Geology topics

Jaycee Favela

Publications and source records attributed to Jaycee Favela.

5 recordsLinked to original sources

USGS2026

Spontaneous combustion of metal sulfide minerals and implications for seafloor massive sulfide mining

Metal sulfide minerals are abundant phases in many environments on earth, including seafloor massive sulfide (SMS) deposits, which occur at hydrothermal vents throughout the global ocean. These deep-sea settings are currently undergoing exploration for potential future mining. Here, we describe rocks from Escanaba Trough on the southern Gorda Ridge that underwent spontaneous combustion during laboratory processing. We compared the mineralogy, composition, and thermogravimetric behavior of the combusting samples to rocks from the same location that did not combust. We then used thermodynamic analyses to evaluate the exothermic nature of different metal sulfide oxidation reactions. The combusting rocks are primarily composed of nanocrystalline marcasite pseudomorphing blades of primary pyrrhotite. These altered, nanocrystalline rocks are less stable than their primary predecessors. Typically, recrystallization decreases reactivity as minerals trend towards steady state; however, both of the combusting samples we characterized are secondary, and demonstrate the instability of this metastable intermediate phase. Further, thermodynamic calculations indicate that metal sulfide oxidation is more exothermic when oxygen serves as the oxidant, as occurs in seawater or air. The distinct mineralogy, texture, and setting of SMS deposits in the modern oceans thus suggests a greater potential for self-heating in comparison to their ancient analogues.

Scientific Reports↗

USGS2026

Cruise summary—Samoa Basin abyssal mapping—Box coring leg, Pago Pago, Territory of American Samoa to Pago Pago, American Samoa, April 11– May 1, 2026

Expedition Summary U.S. Geological Survey scientists led a box coring effort to the Samoa Basin to characterize minerals and the surrounding abyssal sediments and fauna. Thirty-eight box cores were deployed between April 13, 2026, and April 28, 2026. Thirty-six box cores recovered sufficient material to determine nodule density, and 35 recovered sufficient material for subcores to be collected. The purpose of this Data Report is to provide a summary of samples collected, initial results that were obtained shipboard, and briefly mention planned future analyses from this expedition.

Data Report↗

USGS2026

Samoa basin abyssal mapping: Box coring leg

This cruise report describes work from leg three of the NOAA American Samoa Abyssal Mapping effort, OPR-T900-KR-26. Leg one preceded this effort and collected ship-based acoustic data. Leg two collected autonomous underwater vehicle (AUV) data and began before, continued contemporaneously, and finished subsequently to leg three. The USGS field activity number assigned to this expedition is 2026-604-FA. The objective of this expedition was to collect representative box cores to inform prospectivity analyses through the region, and co-located interdisciplinary datasets as practical. USGS personnel directed the sampling locations and took custody of the box cores once recovered shipboard. USGS personnel then photographed and described the cores, subsampled, conducted analyses including wet weights and time sensitive measurements, and preserved subsamples and additional components for future work. Data releases co-released with this report are listed in the section “paired data releases.” NOAA released data from all legs of this effort in near-real time. Details of the preceding legs, the data, and NOAA’s project summary available from NOAA, (2026). This report is an expedition summary with preliminary datasets; remaining data releases and publications will be forthcoming.

EarthArXiv↗

USGS2023

A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Scientific Data↗

USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