Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network

A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kikuchi, Kazuki [verfasserIn] Naruse, Hajime [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning

Übergeordnetes Werk:	Enthalten in: Sedimentary geology - Amsterdam [u.a.] : Elsevier, 1967, 461
Übergeordnetes Werk:	volume:461

DOI / URN:	10.1016/j.sedgeo.2023.106570

Katalog-ID:	ELV067028837

Internformat


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245	1	0	\|a Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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520			\|a A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.
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publishDate	2024
allfields	10.1016/j.sedgeo.2023.106570 doi (DE-627)ELV067028837 (ELSEVIER)S0037-0738(23)00242-7 DE-627 ger DE-627 rda eng 550 VZ 38.28 bkl 38.41 bkl Kikuchi, Kazuki verfasserin aut Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity. Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning Naruse, Hajime verfasserin aut Enthalten in Sedimentary geology Amsterdam [u.a.] : Elsevier, 1967 461 Online-Ressource (DE-627)320505863 (DE-600)2012818-6 (DE-576)097934836 0037-0738 nnns volume:461 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.28 Sedimentgesteine VZ 38.41 Sedimentation VZ AR 461
spelling	10.1016/j.sedgeo.2023.106570 doi (DE-627)ELV067028837 (ELSEVIER)S0037-0738(23)00242-7 DE-627 ger DE-627 rda eng 550 VZ 38.28 bkl 38.41 bkl Kikuchi, Kazuki verfasserin aut Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity. Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning Naruse, Hajime verfasserin aut Enthalten in Sedimentary geology Amsterdam [u.a.] : Elsevier, 1967 461 Online-Ressource (DE-627)320505863 (DE-600)2012818-6 (DE-576)097934836 0037-0738 nnns volume:461 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.28 Sedimentgesteine VZ 38.41 Sedimentation VZ AR 461
allfields_unstemmed	10.1016/j.sedgeo.2023.106570 doi (DE-627)ELV067028837 (ELSEVIER)S0037-0738(23)00242-7 DE-627 ger DE-627 rda eng 550 VZ 38.28 bkl 38.41 bkl Kikuchi, Kazuki verfasserin aut Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity. Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning Naruse, Hajime verfasserin aut Enthalten in Sedimentary geology Amsterdam [u.a.] : Elsevier, 1967 461 Online-Ressource (DE-627)320505863 (DE-600)2012818-6 (DE-576)097934836 0037-0738 nnns volume:461 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.28 Sedimentgesteine VZ 38.41 Sedimentation VZ AR 461
allfieldsGer	10.1016/j.sedgeo.2023.106570 doi (DE-627)ELV067028837 (ELSEVIER)S0037-0738(23)00242-7 DE-627 ger DE-627 rda eng 550 VZ 38.28 bkl 38.41 bkl Kikuchi, Kazuki verfasserin aut Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity. Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning Naruse, Hajime verfasserin aut Enthalten in Sedimentary geology Amsterdam [u.a.] : Elsevier, 1967 461 Online-Ressource (DE-627)320505863 (DE-600)2012818-6 (DE-576)097934836 0037-0738 nnns volume:461 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.28 Sedimentgesteine VZ 38.41 Sedimentation VZ AR 461
allfieldsSound	10.1016/j.sedgeo.2023.106570 doi (DE-627)ELV067028837 (ELSEVIER)S0037-0738(23)00242-7 DE-627 ger DE-627 rda eng 550 VZ 38.28 bkl 38.41 bkl Kikuchi, Kazuki verfasserin aut Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity. Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning Naruse, Hajime verfasserin aut Enthalten in Sedimentary geology Amsterdam [u.a.] : Elsevier, 1967 461 Online-Ressource (DE-627)320505863 (DE-600)2012818-6 (DE-576)097934836 0037-0738 nnns volume:461 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.28 Sedimentgesteine VZ 38.41 Sedimentation VZ AR 461
language	English
source	Enthalten in Sedimentary geology 461 volume:461
sourceStr	Enthalten in Sedimentary geology 461 volume:461
format_phy_str_mv	Article
bklname	Sedimentgesteine Sedimentation
institution	findex.gbv.de
topic_facet	Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning
dewey-raw	550
isfreeaccess_bool	false
container_title	Sedimentary geology
authorswithroles_txt_mv	Kikuchi, Kazuki @@aut@@ Naruse, Hajime @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	320505863
dewey-sort	3550
id	ELV067028837
language_de	englisch
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author	Kikuchi, Kazuki
spellingShingle	Kikuchi, Kazuki ddc 550 bkl 38.28 bkl 38.41 misc Ichnology misc Submarine-fan deposits misc IODP Exp.362 misc U-Net misc Semantic segmentation misc Deep learning Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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topic_title	550 VZ 38.28 bkl 38.41 bkl Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network Ichnology Submarine-fan deposits IODP Exp.362 U-Net Semantic segmentation Deep learning
topic	ddc 550 bkl 38.28 bkl 38.41 misc Ichnology misc Submarine-fan deposits misc IODP Exp.362 misc U-Net misc Semantic segmentation misc Deep learning
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title	Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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title_full	Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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title_sort	abundance of trace fossil phycosiphon incertum in core sections measured using a convolutional neural network
title_auth	Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
abstract	A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.
abstractGer	A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.
abstract_unstemmed	A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.
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title_short	Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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up_date	2024-07-06T19:50:03.955Z
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Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network

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