ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

The endeavor to explore and characterize oil and gas reservoirs presents significant challenges due to the inherent heterogeneities that are further compounded by the existence of thin sand layers encapsulated in shale strata. This complexity is intensified by limited and low-resolution seismic data, missing critical well-log information, and inaccessible angle stack data. Conventional reservoir classification approaches have struggled to address these issues, primarily due to their limitations in handling missing data effectively and, hence, precise estimations. This study focuses on the characterization of thin, heterogeneous potential sands of the B-interval within the Lower Goru Formation, a proven gas reservoir in the Badin area. The reservoir sands with varying thicknesses are assessed in detail for their optimized description and field productions by handling challenges, including low seismic resolutions, heterogeneities, and missing data sets. An innovative solution is developed based on the integration of continuous wavelet transform (CWT) and machine learning (ML) techniques for the approximation of missing data sets, i.e., S-wave (DTS), along with enhanced elastic and petrophysical properties. The improved properties are augmented by the high resolution attained by CWT and captured variability more profoundly through the implication of residual neural networks (ResNet). The limitations of conventional approaches are harnessed by ML solutions that operate with limited input data and deliver significantly improved results in characterizing enigmatic thin sand reservoirs. The high-frequency petroelastic properties reliably determined the thin heterogeneous potential sand bodies and illuminated a channelized play fairway that can be tested for additional wells with low-risk involvement.

Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to people's lives and property [...].

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R² in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R² increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

Seismic evidence for a 1000 km mantle discontinuity under the Pacific

Seismic discontinuities in the mantle are indicators of its thermo-chemical state and offer clues to its dynamics. Ray-based seismic methods, though limited by the approximations made, have mapped mantle transition zone discontinuities in detail, but have yet to offer definitive conclusions on the presence and nature of mid-mantle discontinuities. Here, we show how to use a wave-equation-based imaging method, reverse-time migration of precursors to surface-reflected seismic body waves, to uncover both mantle transition zone and mid-mantle discontinuities, and interpret their physical nature. We observe a thinned mantle transition zone southeast of Hawaii, and a reduction in impedance contrast around 410 km depth in the same area, suggesting a hotter-than-average mantle in the region. Here, we furthermore reveal a 4000-5000 km-wide reflector in new images of the mid mantle below the central Pacific, at 950-1050 km depth. This deep discontinuity exhibits strong topography and generates reflections with polarity opposite to those originating at the 660 km discontinuity, implying an impedance reversal near 1000 km. We link this mid-mantle discontinuity to the upper reaches of deflected mantle plumes upwelling in the region. Reverse-time migration full-waveform imaging is a powerful approach to imaging Earth's interior, capable of broadening our understanding of its structure and dynamics and shrinking modeling uncertainties.

Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km³ from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km³ yr^-1 and -9.8 ± 1.7 km³ yr^-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

Surface rupture and landscape response in the middle of the great Mw 8.3 1934 earthquake mesoseismal area: Khutti Khola site

Large earthquakes breaking the frontal faults of the Himalayan thrust system produce surface ruptures, quickly altered due to the monsoon conditions. Therefore, the location and existence of the Mw8.3 1934 Bihar-Nepal surface ruptures remain vividly disputed. Even though, previous studies revealed remnants of this surface rupture at the western end of the devastated zone, ruptures extent remains undocumented in its central part. Evidence for recent earthquakes is revealed along the frontal thrust in this region. The Khutti Khola river cuts an 8 m-high fault scarp exposing Siwalik siltstone thrusted over recent alluvial deposits, with faults sealed by a colluvial wedge and undeformed alluvial sediments. Detrital charcoals radiocarbon dating reveals that the last event occurred between the seventeenth century and the post-bomb era, advocating for the 1934 earthquake as the most recent event. In the hanging wall, fluvial terraces associated with fault scarps were abandoned after a penultimate event that happened after the tenth century, a rupture we associate with the historic earthquake of 1255CE. Slips of 11-17 m and 14-22 m for the 1934 and 1255 earthquakes, respectively, compare well with the ~ 10-15 m slip deficit accumulated between the two earthquakes, suggesting that most of the deformation along the front is accommodated by surface-rupturing earthquakes.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Wave-induced motion of rigid bodies: beads, boats and buildings

The determination of rigid-body motion caused by incident waves is a familiar problem in mechanics. Good examples are the motion of a ship in the presence of water waves and the motion of a rigid structure in the presence of seismic waves. The basic goals are to determine the motion of the rigid body and the effects of the motion on the wave field, assuming linear theory is adequate. Although the underlying mathematical problems are similar, several solution methods have evolved, depending on the physical problems of interest. For ship motions, the standard approach is to decompose the problem into seven subproblems, one for each of the six rigid-body modes and one to take account of the incident wave. This approach is reviewed and then adapted to problems in acoustics and to problems in elastodynamics, such as those that arise in simple examples of soil–structure interaction. It is argued that the resulting approach for elastodynamic problems has clear advantages over those currently in use.

Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts

The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.

Study of Propeller Vortex Characteristics under Loading Conditions

Marine load is an important factor affecting propeller propulsion efficiency, and the study of the wake evolution mechanism under different conditions is an essential part of the propeller equipment design, which needs to meet the requirements of complex engineering. Based on the large eddy simulation (LES) method, the wake instability characteristics are researched with the hydrodynamic load and wake dynamics theory, and the vortices composition and evolution mechanism under various load conditions are analyzed. Meanwhile, the propeller wake using the unsteady Reynolds-averaged Navier–Stokes (URANS) and LES methods is numerically simulated and compared. In addition, a comparison between a simulation and an experiment is carried out. The vortices evolution is described by dimensionless values of the velocity, pressure field, and vorticity field. The breaking and reassembling of different vortices are discussed. The results show that the pitch of the helicoidal tip vortices is larger under light loading conditions with high advance coefficients, and the wake is more stable, in contrast, which is smaller and the vortices break down earlier. By comparison, the topology of the vortices system is more complex under the low advance coefficient. Considering the interference effect between adjacent tip vortices, the energy dissipation is accelerated, resulting in the increased instability of vortices.

