Prediction of shoreline–shelf depositional process regime guided by palaeotidal modelling

Tanjung Formation is one of the major coal-bearing deposit in the Barito Basin, Central Kalimantan. The distribution of total sulfur and ash yield in coal is closely related to the depositional environment. This study was to determine the total sulfur and ash yield and the interpretation of the dynamics of depositional process. Coal seam A and B generally have low to medium ash yield 2.82 to 9.23 (wt.%, db) and low total sulfur content of <1 (wt.%, db), except for the 6PLY1 coal sample which has total sulfur content that relatively high at 1.55 (wt.%, db). Coal samples 5PLY1A, 5PLY1B, 5PLY3, 5PLY5, 6PLY2, 6PLY4, 6PLY5, 6PLY7, and 6PLY9 which have low to medium ash yield and low total sulfur content <1% (wt.%, db) are formed in the topogeneous mire (freshwater swamp) in a fluvial environment. The total sulfur content was interpreted to be derived mainly from the parent plant materials. Meanwhile, the 6PLY1 coal sample which has an ash yield of 5.83 (wt.%, db) and total sulfur content of 1.55 (wt.%, db) formed in topogeneous mire in an environment that is invaded by sea water, and the total sulfur content were interpreted coming from the parent plant materials and the effect of seawater invasion which is rich in sulfate (SO4) compounds. It is also supported by the occurrence of syngenetic mineral content (framboidal pyrite) and epigenetic pyrite of 1.23 (vol.%).

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Predicting shoreline depositional process regimes using insights from palaeotidal modelling

10.31219/osf.io/3pzv5 ◽

2020 ◽

Author(s):

Daniel Collins ◽

Alexandros Avdis ◽

Martin R. Wells ◽

Andrew J. Mitchell ◽

Peter Allison ◽

...

Keyword(s):

Numerical Simulations ◽

Depositional Processes ◽

Depositional Process ◽

Stratigraphic Record ◽

Refined Classification

This review demonstrates the benefit of numerical tidal modelling, calibrated by integrated comparison to the preserved stratigraphic record, and offers a refined classification and prediction of shoreline process regimes. Wider and consistent utilisation of these concepts, and numerical simulations of other depositional processes, will further improve process-based classifications and predictions of modern and ancient shoreline systems.

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Fault-Guided Seismic Stratigraphy Interpretation via Semi-Supervised Learning

10.2118/207218-ms ◽

2021 ◽

Author(s):

Haibin Di ◽

Chakib Kada Kloucha ◽

Cen Li ◽

Aria Abubakar ◽

Zhun Li ◽

...

Keyword(s):

Machine Learning ◽

Supervised Learning ◽

Model Building ◽

Structural Information ◽

Mapping Function ◽

Seismic Stratigraphy ◽

Training Data ◽

Entire Study ◽

Depositional Process ◽

Convolutional Autoencoder

Abstract Delineating seismic stratigraphic features and depositional facies is of importance to successful reservoir mapping and identification in the subsurface. Robust seismic stratigraphy interpretation is confronted with two major challenges. The first one is to maximally automate the process particularly with the increasing size of seismic data and complexity of target stratigraphies, while the second challenge is to efficiently incorporate available structures into stratigraphy model building. Machine learning, particularly convolutional neural network (CNN), has been introduced into assisting seismic stratigraphy interpretation through supervised learning. However, the small amount of available expert labels greatly restricts the performance of such supervised CNN. Moreover, most of the exiting CNN implementations are based on only amplitude, which fails to use necessary structural information such as faults for constraining the machine learning. To resolve both challenges, this paper presents a semi-supervised learning workflow for fault-guided seismic stratigraphy interpretation, which consists of two components. The first component is seismic feature engineering (SFE), which aims at learning the provided seismic and fault data through a unsupervised convolutional autoencoder (CAE), while the second one is stratigraphy model building (SMB), which aims at building an optimal mapping function between the features extracted from the SFE CAE and the target stratigraphic labels provided by an experienced interpreter through a supervised CNN. Both components are connected by embedding the encoder of the SFE CAE into the SMB CNN, which forces the SMB learning based on these features commonly existing in the entire study area instead of those only at the limited training data; correspondingly, the risk of overfitting is greatly eliminated. More innovatively, the fault constraint is introduced by customizing the SMB CNN of two output branches, with one to match the target stratigraphies and the other to reconstruct the input fault, so that the fault continues contributing to the process of SMB learning. The performance of such fault-guided seismic stratigraphy interpretation is validated by an application to a real seismic dataset, and the machine prediction not only matches the manual interpretation accurately but also clearly illustrates the depositional process in the study area.

