Using integrated geophysics data set to delineate Phetchabun active fault in Thailand

Arjwech, Rungroj; Boonsungnern, Wanwisa; Sriwangpon, Pakawat; Somchat, Kittipong; Pondthai, Potpreecha

doi:10.1016/j.dib.2020.105608

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fault zone's general location, geometry, internal structure, and affected lithologies can be determined by forward modeling, which accurately replicates the crucial components of the observed electrical resistivity data [16]. Measured data began with a Digital Elevation Model (DEM) and remote sensing techniques to find traces of possibly active faults and possible sites for field investigations to locate active faults in the Kajai Village and its surroundings precisely [17]. Furthermore, overlaying these two data to create a realistic appearance and alignment of the Kajai Fault using a matching analysis method between the findings of the fault alignment using the most reasonable model that caused or potentially generated earthquake hazard that is carried out in detail on a large [18].…”

Section: Methodsmentioning

confidence: 99%

Development of Active Fault Mapping Method Using Geoelectric and Geographical Information System

Suasti

2024

GEOMATE

View full text Add to dashboard Cite

On February 25 th , 2022, an earthquake with a magnitude of 6.2 in Nagari Kajai, Talamau District, West Pasaman Regency, resulted in considerable structural damage because it originated from a shallow earthquake with a hypocenter depth of 12.6 km and is a manifestation of the strike-slip faulting system linked to the Sumatra Fault System (SFS), which is adjacent to the Angkola segment, Barumun Segment, Sumpur Segment, and Sianok Segment is a newly-found active fault in this area. All of Nagari Kajai (Kajai Village) Sub-district is covered in volcanic deposits, whether they are new (quaternary deposits) or a combination of old (tertiary deposits) and young (quaternary deposits) as a product from the Talamau Mountain and the Maninjau Mountain. These geological activities will provide a good impression of the topography and geomorphology of a newly found active fault, The Kajai Fault. The geomorphological analysis is used to get new characteristic features for this new fault line continuation using Landsat 8 imagery and supported by aerial photography using Unmanned Aerial Vehicles (UAV). A 2D subsurface image was obtained from the Wenner-Schlumberger geoelectric survey configuration to get an excellent subsurface interpretation of lithology in this area. From this interpretation, the Kajai Fault is present in a valley filled with volcanic products and a narrow diversion of the Batang Talu River. The geoelectrical survey images show a double juxtaposition from two interpreted faults, namely F1 and F2, which run through The Kajai village and are suspected to be one of the shallow earthquake sources.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of Active Fault Mapping Method Using Geoelectric and Geographical Information System

Suasti

2024

GEOMATE

View full text Add to dashboard Cite

show abstract

“…Geoscientists use geophysical survey data to make geological interpretations and construct geological maps. For example, in Brazil, gravity and magnetic data were used to estimate the location of geological structures (Domingues Teixeira et al., 2021); airborne geophysical surveys were used in structural complexity analysis above the underground mineralization in Canada (Mir et al., 2019; Perrouty et al., 2017;); and remote sensing combined with a digital elevation model was used to find active faults in Thailand (Arjwech et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Inferring geological structural features from geophysical and geological mapping data using machine learning algorithms

Green

2023

Geophysical Prospecting

View full text Add to dashboard Cite

We present an automated approach for inferring surface geological structures from geophysical survey data. Our method employs machine learning, using mapped geological structures as labels and filtered geophysical surveys as reference maps. We compared the performance of the eight main machine learning algorithms and their 32 branches. Applied to the Geological Survey of Victoria's database for the Bendigo Zone, following an appropriate choice of geological features, the 3‐class classification model using subspace K‐nearest neighbour methods achieves a stable and validated 92% accuracy in around 1 min. The fault‐only classification model achieves a stable and validated 97% accuracy in around 6 min. This shows that geological structural features on the surface may be inferred from between one and three of the following geophysical data types: gravity, airborne total magnetic intensity and first vertical derivative of total magnetic intensity. It shows the prospect of machine learning in geological research and suggests that geophysical data combined with machine learning may be useful and efficient in determining the existence of geological structural features.

show abstract

Geoelectrical characterization of non-filled active faults in Jaral de Berrios, Guanajuato, México

Monge-Cerda,

Delgado-Rodríguez,

Ramos-Leal

et al. 2024

Journal of Applied Geophysics

View full text Add to dashboard Cite

Using integrated geophysics data set to delineate Phetchabun active fault in Thailand

Cited by 4 publications

References 4 publications

Development of Active Fault Mapping Method Using Geoelectric and Geographical Information System

Development of Active Fault Mapping Method Using Geoelectric and Geographical Information System

Inferring geological structural features from geophysical and geological mapping data using machine learning algorithms

Geoelectrical characterization of non-filled active faults in Jaral de Berrios, Guanajuato, México

Contact Info

Product

Resources

About