Computational analysis of human upper airway aerodynamics

There is a considerable interest in understanding transient human upper airway aerodynamics, especially in view of assessing the effects of various ventilation therapies. Experimental analyses in a patient-specific manner pose challenges as the upper airway consists of a narrow confined region with complex anatomy. Pressure measurements are feasible, but, for example, PIV experiments require special measures to accommodate for the light refraction by the model. Computational fluid dynamics can bridge the gap between limited experimental data and detailed flow features. This work aims to validate the use of combined lattice Boltzmann method and a large eddy scale model for simulating respiration, and to identify clinical features of the flow and show the clinical potential of the method. Airflow was computationally analyzed during a realistic, transient, breathing profile in an upper airway geometry ranging from nose to trachea, and the resulting pressure calculations were compared against in vitro experiments. Simulations were conducted on meshes containing about 1 billion cells to ensure accuracy and to capture intrinsic flow features. Airway pressures obtained from simulations and in vitro experiments are in good agreement both during inhalation and exhalation. High velocity pharyngeal and laryngeal jets and recirculation in the region of the olfactory cleft are observed. Graphical Abstract The Lattice-Boltzmann Method combined with Large Eddy Simulations was used to compute the aerodynamics in a human upper airway geometry. The left side of this graphical abstract shows the velocity and vorticity (middle figure in bottom row, and right figure of the right bottom figure) profiles at peak exhalation. The simulations were validated against experiments on a 3D-print of the geometry (shown in the top figures on the right hand side). The pressure drop (right bottom corner) shows a good agreement between experiments and simulations.

Monitoring storm evolution using a high-density seismic network

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography, where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have an exponential relationship and the periods with larger seismic power are coincident. The time intervals with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops. Thunderstorms can be identified by the recording of the sonic waves generated by thunders, with. Single thunders detected to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms.

Crystal growth in confinement

The growth of crystals confined in porous or cellular materials is ubiquitous in Nature and forms the basis of many industrial processes. Confinement affects the formation of biominerals in living organisms, of minerals in the Earth's crust and of salt crystals damaging porous limestone monuments, and is also used to control the growth of artificial crystals. However, the mechanisms by which confinement alters crystal shapes and growth rates are still not elucidated. Based on novel in situ optical observations of (001) surfaces of NaClO₃ and CaCO₃ crystals at nanometric distances from a glass substrate, we demonstrate that new molecular layers can nucleate homogeneously and propagate without interruption even when in contact with other solids, raising the macroscopic crystal above them. Confined growth is governed by the peculiar dynamics of these molecular layers controlled by the two-dimensional transport of mass through the liquid film from the edges to the center of the contact, with distinctive features such as skewed dislocation spirals, kinetic localization of nucleation in the vicinity of the contact edge, and directed instabilities. Confined growth morphologies can be predicted from the values of three main dimensionless parameters.

A reservoir induced earthquake swarm in the Central Highlands of Sri Lanka

An anomalous seismic sequence of five small (M_W < 3) felt earthquakes occurred between 29 August 2020 and 05 December 2020 around the Victoria Reservoir in the central highlands of Sri Lanka that clearly exceeded the established national background seismic rate. Using seismic waveform template-matching and a newly developed single-station earthquake location method based on travel-time back-projection, we detected an additional co-located 23 microseismic events, of which 18 occurred within the same period as the felt events. This hitherto undetected seismic swarm defines a seismogenic zone beneath the western flank of the reservoir between 1.5 and 3 km depths. The reservoir-induced peak stresses, resolved on E-W striking faults, predicted from the poroelastic theory that include both drained and undrained crustal responses are ~ 15 kPa in an area overlapping the seismogenic zone, which, together with the physical and spatio-temporal characteristics of the seismic swarm, establish a causal link between reservoir-induced stresses and the earthquake swarm with implications to seismic hazard. This is the first record of induced seismicity in Sri Lanka. The newly developed efficient computational workflows with minimal operational costs described in our study provide a blueprint for monitoring reservoir-induced seismicity in developing countries with severe resource limitations.

Sea ice in the Weddell Sea and its relationship with the South Atlantic Subtropical High and precipitation in South America

This study aims to evaluate the position and intensity of the South Atlantic Subtropical High (SASH), related to sea ice extent (SIE) retraction and expansion in the Weddell Sea, assessing precipitation in South America. We assess the difference between atmospheric fields related to SIE (four most intense retraction events minus four most intense expansion events) in February. To this end, we used NSIDC SIE, ERA-5 reanalysis, CHIRPS precipitation, ICOADS SST, ONI/SAM indexes (CPC/NOAA). In the following month, under neutral ENSO and SAM, we observed tropospheric warming in the Weddell Sea and cooling in the mid-latitudes South Atlantic. There is a weakening of both the Weddell Sea circumpolar low and the high pressures between tropical and subtropical latitudes, in addition to the equatorward shift of the Ferrel cell. Therefore, SASH weakens and contracts, resulting in a reduction of the tropical Atlantic moisture supply to South America and negative precipitation anomalies in the tropical region - similar to the suppression pattern of the South Atlantic Convergence Zone. Our results suggest that SIE retraction (expansion) in the Weddell Sea may contribute to the weakening (strengthening) of the SASH and an early-ending (longer-ending) or drier-ending (wetter-ending) rainy season in tropical South America.

