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Abstract

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL’s GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second \(M_w\)  7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first \(M_w\)  7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip.

Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.

Publication date: Available online 2 December 2024

Source: Earth and Planetary Science Letters

Author(s): Shuo Chen, Pu Sun, Yaoling Niu, Pengyuan Guo, Tim Elliott, Remco C. Hin

Publication date: 1 January 2025

Source: Advances in Space Research, Volume 75, Issue 1

Author(s): D. Sierra-Porta

Publication date: 1 January 2025

Source: Advances in Space Research, Volume 75, Issue 1

Author(s): Yuqing Liu, Hu Wang, Lina He, Hongyang Ma, Yingying Ren, Yafeng Wang, Jing Jiao, Yamin Dang

Publication date: 1 January 2025

Source: Advances in Space Research, Volume 75, Issue 1

Author(s): Ehsan Abbasali, Amirreza Kosari, Majid Bakhtiari

Extreme climate events endanger groundwater quality and stability when rain water evades natural purification processes in the soil. This was demonstrated in long-term groundwater analyses using new analytical methods, as described in a recent study in Nature Communications. As billions of people rely on sufficient and clean groundwater for drinking, understanding the impacts of climate extremes on future water security is crucial.

Directly measuring soil carbon rather than relying on predictive models can provide hard evidence of how much carbon is being stored, allowing for better assessments of confidence in carbon markets for croplands, according to a study co-authored by Yale School of the Environment scientists and recently published in Environmental Research Letters.

The Arctic is heating up particularly fast as a result of global warming—with serious consequences. The widespread permafrost in this region, where soils currently store twice as much carbon as the atmosphere, is thawing. Scientists are using increasingly detailed climate models to investigate what this means for the global climate and which striking feedbacks need to be taken into account.

SummaryStress accumulation on the plate interface of subduction zones is a key parameter that controls the location, timing and rupture characteristics of earthquakes. The diversity of slip processes occurring in the megathrust indicates that stress is highly variable in space and time. Based on GNSS and InSAR data, we study the evolution of the interplate slip-rate along the Oaxaca subduction zone, Mexico, from October 2016 through October 2020, with particular emphasis on the pre-seismic, coseismic and post-seismic phases associated with the June 23, 2020 Mw 7.4 Huatulco earthquake (also known as La Crucecita earthquake), to understand how different slip regimes contribute to the stress accumulation in the region. Our results show that continuous changes in both the aseismic stress-releasing slip and the coupling produced a high stress concentration (i.e., Coulomb Failure Stress (CFS) of 80 kPa) prior to the event on the region with the highest moment release of the Huatulco earthquake (between 17 and 30 km depth) and a stress deficit zone in the adjacent updip region (i.e., shallower than 17 km depth with CFS around -90 kPa). This region under negative stress accumulation can be explained by possible recurrent shallow Slow Slip Events (SSE) offshore Huatulco as well as by the stress shadow from adjacent locked segments. Absent in the literature, the shallow rupture is characterized by a secondary slip patch (between 7 and 14 km depth) that overlaps with the highest concentration of aftershocks. Two months prior to the event, a Mw 6.6 long-term SSE also occurred about 80 km northwest from the hypocenter, between 25 and 55 km depth. Transient increments of the interplate coupling around the adjacent 1978 (Mw 7.8) Puerto Escondido rupture zone correlate with the occurrence of the last three SSEs in Oaxaca far downdip of this zone, possibly associated with along-dip fluid diffusion at the subduction interface. Throughout the four-year period analyzed, the interface region of the 1978 event experienced a high CFS build up of 80-150 kPa, primarily attributable to both the co-seismic and early post-seismic slip of the Huatulco rupture, that, considering the 55 year average return period of the region, indicates large earthquake potential near Puerto Escondido. Continuous monitoring of the interplate slip-rate thus provides a better estimation of the stress accumulation in seismogenic regions than those given by long-term, time-invariant coupling models, and improves our understanding of the megathrust mechanics where future earthquakes are likely to occur.

SummaryMonitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements:(i) A likelihood function that describes the distribution of detection and travel time data from the sensor network,(ii) A prior distribution that describes a priori belief about seismic events,(iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data,(iv) An algorithm to compute EIG about seismic events over a dataset of hypothetical prior events,(v) An optimizer that finds a sensor network which maximizes EIG.Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number, and locations influence uncertainty; and how prior models and constraints influence sensor placement.

