Surveying the seafloor with new technologies and techniques provides a new generation of higher-resolution imagery of both bathymetry and benthic habitat. Recent surveys using an Edgetech 4600 interferometric sonar have identified previously unknown patterns in these properties in the estuarine and nearshore areas in coastal Georgia, USA. As part of a multi-year mapping effort, four of the seven sounds in coastal Georgia have been mapped: Wassaw Sound, Ossabaw Sound, St. Catherine’s Sound and Sapelo Sound. Interferometry, sidescan sonar imagery and bottom sediment character have been combined to identify bathymetric patterns, diverse benthic habitats and to produce comprehensive habitat maps. Although the distribution of habitat types differs within each of these sites, all habitat types are common to all areas, and some general patterns emerge. Each site exhibits regions of mobile sediments, but of critical importance, also exhibits deep scours into semi-indurated materials that create vertical relief and hard substrates. Each project area can be divided into geomorphological features i.e., channels, deep depressions, vertical walls, ridges and flats. Bedforms are commonly visible, particularly in channels and depressions. Sediments are dominated by sand. These data, along with derivatives e.g., slope, rugosity and bathymetric position index, identify potentially important, complex habitat for sessile and mobile organisms. Scarps and ledges (up to 2 m high, up to 1000 m long) are distinctly recognizable in the sidescan imagery and slope analysis. This presentation will describe the procedures for working with shallow water interferometric sonar data and interpretations that can be reached using these tools.

The importance of soil moisture is recognized globally because it controls hydrological processes that are relevant to agriculture and climate studies. Currently, estimation of root zone soil moisture is largely accomplished using physical models, which are based on flow and transport equations. However, with the complexity of the processes operating in the vadose zone as well as their interactions with each other, parameterizing all the relevant processes is quite a challenge. This complexity is further enhanced by spatio-temporal variability in soil and vegetation properties which demand model parameters to be dynamic. Alternatively, purely data-based methods for root zone soil moisture estimation are still limited despite the growing availability of datasets from networks established within the last decade. Currently, these datasets are used largely for calibration and validation of physical models and retrieval methods from satellites. In this study, we explored the utility of Random Forest (RF) as an approach for predicting and forecasting daily root zone soil moisture from selected stations in the Raam and Twente network. We trained a single RF using meteorological datasets, soil type, land cover type, and LAI as predictor variables. The model was also tuned in order to obtain the optimal hyperparameters (mtry and ntree) and number of training samples. A comparison with model simulation results using Hydrus-1D was also performed. Our results show that RF can accurately predict and forecast root zone soil moisture at the study sites based on RMSE of 0.02 – 0.12 m3m-3, in comparison with Hydrus-1D simulations having RMSE of 0.05-0.22 m3m-3. However, poor results were obtained for saturated water conditions. In addition, 5-95% RF prediction intervals become wider at saturated water conditions for some sites, which indicates higher prediction and forecast uncertainties. RF can be used for root zone soil moisture estimation, especially at data poor regions where information on soil hydraulic parameters are sparse or lacking. It can also be used for estimating missing values at gaps in time series datasets.

Estimation of the ionospheric conductance is a crucial step in coupling the magnetosphere & ionosphere (MI). Since the high-latitude ionosphere closes magnetospheric currents, conductance in this region is pivotal to examine & predict MI coupling dynamics, especially during extreme events. In spite of its importance, only recently have impacts of key magnetospheric & ionospheric contributors affecting auroral conductance (e.g., particle distribution, ring current, anomalous heating, etc.) been explored using global models. Addressing these uncertainties require new capabilities in global magnetosphere - ionosphere - thermosphere models, in order to self-consistently obtain the multi-scale, dynamic sources of conductance. This work presents the new MAGNetosphere - Ionosphere - Thermosphere (MAGNIT) auroral conductance model, which delivers the requisite capabilities to fully explore the sources of conductance & their impacts. MAGNIT has been integrated into the Space Weather Modeling Framework to couple dynamically with the BATSRUS magnetohydrodynamic (MHD) model, the Rice Convection Model (RCM) of the ring current, the Ridley Ionosphere Model (RIM) & the Global Ionosphere Thermosphere Model (GITM). This new model is used to address the precise impact of diverse conductance contributors during geomagnetic events. First, the coupled MHD-RIM-MAGNIT model is used to establish diffuse & discrete precipitation using kinetic theory. The key innovation is to include the capability of using distinct particle distribution functions (PDF) in a global model: in this study, we explore precipitation fluxes estimated using isotropic Maxwellian & Kappa PDFs. RCM is then included to investigate the effect of the ring current. Precipitating flux computed on closed field lines by RCM is compared against MAGNIT results, to show that expected results are alike. Lastly, GITM is coupled to study the impact of the ionosphere thermosphere system. Using the MAGNIT model, aforementioned conductance sources are progressively applied in idealized simulations & compared against the OVATION Prime Model. Finally, data-model comparisons against SSUSI, AMPERE & SuperMAG measurements during the March 17, 2013 Storm are shown. Results show remarkable progress of conductance modeling & MI coupling layouts in global models.