The Evolution of Paleo-Porosity in Basalts: Reversing Pore-Filling Mechanisms Using X-Ray Computed Tomography

Often carrying a high-volume fraction of vesicles, basaltic rocks can be an important reservoir horizon in petroleum systems, and are considered an excellent candidate for CO2 storage by in situ mineral trapping. The frequency of amygdaloidal basalts in many sequences highlights the prevalence of mineralisation, but when the vesicle network has been filled, the basalts can act as impermeable seals and traps. Characterising the spatial and temporal evolution of the porosity and permeability is critical to understanding the petro-physical properties and CO2 storage potential of basalts. We exploit X-ray computed tomography (XCT) to investigate the precipitation history of an amygdaloidal basalt containing a pore-connecting micro fracture network now partially filled by calcite as an analogue for CO2 mineral trapping in a vesicular basalt. The fracture network likely represents a preferential pathway for CO2-rich fluids during mineralisation. We investigate and quantify the evolution of basalt porosity and permeability during pore-filling calcite precipitation by applying novel numerical erosion techniques to “back-strip” the calcite from the amygdales and fracture networks. We provide a semi-quantitative technique for defining reservoir potential and quality through time and understanding sub-surface flow and storage. We found that permeability evolution is dependent on the precipitation mechanism and rates, as well as on the presence of micro fracture networks, and that once the precipitation is sufficient to close off all pores, permeability reaches values that are controlled by the micro fracture network. These results prompt further studies to determine CO2 mineral trapping mechanisms in amygdaloidal basalts as analogues for CO2 injections in basalt formations.

Elucidation of Prebiotics, Probiotics, Postbiotics, and Target from Gut Microbiota to Alleviate Obesity via Network Pharmacology Study

The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota-substrate-metabolite-target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein-protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via Lactobacillus paracasei JS1 (probiotics) can bind the most stably on IL6 (target) compared with the other four metabolites (3-indolepropionic acid, trimethylamine oxide, butyrate, and acetate). In this study, we demonstrated that the promising substate (prebiotics), microbe (probiotics), metabolite (postbiotics), and target are suitable for obsesity treatment, providing a microbiome basis for further research.

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

Revealing the spatiotemporal complexity of the magnitude distribution and b-value during an earthquake sequence

The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present remarkable MFD variability during the complex 2016/17 central Italy sequence using a high-resolution earthquake catalog. Isolating seismically active volumes (‘clusters’) reveals that the MFD differed in nearby clusters, varied or remained constant in time depending on the cluster, and increased in b-value in the cluster where the largest earthquake eventually occurred. These findings suggest that the fault system’s heterogeneity and complexity influence the MFD. Our findings raise the question “b-value of what?”: interpreting and using MFD variability needs a spatiotemporal scale that is physically meaningful, like the one proposed here.

The authors introduce a new perspective to study the spatiotemporal behavior of the magnitude–frequency distribution: spatially isolating seismogenic zones to provide an appropriate scale to resolve the b-value. Among those zones, the b-value behaved remarkably throughout the 2016 central Italy sequence.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Deep-learning seismology

Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology.

Comparison of Long-Term Albedo Products against Spatially Representative Stations over Snow

Multiple satellite products are available to monitor the spatiotemporal dynamics of surface albedo. They are extensively assessed over snow-free surfaces but less over snow. However, snow albedo is critical for climate monitoring applications, so a better understating of the accuracy of these products over snow is needed. This work analyzes long-term (+20 years) products (MCD43C3 v6/v6.1, GLASS-AVHRR, C3S v1/v2) by comparing them against the 11 most spatially representative stations from FLUXNET and BSRN during the snow-free and snow-covered season. Our goal is to understand how the performance of these products is affected by different snow cover conditions to use this information in an upcoming product inter-comparison that extends the analysis spatially and temporally. MCD43C3 has the smallest bias during the snow season (−0.017), and more importantly, the most stable bias with different snow cover conditions. Both v6 and v6.1 have similar performance, with v6.1 just increasing slightly the coverage at high latitudes. On the contrary, the quality of both GLASS-AVHRR and C3S-v1/v2 albedo decreases over snow. Particularly, the bias of both products varies strongly with the snow cover conditions, underestimating albedo over snow and overestimating snow-free albedo. GLASS bias strongly increases during the melting season, which is most likely due to an artificially extended snow season. C3S-v2 has the largest negative bias overall over snow during both the AVHRR (−0.141) and SPOT/VGT (−0.134) period. In addition, despite the improvements from v1 to v2, C3S-v2 still is not consistent enough during the transition from AVHRR to SPOT/VGT.

The Influence of a Cold Front and Topography on the Initiation and Maintenance of a Precipitation Convective System in North China: A Case Study

By using the convection-resolving weather research and forecasting simulation, a convective rainfall event over the middle portions of the eastern foothills of the Taihang Mountains in North China is investigated in this study. The influences of the cold front and complex topography on the initiation and maintenance of the convective system are analyzed. Results show two reasons why the convective clusters are initiated near noon on the hillsides at an elevation of 800 m. First, a local topographic convergence zone usually appears on the eastern slope of the Taihang Mountains near noon in May. Second, such a topographic convergence zone is enhanced by a cold front system and then triggers the convective clusters. Subsequently, the convective cells strengthen when moving downslope and weaken when moving eastward on the plain. When moving downslope, the atmospheric stratification is convectively unstable, and the mountain–plains solenoid (MPS) is strong near the foot of the mountain. The large amount of water vapor carried by the MPS-induced easterly wind is forced to ascend by topographic obstructions, and therefore the convective cells develop. As a result, heavy rainfall occurs on the hillsides with an elevation of 200–600 m. When the convective cells move eastward on the plain, the atmospheric stratification is stable, and the MPS is weak. Thus, convective activities weaken. Moreover, the results reveal that the mesoscale convergence line, slope gradient and slope aspect of the local terrain, local atmospheric instability, and the MPS play different roles in maintaining the convective system at elevations of 200–600 m along the eastern foothills of the Taihang Mountains.

