California Earthquake Research Articles

An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs

AbstractEarthquake catalogs are essential to analyze the evolution of active fault systems. The background seismicity rate, or rate of earthquakes that are not directly triggered by other earthquakes, directly relates to the stressing rate, a crucial quantity for understanding the seismic hazards. Determining the background seismicity rate is challenging because aftershock sequences may dominate the seismicity rate. Classifying these events in earthquake catalogs—known as catalog declustering—is a common practice and most declustering solutions rely on spatiotemporal distances between events, such as the nearest‐neighbor‐distance algorithm, widely used in various contexts. This algorithm assumes that the nearest‐neighbor distance (NND) follows a bimodal distribution related to the background seismicity and to the aftershocks. Constraining these two distributions is crucial to distinguish the aftershocks from the background events accurately. Recent work often uses linear splitting based on the NND, ignoring the potential overlap between the two populations and resulting in a biased identification of background earthquakes and aftershock sequences. We revisit this problem with machine‐learning algorithms. After testing several popular algorithms, we show that a random forest trained with various synthetic catalogs generated by an Epidemic‐Type Aftershock Sequence model outperforms approaches such as k‐means, Gaussian‐mixture models, and support vector machine classification. We apply our model to two different earthquake catalogs: the relocated Southern California earthquake center catalog and the GeoNet catalog of New Zealand. Our model capably adapts to these two different tectonic contexts, highlighting the differences in aftershock productivity between crustal and intermediate‐depth seismicity.

Observation‐Constrained Multicycle Dynamic Models of the Southern San Andreas and the Northern San Jacinto Faults: Addressing Complexity in Paleoearthquake Extent and Recurrence With Realistic 2D Fault Geometry

AbstractUnderstanding mechanical conditions that lead to complexity in earthquakes is important to seismic hazard analysis. In this study, we simulate physics‐based multicycle dynamic models of the San Andreas fault (Carrizo through San Bernardino sections) and the San Jacinto fault (Claremont and Clark strands). We focus on a complex fault geometry based on the Southern California Earthquake Center Community Fault Model and its effect over multiple earthquake cycles. Using geodetically derived strain rates, we validate the models against geologic slip rates and recurrence intervals at various paleoseismic sites. We find that the interactions among fault geometry, dynamic rupture and interseismic stress accumulation produce stress heterogeneities, leading to rupture segmentation and variability in earthquake recurrence. Our models produce earthquakes with rupture extents similar to a recent comprehensive paleoseismic catalog. The “earthquake gates” of the Big Bend and the Cajon Pass occasionally impede dynamic ruptures. The angle of compression, which is the subtraction of the maximum shear strain rate direction from the local fault strike, can better determine the likelihood of the impedance of restraining bends to dynamic ruptures. Because the Big Bend has an angle of compression of ∼20°, ruptures that traverse the Big Bend, like the 1857 Fort Tejon earthquake, are more frequent than expected based on empirical relations which predict the ∼40° restraining bend to terminate most ruptures. Our models indicate that large ruptures tend to initiate north of the Big Bend and propagate southwards, similar to the 1857 earthquake, providing critical information for ground shaking assessment in the region.

Seismic isolated house reaction upon condition of the repeatability of earthquakes

To study the seismic properties of buildings in Almaty, a special test site operates. It includes buildings with conventional strip foundations with a system of cross belts and seismic support of 2 types. The landfill buildings were previously tested under static and dynamic influences. With the use of real accelerograms of earthquakes, a forecast of the behavior of a seismically isolated house on kinematic foundations under seismic influences has been carried out. The seismic impact is represented by a set of real accelerograms of strong, mainly Californian earthquakes. The hearts depths and magnitudes correspond to possible Almaty earthquakes. Two design schemes of a seismically isolated building are considered - single-mass and 10-mass dynamic building models. It is shown that the magnitudes of displacements in the kinematic foundations (KF) level obtained by two dynamic models usually differ within 5-10%. Therefore, the calculation of seismically isolated buildings can be applied to a single-mass non-linear dynamic model. A linear regression connection was obtained between the values of acceleration at the base and displacements at the level of the foundation. For the values of acceleration according to the new "Map of seismic zoning of the Republic of Kazakhstan" were obtained the predicted values of displacement at the level of the kinematic support of the order of 8.0-11.5 cm.