Earthquakes are commonly modeled as shear cracks, where the slip profile of an earthquake rupture is the spatial distribution of relative displacement between fault surfaces. It is an accumulated result of all the processes during the earthquake: nucleation, propagation, and arrest. Understanding the characteristics of a slip profile gives insight into the associated stress changes, which is generally immeasurable on natural faults, and is useful for understanding the underlying friction law. Most models focus on simplicity for application purposes. For instance, the elastic crack model (Bilby & Eshelby, 1968) established that a perfect crack with uniform shear stress drop leads to an elliptical slip profile; Cowie and Scholz (1992) proposed a crack model with constant-stress cohesive zones at the crack tips, which results in “bell-shaped” slip profiles. However, the elliptical model results in unphysical stress singularities at crack tips, and the plateau in stress drop distribution near crack tips in the “bell-shaped” model is infeasible for friction dominated ruptures because it is commonly believed that slip is always accompanied by shear stress drop, e.g. slip-weakening friction law (Andrews, 1976). ­ We present results from recent large-scale laboratory experiments where all the rupture processes are contained in a 3-meter long saw-cut granite fault (Ke et al., 2018) and slip local fault slip and shear stress changes are measured at 16 locations along the fault. Guided by the laboratory experiments, we derived an analytical model to faithfully represent measured slip profiles δ(x), and shear stress changes Δτ(x), resulting from laboratory earthquakes. Field measurements of slip profiles revealed that slip profiles are commonly tapering roughly linearly toward the tips. The proposed model includes this feature, and thus fits slip profiles measured from natural earthquakes on isolated faults better than other idealized analytical models. For more complex natural earthquakes, our model can be used as a basis function. Our results suggest that inelastic earthquake processes can be solely originated from friction, and the shape of an earthquake rupture is likely between the elliptical and bell-shaped idealized models.

The citizen science app, MyShake, represents a new source of global seismic data that can be provided with much higher density sampling than a traditional seismic network, and at a relatively low cost. Because phones are most frequently located in buildings, the signals they record include structural response information in addition to ground shaking. This work explores the capabilities of MyShake as a remote damage detection tool within the field of structural health monitoring. There are several common, empirically determined parameters, such as modal frequency and inter-story drift, which characterize buildings and their response to shaking. Structural damage causes these values to deviate from their ‘healthy’ baseline. Using data collected during smaller excitations, we aim to generate a database of healthy values for buildings in which MyShake users live and work. Then, when a significant earthquake occurs, data from the mainshock and aftershock coda seismic records can be analyzed to detect deviation from this baseline, permitting a rapid, remote diagnosis of damage that could provide critical information to engineers on the ground. To gauge the expected capabilities of MyShake while the system is still relatively new, we compile a database of waveforms collected by the Center for Engineering Strong Motion Data (CESMD), which has instrumented buildings across California. Available CESMD data spans several years, building types, and earthquake magnitudes. After ‘downgrading’ these data to emulate the quality of waveforms collected by MyShake, we determine how big and how close an earthquake needs to be to excite buildings so that key structural health parameters can be determined. These data also inform our expectations for the range of values that typify each building type and size, permitting a gauge for the reasonableness of our results from true phone data. Demonstrative examples from this CESMD-based database in addition to real MyShake examples will be presented.