Impact of an Uncertain Structural Constraint on Electrical Resistivity Tomography for Water Content Estimation in Landslides

Geoelectrical methods can be part of early warning systems for landslide-prone hillslopes by giving estimates of the water content distribution. Structurally constrained inversions of geoelectrical data can improve the water content estimation by reducing the smoothness constraint along known lithological boundaries, which is especially important for landslides, as often layers with strongly divergent hydrological parameters and varying electrical signatures are present in landslides. However, any a priori information about those boundaries has an intrinsic uncertainty. A detailed synthetic study and a field investigation are combined to study the influence of misplaced structural constraints and the strength of the smoothness reduction via a coupling coefficient on inversion results of electrical resistivity data. While a well-known lithological boundary with a substantial reduction of the smoothness constraint can significantly improve the inversion result, a flawed constraint can cause strong divergences from the synthetic model. The divergence can even grow above the divergence of a fully smoothed inversion result. For correctly placed structural constraints, a coupling coefficient smaller than 10−4 uncovers previously unseen dynamics in the resistivity distribution compared to smoothed inversion results. Uncertain layer boundaries can be included in the inversion process with a larger coupling coefficient to avoid flawed results as long as the uncertainty of the layer thickness is below 20%. The application to field data confirms these findings but is less sensitive to a further reduction of the coupling coefficient, probably due to uncertainties in the structural information.

Reflectance Anisotropy from MODIS for Albedo Retrieval from a Single Directional Reflectance

Surface reflectance anisotropy and insufficient multi-angular observations are the main challenges in albedo estimation from satellite observations. Numerous studies have been developed for albedo retrieval from a single directional reflectance by associating the anisotropy information extracted from coarse-resolution bidirectional-reflectance distribution function (BRDF) data. The contribution of land-cover type (LCT) and the Normalized Difference Vegetation Index (NDVI) in distinguishing reflectance anisotropy in these methods remains controversial. This study first proposed an approach to extracting a priori BRDF (F) from the MODIS BRDF/albedo product by considering the distribution characteristics of the model parameters. LCT- and NDVI-based F were also extracted from the corresponding subset. Then, the F-based albedo was derived from simulated or satellite directional reflectance and the anisotropic information of F. Finally, the directional reflectance and F-based albedo were compared with the MODIS albedo or ground measurement, in order to show the ability of F to compensate for the effect of reflectance anisotropy in the albedo retrieval process. The method was fully validated by the global and time-series MODIS BRDF data. The results showed that reflectance anisotropy has an aggregated distribution pattern, and F can represent the reflectance anisotropy of most pixels within a tile. The improvement of LCT and NDVI only occurs when the tile contains a large area of vegetated and barren ground. With the exception of the hotspot and large viewing-zenith-angle area in the forward hemisphere, the F-based shortwave albedo has high consistency with the MODIS albedo product. A comparison with the ground measurements and MODIS albedo showed that the F-based albedo from a single directional reflectance generally achieves an absolute accuracy requirement, with a root-mean-square error (RMSE) of 0.027 and 0.036.

On the generation of realistic synthetic petrographic datasets using a style-based GAN

Deep learning architectures have transformed data analytics in geosciences, complementing traditional approaches to geological problems. Although deep learning applications in geosciences show encouraging signs, their potential remains untapped due to limited data availability and the required in-depth knowledge to provide a high-quality labeled dataset. We approached these issues by developing a novel style-based deep generative adversarial network (GAN) model, PetroGAN, to create the first realistic synthetic petrographic datasets across different rock types. PetroGAN adopts the architecture of StyleGAN2 with adaptive discriminator augmentation (ADA) to allow robust replication of statistical and esthetical characteristics and improve the internal variance of petrographic data. In this study, the training dataset consists of > 10,000 thin section images both under plane- and cross-polarized lights. Here, using our proposed novel approach, the model reached a state-of-the-art Fréchet Inception Distance (FID) score of 12.49 for petrographic images. We further observed that the FID values vary with lithology type and image resolution. The generated images were validated through a survey where the participants have various backgrounds and level of expertise in geosciences. The survey established that even a subject matter expert observed the generated images were indistinguishable from real images. This study highlights that GANs are a powerful method for generating realistic synthetic data in geosciences. Moreover, they are a future tool for image self-labeling, reducing the effort in producing big, high-quality labeled geoscience datasets. Furthermore, our study shows that PetroGAN can be applied to other geoscience datasets, opening new research horizons in the application of deep learning to various fields in geosciences, particularly with the presence of limited datasets.

