Results 101 to 110 of about 4,702 (274)
This study on close coal seams shows that multi‐seam mining increases the height of the fractured zone from approximately 26 m (single upper‐seam mining) to about 49 m. When the lower seam is mined after the extraction of the upper seam, the heights of both the caved zone and the fractured zone increase by approximately two times.
Xiaoyu Wu +4 more
wiley +1 more source
Quantitative identification and prediction of mixed lithology, Bohai Sea, China [PDF]
The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions.
Chao Ma +4 more
core +1 more source
This work advances landslide susceptibility mapping by incorporating short‐term trigger data with landscape susceptibility mapping. We also examine the importance of downsampling, watershed delineation and geospatial correlations in evaluating outcomes.
Kanta Kotsugi +3 more
wiley +1 more source
Integrated petrographic, mineralogical and geochemical data from the Hastanetepe Pb–Zn deposit (Balya, Türkiye) reveal multi‐stage hydrothermal alteration and a magmatic sulphur source. Fluid inclusion and sulphur isotope results indicate a complex fluid evolution responsible for skarn‐type Pb–Zn mineralization along limestone–dacite contacts ...
Esra Ünal‐Çakır
wiley +1 more source
Lithology Identification of Buried Hill Reservoir Based on XGBoost with Optimized Interpretation [PDF]
Buried hill reservoirs are characterized by complex formation conditions and highly heterogeneous rock structures, which result in the poor performance of traditional crossplot methods in stratigraphic lithology classification.
Bin Zhao, Wenlong Liao
core +1 more source
Lithology identification technology of seismic measurement while drilling based on EEMD and DE-PNN
In view of the lithology identification technology problems encountered by mine drilling rigs in the process of drilling construction, a lithology identification method based on Ensemble Empirical Mode Decomposition (EEMD) and Product Neural Networks ...
Jinsuo LIU +6 more
doaj +1 more source
Research on Lithology Identification based on Machine Learning
Machine learning has great potential in lithology identification. Through supervised learning, unsupervised learning, semi-supervised learning, deep learning and other methods, features can be automatically extracted from complex seismic data and logging data to achieve efficient and accurate lithology classification. These methods not only improve the
openaire +1 more source
A classic oil producing interval of the Campos Basin—Macaé Group is revisited through seismic stratigraphic analysis, providing a stratigraphic framework, characteristic depositional and relative time positioning for several complex structural settings.
Renata Alvarenga +12 more
wiley +1 more source
Lithology identification using semantic segmentation for well log data
Abstract In the past decade, machine learning techniques were responsible for a revolution in classification and regression tasks, making it possible to automate some laborious activities, saving time and reducing errors. It is known that the geological logging process is one of the most time-consuming activities accomplished by mining companies ...
Áttila Leães Rodrigues +3 more
openaire +3 more sources
The composition of intrusive rocks suggests that the magma was likely generated in a subduction‐related setting. The arc crustal thickness in the Phnom Sro Ngam and Halo Prospects was probably < 40 km during emplacement. Zircon U–Pb age range indicates a correlation with Loei Fold Belt magmatic activity.
Sirisokha Seang +6 more
wiley +1 more source