Nearly one million Rohingya refugees of Myanmar have fled to neighboring Bangladesh for seeking shelter from systematic oppression since 25 August 2017. The speed and scale of the influx has led to unprecedented growth of the Kutupalong refugee camp of eastern coast of Bangladesh within few months. There is a few study focused on environmental degradation of the refugee camp, however, no study has been done so far to estimate the impact of camp expansion on ecosystem services loss. This study, therefore, made an attempt to estimate the changes of Ecosystem Services Value (ESV) in response to camp expansion for the year July 2017 (pre-camp) and July 2018 by GIS technique and corresponding global value coefficient developed by Costanza et.al. (1997). Land cover map of the study area was prepared by using Landsat 8 satellite data. Results show an overall decrease of vegetation of 2486 hectares, of which 20% were used to expand the camp and 80% were deforested. Total ecosystem service values of the study area reduced dramatically, from 51.53×106 US$ to 49.12×106 US$ in the study period. This represents a 4.63% net decline in annual value of ecosystem services in the study area. In terms of 2 km buffer of the camp, the net decline rate is found 32.58%. The significant changes are also recorded in individual ecosystem services function of the area. Hence, the findings of this study may motivate the Bangladesh government to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps and assist in more sustainable resource mobilization for the Rohingya refugees.

The poor across the world is very vulnerable to floods and drought disasters and have a detrimental effect on the lives and livelihoods of the poor. Weather based index insurance is one of the ways of dealing with these disasters. Protecting against floods and providing risk cover against losses due to floods has been a major area of concern for any government. Risk transfer through insurance is an important component in managing agricultural risks from extreme flood events. The study developed the first of its kind of designing and implementation of an index-based flood insurance (IBFI) product with the advanced use of satellite data and flood models to estimate crop losses due to floods. IBFI insurance product uses two different data elements, the first one is based on the flood model using HEC-HMS and HEC-RAS that uses inputs from NASA GPM bias-corrected satellite rainfall estimates, observed water level and discharge data, river characteristics, and digital elevation model to generate flood depth and flood duration to develop pre-determined thresholds based on the historical flood events between 1991 to 2015 and the second IBFI product uses only satellite data from NASA MODIS Terra and Aqua satellite data and the Copernicus Sentinel-1 SAR data to generate flood depth and flood duration to develop pre-determined thresholds based on the historical flood events and economic losses. More than 7,000 farming households in Bihar (India) and northern Bangladesh have signed up for a pilot IBFI scheme, which went live in 2017. The participating farmers have received insurance compensation for crop losses of over USD 160,000. In addition to the insurance product implementation, the research evaluated the farming willingness to pay, developing business models for scaling; social equity, and economic benefits of derisk disasters. IBFI initiative promotes a closer linkage between risk transfer and risk reduction that could make this more sustainable and robust financial instruments for flood-affected communities and reducing the burden of post-disaster relief funds for the government. In summary, the index insurance using open-access satellite imagery is a win-win opportunity as it brings down the data development cost, lower insurance premium, quick settlement, and greater transparency among various users.