The Classification of Reflectance Anisotropy and Its Application in Albedo Retrieval

The land surface albedo reflects the ability of the surface to reflect solar radiation and is a critical physical variable in the study of the Earth’s energy budget and global climate change. Algorithms for the retrieval of albedo usually require multi-angle measurements due to surface anisotropy. However, most of the satellites cannot currently provide sufficient and well-distributed observations; therefore, the accuracy of remotely sensed albedo is constrained. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo product (MCD43A1), this study proposed a method to further subdivide reflectance anisotropy and build an updated database of BRDF archetype, using both the Anisotropic Flat Index (AFX) and Perpendicular Anisotropic Flat Index (PAFX). The BRDF archetypes were used to fit the corresponding MODIS BRDF, and the optimal number of BRDF archetype categories was determined according to the tendency of fitting error. The effect of surface anisotropy and observation noise on albedo retrieval were explored based on simulated MODIS reflectance. Finally, the BRDF archetype A2P2 was taken as prior knowledge to retrieve albedo from a different number of MODIS observations, and the result was validated by the high-quality MODIS albedo product. The results show that the fitting error between BRDF archetypes and MODIS BRDF shows a rapid decline when introducing the PAFX in the classification process. A 3-by-3 matrix of BRDF archetypes, which occupy 73.44% and 70.13% of the total decline in the red and NIR band, can be used to represent the characteristics of reflectance anisotropy. The archetype A2P2 may be used as prior knowledge to improve the albedo retrieval from insufficient observations. The validation results based on MODIS observations show that the archetype A2P2-based albedo can reach root-mean-square errors (RMSEs) of no more than 0.03.

Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma

An empirical model to estimate global solar radiation was developed at Qomolangma Station using observed solar radiation and meteorological parameters. The predicted hourly global solar radiation agrees well with observations at the ground in 2008–2011. This model was used to calculate global solar radiation at the ground and its loss in the atmosphere due to absorbing and scattering substances in 2007–2020. A sensitivity analysis shows that the responses of global solar radiation to changes in water vapor and scattering factors (expressed as water-vapor pressure and the attenuation factor, AF, respectively) are nonlinear, and global solar radiation is more sensitive to changes in scattering than to changes in absorption. Further applying this empirical model, the albedos at the top of the atmosphere (TOA) and the surface in 2007–2020 were computed and are in line with satellite-based retrievals. During 2007–2020, the mean estimated annual global solar radiation increased by 0.22% per year, which was associated with a decrease in AF of 1.46% and an increase in water-vapor pressure of 0.37% per year. The annual mean air temperature increased by about 0.16 °C over the 14 years. Annual mean losses of solar radiation caused by absorbing and scattering substances and total loss were 2.55, 0.64, and 3.19 MJ m⁻², respectively. The annual average absorbing loss was much larger than the scattering loss; their contributions to the total loss were 77.23% and 22.77%, indicating that absorbing substances play significant roles. The annual absorbing loss increased by 0.42% per year, and scattering and total losses decreased by 2.00% and 0.14% per year, respectively. The estimated and satellite-derived annual albedos increased at the TOA and decreased at the surface. This study shows that solar radiation and its interactions with atmospheric absorbing and scattering substances have played key but different roles in regional climate and climate change at the three poles.

Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland

Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based machine learning ensemble approach to evaluate and map wetland ET by linking tower measured ET with key predictors from MODIS products and meteorological variables. The results showed MOD16A2 had poor performance in characterizing ET patterns and was unsatisfactory for estimating ET over four wetland communities where Nash-Sutcliffe model Efficiency (NSE) was less than 0.5. In contrast, the site-specific machine learning ensemble model had a high predictive power with a NSE larger than 0.75 across all EC sites. We mapped the ET rate for two distinctive seasons and quantified the prediction diversity to identify regions easier or more challenging to estimate from model-based analyses. An integration of MODIS products and other datasets through the machine learning upscaling paradigm is a promising tool for local wetland ET mapping to guide regional water resource management.

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

Critical hydrometeorological events are generally triggered by heavy precipitation. In complex terrain, precipitation may be perturbed by the upslope raising of the incoming humid airflow, causing in some cases extreme rainfall. In this work, the application of LUME—Linear Upslope Model Extension—to a group of extreme events that occurred across mountainous areas of the Central Alps and Apennines in Italy is presented. Based on the previous version, the model has been “extended” in some aspects, proposing a methodology for physically estimating the time-delay coefficients as a function of precipitation efficiency. The outcomes of LUME are encouraging for the cases studied, revealing the intensification of precipitation due to the orographic effect. A comparison between the reference rain gauge data and the results of the simulations showed good agreement. Since extreme precipitation is expected to increase due to climate change, especially across the Mediterranean region, LUME represents an effective tool to investigate more closely how these extreme phenomena originate and evolve in mountainous areas that are subject to potential hydrometeorological risks.

Microphysical Analysis of Precipitation in the Central and Eastern Margins of the Tibetan Plateau

Through the observation and study of the raindrop spectrum, we can not only explore the evolution law of precipitation but also understand the microphysical characteristics of different types of precipitation clouds. This paper uses the raindrop spectrum data observed at Naqu Station, Yushu Station, Linzhi Station, and Emei Mount Station, as well as cloud radar data in the Yushu region in the Tibetan Plateau. Raindrop spectral characteristics are studied, and the raindrop size distribution (DSD) characteristics of the four stations are analyzed. The results are as follows: (1) The overall raindrop spectral concentration of the four stations decreases with the increase in particle size after the peak value. The downtrend is most gentle at the peak. (2) All rain intensity levels show the characteristics of multiple vertices. At Linzhi Station, Naqu Station and Yushu Station, the particles with a diameter of <1 mm contributed the most to the precipitation rate, while the particles with a diameter of <1 mm at Emei Mount Station contributed the least to the precipitation rate. (3) The precipitation in the central and eastern margins of the Tibetan Plateau is dominated by small and medium-sized particles, accounting for 95–99% of the precipitation particle number concentration. (4) The raindrop spectra of the three types of precipitation clouds at the four sites are all cumulonimbus precipitation Cumulonimbus in the uppermost layer, stratiform cloud precipitation stratiform cloud in the lowermost layer, and mixed cloud precipitation stratiform cloud in between. (5) Cloud radar analysis of precipitation structure shows that cumulonimbus and mixed clouds develop vigorously, and the highest cloud height can reach 13 km, but the average precipitation duration of mixed clouds is shorter than that of cumulonimbus; stratiform clouds develop relatively smoothly, with cloud heights of 6–7 km, the average precipitation duration is the shortest.