High‐Precision Earthquake Location Using Source‐Specific Station Terms and Inter‐Event Waveform Similarity

AbstractEarthquake monitoring and many seismological studies depend on earthquake locations from phase arrival‐times. We present an extended, arrival‐time earthquake location procedure (NLL‐SSST‐coherence) which approaches the precision of differential‐timing based, relative location methods. NLL‐SSST‐coherence is based on the probabilistic, global‐search NonLinLoc (NLL) location algorithm which defines a probability density function (PDF) in 3D space for hypocenter location and is highly robust to outlier data. NLL‐SSST‐coherence location first reduces velocity model error through iteratively generated, smooth, source‐specific, station travel‐time corrections (SSST). Next, arrival‐time error is reduced by consolidating location information across events based on inter‐event waveform coherency. If the waveforms at a station for multiple events are very similar (have high coherency) up to a given frequency, then the distance separating these “multiplet” events is small relative to the seismic wavelength at that frequency. NLL‐coherence relocation for a target event is a stack over 3D space of the NLL‐SSST location PDF for the event and the PDF's for other multiplet events, each weighted by its waveform coherency with the target. NLL‐coherence relocation requires waveforms from only one or a few seismic stations, enabling precise relocation with sparse networks, for foreshocks and early aftershocks of significant events before installation of temporary stations, and for older data sets with few waveform data. We show the behavior and performance of NLL‐SSST‐coherence with synthetic and ground‐truth tests, and through application and comparison to relative locations for California earthquake sequences with dense and sparse station coverage.

Estimating the Epicenter of a Future Strong Earthquake in Southern California, Mexico, and Central America by Means of Natural Time Analysis and Earthquake Nowcasting.

It has recently been shown in the Eastern Mediterranean that by combining natural time analysis of seismicity with earthquake networks based on similar activity patterns and earthquake nowcasting, an estimate of the epicenter location of a future strong earthquake can be obtained. This is based on the construction of average earthquake potential score maps. Here, we propose a method of obtaining such estimates for a highly seismically active area that includes Southern California, Mexico and part of Central America, i.e., the area NW. The study includes 28 strong earthquakes of magnitude M that occurred during the time period from 1989 to 2020. The results indicate that there is a strong correlation between the epicenter of a future strong earthquake and the average earthquake potential score maps. Moreover, the method is also applied to the very recent 7 September 2021 Guerrero, Mexico, M7 earthquake as well as to the 22 September 2021 Jiquilillo, Nicaragua, M6.5 earthquake with successful results. We also show that in 28 out of the 29 strong M EQs studied, their epicenters lie close to an estimated zone covering only 8.5% of the total area.

Contributed Reports of Widely Felt Earthquakes in California, United States: If They Felt it, Did They Report it?

In a recent study,Hough and Martin(2021) considered the extent to which socioeconomic factors influence the numbers and distribution of contributed reports available to characterize the effects of both historical and recent large earthquakes. In this study I explore the question further, focusing on analysis of widely felt earthquakes near major population centers in northern and southern California since 2002. For most of these earthquakes there is a correlation between average household income in a postal ZIP code and the population-normalized rate of responses to the DYFI system. As past studies have demonstrated, there is also a strong correlation between DYFI participation and the severity of shaking. This first-order correlation can obscure correlations with other factors that influence participation. Focusing on five earthquakes between 2011 and 2021 that generated especially uniform shaking across the greater Los Angeles, California, region, response rate varies by two orders of magnitude across the region, with a clear correlation with demographics, and consistent spatial patterns in response rate for earthquakes 10 years apart. While there is no evidence that uneven DYFI participation in California impacts significantly the reliability of intensity data collected, the results reveal that DYFI participation is significantly higher in affluent parts of southern California compared to economically disadvantaged areas.

