to the seismicity of California. [Copyright &y& Elsevier]

Abstract: A method for the computation and interpretation of gravity and height changes and vertical gravity gradients produced by magmatic intrusions in a layered elastic–gravitational medium is presented. The methodology assumes a planar medium geometry, which consists of welded layers overlying a half-space. The medium is elastic and gravitating. The intrusion is treated as a point source and can be located at any depth inside the medium. The theoretical elastic–gravitational model allows the computation of the so-called geometric and orthometric vertical displacements as well as gravity changes of different types. The corresponding vertical gravity gradients can also be computed. We present several examples of theoretical computations and study different types of these geometric and orthometric gravity gradients and the information we can get from the application of the methodology described. The results presented show that the use of these gradients is a useful tool to obtain information on the dynamics of the injection processes, including the detection of new magma recharge. We show that using the elastic–gravitational deformation model we can explain non-linear gravity–height relationships that appear in volcanic areas. We also present the application of the methodology to Mayon volcano, Philippines, delineating the intrusion of new magma, consistent with the produced eruptions after the observation period. [Copyright &y& Elsevier]

The statistical properties of aftershock sequences are associated with three empirical scaling relations: (1) Gutenberg-Richter frequency-magnitude scaling, (2) Båth’s law for the magnitude of the largest aftershock, and (3) the modified Omori’s law for the temporal decay of aftershocks. In this paper these three laws are combined to give a relation for the aftershock decay rate that depends on only a few parameters. This result is used to study the temporal properties of aftershock sequences of several large California earthquakes. A review of different mechanisms and models of aftershocks are also given. The scale invariance of the process of stress transfer caused by a main shock and the heterogeneous medium in which aftershocks occur are responsible for the occurrence of scaling laws. We suggest that the observed partitioning of energy could play a crucial role in explaining the physical origin of Båth’s law. We also study the stress relaxation process in a simple model of damage mechanics and find that the rate of energy release in this model is identical to the rate of aftershock occurrence described by the modified Omori’s law. [ABSTRACT FROM AUTHOR]

Examines viscoelastic displacement and gravity changes due to point magmatic intrusions in a gravitational layered solid Earth. Extension of a deformation model; Planar earth geometry of a model.

Examines the ground deformation in a viscoelastic medium. Computation of numerical solutions for an axisymmetric extended source; Determination of analytical solutions for a point source using the dislocation theory and the propagator matrix technique; Assessment on the correspondence principle of linear viscoelasticity.

Following the Mw 6.9 Hawaiian earthquake on 4 May 2018, a remarkable quasiperiodic sequence of collapse events began at Halema'uma'u Crater at the summit of Kīlauea Volcano. The collapse events were associated with the drainage of magma from beneath the summit to the Lower East Rift Zone where fissure eruptions occurred. From 4 June 2018 to 2 August 2018 forty‐seven collapse events Mw 5.3 ± 0.1 occurred with the same temporal pattern of seismicity occurring between sequential pairs of collapse events. This paper focuses on this interevent seismicity pattern. Following a collapse event, there was a relatively quiescent period. This was followed by a sudden increase in seismicity, occurring at a nearly linear rate of 397 ± 96 earthquakes per day. These seismically active periods lasted until the next collapse event occurred. The pattern then repeated itself beginning again with postcollapse quiescence. We provide a statistical summary of this seismicity behavior by isolating the quiescent and active times to look at immediate precollapse and postcollapse activity. In mid‐June there were significant changes in the quiescent time lengths (decreased), the number of earthquakes during the interevent times (increased), and the rates of seismicity during the active times (increased). This type of interevent study could be conducted with other seismically well recorded, sequential caldera collapse events and also with other data types to look for potential physical explanations and an improved understanding of precollapse and postcollapse activity. Key Points: The same pattern of seismically quiescent and active periods occurred between every pair of collapse events at the summitThe active times had linear rates of seismicity, and the quiescent times had fewer earthquakesFollowing a large increase in June 2018, there was a general decrease in the active time seismicity rates until the end of the sequence [ABSTRACT FROM AUTHOR]

During May 12–14, 2018, the International Conference for the Decade Memory of the Wenchuan Earthquake, in connection to the 4th International Conference on Continental Earthquakes (4th ICCE) and the 12th General Assembly of the Asian Seismological Commission (ASC), was held in Chengdu, China. There have been several outcomes of the Conference (for example, the book published jointly by Higher Education Press and Springer) as well as the present special issue. Among the 58 submissions, 34 have been accepted after peer review. Some of the papers came from the presentations during the Conference, but with additional submissions, the scope of this special issue is far beyond that of the proceedings of the meeting. [ABSTRACT FROM AUTHOR]