General circulation and global heat transport in a quadrupling CO2 pulse experiment

To investigate the response of the general circulation and global transport of heat through both atmosphere and ocean to two-types of carbon dioxide removal scenario, we performed an earth system model experiment in which we imposed a pulse-type quadrupling of CO₂ forcing for 50 years and a gradual peak-and-decline of four-time CO₂ forcing. We found that the results from two experiments are qualitatively similar to each other. During the forcing-on period, a dominant warming in the upper troposphere over the tropics and on the surface at high latitudes led to a slowdown in the Hadley circulation, but the poleward atmospheric energy transport was enhanced due to an increase in specific humidity. This counteracted the reduction in poleward oceanic energy transport owing to the suppression of the meridional overturning circulation in both Hemispheres. After returning the original CO₂ level, the hemispheric thermal contrast was reversed, causing a southward shift of the intertropical convergence zone. To reduce the hemispheric thermal contrast, the northward energy transports in the atmosphere and ocean surface were enhanced while further weakening of the global-scale Atlantic meridional overturning circulation led to southward energy transport in the deep ocean.

Image Human Thorax Using Ultrasound Traveltime Tomography with Supervised Descent Method

The change of acoustic velocity in the human thorax reflects the functional status of the respiratory system. Imaging the thorax’s acoustic velocity distribution can be used to monitor the respiratory system. In this paper, the feasibility of imaging the human thorax using ultrasound traveltime tomography with a supervised descent method (SDM) is studied. The forward modeling is computed using the shortest path ray tracing (SPR) method. The training model is composed of homogeneous acoustic velocity background and a high-velocity rectangular block moving in the domain of interest (DoI). The average descent direction is learned from the training set. Numerical experiments are conducted to verify the method’s feasibility. Normal thorax model experiment proves that SDM traveltime tomography can efficiently reconstruct thorax acoustic velocity distribution. Numerical experiments based on synthetic thorax model of pleural effusion and pneumothorax show that SDM traveltime tomography has good generalization ability and can detect the change of acoustic velocity in human thorax. This method might be helpful for the diagnosis and evaluation of respiratory diseases.

Assessment of Deep Convective Systems in the Colombian Andean Region

In tropical regions, deep convective systems are associated with extreme rainfall storms that usually detonate flash floods and landslides in the Andean Colombian region. Several studies have used satellite data to address the structure and formation of tropical convective storms. However, there is a local gap in the characterization, which is essential for a better understanding of flash floods and preparedness, filling a gap in a region with scarce information regarding extreme events. In this work, we assess the deep convective storms in a mountainous region of Colombia using meteorological radar observations between 2014 and 2017. We start by identifying convective and stratiform formations. We refine the convective identification by classifying convective systems into enveloped (contained in a stratiform system) and unenveloped (not contained). Then, we analyze the systems’ temporal and spatial distributions and contrast them with the watersheds’ features. According to our results, unenveloped convective systems have higher reflectivity and hence higher rainfall intensities. Moreover, they also have a well-defined spatial and temporal distribution and are likely to occur in watersheds with elevation gradients of around 2000 m and an aspect contrary to the wind direction. Our assessment of the convective storms is of significant value for the hydrologic community working on flash floods. Moreover, the spatiotemporal description is highly relevant for stakeholders and future local analysis.

Decarbonisation of calcium carbonate in sodium hydroxide solutions under ambient conditions: effect of residence time and mixing rates

The decarbonisation of CaCO₃ is essential for the production of lime (Ca(OH)₂ and CaO), which is a commodity required in several large industries and the main precursor for cement production. CaCO₃ is usually decarbonised at high temperatures, generating gaseous CO₂ which will require post-process capture to minimise its release into the environment. We have developed a new process that can decarbonise CaCO₃ under ambient conditions, while sequestering the CO₂ as Na₂CO₃·H₂O or Na₂CO₃ in the same stage. Here, the effects of increasing stirring rates and residence times on reaction efficiency of the key reaction occurring between CaCO₃ and NaOH solution are studied. It is shown that the reaction is enhanced at lower stirring rates and longer residence times up to 300 seconds of contact between the reactants. The mass balance performed for Ca and CO₂ revealed that up to the 95% of the process CO₂ embodied in CaCO₃ was sequestered, with maximum capture rate assessed at nn moles CO₂ captured per second of reaction progress. A deeper insight into the precipitation of Na₂CO₃·H₂O or Na₂CO₃ under different reaction conditions was gained, and SEM-EDX analysis enabled the observation of the reaction front by detection of Na migrating towards inner regions of partially-reacted limestone chalk particles.

