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This is the AmeriFlux version of the carbon flux data for the site US-RGW Desha County Rice Farm. Site Description - Commercially farmed rice variety, field in Desha County, Arkansas. Part of a five-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project.
This is the AmeriFlux version of the carbon flux data for the site US-DS3 Staten Rice 1. Site Description - This tower is located in a rice field in the California Delta on Staten Island. The tower is within a few square kilometers of rice fields that comprise the vast majority of the southwestern portion of Staten Island. This is part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project.
This is the AmeriFlux version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm. Site Description - Commercially farmed field in Stanislaus County, California. Part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project (https://arpa-e.energy.gov/news-and-media/blog-posts/smartfarm-changing-whats-possible-agriculture). Field is approx. 31.3 ha in size (1,300 ft by 2,600 ft), part of a 176+ acre site in several fields with commercial corn-wheat rotation for silage, in combination with manure application.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGA Arkansas Corn Farm. This is the FLUXNET version of the carbon flux data for the site US-RGA Arkansas Corn Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed corn-soy rotation in Arkansas County, Arkansas. Part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGB Butte County Rice Farm. This is the FLUXNET version of the carbon flux data for the site US-RGB Butte County Rice Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed, mid-grain japonica rice variety, field in Butte County, California. Part of a five-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project. Field is approx. 30 ha in size in three checks with commercial rice over rice rotation on silty clay loam.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGo Glenn County Organic Rice Farm. This is the FLUXNET version of the carbon flux data for the site US-RGo Glenn County Organic Rice Farm produced by applying the standard ONEFlux (1F) software. Site Description - Organically farmed medium-grain brown rice field in Glenn County, California. Part of a five-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project. Field check is approx. 7 ha in size (1,250 ft by 250 ft), part of a 75+ acre site in several checks with commercial rice over rice rotation on Tehama silt loam, management practices include winter cover crops and AWD.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-DS3 Staten Rice 1. This is the FLUXNET version of the carbon flux data for the site US-DS3 Staten Rice 1 produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located in a rice field in the California Delta on Staten Island. The tower is within a few square kilometers of rice fields that comprise the vast majority of the southwestern portion of Staten Island. This is part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm. This is the FLUXNET version of the carbon flux data for the site US-RGF Stanislaus County Forage Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed field in Stanislaus County, California. Part of a multi-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project (https://arpa-e.energy.gov/news-and-media/blog-posts/smartfarm-changing-whats-possible-agriculture). Field is approx. 31.3 ha in size (1,300 ft by 2,600 ft), part of a 176+ acre site in several fields with commercial corn-wheat rotation for silage, in combination with manure application.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGW Desha County Rice Farm. This is the FLUXNET version of the carbon flux data for the site US-RGW Desha County Rice Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed rice variety, field in Desha County, Arkansas. Part of a five-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project.
Our objective is to improve the view of the seismicity in the Caucasus region using instrumental data between 1951 and 2019. To create a comprehensive catalog, we combine the bulletins of local agencies and the International Seismological Centre, and use an advanced single-event location algorithm, iLoc, to obtain better locations. We show that relocations with iLoc, using travel-time predictions from the 3D upper mantle velocity model, Regional Seismic Travel Time, improve the locations. Then, using the iLoc results as initial locations and the ground-truth events identified in the iLoc results as fix points, we apply Bayesloc, a multiple-event location algorithm, to simultaneously relocate the entire seismicity of the Caucasus region. Here we demonstrate that the simultaneous relocation of the seismicity with Bayesloc clarifies the location and geometry of major active structures accommodating ongoing convergence between the Arabian and Eurasian continents between the Black and Caspian Seas. Among our major findings is the confirmation of widespread seismicity in the mantle beneath the northern flank of the Greater Caucasus and central Caspian, resulting from north-dipping subduction of the Kura and South Caspian basins and the identification of a discrete band of crustal seismicity beneath the southern flank of the Greater Caucasus.
Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.
Robust estimation of moment magnitude (M w ) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of M w for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Therefore, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.
Chemical explosions create blast waves with large overpressure disturbances. It is important to develop a standard blast model based on data to accurately predict acoustic blast-wave amplitudes near detonations and invert for explosion energy from distant observations of blast-wave signals. However, open data from large, controlled chemical explosions with reliable ground truth can be challenging to find. The lack of access to such data could limit the number of contributions to related research and potentially stifle the rate of discoveries or validation of existing models. Here, to address these data scarcity problem, we have curated and compiled a standardized set of 817 blast-wave waveforms from 19 distinct high-explosive events. The blast-wave waveforms are standardized to a 1 kg trinitrotoluene explosion using scaling laws and corrections for location effects. A brief overview of the dataset is presented along with explosion feature models as well as recommendations for extracting explosion features. The resulting dataset is distributed to an open repository in both Seismic Analysis Code and pandas DataFrame formats containing the waveforms, the scaled distances, and the sample rates.
Summary Core-scale measurements are considered the ground truth that oil and gas or electric utility companies use to predict the migration of fluids such as oil, natural gas, carbon dioxide, or brine deep underground during their extraction or injection operations. To provide a greater understanding of petrophysical properties of low-permeability geologic formations such as shales and tight gas sandstones, this study introduces a novel core-analysis procedure. The technique follows conventional pressure-pulse-decay permeametry, where the pressure in an inlet chamber adjacent to a cylindrical core plug undergoes a rapid pressurization, the system is shut in, and the pressure reaches a new equilibrium. However, unlike a standard unidirectional pressure-pulse decay, the full-immersion pressure-pulse decay (Hannon 2019) applies a pressure disturbance to the entire outer surface area of the sample. This article covers the numerical simulator designed to model flow through an anisotropic porous sample in this scenario. The model assumes distinct but uniform permeabilities along the radial and axial directions of the cylindrical plug sample. When extracting a plug vertically (or perpendicular to bedding), the permeability along the radial direction associates with the horizontal permeability (i.e., parallel to bedding), whereas in the axial direction, flow occurs perpendicular to bedding (a vertical permeability). Investigations of these model outputs demonstrate an approximately 20-fold decrease in time to complete a full-immersion experiment compared with conventional pressure-pulse decay. Furthermore, the pressure-decay curves resulting from the full-immersion method have slightly different shapes than those resulting from other unidimensional transient methods. These differences begin to demonstrate that, under achievable experimental conditions, the analysis of pressure data from one full-immersion test could enable the simultaneous estimation of the apparent permeabilities parallel and perpendicular to bedding of a cylindrical sample in addition to its porosity. A follow-up article finalizes the proof of this capability with a parameter-estimation procedure and presents experimental verification through a proof-of-concept study. Described in greater detail in that article, the parameter estimator requires multiple forward simulations to analyze the data, which behooves minimizing the compute time for each simulation. By using an alternating direction implicit time-marching scheme and a structured but variably spaced grid, the numerical simulator built for this purpose provides a forward model output with suitable accuracy in approximately 0.5 seconds.
Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.
This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.
Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.
This document reports the results of a 4-month study analyzing data provided to Los Alamos National Laboratory (LANL) by the performer teams for the Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. The MOSAIC performer teams monitored hundreds of volunteers from several organizations in their daily activities, using wearable sensors to capture data about those activities. The project also collected “ground truth” data on individual behavior and psychological state using survey instruments throughout the study. The LANL MOSAIC analysis team approached this data with the goal of capturing and analyzing social dynamics within organizations. We focused on two key analysis objectives: identifying and characterizing groups as the evolve over time, and identifying and modeling patterns of social influence.