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DEPOSITIONAL PROCESSES AND CHARACTERISTICS OF 21 DEPOSITIONAL PROCESS/MEDIUM ASSOCIATIONS WITH EXAMPLES FROM THE TERTIARY OF THE SANTA MONICA MOUNTAINS

Field Conference Guide, AAPG National Convention, San Diego, California ◽

10.32375/1996-gb73.22 ◽

1996 ◽

Author(s):

A. Eugene Fritsche

Keyword(s):

Depositional Processes ◽

Santa Monica Mountains ◽

Santa Monica ◽

Depositional Process ◽

Process Medium

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Coupled partitioning of Au and As into pyrite controls formation of giant Au deposits

Science Advances ◽

10.1126/sciadv.aav5891 ◽

2019 ◽

Vol 5 (5) ◽

pp. eaav5891 ◽

Cited By ~ 15

Author(s):

C. Kusebauch ◽

S. A. Gleeson ◽

M. Oelze

Keyword(s):

Partition Coefficients ◽

Hydrothermal Fluid ◽

Iron Sulfide ◽

Gold Deposits ◽

Hydrothermal Systems ◽

Hydrothermal Fluids ◽

Balance Model ◽

Mass Balance Model ◽

Depositional Process ◽

Partitioning Behavior

The giant Carlin-type Au deposits (Nevada, USA) contain gold hosted in arsenic-rich iron sulfide (pyrite), but the processes controlling the sequestration of Au in these hydrothermal systems are poorly understood. Here, we present an experimental study investigating the distribution of Au and As between hydrothermal fluid and pyrite under conditions similar to those found in Carlin-type Au deposits. We find that Au from the fluid strongly partitions into a newly formed pyrite depending on the As concentration and that the coupled partitioning behavior of these two trace elements is key for Au precipitation. On the basis of our experimentally derived partition coefficients, we developed a mass balance model that shows that simple partitioning (and the underlying process of adsorption) is the major depositional process in these systems. Our findings help to explain why pyrite in Carlin-type gold deposits can scavenge Au from hydrothermal fluids so efficiently to form giant deposits.

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Process regime, salinity, morphological, and sedimentary trends along the fluvial to marine transition zone of the mixed-energy Mekong River delta, Vietnam

Continental Shelf Research ◽

10.1016/j.csr.2017.03.001 ◽

2017 ◽

Vol 147 ◽

pp. 7-26 ◽

Cited By ~ 40

Author(s):

Marcello Gugliotta ◽

Yoshiki Saito ◽

Van Lap Nguyen ◽

Thi Kim Oanh Ta ◽

Rei Nakashima ◽

...

Keyword(s):

Transition Zone ◽

River Delta ◽

Mekong River ◽

Mekong River Delta ◽

Process Regime

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MRI signal changes in the liver following multiple transfusions.

South African Journal of Radiology ◽

10.4102/sajr.v12i4.550 ◽

2008 ◽

Vol 12 (4) ◽

pp. 93

Author(s):

M Durand ◽

J C Abrahams

Keyword(s):

Blood Transfusion ◽

Multiple Blood Transfusion ◽

Depositional Process ◽

Multiple Blood ◽

Signal Changes

Diffuse signal changes in the liver on MRI often represent a depositional process with decreased signal when iron or copper is deposited or increased signal with fatty deposition. We present a 68y male with myelodysplasia requiring multiple blood transfusion, resulting in such signal changes.

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