A preliminary study on the element abundance in the Hulusimpang Formation, Way Kalianda, Pesawaran, Lampung, Indonesia

Harbowo, Danni Gathot; Priadi, Bambang; Julian, Timothy R.; Amelia, Risky Nuri; Sihombing, Dirman; Kencana, F S

doi:10.1088/1755-1315/882/1/012078

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Silica is a naturally occurring mineral that is found in abundance in the environment, and it can bond with the organic material in wood to create a strong and durable composite material. In nature, wood silification occurs over long periods of time, as fallen trees and other woody debris become buried in sediment and are exposed to mineral-rich groundwater [4,21,22]. As the wood decays, the silica in the groundwater replaces the organic material in the cell walls, creating a fossilized wood material known as petrified wood.…”

Section: Data Analyses Resultsmentioning

confidence: 99%

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k-Means Clustering to Enhance the Petrified Wood Composition Data Analyses and Its Interpretation

Muliawati,

Harbowo,

Lubis

et al. 2023

Indo. J. Appl. Math

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Geologically, the fossilization of wood materials into fossils requires appropriate conditions, some of which have been preserved for millions of years. In nature, the organic mass of wood must be quickly replaced by inorganic elements before it decomposes under harsh geological conditions. Anorganic oxides such as silica-oxide, are known to be the main components of most wood specimens (up to 80%). The presence of alkaline oxides such as sodium and potassium oxide seems to play a major role in the presence of dissolved silica during petrification. However, their significance in the petrification phenomenon that occurs in fossilized plant wood is not yet known. Therefore, in this study, cluster analysis was conducted to determine the relationship between the presence of silica and alkaline compounds in petrified wood fossils. The approach used was -means clustering supported by the Elbow Method, which aims to review and order a complex set of data into subsets, thus allowing interpretation. The results showed that the clustering of the fossil wood composition data was optimal at = 3. There is a fair correlation between the presence of silica and alkali oxide compounds (-0.504 to -0.387), as well as with another inorganic compounds (+0.957). The presence of sodium and potassium is strongly correlated during silicification (+0.905). Additionally, the results of data clustering made the wood fossilization process susceptible to describe, especially through data regression. The data visualization provides more facts and proper explanations of the role of alkaline oxides in wood silicification. This study furthers our understanding of wood fossilization, especially the diagenesis of wood chemical composition in geological history.

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Section: Data Analyses Resultsmentioning

confidence: 99%

“…It forms sodium-potassium silicates. When wood is treated with sodium and potassium silicate, silicate ions react with the wood fibres to create a silica gel within the cell walls [2,3,22]. This gel can then be transformed into a solid silica-rich material via a process called consolidation.…”

Section: Data Analyses Resultsmentioning

confidence: 99%

k-Means Clustering to Enhance the Petrified Wood Composition Data Analyses and Its Interpretation

Muliawati,

Harbowo,

Lubis

et al. 2023

Indo. J. Appl. Math

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“…Foraminifera play a crucial role in various geological studies [1,2], serving as paleontological proxies for dating rock strata and interpreting paleoenvironments. However, the procedures involved in analyzing foraminiferal samples are time-consuming and require careful attention.…”

Section: Introductionmentioning

confidence: 99%

Advancing the automated foraminifera fossil identification through scanning electron microscopy image classification: A convolutional neural network approach

Harbowo,

Muliawati

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Handling more than thousand fossil foraminifera data is very challenging, especially for old-way identification. Determining morpho-taxonomy by conventional microscopic observation is very time-consuming and can lead to innacuracy identification. We are certain that the process could be advanced through big data analysis using a machine learning approach. Foraminifera fossils have already become a common standard for biostratigraphic proxies and paleoenvironmental interpretation. Therefore, the objective of this study was to develop an automated identification method using Convolutional Neural Networks (CNN). We used standardized Scanning Electron Microscopy (SEM) images of foraminifera acquired from various open-source databases for image classification. The analysis was conducted using Python programming language in Google Colaboratory. The image dataset is categorized based on its genus (n: 138) and divided into training and test/validation data for accuracy simulation (total image: 1833; data training: 1387; test/validation: 237/237). The best-fit accuracy values of the training-data and testing-data were between 97-86%:73-77%, with parameters including epoch number ranging up to 40, learning rates of 0.05, and a batch size of 64. These values indicate good prospects for foraminifera SEM Image taxonomic classification, demonstrating a noteworthy level of identification accuracy (63%). The outcomes of this study offer a new method for further effective automated morpho-taxonomic identification of foraminifera fossils using other conventional optic microscopy.

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“…Menanga Formation consists of alternating shale and claystone with basalt intercalation of chert and limestone lenses [11,12]. Menanga Formation is the oldest sedimentary rock unit, followed by Tertiary Hulusimpang Formation, conglomeratic Sabu Formation; Miocene dacite intrusion, and covered by Quarternary volcanic deposit from Ratai Volcanic Complex [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

The Significances of Cretaceous Petrified Wood Fossils from Padangcermin, Lampung in Paleoenvironmental Perspectives

Harbowo

Nahar²,

Sari³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Reports on the presences of petrified wood fossils in Padangcermin, Lampung, are rare, no strong examination that expose the scientific concern on its paleoenvironment perspective. By the published geological map, these fossils was found in the area that labelled as Cretaceous sedimentary rock (145-66 mya). Recently, the known oldest petrified wood fossil in Indonesia, is from Miocene epoch (23.03-5.33 mya). It is an interesting circumstance to examine. By this study, we are trying to confirm this circumstances by conducting the geological mapping, collecting, and identifying the petrified wood fossils that could be found as in situ fossils in the area. This study reveals a new update in paleoenvironmental perspective. The petrified woods in this area are commonly preserved in cherty layer. There are also several fossilized aquatic freshwater plant remains that presences in this area. We suggest the fossilization of the petrified wood fossil in this area is related to high alkaline-freshwater lacustrine. Supposedly, it associated with the Cretaceous terrestrial landmass and freshwater lacustrine environment in southern paleo-Sumatra by paleontological interpretation of data in this study.

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A preliminary study on the element abundance in the Hulusimpang Formation, Way Kalianda, Pesawaran, Lampung, Indonesia

Cited by 3 publications

References 3 publications

k-Means Clustering to Enhance the Petrified Wood Composition Data Analyses and Its Interpretation

k-Means Clustering to Enhance the Petrified Wood Composition Data Analyses and Its Interpretation

Advancing the automated foraminifera fossil identification through scanning electron microscopy image classification: A convolutional neural network approach

The Significances of Cretaceous Petrified Wood Fossils from Padangcermin, Lampung in Paleoenvironmental Perspectives

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