The Culprit of Bias in the Shaw-type Method of Estimating Geomagnetic Paleointensity and an Innovative Computational Method for Enhanced Reliability

Retrieving precise information about the ancient geomagnetic field strength (paleointensity) is crucial for understanding the Earth's interior evolution. The Shaw-type method is one of the major protocols used to estimate paleointensity, characterized by relatively expeditious processes and high success rates. However, albeit employing strict selection criteria, Shaw-type paleointensity results occasionally exhibit bias, necessitating further methodological development. Despite the significance of identifying the causes of these biased estimates, there has been limited detailed discussion on this issue. To clarify the culprit of bias in Shaw-type paleointensity, we conducted a pseudo-Tsunakawa-Shaw experiment using Permian basalts from the Tarim large igneous province (TLIP). In this experiment, the "NRMs (natural remanent magnetizations)" of the samples were acquired in a controlled lab field. We then monitored changes in various rock magnetic parameters, such as squareness (Mrs/Ms), coercivity (Bc), ARM, amtLTD (amount of remanence demagnetized by low-temperature treatment), MDF (median destructive field), and R (the ratio of thermal remanent magnetization (TRM) to anhysteretic remanent magnetization (ARM)), before and after heating, and compared them to the recovered paleointensities. Our analysis uncover a proportional relationship between changes in R and the bias in paleointensity, likely attributed to variations in grain sizes. Combining with microscopic observations, we further reveal samples with particular magnetic grain sizes (~200 nm) are more prone to alteration in the integral TRM/ARM efficiency during heating. Building on these findings, we propose an innovative computational method exploiting the linear regression of R with various cut-off coercivity intervals for Shaw-type paleointensities (LoRA-Shaw), which yield accurate results in both laboratory-tested and modern lava samples. We also invoke curve fits for samples with “folding” phenomenon which are not suitable for linear regression. The LoRA-Shaw method combined with curve fits may help in mitigating bias from the thermal alteration and multi-domain effect in paleointensity study, and enhance the success rate in paleointensity determinations for constraining geomagnetic field evolution.