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High‐Resolution Seismicity and Ground Motion Variability Across the Highly Locked Southern Anninghe Fault With Dense Seismic Arrays and Machine Learning Techniques

JGR–Solid Earth - Sat, 08/31/2024 - 18:30

Abstract

Fault activity and structure are important factors for the assessment of seismic hazards. The Anninghe fault is one of the most active strike-slip faults in southwestern China but has been experiencing seismic quiescence for M > 4 earthquakes since the 1970s. To understand better the characteristics of its highly locked southern segment, we investigate seismicity and ground motion variability using recently deployed multi-scale dense arrays. Assisted by machine learning (ML) seismic phase picking and event discrimination models, we first compile a high-resolution catalog of local seismic events. We find limited earthquakes that occurred on the Anninghe fault, consistent with its generally acknowledged high locking degree. Whereas, most newly detected events appear within off-fault clusters, among which four are closely related to anthropogenic activities (e.g., mining blasts), and two neighboring faults host the remaining ones. We further apply an ML-based first-motion polarity (FMP) classifier and successfully obtain a reliable small earthquake focal mechanism, which agrees well with the geologically inferred north-south trending and eastward dipping of the Anninghe fault. Analyses of ground motion variations along two across-fault linear arrays show abrupt changes in FMPs and obvious frequency-dependent site amplifications near the mapped fault traces. It further suggests that, at finer scales, the damaged Anninghe fault zone may have split into two smaller damaged zones at shallower depths, resulting in a typical “flower-type” fault structure. The efficient workflow developed in this study can be well applied for the longer-term monitoring and better characterization of the southern Anninghe fault, or other similar regions.

Spontaneous Imbibition in Dual Permeable Media Using Dynamic Pore Network Model

JGR–Solid Earth - Sat, 08/31/2024 - 17:54

Abstract

Understanding preferential flow in porous media holds substantial theoretical significance on the design and optimization of hydrocarbon exploitation in shale reservoir. Previous researches discussed the competition of imbibition front in layered porous media while the underlining mechanism for interfacial dynamics and induced displacement efficiency of multiphase flow remains ambiguous. In this paper, we investigate the spontaneous imbibition in dual permeable media and analyze the flux exchange between the neighboring porous zones with permeability contrast using dynamic pore network model. The impact of fluid viscosity ratio and permeability contrast on the spontaneous imbibition preference have been addressed, and finally a phase diagram for displacement efficiency has been obtained. The results reveal that the dual permeable structure enhances the invasion rate of wetting fluid in the low-permeable zone and induces unstable displacement patterns, leading to reduction of the long-term displacement efficiency. The interfacial pattern transition from stable displacement to unstable pattern in dual permeable media could be ascribed into the flux exchange between dual permeable zones, which shows a contrary impact on the fluid flow within the low-permeable zone under favorable and unfavorable viscosity ratios. The permeability contrast in dual permeable media intensifies this impact during spontaneous imbibition. These results help us to understand the occurrence and mutual interaction of multiphase flow in layered porous media, and provide a theoretical guidance for the hydrocarbon exploitation in shale reservoir.

Thermal Emissions of Active Craters at Stromboli Volcano: Spatio‐Temporal Insights From 10 Years of Satellite Observations

JGR–Solid Earth - Sat, 08/31/2024 - 17:48

Abstract

Open-vent volcanoes continuously emit magmatic products and frequently feature multiple adjacent craters. Temporal shifts of thermal emissions between craters are especially detectable by InfraRed satellites. Here, SENTINEL-2 and LANDSAT-8/9 Short Wave InfraRed (SWIR) high-spatial resolution satellite data, are combined to investigate 10 years (2013–2023) of thermal activity at Stromboli volcano (Italy). The correlation between Volcanic Radiative Power (VRP, in Watts) and Volcanic Radiative Energy (VRE, in Joules), retrieved by moderate MODIS and VIIRS Middle InfraRed (MIR) data, with the Thermal Index SWIR (TISWIR) data, allows us to quantify long-term series of heat fluxes (VRPSWIR) and energy (VRESWIR). Combining moderate and higher spatial resolution data and fitting cumulative trends of TISWIR with VREMIR allows to measure thermal activity sourced by single craters during Strombolian activity. Long-term results highlight that thermal emissions are clustered in the northern and southern parts of the crater terrace, with total energy emitted (∼12 × 1014 J) equally distributed. The thermal increase since April 2017 marked a reactivation of shallow magma transportation and an intensification of the activity after the 2014 eruption. Distinct thermal behaviors are shown by the NE, C, and SW craters, related to mechanisms of explosions. We found that short-term thermal variations match well those resolved by ground-based signals, and the NE crater as the most sensitive to the transition to higher-intensity activity. Our multispatial/multisensory investigation allows, for the first time, the long-term quantification of heat flux from Stromboli's craters, with an improved understanding of open-vent dynamics and a new approach to monitor multiple active craters.