The ratio between slow or thermal (<2.2 km/s) and fast (>2.2 km/s) neutrons is known to be a good measure of the amount of water present in a radius of about 300m from the measurement. COSMOS detectors use this principle and measure neutrons by means of the helium isotope 3He. COSMOS has been in use for some time now and its large-scale observations are central to bridging the scaling gap between direct gravimetric observation of soil moisture (<<1m2) and the scale at which soil moisture is represented in hydrological models and satellite observations (>100m2). The main sources of 3He were nuclear warheads. The fortunate demise of nuclear weapons has had the less fortunate consequence that 3He has become expensive, leading to a search for more affordable alternatives. Here, we present laboratory results of a boron-based neutron detector called BLOSM. About 20% of naturally occurring boron is 10B, which has a large cross-section for thermal neutrons. When 10B absorbs a neutron, it decays into lithium and alpha particles. Alpha particles can then be detected by ZnS(Ar), which sends out UV photons. Because real-estate is at a premium for most neutron detection applications, most boron detectors are based on relatively expensive enriched boron with >99% 10B. In hydrology, space is usually less of an issue, so one innovation here is that we use natural boron in a detector that is simply a bit larger than one based on enriched boron but much cheaper. A second innovation, put forward by Jeroen Plomp of the Delft Reactor Institute, are wavelength shifting fibers that capture UV photons by downshifting the wavelength to green. Green photons have a wider angle of total internal reflection and tend to stay in the fiber until they exit at the end. Here, a third innovation comes into play, inspired by Spencer Axani’s $100 muon detector, namely the use of simple electronics and silicon photon multipliers (SiPMs). Because we want to know the ratio between fast and slow neutrons, we need two detectors, one that just counts the thermal neutrons that continuously zap around and through us, and one covered by a moderator that slows down faster neutrons to thermal levels, so that they can be detected. Presently, we can build two detectors for about EU 1000. We expect that after the development of some custom electronics, this will come down to around EU 500. Ideally, we would like to build a network of these detectors in Africa in conjunction with the TAHMO network (www.tahmo.org).

Urban areas (i.e. cities, towns and suburbs) provide a home to over 70% of the EU‑population, and this number is expected to exceed 80% by 2050 (Tapia et al., ECOL INDIC, 2017). The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g. cloudbursts, rainstorms, heavy rainfall, hail, heavy snow) combined with the high population density and concentration of assets in urban areas makes them particularly vulnerable to pluvial flooding, hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modelling is resource intensive and therefore scarcely suitable for a consistent hazard assessment across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential for consistent pluvial flood hazard characterization of fast-processing DEM-based methods, such as the Hierarchical Filling and Spilling or Puddle-to-Puddle Dynamic Filling and Spilling (see e.g. Zhang et al., J HYDROL, 2014; Chu et al., WATER RESOUR RES, 2013). As part of the activities of the EIT Climate-KIC Demonstrator project SAFERPLACES (https://saferplaces.co/), we developed a fast-processing algorithm, named Safer_RAIN, that enables one to map pluvial flooding in large urban areas by implementing a filling and spilling procedure that accounts for spatially distributed rainfall input and infiltration processes (Green Ampt method). We present the first applications of the algorithm to model recent urban inundations occurred in Northern Italy. These preliminary applications, compared against ground evidence and detailed output from a two-dimensional hydrologic and hydraulic numerical model, highlight limitations and potential of Safer_RAIN for identifying pluvial-hazard hotspots across large urban environments.

The Dobi graben is a NW-trending, Quaternary continental rift found within the East-Central Block (ECB) of the Afar Depression (AD) in northeastern Ethiopia. Extension occurs on steeply dipping faults, where the ratio of maximum displacement to traced fault length extends to four orders of magnitude. We conducted fault population analysis in the Dobi graben using a 30 m resolution Shuttle Rader Topography Mission (SRTM) Digital Elevation Model (DEM). We traced a total of 953 faults. We used the fault displacement length profiles’ tapering directions and the different types of fault propagations’ termination styles to characterize the fault kinematics pattern. Our population analysis results show that ~45% of the normal faults in the Dobi graben are tapering towards the southeast in a manner similar to the Red Sea Rift (RSR) regional strain gradient. On the other hand, our analysis showed that ~40% of the faults in the Dobi graben are tapering towards the northwest direction in a manner similar to the Gulf of Aden Rift (GAR) regional strain gradient. We found the statistical regression correlation coefficients (R-square value) for both the southeast and the northwest tapering faults to be ~0.7. Therefore, we suggest that ~85% of the lateral propagation of the 953 faults in the Dobi graben is highly influenced by the regional strain transfer of the RSR and GAR. Additionally, based on the fault propagation termination styles, our faults population analysis shows that faults which exhibit the half-restricted termination style account for 85% of the 953 faults. The maximum displacement over the maximum length (Dmax/Lmax) ratio of these faults is 0.03, which is in accord with the constant displacement length fault growth model.

