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Results of a detailed analysis of solar activity in 1957-1965 are compared with the data on solar activity with variations of cosmic ray activity, and geomagnetic, ionospheric and terrestrial currents. Cosmic ray variations are investigated with reference to the asymmetry of solar hemispheres. All storms in the IGY are classified and a symbolic diagram of the time sequence is drawn up.
This data package is associated with the publication “Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization” published in European Geophysical Union - Biogeosciences (Barnes et al. 2025). This study investigates how phosphorus (P) biogeochemistry is altered by burn severity in contrasting types of vegetation chars. This data package documents the workflow used to process and generate the main figures and statistics in the manuscript. The R scripts reference minimally processed P nuclear magnetic resonance (P-NMR) and X-ray absorption near edge structure (P-XANES) data, as well as fully processed data including total elemental composition of the solid chars, total elemental composition of the char leachates (particulate and aqueous phases), and leachate aqueous phase molybdate reactive P concentration. These source data and associated metadata can be found on ESS-DIVE at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022; v3). Files and scripts included in this data package finish the processing workflow for P-NMR and P-XANES data. These data can be used to gain a better understanding of bulk chemical changes in chars and their leachates, as well as detailed molecular changes to P. This data package is associated with the GitHub repository found at https://github.com/river-corridors-sfa/rcsfa-RC3-BSLE_P. This data package is comprised of a “data” folder and a series of data processing and analysis scripts. Details on how to recreate the workflow can be found in the Critical Details section of the readme and the “workflow_readme.md” file. The file-level metadata file (file ending in “flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (file ending in “dd.csv”) describes all tabular data columns and their respective definitions and units.
The successful combination of data from different ocean color sensors depends on the correct interpretation of signal from each of these sensors. Ideally, the sensor measured signals are calibrated to geophysical units of spectral radiance, and sensor artifacts are removed and corrected. The calibration process resamples the signal into a common radiometric data space so that subsequent ocean color algorithms that are applied to the data are based on physical processes and are inherently sensor independent. The objective of this project is to calibrate and validate the on-orbit radiometric characteristics of Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) with underflights of NASA's calibrated Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This objective is feasible because AVIRIS measures the same spectral range as SeaWIFS at higher spectral resolution. In addition to satellite sensor underflights, the AVIRIS project has supported comparison and analysis of the radiometric calibration standards used for AVIRIS and SeaWIFS. To date, both the OCTS and SeaWIFS satellite sensors have been underflown by AVIRIS with matching spectral, spatial, geometric, radiometric, and temporal domains. The calibration and validation objective of this project is pursued for the following reasons: (1) Calibration is essential for the quantitative use of SeaWIFS and other SIMBIOS (Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies) sensor data; (2) Calibration in the laboratory of spaceborne sensors is challenging; (3) Satellite sensors are subjected aging on the ground and to trauma during launch; (4) The Earth orbit environment is significantly different than the laboratory calibration environment; (5) Through years of effort AVIRIS has been demonstrated to be well calibrated; and (6) AVIRIS can match the spectral and spatial observation characteristics near the top of the atmosphere at the time of SeaWIFS measurements.
Abstract Seismic observations of underground nuclear explosions provide crucial data on source yield and depth that cannot easily be estimated from other geophysical methods. However, it is difficult to obtain reliable yield estimates for test sites for which we do not have direct seismic calibration experiments. To obtain source information from uncalibrated sites and paths, local and regional seismic records of six, proximal, declared underground nuclear explosions in North Korea are used to compute spectral ratios of narrow‐band waveform envelopes of body‐wave coda that remove path and site effects to reveal precise, relative source moment. The yields of these explosions are obtained from the observed source ratios by simultaneously fitting the classical source model of Mueller and Murphy (1971), https://doi.org/10.1785/bssa0610061675 to all event pairs. The source model provides an impressive fit to the observations considering that the P phase coda derived source spectral ratios did not require a prior knowledge of the source or regionally calibrated corrections to be applied to the data. However, the observed corner frequencies from S wave coda spectral ratios are lower than the source model predictions, but are well fit by models calculated using the corner frequency consistent with the Fisk conjecture. The results presented here provide novel constraints on the spectral distributions of the energy radiated by the sources of the DPRK test series, and allows for an independent evaluation of existing estimated source model parameters.
Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.
This dataset contains estimates of dissolved organic carbon (DOC) yield (mg C m-2), runoff (mm day-1), snow water equivalent (SWE, mm day-1), and snowmelt (mm day-1) from a simulation of the Permafrost Water Balance Model (PWBM v4). The simulation effectively quantifies snowpack accumulation and melt, runoff, and DOC leaching, and broadly capture the seasonal cycle in DOC concentration and mass loadings. Daily air temperature, precipitation, and wind speed data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) were used for meteorological forcings. The model outputs are organized by grid and day of the month in gzipped ASCII text files contained within each archived ZIP file. No special software is required to work with these data. The basin and domain information files are also ASCII text. The gridded estimates are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and leachate DOC concentrations and loadings across parts of the western Arctic. A manuscript describing the data and associated analysis has been accepted for publication in Journal of Geophysical Research - Biogeosciences (Rawlins et al., 2021).
Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.
The Earth’s interconnected and dynamical systems operate on a spectrum of time scales from millions of years to fractions of a second. While the evolution of the Earth and its deep interior are beyond the time span of human observations, understanding of many natural phenomena operating on human times scales have benefited from direct scientific observation. Continuous processes and those that are repeated over time shape the environment we live in. Furthermore, as we are faced with unprecedented changes to climate, understanding the deeper patterns and trends in natural systems through time have taken on new importance (Research Data Alliance, 2019). The call to reuse data is driven not only by economics but also by the recognition of their complete uniqueness (observations of natural systems are not repeatable) and scientific value in enhancing current understandings as well as potential new discoveries especially in the era of big data. These data are part of the historical record and our scientific heritage (American Geophysical Union, 2019) not only in explicitly recording earth observations but implicitly recording, and thus providing the evidence that addresses the manner in which science was conducted.
Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.
Computed tomography and special core analysis data associated with the technical report series document Computed Tomography Scanning and Geophysical Measurements of the Enhanced Geothermal Systems (EGS) Collab SURF Core. Paronish, T.; Mackey, P.; Schmitt, R.; Crandall, D.; Moore, J.; Brown, S.; Roggenthen, W.; Schwering, P. C.; Dobson, P. F.; Kneafsey, T. Computed Tomography Scanning and Geophysical Measurements of the Enhanced Geothermal Systems (EGS) Collab SURF Core; DOE.NETL-2021.2866; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2022; p 76.
Data described in the NETL Technical Report Series "Computed Tomography Scanning and Geophysical Measurements of the Wellington 1-32 Core", including processed and raw CT and measurements from NETL's multi-sensor core logger. Paronish, T.; Schmitt, R.; Mitchell, N; Brown, S; Crandall, D.; Moore, J.; Hasiuk, F.; Potter, N.; Holubnyak, Y.E. Computed Tomography Scanning and Geophysical Measurements of the Wellington 1-32 Core; DOE.NETL-2021.2882; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2022; p 108. DOI: https://doi.org/10.2172/1894355 https://edx.netl.doe.gov/dataset/computed-tomography-scanning-and-geophysical-measurements-of-the-wellington-1-32-core
A geological study of a 27,500 sq km area in the Los Andes region of northwestern Venezuela was performed which employed both X-band radar mosaics and computer processed Landsat images. The 3.12 cm wavelength radar data were collected with horizontal-horizontal polarization and 10 meter spatial resolution by an Aeroservices SAR system at an altitude of 12,000 meters. The radar images increased the number of observable suspected fractures by 27 percent over what could be mapped by LANDSAT alone, owing mostly to the cloud cover penetration capabilities of radar. The approximate eight fold greater spatial resolution of the radar images made possible the identification of shorter, narrower fractures than could be detected with LANDSAT data alone, resulting in the discovery of a low relief anticline that could not be observed in LANDSAT data. Exploration targets for petroleum, copper, and uranium were identified for further geophysical work.
