Seismic Data Acquisition Utilizing a Group of UAVs

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging.

Authors

Institutions

Publication Details

Journal
Drones
Published
2025-02-20
DOI
https://doi.org/10.3390/drones9030156
Citations
2
Primary Topic
Geological Modeling and Analysis
Type
article
Field-Weighted Citation Impact
2.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Seismic Data Acquisition Utilizing a Group of UAVs

Pavel Golikov, Valerii Serpiva, A. N. Timoshenko, Grigoriy Yashin et al.
2 citations
Drones
Geological Modeling and Analysis
2.67
article

Seismic Data Acquisition Utilizing a Group of UAVs

Pavel Golikov, Valerii Serpiva, A. N. Timoshenko, Grigoriy Yashin, Rustam Hamadov, Dmitry Fedotov, Mariia Kartashova
article en
2 citations

Abstract

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging.

DronesVol. 9(3)
Saudi Aramco (Saudi Arabia) (SA)
Openalex Percentile: Top 13%
Geological Modeling and Analysis
2.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.