Reconstruction of complete seismic data is a crucial step in seismic data processing, which has seen the application of various convolutional neural networks (CNNs). These CNNs typically establish a direct mapping function between input and output data. In contrast, diffusion models which learn the feature distribution of the data, have shown promise in enhancing the accuracy and generalization capabilities of predictions by capturing the distribution of output data. However, diffusion models lack constraints based on input data. In order to use the diffusion model for seismic data interpolation, our study introduces conditional constraints to control the interpolation results of diffusion models based on input data. Furthermore, we improving the sampling process of the diffusion model to ensure higher consistency between the interpolation results and the existing data. Experimental results conducted on synthetic and field datasets demonstrate that our method outperforms existing methods in terms of achieving more accurate interpolation results.
The Andean foreland is divided into morphotectonic provinces characterized by diverse deformation styles and seismogenic behavior partially stemming from distinct geological histories that preceded the current phase of subduction. The transition between the high Andes and the eastern foreland is exposed to numerous natural hazards and contains critical economic infrastructure, yet we know relatively little about regional active tectonics due to few geophysical investigations. Here we use waveforms collected during a 15-month-long seismic network deployment in the Santa Bárbara System (SBS) of northwest Argentina following the 2015 Mw 5.7 El Galpón earthquake to determine the distribution and magnitude of local earthquakes, obtain a regional 1D seismic velocity model, and improve our overall understanding of SBS neotectonics. Of the nearly 1200 recorded earthquakes, ~700 occurred in the crust with half of the moment release associated with events deeper than 25 km. The depth extent of seismicity supports the notion that the SBS upper and middle crust are homogeneous and that the lower crust is composed of granulites. These conditions likely formed during Paleozoic mountain building and Salta Rift-related Cretaceous magmatism, which dehydrated the crust. We find no clear indications that a shallow, low-angle detachment fault inferred to have been active during Cretaceous rifting exerts a strong control on modern deformation in contrast to the active décollement beneath the adjacent fold-and-thrust belt of the Subandes to the north. It remains unclear how active, inverted normal faults in the SBS shallow crust connect to the deeper zones of seismicity.
According to the principle of thermal expansion and cold shrinking, it is found that there is a magic one-to-one correspondence between the existing eight ancient plates of the earth and the eight planets of the solar system. It is found with further studying that there is a one-to-one correspondence between the nine celestial bodies of the solar system with the sun as the core and the nine ancient plates splitting from the unique prehistoric supercontinent. Therefore, in order to restore the prehistoric unique supercontinent, it is not to simply pile up the existing residual dominant ancient plates, but to restore the hidden ancient plate sunk into the ocean floor and take it as the core to restore the prehistoric unique supercontinent. The unique prehistoric supercontinent reconstructed by such a jigsaw recovery is similar to the shape of the solar system or an egg, presenting with a clear core and circle structure. The core ancient plate, which is recovered from the occult ancient plate on the ocean floor, determines the uniqueness, irreversibility and unrepeatability of the prehistoric supercontinent. The sudden extinction of the dinosaur family at the end of the cretaceous about 66 million years ago provides the easiest reasonable guess as to the time point when the Earth's unique supercontinent broke-up. The clam shape distribution of the geological age on the Northwestern Pacific Ocean floor gives us a hint meaning for the symbolization of the black hole in the universe which always dominant the spiral galaxy as the core of it.
Mineral precipitation can form complex patterns under non-equilibrium conditions, in which two representative patterns are rhythmic Liesegang stripes and fractal dendrites. Interestingly, both patterns occur in the same rock formations, including various dendritic morphologies found in different rocks, such as limestone and sandstone. However, the underlying mechanism for selecting the vastly different mineral precipitation patterns remains unclear. We use a phase-field model to reveal the mechanisms driving pattern selection in mineral precipitation. Simulations allow us to explore the effects of diffusion parameters on determining the dendritic morphologies. We also propose a general criterion to distinguish the resulting dendrites in simulations and field observations based on a qualitative visual distinction into three categories and a quantitative fractal dimension phase diagram. Using this model, we reproduce the classified dendrites in the field and invert for the key parameters that reflect the intrinsic material properties and geological environments. This study provides a quantitative tool for identifying the morphology selection mechanism with potential applications to geological field studies, exploration for resource evaluation, and other potential industrial applications.
