A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series

Abstract EN

Landslides endanger regular industrial production and human safety.

Displacement trend analysis gives us an explicit way to observe and forecast landslides.

Although satellite-borne remote sensing methods such as synthetic aperture radar have gradually replaced manual measurement in detecting deformation trends, they fail to observe displacement in a north-south direction.

Wireless low-cost GPS sensors have been developed to assist remote sensing methods in north-south deformation monitoring because of their high temporal resolution and wide usage.

In our paper, a DLM-LSTM framework is developed to extract and predict north-south land deformation trends from meter accuracy GPS receivers.

A dynamic linear model is introduced to model the relation between measurement and the state vector, including the trend, periodic variation, and autoregressive factors in a discontinuous low-cost latitude time series.

The deformation trend with submeter-level accuracy is extracted by a Kalman filter and smoother.

With validated input as in previous work, the power of an LSTM network is also shown in its ability to predict deformation trends in submeter-level accuracy.

A submeter-level deformation trend is detected from wireless low-cost GPS sensors with meter-level navigation error.

The framework will have broad application prospects in geological disaster monitoring.