Results from Seasat-A scatterometer (SASS) remote sensing of oceanic wind vectors are reviewed, together with the theory of radar backscattering. The SASS was designed to measure the surface wind stress and neutral stability wind vector at 19.5 m altitude. Requirements included windspeeds from 4-24 m/sec to within 2 m/sec or 10%, a 1000 km swath, directions from 0-360 deg to within 20 deg, a 50 km resolution cell, and cross-track and along-track spacing between resolution cells of 100 km. The method was based on Bragg scattering of microwaves from cm-length capillary ocean waves. The strength of the backscatter is proportional to the capillary wave amplitude, which is in equilibrium with the ocean surface wind speed. The measurement grouping, least-squares estimator, and dB deviation model function table for the SASS geophysical algorithm are discussed. Comparisons of SASS, GOASEX, and ground truth wind data are made, and a 0.92 correlation between SASS and ground truth data is determined from regression analysis.
A technique is described for estimating spectral admittance functions using Pioneer Venus gravity and topography data. These admittance functions provide a convenient means to carry out systematic geophysical studies over much of the surface of Venus with a variety of interior density models. The admittance functions are calculated in the observation space of line-of-sight (LOS) gravity. Both closed and open system petrological models are considered for the Tharsis region of Mars. An analytic theory for isostatic compensation on one-plate planet is applied, including membrane stresses in the lithosphere, self gravitation, and rotational ellipticity. Crucial to this stress modeling and also to the petrological modeling is the observation that the earliest fracturing seen in the Tharsis region is associated with isostatic stresses. The radial fractures that extend far from Tharsis are associated with an additional and/or a completely different mechanism.
The Gravity Probe-B Mission will carry the Stanford Gyroscope relativity experiment into orbit in the mid 1990's, as well as a Global Positioning System (GPS) receiver whose tracking data will be used to study the earth gravity field. Estimates of the likely quality of a gravity field model to be derived from the GPS data are presented, and the significance of this experiment to geodesy and geophysics are discussed.
It is often the case that some instruments being used for geophysical measurements cannot measure some parameters that are important for processing the data obtained using the instrument. However, the parameters of interest may be measured by other instruments and these data made available to the operators of the first instrument. Processing the data immediately after it is acquired is useful in directing the operation of the same or different instrument or in providing a quick look data set to users on the ground. The four applications which are considered are: the decision to acquire data due to some important occurrence detected by Eos instruments; the decision not to acquire data at a scheduled time and/or location; the decision to acquire additional data to improve data quality; and combining data from several sources to enhance data quality. General examples are presented, which may or may not apply directly to Eos instruments on the various platforms.
Pulse-limited, airborne radar data taken in June and July 1984 with a 13.8-GHz altimeter over the Fram Strait marginal ice zone are analyzed with the aid of large-format aerial photography, airborne synthetic aperture radar data, and surface observations. Variations in the radar return pulse waveforms are quantified and correlated with ice properties recorded during the Marginal Ice Zone Experiment. Results indicate that the wide-beam altimeter is a flexible instrument, capable of identifying the ice edge with a high degree of accuracy, calculating the ice concentration, and discriminating a number of different ice classes. This suggests that microwave radar altimeters have a sensitivity to sea ice which has not yet been fully exploited. When fused with SSM/I, AVHRR and ERS-1 synthetic aperture radar imagery, future ERS-1 altimeter data are expected to provide some missing pieces to the sea ice geophysics puzzle.
Results are presented on measurements of crater depths and other morphological parameters (such as central peak and terrace frequency) of fresh craters on Ganymede and Callisto, two geophysically very similar but geologically divergent large icy satellites of Jupiter. These data were used to investigate the crater mechanics on icy satellites and the intersatellite crater scaling and crustal properties. The morphological transition diameters of and complex crater depths on Ganymede and Callisto were found to be similar, indicating that the crusts of both satellites are dominated by water ice with only a minor rocky component.