arXiv subjects

Spencer Jones

Publications and source records attributed to Spencer Jones.

3 recordsLinked to original sources

arXiv2026

Estimating the Kinetic Energy Spectrum from the Second-Order Velocity Structure Function using a Regularized Fitting Approach

Ocean turbulence plays a key role in shaping large-scale circulation, heat uptake, and biogeochemical processes. The kinetic energy (KE) wavenumber spectrum is a fundamental diagnostic, quantifying how KE is distributed across spatial scales. The second-order structure function -- computed from velocity differences between spatially separated observations -- provides a complementary measure, but unlike the KE spectrum, it reflects a non-local, weighted integral of KE over all scales. Analytic relationships link the two metrics, permitting forward and inverse transformations between them. However, recovering the KE spectrum from the structure function via the inverse relationship is highly sensitive to sampling limitations and numerical discretization errors. Here we propose a regularized approach in which the spectrum is assumed to consist of a finite number of segments with distinct slopes and amplitudes, and the inversion is formulated as an optimization problem. The approach is first validated in an idealized setting; for a number of idealized KE spectra with prescribed sets of spectral slopes and amplitudes, the corresponding structure functions are computed by numerically evaluating the forward relationship. These structure functions are then used to determine the underlying parameters using our proposed approach, which shows that we are able to perfectly recover the parameters and consequently the KE spectra. The method is further evaluated on high-resolution ocean model output, where it reconstructs the underlying spectra well even in the presence of noise. Finally, we apply the method to surface drifter observations (GLAD and LASER experiments). The results show that the framework enables estimation of the KE spectrum from sparse Lagrangian data, extending spectral diagnostics beyond gridded Eulerian measurements.

physics.ao-ph↗

arXiv2024

Multi-scale decomposition of sea surface height snapshots using machine learning

Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales.

physics.ao-ph↗

arXiv2022

Biographical: A Semi-Supervised Relation Extraction Dataset

Extracting biographical information from online documents is a popular research topic among the information extraction (IE) community. Various natural language processing (NLP) techniques such as text classification, text summarisation and relation extraction are commonly used to achieve this. Among these techniques, RE is the most common since it can be directly used to build biographical knowledge graphs. RE is usually framed as a supervised machine learning (ML) problem, where ML models are trained on annotated datasets. However, there are few annotated datasets for RE since the annotation process can be costly and time-consuming. To address this, we developed Biographical, the first semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), we match information with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain. Furthermore, we demonstrate the effectiveness of the dataset by training a state-of-the-art neural model to classify relation pairs, and evaluate it on a manually annotated gold standard set. Biographical is primarily aimed at training neural models for RE within the domain of digital humanities and history, but as we discuss at the end of this paper, it can be useful for other purposes as well.

cs.IR↗