The earth’s crust is a leaky geofluid system where surface trace gas emissions relate to open migration pathways and the presence of subsurface source(s). Seismic activity can open sealed migration pathways leading to trace gas emissions from the surface intersection of the active fault, which may not relate to observable surface fault rupture or offset. After the M7.1 Ridgecrest earthquake, we collected mobile surface trace gas and meteorology data with AMOG (AutoMObile trace Gas) Surveyor, a mobile atmospheric chemistry and meteorology lab, in the Death Valley Park and Searles Valley within 24 hours of the quake, the following week, and after several weeks with air samples also were collected for detailed later laboratory analysis. We found widespread highly elevated CO2 emissions along Panamint Valley including overall elevated SO2 and H2S with strong enhancements around Manly Pass, where aftershocks occurred at the northern edge of the Slate Range and along a trend parallel to Water Canyon. This is in contrast to AMOG data collected in Death and Panamint Valleys in 2014, where concentrations were typical of California desert levels–near ambient and uniform. Significant sulfur trace gas emissions were discovered escaping from the rim of Ubehebe Volcano, last active ~2500 B.P., 115-km north of the Slate Range. Faults appear to play an important role in these geogas emissions, activated by the major earthquake and aftershocks. Further investigations are planned to characterize the system’s return towards quiescence.

Fuego volcano in Guatemala began its current eruptive episode in 1999. From 2008-2015 we observed repeating and near-repeating seismic events in the long period (LP: 0.5-5 Hz) and very long period (VLP: 100-10s) bandwidths. Two separate types of repeating VLP events indicate pressurization within the shallow conduit prior to explosions with different surficial expressions, including emissions from two separate vents from at least 2008-2012. Between explosions, repeating LP events which do not have associated visible emissions provide a mechanism for small magnitude degassing. The seismic amplitudes of the LP events are 1-3 orders of magnitude lower than the amplitudes of the VLP events. The coefficient of variation of the interevent times for these repeating LP events in 2012 were all above 1.5, which suggests a renewal process driven by interactions of more than one factor. Based on the at least eight-year stability of both the LP and VLP signals and coupled with various other visual datasets, we present an updated model of the shallow conduit dynamics controlling explosive events. In this model, the VLP source acts as a possible constriction point allowing for crystal and volatiles to form local concentrations out of an otherwise steady supply of magma. High water content leads to undercooled magma and promotes rapid crystallization and the formation of partial seals within the conduit. Pressurization due to and breaking of these seals results in the modeled VLP source. Strain along the conduit margins promotes the formation of fracture networks which facilitate degassing, the opening and closing of which are sources for the LP signals. Small fluctuations in magma ascent rates therefore have drastic effects on changes in shallow magma rheology and eruption style. These small fluctuations average out over the intermediate term (week to month) to maintain observed, stable, long-term (year to multi-year) degassing rates from the volcano.

The dithiothreitol (DTT) assay is widely used to characterize the Oxidation Potential (OP) of atmospheric particulate matter (PM), which can cause adverse effects on human health. However, it’s under debate which chemical species determines the consumption rate of DTT. During January and April 2018, we measured the improved DTT assay of daily PM2.5 samples collected in Guangzhou, China with complimentary measurements of water-soluble ions, organic/elemental carbon (OC/EC) and metal elements. The average sampled air volume normalized consumption rate of DTT (DTTv) was 4.67 ±1.06 and 4.45 ± 1.02 nmol min-1 m-3, in January and April, respectively while the average PM2.5 mass normalized consumption rate of DTT (DTTm) was 13.47 ± 3.86 and 14.66 ± 4.49 pmol min-1 μg-1. Good correlations were found between DTTv and concentration of PM2.5, OC, and EC while no correlation was found between DTTm and concentrations of water-soluble ions, OC, EC or metal element, which is consistent with most early observations. We also evaluated the contribution of soluble metals to DTT assay by addition of EDTA, a strong metal chelator. We found that nearly 90% of DTTv and DTTm were reduced by EDTA, suggesting a dominant role of soluble metals in determining the response of DTT to ambient PM2.5. Based on responses of DTT to soluble metals in literature, we found that Cu(II) and Mn(II) are the major contributors to OP of PM2.5 in Guangzhou. The correlation coefficient between DTTm and OC shows a clear increase after addition of EDTA, implying that the response of DTT to quinones is not strongly suppressed by EDTA.

