nasa-gesdisc/es-publications-researchareas huggingface dataset:
School Location Analysis by Integrating the Accessibility, Natural and (2022)

This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parametersaccessibility, multi natural hazard, biohazard, comfort index, and land valueto determine potential areas for new schools to achieve educational equity targets.

Research areas: Habitat Conversion/Fragmentation
Natural disasters and regional development  the case of earthquakes (2019)

We analyze the impact of earthquakes on nighttime lights at a sub-national level, i.e., on grids of different size. We argue that existing studies on the impact of natural disasters on economic development have several important limitations, both at the level of the outcome variable as well as at the level of the independent variable, e.g., the timing of an event and the measuring of its intensity. We aim to overcome these limitations by using geophysical event data on earthquakes together with satellite nighttime lights. Using panel fixed effects regressions covering the entire world for the period 19922013, we find that earthquakes reduce both light growth rates and light levels significantly. The effects persist for approximately 5 years, but we find no long-run effects. Effects are stronger the smaller the area of a unit of observation. National institutions and economic conditions are relevant moderating factors.

Research areas: Earthquakes
Satellite-Observed Thermal Anomalies and Deformation Patterns Associated to the 2021, Central Crete Seismic Sequence (2022)

Nowadays, there has been a growing interest in understanding earthquake forerunners, i.e., anomalous variations that are possibly associated with the complex process of earthquake evolution. In this context, the Robust Satellite Technique was coupled with 10 years (20122021) of daily night-time MODIS-Land Surface Temperature remote sensing data to detect thermal anomalies likely related to the 27 September 2021, strong onshore earthquake of magnitude Mw6.0 occurring near the Arkalochori village in Central Crete, Greece. Eight intense (signal-to-noise ratio > 3) and infrequent, quite extensive, and temporally persistent thermal signal transients were detected and characterized as pre-seismic anomalies, while one thermal signal transient was identified as a co-seismic effect on the day of the main tectonic event. The thermal anomalies dataset was combined with tectonic parameters of Central Crete, such as active faults and fault density, seismogenic zones and ground displacement maps produced using Sentinel-1 satellite imagery and the Interferometric Synthetic Aperture Radar technique. Regarding the thermal anomaly of 27 September, its greatest portion was observed over the footwall part of the fault where a significant subsidence up to 20 cm exists. We suggest that the thermal anomalies are possibly connected with gas release which happens due to stress changes and is controlled by the existence of tectonic lines and the density of the faults, even if alternative explanations could not be excluded.

Research areas: Earthquakes
Optimizing Earthquake Early Warning Alert Distance Strategies Using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, Earthquakes (2020)

The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate medianexpected peak ground motions with distance, then using these ground motions to determine medianexpected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 Did You Feel It? survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which groundmotion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the groundmotion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating groundmotion uncertainties, we find MMI IV alert regions using medianexpected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find >94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of groundmotion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the tradeoff between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.

Research areas: Earthquakes
Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice (2021)

Advances in machine learning (ML) techniques and computational capacity have yielded state-of-the-art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34-station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for the comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower-dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island.

Research areas: Earthquakes
The modern Swedish national seismic network: two decades of intraplate microseismic observation (2021)

The Swedish National Seismic Network (SNSN) was modernized and rapidly expanded during the period 19982012. The network currently operates 68 permanent seismic stations, all with broadband instruments supplying realtime continuous data at 100 samples per second. Continuous data from 10 stations are shared with the international community via Orfeus, and approximately 10 stations of their individual choice are shared with institutes in neighboring countries (Denmark, Finland, Norway, and Germany). The SNSN uses the South Iceland Lowland (SIL) system as the primary system for automatic detection and event definition. In addition, an inhouse system based on migration and stacking is used for automatic detection of small events, and implementations of SeisComP (SC) and Earthworm are used primarily for rapid detection of larger regional events. Global monitoring is performed with SC, using approximately 250 global stations, and we operate a continuous rapid risk assessment system serving Swedish crisis management authorities. Since the start of automatic processing in August 2000, the SNSN has recorded and interactively analyzed more than 171,000 seismic events, of which 10,700 were earthquakes with local magnitudes ranging from around 1 to 4.3. The microearthquake activity detected in the last 20 yr has significantly improved the identification and understanding of seismically active structures in Sweden.