Complex flow dynamics have been observed, at the pore-scale, during multiphase through porous rocks. These dynamics are not captured in large scale models exploring the migration and trapping of subsurface fluids e.g., CO2 or hydrogen. Due to limitations in imaging capabilities, these dynamics cannot be observed directly at the larger, Darcy scale. Instead, by using pressure data from pore-scale (mm-scale) and core-scale (cm-scale) experiments, we show that fluctuations in pressure measured at the core-scale reflect specific fluid displacement events taking place at the pore-scale. The spectral characteristics of the pressure data depends on the flow dynamics, size of the rock sample, and heterogeneity of pore space. While high resolution imaging of large samples would be useful in assessing flow dynamics across many of the scales of interest, such an approach is currently infeasible. We suggest an alternative, pragmatic, approach examining pressure data in the time-frequency domain using wavelet transformation.
By using the magnetic field data from of Van Allen Probes, we analyzed the distribution characteristics of the electromagnetic environment in the inner magnetosphere on different Dst* index and magnetic local time (MLT). Our results show that for the response of different current systems, the dawn-dusk and noon-midnight asymmetry distribution of the residual magnetic field δB increases with Dst* index. When Dst* < -60 nT, a ‘banana’-shaped geomagnetic field negative disturbance peak region appears in the sector from midnight to dusk. Then, we obtained the azimuthal current density and found the asymmetric internal eastward and external westward ring current. Through the vector analysis of three-dimensional current density, the current density vector distribution in the magnetic equatorial plane (MEP) is completely displayed for the first time, which directly proves the existence of banana current near r = 3.0 - 4.0 RE during strong geomagnetic storms.
The aim of this work is to present a global ionospheric prediction model based on deep learning (DL) to forecast Total Electron Content 24 hours in advance under different space weather conditions. Three different DL techniques have been compared to select the most suitable for the purpose of an operational service: Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The modeling approach inherits and extends what has been proposed by Cesaroni and co-authors (2020). We use TEC on 18 selected grid points of Global Ionospheric Maps (GIMs) as the target parameter and Kp index as the external input. We use a dataset from 2005-2016 for training and testing, we also analyze case studies from 2017 under different geomagnetic conditions. Results show that CNN models have better predictive capabilities than the other two DL models, even under geomagnetically disturbed conditions. Considering the first 24 hours of forecasting, CNN exhibits errors between 0.5 and 2 TECu, while LSTM and GRU errors can reach 3 TECu. We also show how all the proposed models outperform the two naive models: the so-called “frozen ionosphere” and a 27 days averaged model. Moreover, we implemented the models using incremental training to update them as new data arrives and thus the trained model is able to adapt to rapid changes within the previous 24 hs to the forecasting. Thus, the proposed model can be implemented in an operative manner for Space Weather applications and services.
Over the past decade, the seismicity rate in the state of Oklahoma has increased significantly, which has been linked to industrial operations, such as saltwater injection. Taking advantage of induced earthquakes and recently deployed seismometers, we construct a 3-D radially anisotropic seismic velocity model for the crust of Oklahoma by using full waveform inversion. To mitigate the well-known cycle-skipping problem, we use misfit functions based on phase and waveform differences in several frequency bands. Relative velocity perturbations in the inverted model allow us to delineate major geological provinces in Oklahoma, such as the Anadarko and Arkoma Basins, as well as the Cherokee Platform and Shelf. In addition, radial anisotropy in the inverted model reflects deformation within the crust of Oklahoma, which might correlate with sedimentary layers, micro-cracks/fractures, as well as the dominant orientation of anisotropic minerals. The crystalline basement beneath Oklahoma can be inferred from the new velocity model, which enables us to classify induced seismicity in current earthquake catalogs better. Furthermore, synthetic experiments suggest that the new velocity model enables us to better constrain earthquake location in Oklahoma, especially for determining their depths, which are important for investigating induced seismicity.