Fracture Network Localization Preceding Catastrophic Failure in Triaxial Compression Experiments on Rocks

We quantify the spatial distribution of fracture networks throughout six in situ X-ray tomography triaxial compression experiments on crystalline rocks at confining stresses of 5–35 MPa in order to quantify how fracture development controls the final macroscopic failure of the rock, a process analogous to those that control geohazards such as earthquakes and landslides. Tracking the proportion of the cumulative volume of fractures with volumes >90th percentile to the total fracture volume, ∑v90/vtot indicates that the fracture networks tend to increase in localization toward these largest fractures for up to 80% of the applied differential stress. The evolution of this metric also matches the evolution of the Gini coefficient, which measures the deviation of a population from uniformity. These results are consistent with observations of localizing low magnitude seismicity before large earthquakes in southern California. In both this analysis and the present work, phases of delocalization interrupt the general increase in localization preceding catastrophic failure, indicating that delocalization does not necessarily indicate a reduction of seismic hazard. However, the proportion of the maximum fracture volume to the total fracture volume does not increase monotonically. Experiments with higher confining stress tend to experience greater localization. To further quantify localization, we compare the geometry of the largest fractures, with volumes >90th percentile, to the best fit plane through these fractures immediately preceding failure. The r2 scores and the mean distance of the fractures to the plane indicate greater localization in monzonite than in granite. The smaller mean mineral diameter and lower confining stress in the granite experiments may contribute to this result. Tracking these various metrics of localization reveals a close association between macroscopic yielding and the acceleration of fracture network localization. Near yielding, ∑v90/vtot and the Gini coefficient increase while the mean distance to the final failure plane decreases. Macroscopic yielding thus occurs when the rate of fracture network localization increases.

Response Modal Nonlinear Time-History Dynamic Analyses of the Northridge Earthquake of a Two- Storied Steel Structure with Rubber Isolator and Fixed-base Systems

This paper analyzes and compares the responses of a two-storied steel structure to the 1994 Northridge earthquake in California. Response modal nonlinear time-history dynamic analysis was especially used on the structure with fixed-base and base-isolated systems to show the realistic behavior of the structure. Intuitively, the best choice for the seismic isolation technique is the use of the flexible element, or an isolator, at the structure’s base, which ensures damping. The current research analyzed in depth the application of a base isolator in the structure. In this study, one structure with different support systems was analyzed to investigate the use of base isolation. The research compared the features and performance of a structure with and without base isolation. The steel building was analyzed subjected to earthquake-induced load with and without a base-isolated rubber bearing. The building was modeled by SAP2000 for nonlinear time-history analysis. Ground-motion simulations of the M6.69 1994 Northridge earthquake in California were applied to the steel base-isolated building model. Study results showed that the presence of an isolation system in a structure with 24 feet (7.31 m) height increased the time period of the structure significantly due to reduced resonance throughout the building.

A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress

AbstractRepeating earthquakes, earthquakes that rupture the same fault patch more than once, are playing an important role in the study of source processes and fault behavior, and have the potential to improve hazard assessment and earthquake forecast. They are believed to represent brittle failure of a stuck patch (asperity) on an otherwise creeping fault, and as such may offer insight into slip behavior at depth. We carried out a systematic analysis in search for repeating earthquakes in northern California between 1984 and 2014 by identifying events that are co‐located, are of similar size, and share similar waveforms. We find 27,675 events in 7,713 repeating earthquake sequences (RES) that include between 2 and 32 events with magnitudes between −0.5 and 6.5. Temporal behavior of the events within each RES ranges between random and quasi‐periodic over the 30‐year observation period, and includes temporal clustering (bursts). The RESs occur throughout northern California, with some of the largest and most periodic sequences predominately, but not exclusively, along faults within the San Andreas Fault system that exhibit surface creep. Piecewise periodic RESs are found near large earthquakes where they are modulated by transient stress. Slip rates inferred from the new data, while generally consistent with published estimates from geodetic and surface data, offer a high‐resolution image of subsurface creep along individual fault strands and resolve slip partitioning across wider zones of deformation.

How Fault Rocks Form and Evolve in the Shallow San Andreas Fault

AbstractWe document the mechanical and geochemical processes of fault rock development in the shallow San Andreas fault (Mojave segment), and quantify their importance in shaping the mineralogy, grain size, fabric, and frictional characteristics of gouge. Through a combination of field and laboratory analysis of an extensive suite of shallow (<150 m) drill cores, we show that fault rocks evolved from a granodiorite protolith via three main processes: distributed microfracturing/pulverization; cataclastic flow and incipient fabric development; and production of authigenic illite/smectite during fluid‐rock interaction. The interdependence of these mechanical and geochemical processes results in a diverse suite of fault rocks, and causes significant changes in frictional strength. Spatial variations in the effects of these mechanisms, as manifested in fault‐rock mineralogy and geochemistry, indicate marked variations in their relative contribution to fault‐rock evolution. These data reveal a complex San Andreas fault with multiple principal slip zones and damaged and altered rock hosting numerous interconnected secondary slip surfaces. The resulting picture of the San Andreas Fault zone suggests a substantial departure from the simple structures envisioned for near‐surface seismogenenic faults in numerical models is required, and may inform future efforts to forecast peak ground accelerations during southern California earthquakes.