The 2019 Ridgecrest earthquakes pose interesting questions about the nature of intersecting conjugate ruptures, and also the possibility of re-rupture of fault segments. Aftershocks of the July 4th M6.4 event suggest the possibility of a secondary rupture along the fault that subsequently ruptured in the July 5th M7.1 event. Unfortunately, neither InSAR nor rupture mapping will be able to resolve this question, as no SAR acquisitions were made between the two earthquakes, and the critical ‘nexus’ of the two ruptures was located on the China Lake Navy base, and was not accessible between the events. Campaign GPS data and seismic data may provide clues to resolving these questions. We reoccupied 5 previously surveyed GPS benchmarks in the hours following the M6.4 event, meaning that we can separately measure the deformation from the two earthquakes at those locations. We construct a joint inversion of our campaign GPS data, along with the daily displacements of nearby continuous GPS stations, and ascending and descending Sentinel-1 InSAR data, in order to separate the fault geometry and slip of the two earthquakes, and address the question of re-rupture. This approach also allows us to precisely estimate the contribution of the early postseismic deformation to the InSAR data.

This study aims to establish an inverse model for reconstructing properties of turbidity currents from their deposits. Although several attempts of in-situ measurements have been succeeded, flow properties of large-scale turbidity currents that build submarine fans remain unclear. Recurrence interval of turbidites in natural levees or lobes of submarine fans is generally 100s years, while in-situ observation in modern submarine canyons reported yearly to monthly occurrence of turbidity currents. That is, turbidity currents that substantially form submarine fans may be extremely low frequency and large-scale than currently observed. To understand the behavior of fan-building flows occurring with sub-millennial scale recurrence interval, the quantitative reconstruction of flow conditions from geologic records is required. However, inversion of flow properties in natural scale has been impractical because the heavy computational load of 2D or 3D models of turbidity currents has prevented to perform inversion requiring iteration of calculations. To this end, we propose a method using 2D shallow-water model with the deep-learning neural network (DNN). In this method, a horizontal 2D shallow-water model of turbidity currents was employed as the forward model. Numerical simulation was repeated to obtain horizontal distribution of thickness of turbidites under various initial conditions, and then this synthetic data set was used for supervised training of DNN. After the training phase finished, DNN properly estimated initial conditions of turbidity currents (e.g. initial flow height, velocity, etc.) from artificial test data set that was also produced from the forward model. In previous methods, the computational cost of 2D model was too high to be employed as the forward model for turbidite inversion, while our methodology can reconstruct initial conditions of turbidity currents instantaneously. Although production of the training data set requires 1000s times repetition of calculation, all calculation of forward model can be perfectly parallelized, so that the problem of calculation efficiency can be easily solved by using PC cluster. Future application of our method to actual deposits will contribute to understand the development processes of submarine channels or lobes in the long term.

Accurate characterization of hydraulic and poroelastic rock properties is crucial for successful management of groundwater, petroleum resources and subsurface contaminant remediation. The oscillating pore pressure method is a popular laboratory technique for permeability and specic storage measurements of rock samples. We present an improvement of the oscillating pore pressure method by simultaneously measuring hydraulic and poroelastic properties of rocks. Measurements were carried out for four conventional reservoir rock quality samples at oscillation frequencies of 0.025–1 Hz and effective pressures of 3.5–62 MPa. Interpreted permeability values decreased with increasing effective pressure and increased sharply above a frequency range of 0.3–0.4 Hz. We established that hydraulically measured storage capacities are overestimated by an order of magnitude when compared to elastically derived ones. Biot coecient was estimated both from pore pressure and strain measurements, and comparison of two data sets reveals high uncertainty of the hydraulic specic storage measurements. We documented grain crushing and pore collapse in a dolostone sample, observed as a permanent and drastic decrease of permeability and bulk modulus. We validated our method by detecting irreversible microstructural changes independently by hydraulic, elastic, X-ray microtomography ( CT) and nuclear magnetic resonance (NMR) measurements. We further developed a novel data processing approach that utilizes a broad, multifrequency range of data which are inverted for permeability. We re- process published data and demonstrate that our methodology outperforms traditional data reduction techniques, as our inversion results show a better t to pressure trends. To better understand the effect of frequency on phase and amplitude data and to verify our inversion approach we numerically simulate oscillating pore pressure experiments. We document a strong deviation of experimentally obtained phase data starting at 0.3 Hz oscillation frequency. Our method can be used for robust determination of permeability and rapid prediction of experimental results using numerical simulation, ultimately improving experimental permeability measurements.