Research areas: Earthquakes
Cascading Partial Rupture of the Flores Thrust during the 2018 Lombok Earthquake Sequence, Indonesia (2020)

A series of four Mw>6 earthquakes struck the northern region of Lombok, eastern Indonesia, in a span of three weeks from late July to midAugust 2018. The series was thought to be associated with the Flores thrust, but the exact mechanism causing the unusual earthquake series has remained elusive. Our Interferometric Synthetic Aperture Radar analysis, combined with insights from seismology, indicates that the events originated at different hypocenter depths with differing fault geometries, which may explain the cascading behavior of the events, and indicates that better imaging of active fault geometry might provide some insight into future rupture behavior on other similar thrust systems. Our static stress change calculations suggest that the earlier events in the sequence played a role in promoting the later events. In addition, the second event brought the most significant impact on a nearby volcano, by causing volumetric expansion at its shallow magma plumbing system and unclamping its magma ascent zone, which may potentially have an impact on its future eruptive activity. However, no volcanic activity has so far occurred after the earthquakes. Finally, our damage proxy maps suggest that the second event caused the greatest damage to buildings.

Research areas: Earthquakes
Predicting fatality rates due to earthquakes accounting for community vulnerability (2019)

The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality data from 61 earthquakes affecting Taiwan from 1999 to 2016, as well as information on the socioeconomic and environmental characteristics of the affected communities. Using a local seismic hazard map, the calibrated prediction model is used in a seismic risk analysis for Taiwan that predicts the expected fatality rates and counts caused by earthquakes in future years.

Research areas: Earthquakes
Real-time earthquake intensity estimation using streaming data analysis of social and physical sensors (2017)

Earthquake intensity is one of the key components of the decision-making process for disaster response and emergency services. Accurate and rapid intensity calculations can help to reduce total loss and the number of casualties after an earthquake. Modern intensity assessment procedures handle a variety of information sources, which can be divided into two main categories. The first type of data is that derived from physical sensors, such as seismographs and accelerometers, while the second type consists of data obtained from social sensors, such as witness observations of the consequences of the earthquake itself. Estimation approaches using additional data sources or that combine sources from both data types tend to increase intensity uncertainty due to human factors and inadequate procedures for temporal and spatial estimation, resulting in precision errors in both time and space. Here we present a processing approach for the real-time analysis of streams of data from both source types. The physical sensor data is acquired from the U.S. Geological Survey (USGS) seismic network in California and the social sensor data is based on Twitter user observations. First, empirical relationships between tweet rate and observed Modified Mercalli Intensity (MMI) are developed using data from the M6.0 South Napa, CAF earthquake that occurred on August 24, 2014. Second, the streams of both data types are analyzed together in simulated real-time to produce one intensity map. The second implementation is based on IBM Info-Sphere Streams, a cloud platform for real-time analytics of big data. To handle large processing workloads for data from various sources, it is deployed and run on a cloud-based cluster of virtual machines. We compare the quality and evolution of intensity maps from different data sources over 10-min time intervals immediately following the earthquake. Results from the joint analysis shows that it provides more complete coverage, with better accuracy and higher resolution over a larger area than either data source alone.

Research areas: Earthquakes
Dynamic Relationship Study between the Observed Seismicity and (2021)

The Palghar region (north Maharashtra, India), located in the northwestern part of the stable continental region of India, experienced a low magnitude earthquake swarm, which was initiated in September 2018 and is continuing to date (as of October 2021). From December 2018 to December 2020, ~5000 earthquakes with magnitudes from M1.2 to M3.8 occurred in a small region of 20  10 km2. These earthquakes were probably triggered by fluid migration during seasonal rainfall. In this study, we have used multi-temporal Landsat satellite data of the year 2000, 2015, 2018, 2019, and 2020, extracted lineaments, and studied the changes in frequency and pattern of lineaments before and after the initiation of the swarm in the Palghar region. An increase in the lineament density and amount of rainfall are found to be associated with the increasing frequency of earthquakes.

Research areas: Earthquakes
Filling the Gap in Cascadia: The Emergence of Low‐Amplitude Long‐Term Slow Slip (2021)

Long term slow slip events have been observed at several subduction zones around the globe, where they play an integral part in strain release along megathrust faults. Nevertheless, evidence for long term slow slip has remained elusive in the Cascadia subduction zone. Here we conduct a systematic analysis of 13 years of GNSS time series data from 2006 to 2019 and present evidence of at least one low amplitude long term slow slip event on the Cascadia subduction zone, with the possibility of others that are less resolved. Starting in mid 2012, a 1.5 year transient is observed in southern Cascadia, with a group of coastal GNSS stations moving ∼2 mm to the west. The data are modeled as a Mw 6.4 slow slip event occurring at 15-35 km depth on the plate interface, just updip of previously recognized short term slow slip and tremor. The event shares many characteristics with similar long term transient events on the Nankai subduction zone. However, the total fault slip amplitude is an order of magnitude smaller in Cascadia when compared to large events elsewhere, making long term slow slip detection challenging in Cascadia. While there are other westward long duration transients in the refined data set, the surface displacements are below the level of the noise or are limited spatially to a few neighboring stations, making interpretation unclear.

