The necessity of data availability in maintaining the value and longevity of paleointensity results

Asserting the reliability of paleointensity estimates, or comparing relative reliabilities between vastly or subtly different results is a key challenge for paleointensity studies, which often leaves interpretations of these data rife with ambiguities. How we define reliable data is a concept that changes as our understanding of data and experiments advances. As a community we need to take steps to push this forward in an objective fashion that provides the most benefit, not just for paleointensity analysts, but also for those who ultimately wish to use the data to better understand deep Earth processes. However, in this ever-changing landscape, we must also ensure that the data we obtain do not lose their value as our advances threaten to make published data obsolete. It remains unknown exactly how our ability to assess the reliability of data will change and what information will become relevant. It is therefore essential for paleointensity studies to report as much data and meta-data as possible and, ideally, publically archive their measurement data for future reanalysis. Such practices are important, not only for paleointensity studies, but science in general and their implementation is vital to the future of paleomagnetism

New methods for unmixing sediment grain size data

Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End-member analysis (EMA), which fits one set of end-members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end-members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single-specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end-members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data

Measuring, Processing, and Analyzing Hysteresis Data

Magnetic hysteresis loops are important in theoretical and applied rock magnetism with applications to paleointensities, paleoenvironmental analysis, and tectonic studies, among many others. Information derived from these data is among the most ubiquitous rock magnetic data used by the Earth science community. Despite their prevalence, there are no general guidelines to aid scientists in obtaining the best possible data and no widely available software to allow the efficient analysis of hysteresis loop data using the most advanced and appropriate methods. Here we outline detrimental factors and simple approaches to measuring better hysteresis data and introduce a new software package called Hysteresis Loop analysis box (HystLab) for processing and analyzing loop data. Capable of reading a wide range of data formats, HystLab provides an easy‐to‐use interface allowing users to visualize their data and perform advanced processing, including loop centering, drift correction, high‐field slope corrections, and loop fitting to improve the results from noisy specimens. A large number of hysteresis loop properties and statistics are calculated by HystLab and can be exported to text files for further analysis. All plots generated by HystLab are customizable and user preferences can be saved for future use. In addition, all plots can be exported to encapsulated postscript files that are publication ready with little or no adjustment. HystLab is freely available for download at https://github.com/greigpaterson/HystLab and in combination with our simple measurement guide should help the paleomagnetic and rock magnetic communities get the most from their hysteresis data.G. A. P. acknowledges funding from a NERC Independent Research Fellowship (NE/P017266/1) and NSFC grants 41574063 and 41621004, and CAS project XDB18010203. M. J. acknowledges support of the Institute for Rock Magnetism, funded by the NSF Instruments and Facilities program and by the University of Minnesota. The data presented here are available with the HystLab software package (https:// github.com/greigpaterson/HystLab)

Bulk magnetic domain stability controls paleointensity fidelity

Nonideal, nonsingle-domain magnetic grains are ubiquitous in rocks; however, they can have a detrimental impact on the fidelity of paleomagnetic records—in particular the determination of ancient magnetic field strength (paleointensity), a key means of understanding the evolution of the earliest geodynamo and the formation of the solar system. As a consequence, great effort has been expended to link rock magnetic behavior to paleointensity results, but with little quantitative success. Using the most comprehensive rock magnetic and paleointensity data compilations, we quantify a stability trend in hysteresis data that characterizes the bulk domain stability (BDS) of the magnetic carriers in a paleomagnetic specimen. This trend is evident in both geological and archeological materials that are typically used to obtain paleointensity data and is therefore pervasive throughout most paleomagnetic studies. Comparing this trend to paleointensity data from both laboratory and historical experiments reveals a quantitative relationship between BDS and paleointensity behavior. Specimens that have lower BDS values display higher curvature on the paleointensity analysis plot, which leads to more inaccurate results. In-field quantification of BDS therefore reflects low-field bulk remanence stability. Rapid hysteresis measurements can be used to provide a powerful quantitative method for preselecting paleointensity specimens and postanalyzing previous studies, further improving our ability to select high-fidelity recordings of ancient magnetic fields. BDS analyses will enhance our ability to understand the evolution of the geodynamo and can help in understanding many fundamental Earth and planetary science questions that remain shrouded in controversy

Assessment of the usefulness of lithic clasts from pyroclastic deposits for paleointensity determination

Paleomagnetic and rock magnetic measurements were carried out on lithic clasts found within pyroclastic deposits to assess their potential for paleointensity determinations. The use of multiple lithologies in a single paleointensity determination would provide confidence that the result is not biased by alteration within one lithology. Lithic clasts were sampled from three historically active volcanoes: Láscar in the Chilean Andes, Mt. St. Helens, United States, and Vesuvius, Italy. At Láscar, triple heating paleointensity experiments allow development of new selection criteria for lithic clasts found within pyroclastic deposits. Using these criteria, the Láscar data yield a mean paleointensity of 24.3 ± 1.3 μT (1σ, N = 26), which agrees well with the expected value of 24.0 μT. This indicates that pyroclastic rocks have promise for paleointensity determinations. Pyroclastics, however, still suffer from the range of problems associated with conventional paleointensity experiments on lava flows. Samples from Mt. St. Helens are strongly affected by multidomain (MD) behavior, which results in all samples failing to pass the paleointensity selection criteria. At Vesuvius, MD grains, magnetic interactions, and chemical remanent magnetizations contributed to failure of all paleointensity experiments. Rock magnetic analyses allow identification of the causes of failure of the paleointensity experiments. However, in this study, they have not provided adequate preselection criteria for identifying pyroclastics that are suitable for paleointensity determination.NERC, Royal Societ

New methods for unmixing sediment grain size data

Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End-member analysis (EMA), which fits one set of end-members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end-members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single-specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end-members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data