Seismic Object Location Detection Using Machine Learning Noise Elimination
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Solution Overview
Problem
Current techniques for object location detection are limited in predicting the location of objects, such as planes crashing into water, and lack the ability to improve prediction accuracy.
Innovation Solution
A system and method utilizing seismic and geophysical data, processed by a processor with machine learning algorithms, to determine object location by identifying seismic attributes, eliminating unwanted noises, and validating the location through image data comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object location detection techniques are used, then the basic location can be determined when the object hits a surface, but the prediction accuracy of object location cannot be improved
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing seismic data before the object actually hits the surface. Machine learning algorithms process seismic attributes and eliminate unwanted noises in advance, creating a predictive model that improves location accuracy before the final detection moment occurs.
Solution Approach 2:
Seismic data serves as an intermediary medium between the object and the detection system. Instead of directly detecting the object impact, the system uses seismic waves generated by the object's movement through water or air as an intermediate signal that can be analyzed to predict the object's location with higher precision.
2Measurement precision
If seismic data and geophysical data are collected and processed using machine learning algorithms to improve location prediction, then the accuracy of object location detection is enhanced, but the complexity of the detection system increases
Solution Approach 1:
The machine learning system is designed to handle multiple types of data (seismic data, geophysical data) and perform multiple functions (noise elimination, attribute determination, location prediction, validation) using a unified processing framework. This multi-functional approach manages complexity by consolidating diverse operations into a single intelligent system.
Solution Approach 2:
The machine learning algorithms automatically process the seismic and geophysical data without requiring manual intervention for each step. The system self-adjusts by comparing predicted locations with validated data, continuously improving its own accuracy through the validation process while maintaining operational autonomy.
3Reliability
If unwanted noises are eliminated from noise classifications based on seismic attributes, then the signal-to-noise ratio is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs noise elimination as a preliminary action before final location prediction. By removing unwanted noises from the seismic data early in the processing pipeline, the system improves signal quality for subsequent analysis steps, preventing noise from propagating through and degrading later processing stages.
Data Source
AI summary
Systems and methods for determining object location may include a memory and a processor. The processor may be configured to collect seismic data and geophysical data to determine object location. The processor may be configured to determine one or more seismic attributes associated with a plurality types of noises based on the seismic data and the geophysical data using one or more machine learning algorithms. The processor may be configured to eliminate unwanted noises from noise classifications based on the one or more seismic attributes. The processor may be configured to predict the object location by comparing time and velocity data of the object with recorded timing and velocity data. The processor may be configured to validate the object location by comparing the determined noise with image data. The systems and methods may be used in, for example, detecting missing planes such as Malaysian Airlines Flight 370.