Seamounts and ridges are often invoked to explain subduction-related phenomena, but the extent of their involvement remains controversial. An analysis of seismicity in the region of the Pampean flat slab through an application of an automated catalogue generation algorithm resulted in 143,716 local earthquake hypocenters, 35,924 of which are associated with at least 12 arrival time estimates, at least 3 of which are from S waves, along with a total of 12,172 focal mechanisms. Several new features related to the subduction of the Juan Fernandez Ridge were discovered, including: (1) a series of parallel lineaments of seismicity in the subducted Nazca plate separated by about 50 km and striking about 20, and (2) a strong spatial correlation between these deeper (> 80 km depth) regions of intense seismicity and concentrations of activity in the crust almost directly above it. Focal mechanisms of the deeper events are almost exclusively normal, while those in the crust are predominantly reverse. The deeper lineaments mirror the origination and spacing of several seamount chains seen on the Nazca plate, suggesting that these patterns are caused by these same types of features at depth. This would imply that relatively minor features persist as slab anomalies long after they are subducted. The correlation of these deeper features with seismicity in the mid to lower crust suggests a genetic relation between the two. We postulate that volatiles from the subducted ridges percolate into the South American crust and induce seismicity essentially by fracking it.
Field-scale observations suggest that rock heterogeneities control subsurface fluid flow, and these must be characterised for accurate predictions of fluid migration, such as during \CO2 sequestration. Recent efforts have focused on simulation-based inversion of laboratory observations with X-ray imaging, but models produced in this way have been limited in their predictive ability for heterogeneous rocks. We address the main challenges in this approach through an algorithm that combines: a 3-parameter capillary pressure model, spatial heterogeneity in absolute permeability, the constraint of history match iterations based on marginal error improvement, and image processsing that incorporates more of the experimental data in the calibration. We demonstrate the improvements on five rocks (two sandstones and three carbonates), representing a range of heterogeneous properties, some of which could not be previously modelled. The algorithm results in physically representative models of the rock cores, reducing non-systematic error to a level comparable to the experimental uncertainty.
Space weather is the phenomenon of solar storms and other events in space that can have impacts on Earth. They are a major concern for power grids which can be severely damaged by geomagnetic field variations during such natural phenomena. To reduce such impact and the possible consequences following, the study aims to determine how the storm's impact spreads across the Earth during a strong event, the October 29th, 2003 Halloween Storm. The impact of the Halloween Storm is analyzed by using global maps of geomagnetic variations to find where it is received and how it propagated. Cross-correlation is done on specific latitudinal and longitudinal distributed chains. The maps show that impacts are received first in high-latitude regions and then propagate toward mid- and low-latitude regions. The regions of impact during the first storm are on the magnetic dayside while the second storm is on the magnetic night side. The cross-correlation study shows that localized patterns occur more in the high-latitude regions with more intensive impacts, such as Norway, Finland, Sweden, Russia, and Canada. Global patterns occur more in the mid and equatorial regions with less intensive impacts. The mid-latitude countries such as France, UK, and the US can also be impacted during extreme events. The visualization package is developed and available to researchers and the industry. The global view of space weather impacts can help us to understand and mitigate the hazardous impacts on modern society.
We show that the return-point memory of cyclic macroscopic trajectories enables the derivation of a thermodynamic framework for quasistatically driven dissipative systems with multiple metastable states. We use this framework to sort out and quantify the energy dissipated in quasistatic fluid-fluid displacements in disordered media. Numerical computations of imbibition--drainage cycles in a quasi-2D medium with gap thickness modulations (imperfect Hele-Shaw cell) show that energy dissipation in quasistatic displacements is due to abrupt changes in the fluid-fluid configuration between consecutive metastable states (Haines jumps), and its dependence on microstructure and gravity. The relative importance of viscous dissipation is deduced from comparison with quasistatic experiments.