# Multi-View Kernels for Low-Dimensional Modeling of Seismic Events

## TL;DR

This paper introduces a kernel-fusion based dimensionality reduction method for seismic data, improving event classification and localization accuracy using multi-view high-dimensional observations.

## Contribution

It presents a novel kernel-fusion framework for seismic data analysis, enhancing low-dimensional modeling and event identification capabilities.

## Key findings

- Achieves promising classification accuracy for seismic event types.
- Effectively estimates the location of seismic events.
- Applicable to other geophysical data types.

## Abstract

The problem of learning from seismic recordings has been studied for years. There is a growing interest in developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability have a reliable identification of man-made explosions. The availability of multiple high-dimensional observations has increased the use of machine learning techniques in a variety of fields. In this work, we propose to use a kernel-fusion based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and in Jordan. The method achieves promising results in classification of event type as well as in estimating the location of the event. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.