Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples

Johnson, Phyllis S.; Eberl, Markus; McBride, Michael; Aguila, Rebecca Estrada

doi:10.1080/01977261.2020.1860351

Cited by 7 publications

(13 citation statements)

References 28 publications

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“…We compare one of Table 2's archaeological soil samples with an experimental microdebitage sample (Figure 5). The latter comes from a chert biface that the modern stone knapper Michael McBride made from a block of the Georgetown variety of Edwards chert (for more details see Johnson et al, 2021). The archaeological sample contains almost 120,000 and the microdebitage almost 5300 particles.…”

Section: Discussing the Output Of The Particle Analyzermentioning

confidence: 99%

“…A good example is angularity. Microdebitage tends to be more angular than other soil particles (in practice, microdebitage varies in shape considerably, though [Johnson et al, 2021]). The PartAn3D measures angularity based on Wang and colleagues (2013).…”

Section: Employing a Dynamic Image Particle Analyzermentioning

confidence: 99%

“…We are working on various ways to analyze the output. This includes statistical analysis (Johnson et al, 2021) as well as different types of machine learning.…”

Section: Discussing the Output Of The Particle Analyzermentioning

confidence: 99%

“…Statistical approaches allow sifting through the spreadsheet output and are successful in identifying microdebitage (e.g. Johnson et al, 2021). We are currently also working on machine learning approaches to facilitate the analysis of millions of measurements as well as the particle photos.…”

Section: A New Approach To Microdebitage Analysismentioning

confidence: 99%

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Studying lithic microdebitage with a dynamic image particle analyzer

Eberl

Johnson

Aguila

2022

North American Archaeologist

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Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image particle analyzer. This machine takes videos of soil particles as they fall from a chute. Software tracks them and measures their dimensions. Since sieving is no longer necessary, microdebitage analysis proceeds more quickly and processes samples within a few minutes. The standardized output allows the objective analysis of lithic microdebitage. We compare the angularity of c. 120,000 particles in an archaeological soil sample with experimental microdebitage. While the distributions show intriguing overlaps, we conclude that the most angular archaeological particles are not microdebitage but reflect a software glitch.

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Section: Discussing the Output Of The Particle Analyzermentioning

confidence: 99%

Section: Employing a Dynamic Image Particle Analyzermentioning

confidence: 99%

“…We are working on various ways to analyze the output. This includes statistical analysis (Johnson et al, 2021) as well as different types of machine learning.…”

Section: Discussing the Output Of The Particle Analyzermentioning

confidence: 99%

Section: A New Approach To Microdebitage Analysismentioning

confidence: 99%

See 2 more Smart Citations

Studying lithic microdebitage with a dynamic image particle analyzer

Eberl

Johnson

Aguila

2022

North American Archaeologist

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“…Because traditional methods for analyzing microdebitage are quite tedious and require significant time and monetary commitments, most archaeologists devote little to no resources to microdebitage analysis, even if stone tool production is an important aspect of their research agenda. To address this issue, my colleagues and I have recently developed a novel method that combines dynamic image analysis and machine learning for analyzing microdebitage in archaeological soil samples that significantly reduces the time needed for analysis while increasing the reproducibility and robusticity of the data (Eberl et al, 2022, n.d.;Estrada Aguila et al, 2022;Johnson et al, 2021). Using experimental microdebitage, we have a demonstrated accuracy of up to 83.5 percent.…”

Section: Introductionmentioning

confidence: 99%

Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito

Johnson

Eberl

Aguila

et al. 2022

North American Archaeologist

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The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take multiple hours spread over several days to complete. Because of this, microdebitage analysis is typically completed in very small areas of sites due to the intensive time and labor commitment required. Recently, however, my colleagues and I have developed a novel, interdisciplinary method that combines dynamic image analysis and machine learning to analyze microdebitage taken from soil samples at archaeological sites. Analyses of experimental microdebitage demonstrated that microdebitage could be accurately and efficiently identified within archaeological soil samples using this method. In the present study, we apply these methods to soil samples taken from the Maya Capital of Tamarindito in Guatemala to verify whether these methods remain accurate when applied to archaeological contexts.

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A Prediction Model for Loess Collapse Coefficient Based on Genetic Algorithm Back Propagation Neural Network

Zhang,

Zhu,

Jin

et al. 2024

Lecture Notes in Civil Engineering

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As an important property of loess, the collapsibility coefficient is commonly used in engineering to evaluate the collapsibility of loess. The main methods for predicting the collapsibility coefficient of loess currently include fuzzy algorithm, principal component analysis, data mining, etc. Due to the numerous indicators that affect the collapsibility of loess and their mutual influence, current research has problems such as incomplete consideration of indicators or insufficient data used to fit prediction models, resulting in insufficient prediction accuracy. Using factor analysis method, factor analysis is conducted on the selected indicator data based on the theory of collapsible deformation structure. Water content, porosity, and plasticity indicators are selected as independent main factors, and a Back Propagation (BP) neural network prediction model is constructed to predict the collapsible coefficient. Genetic algorithm is used to optimize the initial network parameters of the model. Compared to the BP neural network prediction model, the model optimized by genetic algorithm has varying degrees of improvement in accuracy, high prediction accuracy, and better practicality in engineering.

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Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples

Cited by 7 publications

References 28 publications

Studying lithic microdebitage with a dynamic image particle analyzer

Studying lithic microdebitage with a dynamic image particle analyzer

Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito

A Prediction Model for Loess Collapse Coefficient Based on Genetic Algorithm Back Propagation Neural Network

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