The 4 July 2019 Mw 6.4 Earthquake and 5 July Mw 7.1 Earthquake struck near Ridgecrest, California. Caltech-Jet Propulsion Laboratory Advanced Rapid Imaging and Analysis (ARIA) project automatically processed synthetic aperture radar (SAR) images from Copernicus Sentinel-1A and -1B satellites operated by the European Space Agency, and products were delivered to the US and California Geological Surveys to aid field response. We integrate geodetic measurements for the three-dimensional vector field of coseismic surface deformation for thee two events and measure the early postseismic deformation, using SAR data from Sentinel-1 satellites and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by Japanese Aerospace Exploration Agency. We combine less precise large-scale displacements from SAR images by pixel offset tracking or matching, including the along-track component, with the more precise SAR interferometry (InSAR) measurements in the radar line-of-sight direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. InSAR coherence and coherence change maps the surface disruptions due to fault ruptures reaching the surface. Large slip in the Mw 6.4 earthquake was on a NE-striking fault that intersects with the NW-striking fault that was the main rupture in the Mw 7.1 earthquake. The main fault bifurcates towards the southeast ending 3 km from the Garlock Fault. The Garlock fault had triggered slip of about 15 mm along a short section directly south of the main rupture. About 3 km NW of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 meter of down-drop. Further NW is a wide zone of complex deformation. We image postseismic deformation with InSAR data and point measurements from new GPS stations installed by the USGS. Initial analysis of the first InSAR measurements indicates the pull-apart started rebounding in the first weeks and the main fault had substantial afterslip close to the epicenter where the largest coseismic slip occurred. Slip on a NE-striking fault near the northern end of the main rupture in the first weeks, in the same zone as large and numerous aftershocks along NE-striking and NW-striking trends shows complex deformation.

The 2018 eruption of Kīlauea Volcano included caldera collapse at the summit of the volcano that was well recorded by a network of both permanent and temporary seismometers and infrasound microphones. Volcanic activity prior to the start of the eruption was elevated, including high lava lake levels and increased seismicity. Deflation of the summit began shortly after the eruption in the lower East Rift Zone commenced and was accompanied by a drop in the summit lava lake, which eventually disappeared from view after lowering by several hundred meters. Continued volume loss from beneath the summit eventually led to caldera collapse, which was accompanied by increases in earthquake rates and tremor amplitudes. Infrasonic tremor that originally rose from spattering at the surface of the lava lake was replaced by discrete infrasonic events arising from rockfalls and slumps. On May 16, the first of tens of collapse events occurred, beginning a cycle of increasing earthquake rates and energy release in the presence of deflation. After an initial increase in rate, seismicity rates remained constant for several hours prior to the next collapse event. Earthquakes during these events were typically part of repeating earthquake families and occurred on one of several circumferential cracks that marked active collapsing blocks. Through time, earthquake activity has mirrored the morphology of the collapse, migrating primarily north and east as caldera down drop extended in those directions. Collapse-related infrasound arrivals were initially down, suggesting downdropping of the surface, followed several seconds later by higher frequency infrasound, which we interpret to reflect the explosive or expulsion phase reaching the surface. Infrasound signals were 2-3 times more energetic below 0.5 Hz than above. Especially after May 29, when caldera collapse became much larger in surface area, infrasound signals were highly repetitive with a strong downward first motion. The collapse events were also deficient at seismic frequencies > 0.5 Hz, compared to typical tectonic earthquake sources. Despite similarities in waveforms at low frequencies (<0.1 Hz) and in infrasound, the seismic waveforms at high frequencies were not similar, reflecting either a unique source in each collapse event or a change in location.