Curing Effect on Durability of Cement Mortar with GGBS: Experimental and Numerical Study

In this paper, supplementary cementitious materials are used as a substitute for cement to decrease carbon dioxide emissions. A by-product of the iron manufacturing industry, ground granulated blast-furnace slag (GGBS), known to improve some performance characteristics of concrete, is used as an effective cement replacement to manufacture mortar samples. Here, the influence of curing conditions on the durability of samples including various amounts of GGBS is investigated experimentally and numerically. Twelve high-strength Portland cement CEM I 52.5 N samples were prepared, in which 0%, 45%, 60%, and 80% of cement were substituted by GGBS. In addition, three curing conditions (standard, dry, and cold curing) were applied to the samples. Durability aspects were studied through porosity, permeability, and water absorption. Experimental results indicate that samples cured in standard conditions gave the best performance in comparison to other curing conditions. Furthermore, samples incorporating 45% of GGBS have superior durability properties. Permeability and water absorption were improved by 17% and 18%, respectively, compared to the reference sample. Thereafter, data from capillary suction experiments were used to numerically determine the hydraulic properties based on a Bayesian inversion approach, namely the Markov Chain Monte Carlo method. Finally, the developed numerical model accurately estimates the hydraulic characteristics of mortar samples and greatly matches the measured water inflow over time through the samples.

Data-driven spatiotemporal assessment of the event-size distribution of the Groningen extraction-induced seismicity catalogue

For induced seismicity, the non-stationary, heterogeneous character of subsurface stress perturbations can be a source of spatiotemporal variations in the scaling of event sizes; one of the critical parameters controlling seismic hazard and risk. We demonstrate and test a systematic, statistical, penalized-likelihood approach to analysing both spatial and temporal variations in event size distributions. The methodology used is transferable to the risk analysis of any subsurface operation, especially for small earthquake catalogues. We explore the whole solution space and circumvent conventional, arbitrary choices that require a priori knowledge of these variations. We assess the effect of possible bias in the derivation, e.g., due to tapering of the earthquake-size distribution, correlation between the b-value and the magnitude of completeness and correlation between the b-value and the largest magnitude observed. We analyse the spatiotemporal variations in the earthquake-size distribution of the Groningen induced seismicity catalogue (December 1991–November 16, 2021). We find statistically significant spatial variations without any compelling, statistical evidence of a temporal variation. Furthermore, we find that the largest magnitudes observed are inconsistent with the sampling statistics of an unconstrained earthquake-size distribution. Current risk assessment models likely overestimate the probability of larger magnitude events (M ≥ 3.0) and thus the risk posed.

Focal Mechanism and Source Parameters Analysis of Mining-Induced Earthquakes Based on Relative Moment Tensor Inversion

Mining-induced earthquakes (MIEs) in underground coal mines have been a common phenomenon that easily triggers rock bursts, but the mechanism is not understood clearly. This research investigates the laws of focal mechanism and source parameters based on focal mechanism and source parameters analysis of MIEs in three frequent rock burst areas. The relative moment tensor inversion (MTI) method was introduced, and the way to construct the inversion matrix was modified. The minimum ray and source number conditions were calculated, and an optimized identification criterion for source rupture type was proposed. Results show that the geological structure, stress environment, and source horizon influence the focal mechanism. The tensile type sources can distribute in the roof and coal seam, while the shear types are primarily located in the coal seam. In the typical fold structure area, the difference in source rupture strength and stress adjustment between tensile and shear types is negligible, while the disturbance scale of tensile types is distinct. The shear types have higher apparent volume and seismic moment in the deep buried fault area but lower source energy. The apparent stress of the tensile types is higher than that of the shear types, representing that the stress concentration still exists in the roof after the MIEs, but the stress near the faults could be effectively released. In the high-stress roadway pillar area, the primary fracture of the coal pillar easily produces a continuous shear rupture along the dominant stress direction under the extrusion of the roof and floor. The source parameters (except apparent stress) of shear types are higher than tensile types and have higher dynamic risk. The results contribute to expanding the understanding of rock burst mechanisms and guide MIEs’ prevention.

Atmospheric blockings in Coupled Model Intercomparison Project Phase 5 models with different representations of Antarctic sea ice extent

This study investigated whether there are differences in the frequency and position of Southern Hemisphere atmospheric blockings between Coupled Model Intercomparison Project Phase 5 models with different representations of Antarctic sea ice extent in historical experiments. In the model with the greatest sea ice underestimation (Model for Interdisciplinary Research on Climate version 5) there is a weakening of the polar jet and an increase in 500-hPa height. These atmospheric conditions favor the predomination of simulated blocking frequency overestimations (autumn-winter), in relation to the observed (ERA-Interim). On the other hand, in the models with the greatest sea ice overestimations (Community Climate System Model version 4) and the better sea ice representation (Norwegian Earth System Model version 1) there is a strengthening of the polar jet and weaker positive differences in 500-hPa height in the Antarctic region. These atmospheric conditions favor a predominance of simulated blocking frequency underestimations (all seasons). All models present a good representation of the preferred blocking regions (South Pacific), although they do not represent the longitudinal location of the maximum frequency. In years of sea ice retraction (expansion), there is a predominance of a higher (lower) blocking frequency in the 60°S for all models and observed data.

Instantaneous tracking of earthquake growth with elastogravity signals

Rapid and reliable estimation of large earthquake magnitude (above 8) is key to mitigating the risks associated with strong shaking and tsunamis¹. Standard early warning systems based on seismic waves fail to rapidly estimate the size of such large earthquakes^2–5. Geodesy-based approaches provide better estimations, but are also subject to large uncertainties and latency associated with the slowness of seismic waves. Recently discovered speed-of-light prompt elastogravity signals (PEGS) have raised hopes that these limitations may be overcome^6,7, but have not been tested for operational early warning. Here we show that PEGS can be used in real time to track earthquake growth instantaneously after the event reaches a certain magnitude. We develop a deep learning model that leverages the information carried by PEGS recorded by regional broadband seismometers in Japan before the arrival of seismic waves. After training on a database of synthetic waveforms augmented with empirical noise, we show that the algorithm can instantaneously track an earthquake source time function on real data. Our model unlocks ‘true real-time’ access to the rupture evolution of large earthquakes using a portion of seismograms that is routinely treated as noise, and can be immediately transformative for tsunami early warning.