Research areas: Earthquakes
How low should we alert? Quantifying intensity threshold alerting strategies for earthquake early warning in the United States (2022)

We use a suite of historical earthquakes to quantitatively determine earthquake early warning (EEW) alert threshold strategies for a range of shaking intensity targets for EEW along the United States West Coast. The current method for calculating alert regions for the ShakeAlert EEW System does not take into account variabilities and uncertainties in shaking distribution. As a result, if the modified Mercalli intensity (MMI) level used to determine the extent of the alert region (the alert threshold) is the same as the target intensity threshold, the alert region will be too small to include all locations that require alerts even if earthquake source parameters are estimated accurately. Missed alerts can be reduced by using a lower alert threshold than the target threshold. This expands the alert region, increasing the number of precautionary alerts issued to people who experience shaking below the target level. We determine alert thresholds that optimize this tradeoff between missed and precautionary alerts for target thresholds of MMI 4.0-6.0 using 143 M5.0-7.3 earthquake ShakeMaps as ground truth. We examine the quality of each alerting strategy relative to the target MMI, where we define alert quality metrics in terms of both the area and population alerted. Optimal alert thresholds maximize correct alerts while limiting most precautionary alerts to regions that are likely to still feel some shaking. We find these optimal alert thresholds also maximize warning times. This analysis presents a quantitative framework ShakeAlert can use to communicate alerting strategies and performance expectations to ShakeAlert users.

Research areas: Earthquakes
Maximum covariance direction method for unconventional seismic sources (2021)

We propose a new array covariance matrix analysis method, named maximum covariance direction method (MCD method), to detect and locate unconventional seismic sources of weak signals without clear onsets. The MCD method builds a normalized-covariance matrix of time-shifted seismic waveforms recorded in a seismic array and determines the existence of source based on the consistency of the maximum covariance direction with the theoretical prediction. Synthetic tests demonstrate effectiveness of the MCD method in detecting one and multiple isolated sources with low signal-to-noise ratios. As a data application, we study 1-hr long-period tremors (LPTs) around Aso Volcano of Japan in 2014 November 24. A total of 26 LPTs are detected near the Naka-dake first crater of Aso Volcano, with the uncertainties of source location of about 7 km. Using the recorded background noise at the seismic stations, we show that the MCD method can detect LPTs even when the LPT signals are buried in the background noise and become indiscernible in the seismic data. Unlike traditional methods that employ the coherent features of seismic signals for source detection, the MCD method places emphases on both the coherence of seismic signals and consistency of the direction of the coherent signals from a potential source location. The synthetic tests and data application indicate that the MCD method provides a good alternative to other traditional methods for detecting and locating unconventional seismic sources, with a major improvement of avoiding source misidentification in the presence of strong incoherent signals.

Research areas: Earthquakes
The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters (2019)

The ability to rapidly access optical satellite imagery is now an intrinsic component of managing the disaster response that follows a major earthquake. These images provide synoptic data on the impacts, extent, and intensity of damage, which is essential for mitigating further losses by feeding into the response coordination. However, whilst the efficiency of the response can be hampered when cloud cover limits image availability, spatio-temporal variations in cloud cover have never been considered as part of the design of effective disaster mapping. Here we show how annual variations in cloud cover may affect our capacity to respond rapidly throughout the year and consequently contribute to overall earthquake risk. We find that on a global scale when accounting for cloud, the worst time of year for an earthquake disaster is between June and August. During these months, 40% of the global population at risk from earthquakes are obscured from optical satellite view for >3 consecutive days. Southeastern Asia is particularly strongly affected, accounting for the majority of the population at risk from earthquakes that could be obscured by cloud in every month. Our results demonstrate the importance of the timing of earthquakes in terms of our capacity to respond effectively, highlighting the need for more intelligent design of disaster response that is not overly reliant on optical satellite imagery.

Research areas: Earthquakes
A Possible Second Large Subglacial Impact Crater in Northwest Greenland (2019)

Following the discovery of the Hiawatha impact crater beneath the northwest margin of the Greenland Ice Sheet, we explored satellite and aerogeophysical data in search of additional such craters. Here we report the discovery of a possible second subglacial impact crater that is 36.5-km wide and 183 km southeast of the Hiawatha impact crater. Although buried by 2 km of ice, the structure's rim induces a conspicuously circular surface expression, it possesses a central uplift, and it causes a negative gravity anomaly. The existence of two closely spaced and similarly sized complex craters raises the possibility that they formed during related impact events. However, the second structure's morphology is shallower, its overlying ice is conformal and older, and such an event can be explained by chance. We conclude that the identified structure is very likely an impact crater, but it is unlikely to be a twin of the Hiawatha impact crater.

Research areas: Validation