Automatic identification of semi-tracks on apatite and mica using a deep learning method

Li, Rongyan; Xu, Zeyu; Su, Cheng; Yang, Rong

doi:10.1016/j.cageo.2022.105081

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…For instance, Nachtergaele et al successfully developed a deep neural network that demonstrates exceptional capabilities in intelligently identifying apatite fission tracks, yielding highly accurate results (Nachtergaele and Grave 2021). Similarly, Li et al utilized a convolutional neural network (CNN) to extract semi-tracks through image semantic segmentation, thereby contributing to the study of intelligent identification methods for apatite fission tracks (Li et al 2022). These advancements highlight the promising trajectory of research in this area.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligent identification of apatite fission tracks based on machine learning

Ren,

Li,

Xiao

et al. 2023

Mach. Learn.: Sci. Technol.

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Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligent identification of apatite fission tracks based on machine learning

Ren,

Li,

Xiao

et al. 2023

Mach. Learn.: Sci. Technol.

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How many vs which: On confined track selection criteria for apatite fission track analysis

Tamer

Ketcham

2023

Chemical Geology

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Interpreting and reporting fission-track chronological data

Kohn,

Ketcham,

Vermeesch

et al. 2024

Geological Society of America Bulletin

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Fission-track dating is based on the analysis of tracks—linear damage trails—produced by the spontaneous fission of 238U in a range of natural accessory minerals and glasses. The retention of tracks is sensitive to elevated temperatures, and the data serve principally as a tool for recording thermal histories of rocks, potentially over the range of ∼20−350 °C, depending on the specific minerals studied. As such, in most cases, fission-track data generally bear little or no direct relationship to the original formation age of the material studied. The age range of fission-track dating is related to the product of age and uranium content, and ages from several tens of years to older than 1 Ga are reported. Fission-track analysis led to the development of powerful modeling techniques. When used with appropriate geological constraints, these modeling techniques allow important geological processes to be addressed in a broad range of upper crustal settings. Since early attempts to standardize the treatment of fission-track data and system calibration over more than 30 years ago, major advancements were made in the methodology, necessitating the development of new, updated data reporting requirements. Inconsistencies in reporting impede public data transparency, accessibility and reuse, Big Data regional syntheses, and interlaboratory analytical comparisons. This paper briefly reviews the fundamentals of fission-track dating and applications to provide context for recommended guidelines for reporting and supporting essential meta fission-track data for publication and methodological archiving in structured formats that conform with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Adopting such practices will ensure that data can be readily accessed, interrogated, and reused, allowing for further integration with other numerical geoscience techniques.

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Automatic identification of semi-tracks on apatite and mica using a deep learning method

Cited by 4 publications

References 19 publications

Artificial intelligent identification of apatite fission tracks based on machine learning

Artificial intelligent identification of apatite fission tracks based on machine learning

How many vs which: On confined track selection criteria for apatite fission track analysis

Interpreting and reporting fission-track chronological data

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