The Seismic Hazard Implications of Declustering and Poisson Assumptions Inferred from a Fully Time-Dependent Model

ABSTRACT We use the Third Uniform California Earthquake Rupture Forecast (UCERF3) epidemic-type aftershock sequence (ETAS) model (UCERF3-ETAS) to evaluate the effects of declustering and Poisson assumptions on seismic hazard estimates. Although declustering is necessary to infer the long-term spatial distribution of earthquake rates, the question is whether it is also necessary to honor the Poisson assumption in classic probabilistic seismic hazard assessment. We use 500,000 yr, M ≥ 2.5 synthetic catalogs to address this question, for which UCERF3-ETAS exhibits realistic spatiotemporal clustering effects (e.g., aftershocks). We find that Gardner and Knopoff (1974) declustering, used in the U.S. Geological Survey seismic hazard models, lowers 2% in 50 yr and risk-targeted ground-motion hazard metrics by about 4% on average (compared with the full time-dependent [TD] model), with the reduction being 5% at 40% in 50 yr ground motions. Keeping all earthquakes and treating them as a Poisson process increases these same hazard metrics by about 3%–12%, on average, due to the removal of relatively quiet time periods in the full TD model. In the interest of model simplification, bias minimization, and consideration of the probabilities of multiple exceedances, we agree with others (Marzocchi and Taroni, 2014) that we are better off keeping aftershocks and treating them as a Poisson process rather than removing them from hazard consideration via declustering. Honoring the true time dependence, however, will likely be important for other hazard and risk metrics, and this study further exemplifies how this can now be evaluated more extensively.

Effect of Merging Multiscale Models on Seismic Wavefield Predictions Near the Southern San Andreas Fault

AbstractUpdating Earth models used by the scientific community in geologic studies and hazard assessment has a significant societal impact but is computationally prohibitive due to the large spatial scale. The advent of urban seismology allowed rapid development of local high‐resolution models using short‐term dense seismic arrays to become conventional. To incorporate the details in these local models in community models, we developed a technique for constructing window taper functions like the cosine taper in arbitrarily shaped spatial domains on regular grids. We apply our algorithm to the problem of low‐frequency ground shaking estimation near the southernmost San Andreas fault by creating two hybrid models. These models consist of basin‐scale (top 10 km or less) high‐resolution models developed using controlled source data embedded into two popular Southern California Earthquake Center community models. We evaluate the models by computing long period (6–30 s) wavefield energy misfits using 11 earthquakes with moment magnitudes between 3.5 and 5.5 not used in developing any of the models under consideration. One of the hybrid models produces an ∼24% decrease while the other has an ∼0.6% increase in the overall median misfit relative to their original community models. The overlapping misfit values between the models and variability in waveform fit for different events and stations emphasize the difficulties in model validation. Our approach can merge any type of gridded multiscale and multidimensional datasets, and represents a valuable tool for modeling in the computational sciences.

Impacts on catastrophe risk assessments from multi-segment and multi-fault ruptures in the UCERF3 model

The Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) relaxes fault segmentation, allowing multi-segment and multi-fault ruptures through fault-to-fault “jumps,” with lengths up to ∼1200 km along the San Andreas Fault. Local faults are also highly interconnected, including ruptures on the order of hundreds of kilometers. These prescribed long ruptures did not exist in older models. Longer ruptures produce larger aggregate loss estimates for geographically dispersed assets (portfolios) due to the wider areas that are affected by strong ground shaking. In this study, we model probabilistic earthquake losses of a hypothetical state-wide building portfolio in California. We develop risk deaggregation methods to identify multi-segment and multi-fault ruptures that contribute significantly to high portfolio-wide risks. Three risk measures that are commonly used in risk management decisions are examined: Average Annual Loss (AAL), Return Period Loss (RPLα), and Tail Conditional Expectation (TCEα), for an annual exceedance probability “α,” or corresponding return period “1/α.” Our results show that while the super long ruptures (>500 km) contribute modestly (∼7%) to the portfolio AAL estimate, they contribute more significantly to portfolio catastrophe risk estimates. Specifically, at a 250 year return period, these long ruptures contribute about 26% and 32% to RPL250 and TCE250 estimates, respectively. At a 500-year return period, the corresponding contributions reach about 35% and 39%. Ruptures that connect complex fault systems are also found to be highly influential to estimated portfolio risks. At a 500-year return period, a mere six rupture groups contribute nearly 70% to the RPL500 estimate. Due to the importance of the UCERF3 model to many risk management and public policy decisions, a critical examination of the limit and uncertainty of fault connectivity and rupture lengths of future earthquakes, as well as their impacts on catastrophe risk assessments, is warranted in future model updates.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

