Authors : Kakarla Sri Chandana; Upputuri Someswara Sandeep; Pujala Asritha; Radha Mothukuri; Mula Deepak Reddy

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/mr4z63jr

Scribd : https://tinyurl.com/3r5df74w

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN2025

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study focuses largely on earthquake prediction, which is a crucial element of geoscience and emergency and disaster management. We apply state-of- the-art machine learning methods, most notably the Random Forest Regression approach, to examine the intricate link between geographical data analysis and earthquake prediction. Once we have patiently traversed the challenges of seismic data processing, we create prediction models that deliver insights via sophisticated visualization of earthquake occurrences. The research offers confirmation that machine learning approaches perform exceptionally well for forecasting earthquakes. These results show the relevance of these paradigms for enhancing, among other things, early warning systems and catastrophic preparedness measures.

Keywords : Seismic Forecasting; Machine Learning; Predictive Modeling; Algorithmic Discernment; Complexity Analysis.

References :

1. Wang, Min, XinPing Guo, HongMing Tang, WeiMing Yu, Peng Zhao, and XueWen Shi. "The prediction of shale gas reservoir parameters through a multilayer transfer learning network." Geophysical Journal International 234, no. 2 (2023): 1463-1475.
2. Chen, Yangkang, Alexandros Savvaidis, and Sergey Fomel. "Dictionary Learning for Single ‐ Channel Passive Seismic Denoising." Seismological Research Letters 94, no. 6 (2023): 28402851.
3. Sui, Jilei, Yanan Tian, Yue Li, and Ning Wu. "Complete perception self- attention network for weak seismic signal recovery in distributed acoustic sensing vertical seismic profile data." Geophysics 88, no. 6 (2023): WC107-WC119.
4. Zheng, Qiqi, Chao Wei, Xinfei Yan, Housong Ruan, and Bangyu Wu. "Seismic Elastic Parameter Inversion via a FCRN and GRU Hybrid Network with Multi-Task Learning." Applied Sciences 13, no. 18 (2023): 10519.
5. Luo, Renyu, Jinghuai Gao, Hongling Chen, Zhiqiang Wang, and Chuangji Meng. "Deep Learning for Low-Frequency Extrapolation and Seismic Acoustic Impedance Inversion." IEEE Transactions on Geoscience and Remote Sensing (2023).
6. Li, Shuaishuai, Jiangjie Zhang, Qian Cheng, Feng Zhu, and Linong Liu. "Seismic profile denoising based on common-reflection-point gathers using convolution neural networks." Journal of Geophysics and Engineering 20, no. 2 (2023): 240-254.
7. Lu, Shengyu, Chuyang Cai, Zhi Zhong, Zhongxian Cai, Xu Guo, Heng Zhang, and Jie Li. "Ultradeep carbonate reservoir lithofacies classification based on a deep convolutional neural network—A case study in the Tarim Basin, China." Interpretation 11, no. 3 (2023): T551- T566.
8. Choi, Guebin, and Hee-Seok Oh. "Elastic-band transform for visualization and detection." Pattern Recognition Letters 166 (2023): 119-125.
9. Wang, Hongzhou, Jun Lin, Dan Shao, Xintong Dong, and Yue Li. "Multi-scale interactive network in the application of DAS seismic data processing." Frontiers in Earth Science 10 (2023): 991860.
10. Zhu, Guang, Xiaohong Chen, Jingye Li, and Kangkang Guo. "Data- Driven Seismic Impedance Inversion Based on Multi-Scale Strategy." Remote Sensing 14, no. 23 (2022): 6056.
11. Zhang, Fanchang, Lei Zhu, and Xunyong Xu. "A Knowledgeembedded Close-looped Deep Learning Framework for Intelligent Inversion of Multi- solution Problems." Geophysics 89, no. 2 (2023): 1-19.
12. Gouda, Mohammed Fathy, Abdul Halim Abdul Latiff, and Seyed Yasser Moussavi Alashloo. "Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances." Applied Sciences 12, no. 15 (2022): 7754.
13. Park, Min Jun, Julio Frigerio, Bob Clapp, and Biondo Biondi. "DeepNRMS: Unsupervised Deep Learning for Noise-Robust CO2 Monitoring in Time-Lapse Seismic Images." arXiv preprint arXiv:2310. 07897 (2023).
14. Sun, Youzhuang, Junhua Zhang, and Yongan Zhang. "Gate recurrent unit network combines with Adaboost algorithm to classify fluid types by well logging parameters." Physics of Fluids 36, no. 1 (2024).
15. Wang, Chong Ming, Xing Jian Wang, Yang Chen, Xue Mei Wen, Yong Heng Zhang, and Qing Wu Li. "Deep learning based on self- supervised pre-training: Application on sandstone content prediction." Frontiers in Earth Science 10 (2023): 1081998.