Fracking is a popular term referring to hydraulic fracturing when it is used to extract hydrocarbons. We distinguish between low-volume traditional fracking and the high-volume modern fracking used to recover large volumes of hydrocarbons from shales. Shales are fine-grained rocks with low granular permeabilities. During the formation of oil and gas, large fluid pressures are generated. These pressures result in natural fracking, and the resulting fracture permeability allows oil and gas to escape, reducing the fluid pressures. These fractures may subsequently be sealed by mineral deposition, resulting in tight shale formations. The objective of modern fracking is to reopen these fractures and/or create new fractures on a wide range of scales. Modern fracking has had a major impact on the availability of oil and gas globally; however, there are serious environmental objections to modern fracking, which should be weighed carefully against its benefits. [ABSTRACT FROM AUTHOR]

In this manuscript, we introduce a framework for developing earthquake forecasts usingVirtual Quake (VQ), the generalized successor to the perhaps better known Virtual California (VC) earthquake simulator. We discuss the basic merits and mechanics of the simulator, and we present several statistics of interest for earthquake forecasting. We also show that, though the system as a whole (in aggregate) behaves quite randomly, (simulated) earthquake sequences limited to specific fault sections exhibit measurable predictability in the form of increasing seismicity precursory to large m>7 earthquakes. In order to quantify this, we develop an alertbased forecasting metric, and show that it exhibits significant information gain compared to random forecasts. We also discuss the long-standing question of activation versus quiescent type earthquake triggering. We show that VQ exhibits both behaviours separately for independent fault sections, some fault sections exhibit activation type triggering, while others are better characterized by quiescent type triggering. We discuss these aspects of VQ specifically with respect to faults in the Salton Basin and near the El Mayor-Cucapah region in southern California, USA and northern Baja California Norte, Mexico. [ABSTRACT FROM AUTHOR]

Abstract: Tectonic motion across the Los Angeles region is distributed across an intricate network of strike‐slip and thrust faults that will be released in destructive earthquakes similar to or larger than the 1933 M6.4 Long Beach and 1994 M6.7 Northridge events. Here we show that Los Angeles regional thrust, strike‐slip, and oblique faults are connected and move concurrently with measurable surface deformation, even in moderate magnitude earthquakes, as part of a fault system that accommodates north‐south shortening and westerly tectonic escape of northern Los Angeles. The 28 March 2014 M5.1 La Habra earthquake occurred on a northeast striking, northwest dipping left‐lateral oblique thrust fault northeast of Los Angeles. We present crustal deformation observation spanning the earthquake showing that concurrent deformation occurred on several structures in the shallow crust. The seismic moment of the earthquake is 82% of the total geodetic moment released. Slip within the unconsolidated upper sedimentary layer may reflect shallow release of accumulated strain on still‐locked deeper structures. A future M6.1–6.3 earthquake would account for the accumulated strain. Such an event could occur on any one or several of these faults, which may not have been identified by geologic surface mapping. [ABSTRACT FROM AUTHOR]

We quantify the seismicity on the island of Taiwan using the frequency–magnitude statistics of earthquakes since 1900. A break in Gutenberg–Richter scaling for large earthquakes in global seismicity has been observed, this break is also observed in our Taiwan study. The seismic data from the Central Weather Bureau Seismic Network are in good agreement with the Gutenberg–Richter relation taking b ≈ 1 when M < 7. For large earthquakes, M ≥ 7, the seismic data fit Gutenberg–Richter scaling with b ≈ 1.5. If the Gutenberg–Richter scaling for M < 7 earthquakes is extrapolated to larger earthquakes, we would expect a M > 8 earthquake in the study region about every 25 yr. However, our analysis shows a lower frequency of occurrence of large earthquakes so that the expected frequency of M > 8 earthquakes is about 200 yr. The level of seismicity for smaller earthquakes on Taiwan is about 12 times greater than in Southern California and the possibility of a M ≈ 9 earthquake north or south of Taiwan cannot be ruled out. In light of the Fukushima, Japan nuclear disaster, we also discuss the implications of our study for the three operating nuclear power plants on the coast of Taiwan. [ABSTRACT FROM AUTHOR]

Abstract: We identify two distinct scaling regimes in the frequency–magnitude distribution of global earthquakes. Specifically, we measure the scaling exponent b =1.0 for “small” earthquakes with 5.5< m <7.6 and b =1.5 for “large” earthquakes with 7.6< m <9.0. This transition at m t =7.6, can be explained by geometric constraints on the rupture. In conjunction with supporting literature, this corroborates theories in favor of fully self-similar and magnitude independent earthquake physics. We also show that the scaling behavior and abrupt transition between the scaling regimes imply that earthquake ruptures have compact shapes and smooth rupture-fronts. [Copyright &y& Elsevier]