Abstract

Partial ambiguity resolution (PAR) has been widely adopted in real-time kinematic (RTK) and precise point positioning with augmentation from continuously operating reference station (PPP-RTK). However, current PAR methods, either in the position domain or the ambiguity domain, suffer from high false alarm and miss detection, particularly in challenging environments with poor satellite geometry and observations contaminated by non-line-of-sight (NLOS) effects, gross errors, biases, and high observation noise. To address these issues, a PAR method based on machine learning is proposed to significantly improve the correct fix rate and positioning accuracy of PAR in challenging environments. This method combines two support vector machine (SVM) classifiers to effectively identify and exclude ambiguities those are contaminated by bias sources from PAR without relying on satellite geometry. The proposed method is validated with three vehicle-based field tests covering open sky, suburban, and dense urban environments, and the results show significant improvements in terms of correct fix rate and positioning accuracy over the traditional PAR method that only utilizes ambiguity covariances. The fix rates achieved with the proposed method are 93.9%, 81.9%, and 93.1% with the three respective field tests, with no wrong fixes, compared to 72.8%, 20.9%, and 16.0% correct fix rates using the traditional method. The positioning error root mean square (RMS) is 0.020 m, 0.035 m, and 0.056 m in the east, north, and up directions for the first field test, 0.027 m, 0.080 m, and 0.126 m for the second field test, and 0.035 m, 0.042 m, and 0.071 m for the third field test. In contrast, only decimeter- to meter-level accuracy was obtainable with these datasets using the traditional method due to the high wrong fix rate. The proposed method provides a correct and fast time-to-first-fix (TTFF) of 3–5 s, even in challenging environments. Overall, the proposed method offers significant improvements in positioning accuracy and ambiguity fix rate with high reliability, making it a promising solution for PAR in challenging environments.

A new study finds that climate change may have a range of contrasting effects on coastal forests, both slowing and enabling growth in areas where sea levels are rising and storms are more common.

On the basis of current carbon emissions rates and climate policies, average global temperatures are projected to increase to 2.9°C above preindustrial averages by the end of the century. Such an increase would severely strain global agriculture, making large tracts of current production areas unsuitable for crops and livestock. At the same time, the Food and Agriculture Organization of the United Nations estimates that food production needs to increase by 70% to keep pace with population growth.

In a recently published paper, NASA scientists use nearly 20 years of observations to show that the global water cycle is shifting in unprecedented ways. The majority of those shifts are driven by activities such as agriculture and could have impacts on ecosystems and water management, especially in certain regions.

Research co-led by the University of Maryland reveals that drought and increased temperatures in a CO2-rich climate can dramatically alter how grasslands use and move water.

Increasingly common since 1980, persistent multi-year droughts will continue to advance with the warming climate, warns a study from the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), with Professor Francesca Pellicciotti from the Institute of Science and Technology Austria (ISTA) participating.

Approximately 66 million years ago, the Chicxulub asteroid, estimated to be 10–15 kilometers in diameter, struck the Yucatán Peninsula (in current-day Mexico), creating a 200-kilometer-wide impact crater. This impact triggered a chain reaction of destructive events, including a rapid climate change that eventually led to the extinction of the non-avian dinosaurs and, in total, about 75% of species on Earth.

Dichlorodiphenyltrichloroethane (DDT) soil pollution is still a major problem in many parts of the world. Researchers at Chalmers University of Technology, Sweden, have developed a new method to manage ecological risks from the toxin by binding it with biochar. When they mixed biochar into contaminated soil at a former tree nursery, DDT uptake by earthworms in the soil was halved. This method may enable the growing of certain crops on land that is currently considered unusable due to environmental risks.

Permafrost thaw poses multiple risks to local Arctic communities, their livelihoods, infrastructure and environment. A transdisciplinary study led by Umeå University and others has identified key risks across four Arctic regions. This allows communities to adapt and make informed decisions.

The synchronization of data from two natural climate archives—a speleothem from the Herbstlabyrinth Cave in Hesse (Germany) and ice cores from Greenland—offers new insights into the chronology of abrupt climate changes in Central Europe.