A deep learning model trained on prompt elastogravity signal (PEGS) recorded by seismometers in Japan predicts in real time the final magnitude of large earthquakes faster than methods based on elastic waves.

Analysis of Radon Measurements in Relation to Daily Seismic Activity Rates in the Vrancea Region, Romania

Many previous research studies have shown how local and even regional earthquakes can significantly affect the release of radon in the soil. The aim of this work is to investigate the relationship between radon measurements and the daily seismic activity rate and develop a methodology that allows estimating the seismic activity rate using only radon measurements. To carry out this study, the earthquake catalogue of the Vrancea region (Romania) has been used to estimate the daily seismic activity rate during a given time period, in which radon measurements were also recorded, from January 2016 to September 2020. The Vrancea zone represents the most active seismic zone in Europe and is located on the eastern edge of the strongly bent Carpathian arc. In the case of the radon measurements, seasonal behaviours and linear trends due to non-seismic factors have been identified and subsequently removed. The discrete wavelet transform has been used to analyse the radon signal at two different scales: long and short periods. From the analysis carried out on a long-period scale, an approximate linear relationship has been obtained between the radon series and the daily seismic activity rate, which provides insights into the behaviour of the seismic activity in the study region with only the radon information. In addition, the study reveals certain characteristics that could be used as precursors of earthquakes at different scales: weeks in the case of the estimated daily seismic activity rate, and days in the case of the short-period signal obtained by the wavelet analysis. The results obtained for this region allow us to hope that the analysis of the radon time series can become an effective complement to the conventional seismic analysis used in operational earthquake forecasting.

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements' main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.

Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review

Computational Fluid Dynamics (CFD) have been frequently applied to model the growth conditions in photobioreactors, which are affected in a complex way by multiple, interacting physical processes. We review common photobioreactor types and discuss the processes occurring therein as well as how these processes have been considered in previous CFD models. The analysis reveals that CFD models of photobioreactors do often not consider state-of-the-art modeling approaches. As a comprehensive photobioreactor model consists of several sub-models, we review the most relevant models for the simulation of fluid flows, light propagation, heat and mass transfer and growth kinetics as well as state-of-the-art models for turbulence and interphase forces, revealing their strength and deficiencies. In addition, we review the population balance equation, breakage and coalescence models and discretization methods since the predicted bubble size distribution critically depends on them. This comprehensive overview of the available models provides a unique toolbox for generating CFD models of photobioreactors. Directions future research should take are also discussed, mainly consisting of an extensive experimental validation of the single models for specific photobioreactor geometries, as well as more complete and sophisticated integrated models by virtue of the constant increase of the computational capacity.

Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters

Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs), which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating between solid-like and liquid-like water molecules. Here we compare the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations in a range of temperatures. For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural network approach (DeepIce), which are conceptually different methodologies. We find that all the analysis methods give qualitatively similar trends for the behaviours of the QLLs on ice surfaces with temperature, with some subtle differences in the classification sensitivity limited to the solid-liquid interface. The thickness of QLLs on the ice surface increases gradually as the temperature increases. The trends of the QLL size and of the values of the order parameters as a function of temperature for the different facets may be linked to surface growth rates which, in turn, affect crystal morphologies at lower vapour pressure. The choice of the order parameter can be therefore informed by computational convenience except in cases where a very accurate determination of the liquid-solid interface is important.

The Arkalochori Mw = 5.9 Earthquake of 27 September 2021 Inside the Heraklion Basin: A Shallow, Blind Rupture Event Highlighting the Orthogonal Extension of Central Crete

A strong, shallow earthquake occurred near Heraklion (Crete, Greece) on 27 September 2021. The earthquake produced significant ground deformation in the vicinity of Arkalochori village but without any evidence for surface ruptures of primary origin. We used geodetic (InSAR and GNSS) data to map motions of the Earth’s surface that occurred during and shortly after the earthquake. A 14 cm subsidence of the GNSS station ARKL and a maximum of 19 cm distance from the SAR satellite were recorded. The measured surface displacements were used to constrain the rupture geometry and slip distribution at depth. Our best-fitting inversion model suggests that the rupture occurred on a 13 km-long planar normal fault striking N195° E dipping 55° to the northwest, with major slip occurring to the east and updip of the hypocentre. The fault tip is located 1.2 km beneath the surface. The maximum coseismic slip occurred in the uppermost crust, in the depth interval of 4–6 km. A decrease in the fault offsets toward the Earth’s surface is likely caused by an increased frictional resistance of the shallow layers to rapid coseismic slip. Satellite observations made in the first month after the earthquake detected no post-seismic deformation (i.e., below one fringe or 2.8 cm). The seismic fault may be identified with the Avli (Lagouta) segment of the NNE-SSW striking, west-dipping, 23 km-long neotectonic Kastelli Fault Zone (KFZ). Part of the rupture occurred along the Kastelli segment, indicating a fault segment linkage and a history of overlapping ruptures along KFZ. Based on geological data and footwall topography we estimate an average slip rate between 0.17–0.26 mm/yr for the KFZ. The Arkalochori earthquake is a paradigm example for the on-going extension of Heraklion basin (central Crete) in the WNW-ESE direction, which is almost orthogonal to the E-W Messara graben and other active faults along the south coast of Crete.