Multi‐Instrument and SAMI3‐TIDAS Data Assimilation Analysis of Three‐Dimensional Ionospheric Electron Density Variations During the April 2024 Total Solar Eclipse

JGR:Space physics - Sat, 08/31/2024 - 12:24

Abstract

This paper conducts a multi-instrument and data assimilation analysis of the three-dimensional ionospheric electron density responses to the total solar eclipse on 08 April 2024. The altitude-resolved electron density variations over the continental US and adjacent regions are analyzed using the Millstone Hill incoherent scatter radar data, ionosonde observations, Swarm in situ measurements, and a novel TEC-based ionospheric data assimilation system (TIDAS) with SAMI3 model as the background. The principal findings are summarized as follows: (a) The ionospheric hmF2 exhibited a slight enhancement in the initial phase of the eclipse, followed by a distinct reduction of 20–30 km in the recovery phase of the eclipse. The hmF2 in the umbra region showed a post-eclipse fluctuation, characterized by wavelike perturbations of 10–25 km in magnitude and a period of ∼ ${\sim} $30 min. (b) There was a substantial reduction in ionospheric electron density of 20%–50% during the eclipse, with the maximum depletion observed in the F-region around 200–250 km. The ionospheric electron density variation exhibited a significant altitude-dependent feature, wherein the response time gradually delayed with increasing altitude. (c) The bottomside ionospheric electron density displayed an immediate reduction after local eclipse began, reaching maximum depletion 5–10 min after the maximum obscuration. In contrast, the topside ionospheric electron density showed a significantly delayed response, with maximum depletion occurring 1–2.5 hr after the peak obscuration.

MEMPSEP‐II. Forecasting the Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach

Space Weather - Sat, 08/31/2024 - 08:39

Abstract

Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (MEMPSEP-I by Chatterjee et al., 2024a, https://doi.org/10.1029/2023sw003568), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi-variate data set comprising full-disc magnetogram sequences and numerous derived and in-situ data from various sources (MEMPSEP-III by Moreland et al., 2024, https://doi.org/10.1029/2023SW003765). Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.

MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach

Space Weather - Sat, 08/31/2024 - 08:13

Abstract

We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C-class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP-I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP-II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).

MEMPSEP‐I. Forecasting the Probability of Solar Energetic Particle Event Occurrence Using a Multivariate Ensemble of Convolutional Neural Networks

Space Weather - Sat, 08/31/2024 - 07:59

Abstract

The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive data set and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP-III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full-disc magnetogram-sequences and in situ data from different sources to forecast the occurrence (MEMPSEP-I—this work) and properties (MEMPSEP-II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecast.

Africa's Climate Response to Marine Cloud Brightening Strategies Is Highly Sensitive to Deployment Region

JGR–Atmospheres - Fri, 08/30/2024 - 21:04

Abstract

Solar climate intervention refers to a group of methods for reducing climate risks associated with anthropogenic warming by reflecting sunlight. Marine cloud brightening (MCB), one such approach, proposes to inject sea-salt aerosol into one or more regional marine boundary layer to increase marine cloud reflectivity. Here, we assess the potential influence of various MCB experiments on Africa's climate using simulations from the Community Earth System Model (CESM2) with the Community Atmosphere Model (CAM6) as its atmospheric component. We analyzed four idealized MCB experiments under a medium-range background forcing scenario (SSP2-4.5), which brighten clouds over three subtropical ocean regions: (a) Northeast Pacific (MCBNEP); (b) Southeast Pacific (MCBSEP); (c) Southeast Atlantic (MCBSEA); and (d) these three regions simultaneously (MCBALL). Our results suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region. MCBSEP would produce the strongest global cooling effect and thus could be the most effective in decreasing temperatures, increasing precipitation, and reducing the intensity and frequency of temperature and precipitation extremes across most parts of Africa, especially West Africa, in the future (2035–2054) compared to the historical climate (1995–2014). MCB in other regions produces less cooling and wetting despite similar radiative forcings. While the projected changes under MCBALL are similar to those of MCBSEP, MCBNEP and MCBSEA could see more residual warming and induce a warmer future than under SSP2-4.5 in some regions across Africa. All MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes.