Pre-Seismic Temporal Integrated Anomalies from Multiparametric Remote Sensing Data

Self Cite

Earthquake forecasting aims to determine the likelihood of a damaging earthquake occurring in a particular area within a period of days to months. This provides ample preparation time for potential seismic hazards, resulting in significant socioeconomic benefits. Surface and atmospheric parameters derived from satellite thermal infrared observations have been utilized to identify pre-earthquake anomalies that may serve as potential precursors for earthquake forecasting. However, the correlation between these anomalies and impending earthquakes remains a significant challenge due to high false alarm and missed detection rates. To address this issue, we propose a spatially self-adaptive multiparametric anomaly identification scheme based on global strong earthquakes to establish the optimal recognition criteria. Each optimal parameter exhibits significant spatial variability within the seismically active region and indicates transient and subtle anomaly signals with a limited frequency of occurrences (<10 for most regions). In comparison to the fixed criterion for identifying anomalies, this new scheme significantly improves the positive Matthew’s correlation coefficient (MCC) values from ~0.03 to 0.122–0.152. Additionally, we have developed a multi-parameter anomaly synthesis method based on the best MCC value of each parameter anomaly. On average, the MCC increased from 0.143 to 0.186, and there are now more earthquake-prone regions with MCC values > 0.5. Our research emphasizes the critical importance of a multiparametric system in earthquake forecasting, where each geophysical parameter can be assigned a distinct weight, and the findings specifically identify OLR, including all-sky and clear-sky ones, as the most influential parameter on a global scale, highlighting the potential significance of OLR anomalies for seismic forecasting. Encouraging results imply the effectiveness of utilizing multiparametric anomalies and provide some confidence in advancing our knowledge of operational earthquake forecasting with a more quantitative approach.

Section: Optimal Parameters Of Anomaly Recognition Criteriamentioning

confidence: 91%

A Spatially Self-Adaptive Multiparametric Anomaly Identification Scheme Based on Global Strong Earthquakes

Jiao,

Hao,

Shan

2023

Self Cite

“…Unlike randomly distributed phenomena, strong earthquakes exhibit a non-random spatial distribution, predominantly clustering along major fault systems [28]. While their timing remains largely stochastic [29], analyzing their spatial patterns can provide valuable insights into future seismicity. To define seismically active regions for this study (Figure 1), we utilized an earthquake catalog from the United States Geological Survey (USGS) comprising 4719 events with magnitudes ≥ 6 and focal depths ≤ 70 km recorded between 1980 and 2020.…”

Section: Global Seismically Active Regionsmentioning

confidence: 99%

“…However, advancements in anomaly detection methods offer hope for refining the identification of relevant anomalies. A synthetic earthquake method has been devised to assess anomaly detection under such circumstances [29], indicating a long way to enhancing our understanding of pre-earthquake anomalies.…”

Section: Airs Datasetmentioning

confidence: 99%

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

Jiao,

Shan

2024

Self Cite

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.

“…Earthquakes occur because of tectonic stress changes inside the Earth's lithosphere at various hypo-central depths: low, intermediate, and deep extents [1,2]. The application of global navigation satellite system (GNSS) and remote sensing (RS) satellites has provided great insights into the monitoring of a possible earthquake precursor at different altitudes over the seismic zone before the occurrence of future main shocks [3][4][5]. Previous reports have used remotely sensed data to investigate possible seismic anomalies by studying the various aspects of earthquake energy evaluations from epicentral regions using satellite observations with different spatiotemporal datasets before and after the earthquake day [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Haider,

Shah,

et al. 2024

Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.