ABSTRACTGround-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

Regional seismic velocity changes following the 2019 Mw 7.1 Ridgecrest, California earthquake from autocorrelations and P/S converted waves

SUMMARY We examine regional transient changes of seismic velocities generated by the 2019 Mw 7.1 Ridgecrest earthquake in California, using autocorrelations of moving time windows in continuous waveforms recorded at regional stations. We focus on traveltime differences in a prominent phase generated by an interface around 2 km depth, associated with transmitted Pp waves and converted Ps waves from the ongoing microseismicity. Synthetic tests demonstrate the feasibility of the method for monitoring seismic velocity changes. Taking advantage of the numerous aftershocks in the early period following the main shock, we obtain a temporal resolution of velocity changes up to 20 min in the early post-main-shock period. The results reveal regional coseismic velocity drops in the top 1–3 km with an average value of ∼2 per cent over distances up to 100 km from the Ridgecrest event. These average velocity drops are likely dominated by larger changes in the shallow materials and are followed by rapid recoveries on timescales of days. Around the north end of the Ridgecrest rupture and the nearby Coso geothermal region, the observed coseismic velocity drops are up to ∼8 per cent. The method allows monitoring temporal changes of seismic velocities with high temporal resolution, fast computation and precise spatial mapping of changes. The results suggest that significant temporal changes of seismic velocities of shallow materials are commonly generated on a regional scale by large events.

Understanding public engagement on twitter using topic modeling: The 2019 Ridgecrest earthquake case

Twitter is often used to disseminate information during disasters. This study collected and analyzed tweets related to the July 2019 Ridgecrest earthquake in Southern California from selected organizations (e.g., governmental agencies, media companies, nonprofit organizations, and research groups). The topic modeling technique found both similar and different topics for those organizations. Regression analysis showed that topics from media outlets and “others” (Twitter users not included in the selected organizations) positively related to the number of retweets and favorites. These findings contribute to the methodological development of Twitter research and social media use for disaster management.

Detecting Seismo‐Ionospheric Anomalies Possibly Associated With the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES, and Swarm Observations

AbstractThe association between earthquakes and ionospheric precursors has been widely reported in previous studies. The Swarm satellite constellation, the China Seismo‐Electromagnetic Satellite (CSES), and the ground‐based global navigation satellite system (GNSS) observations provide us new opportunities to detect ionospheric precursors to earthquakes for a better understanding of the seismic‐ionospheric coupling. In California, two shallow earthquakes with magnitudes of 6.5 and 7.1, the two higher magnitude events of the Ridgecrest earthquakes sequence, occurred on July 4 and July 6, 2019, respectively. This study adopted total electron content (TEC) data from the GNSS, and electron density (Ne), and electron temperature (Te) data from the CSES and Swarm to study the possible ionospheric perturbations before the Ridgecrest earthquakes. A sliding interquartile range method was used to analyze the TEC data, and a spatial differential method and a revisit trajectory comparison were used to analyze the Ne and Te data. While excluding the influences of various factors including solar and geomagnetic activities, planetary waves, tropospheric convection, and medium‐scale traveling ionospheric disturbances, the cross‐validation of the multi‐source data found that anomalies detected on June 20, June 22, and June 26–29, 2019 could be regarded as precursors of the Ridgecrest earthquakes. A spatial distribution analysis showed that the pre‐earthquake anomalies offset the equatorial direction. TEC anomalies were all positively correlated with the Ne anomalies, with most of them were positive. The Ne and Te anomalies were both positively and negatively correlated with mostly negative, while showing a strong linear negative correlation at nighttime.

