Claudio Meneses’s research while affiliated with Universidad del Norte and other places

Background Probabilistic Seismic Hazard Assessment (PSHA) is a leading methodology for determining key ground motion parameters such as Peak Ground Acceleration (PGA) and spectral acceleration (SA), essential for structural design. This approach uses extensive earthquake data, typically spanning over a century, leveraging frequency and magnitude statistics. However, long-term ground shaking probabilities may not always be accurately captured by traditional data-driven methods. To address these limitations, this study develops a PSHA map for Northern Chile using both seismic and GNSS (Global Navigation Satellite System) data. A curated homogeneous earthquake catalog, based on the advanced seismic moment magnitude scale Mwg(Das Magnitude Scale), replaces the traditional Mw scale to ensure superior accuracy, particularly for intermediate and smaller earthquakes. Results Using the earthquake catalog, seismicity parameters ‘a’ and ‘b’ from the Gutenberg-Richter relationship were derived. Seismogenic modeling and Ground Motion Models (GMMs) were applied to estimate ground motion probabilities for a 475-year return period. Additionally, a PSHA map was constructed using GNSS strain rates, translating velocity-derived strain rates into seismic moment rates and ground shaking probabilities for seismic source zones. Comparative analyses revealed higher PGA values from GNSS strain data compared to seismic catalog data. GNSS strain data proved invaluable for refining seismic segmentation in Northern Chile, enhancing the precision of PSHA calculations. Conclusions A PSHA map for Northern Chile, synthesizing seismic catalog data and GNSS strain rates using a Logic Tree-based algorithm, has been developed for a 475-year return period. This map provides a critical tool for generating seismic hazard assessments aligned with building codes and emergency planning protocols. By integrating GNSS strain rates and seismic data, this study advances the reliability and accuracy of long-term ground shaking predictions.

For any seismic study, particularly seismic hazard estimations, land use control, and other seismological applications, a unified earthquake catalog is essential. It was necessary to convert various earthquake magnitude classes into a single unsaturated magnitude scale. One magnitude is transformed into a preferred magnitude using regression relationships. It is necessary to apply General Orthogonal Regression (GOR) since earthquake magnitudes are inaccurate. For the purpose of estimating the slope of a regression analysis, the use of GOR needs and the proper application of needs are crucial. Regression relationships for magnitude conversion that are peculiar to the Northeast Indian region and have been published in several research studies using the SR, ISR, and GOR methods are covered in this chapter. Regression relationships for the conversion of body and surface wave magnitudes are also presented for Northern Chile and Southern Peru. To cover the complete magnitude range, Das et al(2023) additionally included several GOR relations that were based on global data. A crucial component of creating a single earthquake catalogue is the conversion regression relationship since any error in the magnitude conversion procedure could cause significant bias in the seismicity metrics.

Standard least squares regression (SLR) and general orthogonal regression (GOR) address different questions and make different assumptions about measurement errors in one or both of the variables. SLR minimizes the sum of squares of the vertical deviations and provides estimation of the dependent variable (Y). It assumes that the independent variable (X) is an observed value which is known without error, and only the dependent variable (Y) suffers from measurement error. GOR, on the other hand, yields a linear relationship ($Y_\text{t} = \beta_{0} + \beta_{1} X_\text{t}$) between the dependent (Yt) and the independent (Xt) variables based on observed data (X, Y) having measurement errors in both the variables involved. Therefore, it is mathematically incorrect to use observed value X in place of $X_\text{t}$ in the equation $Y_\text{t} = \beta_{0} + \beta_{1} X_{t,}$ and thus, if done so (as in the conventional GOR method), this procedure will produce biased estimates. The present study is an overview of different methodologies used for preparing a homogeneous earthquake catalog for different seismic environments. The error variance ratio (η) used in GOR has not been addressed in seismological literature, and this overview will address this critical issue as well. This study will also suggest a guideline for the use of regression methods for preparing a homogeneous earthquake database which is an important input to obtain improved seismic hazard assessment.

This study was aimed to build a multi-station automatic classification system for volcanic seismic signatures such as hybrid, long period, tremor, tectonic, and volcano–tectonic events. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed five experiments using different datasets with data that were real, synthetic, two different combinations of these (combined 1 and combined 2), and a balanced subset without synthetic data. The experiment presented the highest scores when a process of data augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was overfitted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results reached high predictive performance, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The results obtained showed the performance of the probabilistic model, reaching high scores over different test datasets. The most valuable benefit of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalizable model which, in the near future, can be extended to multi-volcano database systems. The impact of this work is significant in the evaluation of hazard and risk by monitoring the dynamic evolution of volcanic centers, which is crucial for understanding the stages in a volcano’s eruptive cycle.

Northeast India is one of the world’s most seismically active regions. The event data included in this research for the period 1737–2012 is mostly obtained from worldwide database agencies such as ISC, NEIC, and GCMT. Historical seismicity is collected from published and unpublished documents and some earthquake events are collected from the Indian Meteorological Department Bulletins. As the Mw scale is developed and validated in the southern California region and overestimates the smaller magnitude earthquakes, therefore, recent literature suggested an improved version of the seismic moment magnitude scale (Mwg) applicable for the entire globe considering both long- and short-period frequency-spectra using modern instrumental data. To update the earthquake catalog of Northeast India, we prepared empirical relationships between different magnitudes to Mwg using robust statistical General Orthogonal Regression. A procedure is also suggested for converting different earthquake sizes towards seismic moment scale. The Magnitude of Completeness (Mc) and the Gutenberg–Richter (GR) recurrence parameter values for the declustered homogenized catalog in four time periods, namely 1737–1963, 1964–1990, 1964–2000, and 1964–2012, have been computed. Our analysis suggests that the use of the Mwg scale improves seismicity parameters ‘b’ up to 30%, ‘a’ up to 17%, and ‘Mc’ up to 18% for the Northeast India region. A complete unified earthquake catalog in terms of advanced seismic moment magnitude scale could help understand seismicity and earthquake engineering studies of the region.

This study was aimed to build a multi-station automatic classification system for volcanic seismic signatures. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed four experiments using different datasets with data that was real, artificial, and two different combinations of these (combined 1 and combined 2). The experiment presented the highest scores when a process of data augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was over-fitted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results were more than optimal, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The most valuable benefit of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalisable model which, in near future, can be extended to multi-volcano database systems. The impact of this work is significant in the evaluation of hazard and risk by monitoring the dynamic evolution of volcanic centres, which is crucial for understanding the stages in a volcano’s eruptive cycle.

Earthquake-related studies on seismicity and seismic hazard assessment need a homogenous earthquake catalog for the region studied. A homogenous earthquake catalog for Northeast India region was compiled using derived regional and global empirical relationships between different magnitudes and moment magnitude based on an improved error-corrected methodology suggested in the recent literature. To convert smaller magnitude earthquakes, global empirical equations were derived and used. A procedure is suggested to change different magnitudes into moment magnitude. A homogenous earthquake catalog of 9845 events was compiled for the time period 1897–2012. Entire magnitude range (EMR) was found to be the most reasonable method for estimating magnitude of completeness. Derived local and global empirical equations are useful for every seismic hazard or seismicity study. A complete and consistent homogenized earthquake catalog prepared in this study could provide good data for studying earthquake distribution in Northeast India. By carefully converting these original magnitudes into homogenized Mw magnitudes, an obstacle is removed for the consistent assessment of seismic hazards in Northeast India.

Southern Peru and northern Chile (17–30°S, 67–74°W) make up a seismically active region due to the convergence of the Nazca Plate and the South American Plate. The region has experienced a number of destructive earthquakes and tsunamis over the past few centuries, which have caused loss of human life and significant damage to infrastructure, highlighting the importance of seismic hazard assessment in the region. In fact, a reliable seismic hazard assessment is critical for developing policies for seismic hazard mitigation and risk reduction. In this study, we performed a probabilistic seismic hazard assessment (PSHA) of the study area based on an earthquake catalog that was very carefully analyzed. In earlier studies, we demonstrated that inappropriate treatment of the earthquake catalog can result in a serious bias in evaluations of seismicity parameters (e.g., a bias of up to 42% in the “b” parameter of the Gutenberg–Richter law). To address this issue, we compiled a homogenous earthquake catalog consisting of 39,977 events during the 1513–2016 period and accounted for site-specific local effects by developing site-specific scaling relationships between different measures of magnitude (e.g., mb, Ms, MD) and moment magnitude (Mw). The study area was subdivided into 15 seismogenic zones, accounting for site-specific seismicity patterns. The parameters “a” and “b” of the Gutenberg–Richter law were estimated for each zone based on independent earthquake events. The PSHA was performed using a standard logic tree approach, which allowed us to systematically take into account the model-based uncertainty and its influence on the estimated ground motion parameters. Uniform hazard spectra for return periods of 475 and 2475 years were estimated for peak ground accelerations and spectral accelerations at 0.2 s and 1.0 s to meet the definitions of seismic hazards provided by the International Building Code (IBC, International Code Council [ICC], 2009). This study is expected to provide a basis for design maps for building codes and emergency planning.