Changes in the dynamics of seismic process observed in the fixed time windows; case study for southern California 1980–2020

In the present work we investigated changes in the extent of regularity (randomness) of seismic process in fixed time span windows. A comparison with a set of randomized catalogues was accomplished basing on spatial, temporal and energetic characteristics of a seismic process. Increments of cumulative times, increments of cumulative distances and increments of cumulative seismic energies have been calculated from the southern California earthquake catalogue, 1980 to 2020.The multivariate Mahalanobis' distance calculation, combined with the surrogate data testing procedure, was chosen as the analysis method. An analysis of variability in the extent of regularity of a seismic process has been accomplished for different completeness magnitude thresholds and sliding windows of different time spans.Analysing the features of temporal, spatial and energetic variability, in periods of supposedly aftershock activity, we found that the original seismic process is significantly different from a random process. Such periods, containing windows with nonrandom seismicity, are always ended by a series of windows in which a seismic process is indistinguishable from a random one. This was shown at different magnitude thresholds, comparing the original catalogue with the set of randomized catalogues.It was also found that at small magnitude thresholds (M2.6 and M3.0), the fixed time span windows with a seismic process significantly different from the random process might have occurred also prior to four large (M > 7.0) earthquakes in the considered catalogue. The amount of the released seismic energy in these windows is essentially smaller than in the ones after strong earthquakes of smaller magnitudes. Relying on our results we suggest that causes of regularity in the seismic process prior and after large earthquakes are probably different.At larger-magnitude thresholds, the total number of fixed time windows with a seismic process different from the random one gradually decreases. Moreover, at M3.8 and M4.2 thresholds there are practically no windows with regular seismic process prior to four large catalogue earthquakes. In the periods between strong earthquakes, mostly in the periods of relatively small earthquakes generation, the percentage of windows in which the seismic process is indistinguishable from the random one essentially increases with increasing magnitude threshold.

Lithosphere atmosphere ionosphere coupling associated with the 2019 Mw 7.1 California earthquake using GNSS andmultiple satellites.

Global Navigation Satellite System (GNSS)-based Earthquake (EQ) anomalies in the ionosphere and troposphere provide explicit evidences to study the coupling between seismic events, atmosphere, and ionosphere in epicentral breeding regions consequent to theEQ day in the preparation period. EQs are still not predicted, but the space-based EQ anomalies aid in the development of monitoring pre- and post-seismic precursors around the seismogenic zone and associated fault lineament regions. In this paper, tropospheric and ionospheric anomalies are investigated for the July 06, 2019, Mw 7.1 California EQ from GNSS tropospheric delays and Total Electron Content (TEC), respectively. We noticed that atmospheric and ionospheric anomalies from GNSS stations within 5-10days before the main shock and storm-induced ionospheric variations occur beyond the 5th day after the EQ. Similarly, synchronized and collocated lower atmospheric anomalies are also recorded in the long-term temporal values of SO2 and SO4 within 1-month before and after July 2019, which validates the existence of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) over the EQ epicenter. On the other hand, EQ anomalies occur during quiet geomagnetic storm activity (Kp < 3; Dst < - 20 nT) and geomagnetic storm triggered high-intensity ionospheric variations during Kp > 3. All these atmospheric and ionospheric perturbations support the development in EQ precursors with satellite measurements, which are indispensable towards the forecasting of future EQ.

Early Manifestations of Short-Term Precursors in Stress-Strain State Dynamics of Southern California

—The stress-strain state before the М = 7.1 Ridgecrest earthquake in Southern California is analyzed based on spatiotemporal distribution of shear strains calculated in the geomechanical model within local ~100 × 100 km crustal segments at a depth of 3–7 km. In the epicentral zone of the earthquake, starting from three years before the event, a successive series of the time intervals, up to the occurrence of the earthquake, when shear deformations are completely absent and rocks are farthest from ultimate strength—the so-called quiescence zones—are established. The spatial distribution of shear strains in the vicinity of the epicentral zone is analyzed during the quiescence intervals and subsequent bursts of maximum amplitude in the epicentral zone itself. The time intervals of the bursts are called excursions. The successive emergence of maxima in shear strain amplitudes in the epicentral zone and surrounding medium during the excursions corresponds to the situation of a swing when the entire preparation region of a future earthquake is rocking up to the moment of event. Consistency of the obtained results with the existing theoretical models of earthquake preparation is discussed.