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Papers by Megha Chakraborty

Artificial Intelligence in Geosciences

The detection and rapid characterisation of earthquake parameters such as magnitude are of prime ... more The detection and rapid characterisation of earthquake parameters such as magnitude are of prime importance in seismology, particularly in applications such as Earthquake Early Warning (EEW). Traditionally, algorithms such as short-term average/long-term average (STA/LTA) are used for event detection, while frequency or amplitude domain parameters calculated from 1-3 seconds of first P-arrival data are sometimes used to provide a first estimate of (body-wave) magnitude. Owing to extensive involvement of human experts in parameter determination, these approaches are often found to be insufficient. Moreover, these methods are sensitive to the signal-to-noise ratio and may often lead to false or missed alarms depending on the choice of parameters. We, therefore, propose a multi-tasking deep learning model-the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (i) detects the first earthquake signal, from background seismic noise, (ii) determines first P-arrival time as well as (iii) estimates the magnitude using the raw 3-component waveform data from a single station as model input. Considering, that speed is of the essence in EEW, we use up to two seconds of P-wave information which, to the best of our knowledge, is a significantly smaller data window (5 second window with up to 2 seconds arXiv:2204.02924v1 [physics.geo-ph] 6 Apr 2022 of P-wave data) compared to the previous studies. To examine the robustness of CREIME we test it on two independent datasets and find that it achieves an average accuracy of 98% for event-vs-noise discrimination and is able to estimate first P-arrival time and local magnitude with average root mean squared errors of 0.13 seconds and 0.65 units, respectively. We also compare CREIME architecture with the architectures of other baseline models, by training them on the same data, and also with traditional algorithms such as STA/LTA, and show that our architecture outperforms these methods.

Frontiers in Earth Science, 2022

Many active volcanoes exhibit Strombolian activity, which is typically characterized by relativel... more Many active volcanoes exhibit Strombolian activity, which is typically characterized by relatively frequent mild volcanic explosions and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of volcanoes and the underlying physical and chemical processes. Catalogs of these eruptions and, specifically, seismo-volcanic events may be generated using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative user-friendly, time-saving, automated approach labelled as: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This strategy of creating seismo-volcanic event catalogs c...

Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized... more Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of the volcano and the underlying physical and chemical processes. Catalogs of volcanic eruptions may be established using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative automated approach: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This process of creating event catalogs consists of three main steps: (i) identification of potential volcanic events based on square...

The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Eart... more The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P-wave arrival– 1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes– "noise", "low-magnitude events" and "high-magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high-magnitude. We show that the variation in the results produced by ...

Earthquakes with magnitude M ≥ 6.5 are potentially destructive events which may cause tremendous ... more Earthquakes with magnitude M ≥ 6.5 are potentially destructive events which may cause tremendous devastation, huge economic loss and large numbers of casualties. Models with predictive or forecasting power are still lacking. Nevertheless, the spatial and temporal information of these seismic events can provide important information about the seismic history and the potential future of a region. This paper analyzes the recently updated International Seismological Centre earthquake catalog of body-wave magnitudes, mb, reported for ~313,500 events in the Sunda-arc region during the last 56 years, i.e., from 1964 to 2020. Based on the data, we report a hitherto unreported strong increase in seismicity during the last two decades associated with strong earthquakes with mb ≥ 6.5. A Gaussian Process Regression Analysis of these ISC-data suggests a continuation of this strong rise in number and strength of events with mb ≥ 6.5, in the region. These yearly maxima in the magnitude of the eart...

Earthquake detection and seismic phase picking not only play a crucial role in travel-time estima... more Earthquake detection and seismic phase picking not only play a crucial role in travel-time estimation of body-waves (P and S waves) but also in the localisation of the epicenter of the corresponding event. Generally, manual phase picking is a trustworthy and the optimum method to determine the phase-arrival time, however, its capacity is restricted by available resources and time. Moreover, noisy seismic data renders an additional critical challenge for fast and accurate phase picking. In this study, a deep learning-based model, EPick, is proposed which benefits both from U-shaped neural network (also called UNet) and attention mechanism, as a strong alternative for seismic event detection and phase picking. On one hand, the utilization of UNet structure enables addressing different levels of deep features. On the other hand, attention mechanism promotes the decoder in the UNet structure to focus on the efficient exploitation of the low-resolution features learned from the encoder p...

Marine and Petroleum Geology

Structures hidden at depth can be assessed through drill cores recovered from them. A crucial par... more Structures hidden at depth can be assessed through drill cores recovered from them. A crucial part of petroleum geology study is to interpret structures from these cores collected from vertical and inclined drilling operations reaching different depths. We deduce analytically ideal structures in unrolled/unwrapped images of cylindrical drill cores. These structures are: (i) orthogonal and conjugate fractures, (ii) single generation periodic folds and type-1 and type-2 superposed folds, (iii) listric faults and (iv) different types of angular unconformities. The sinuous curves as found in unrolled images are not always characteristic of the structures. Therefore accurate identification of structures would require additional information, such as the drill core itself.

Marine and Petroleum Geology

Artificial Intelligence in Geosciences

The detection and rapid characterisation of earthquake parameters such as magnitude are of prime ... more The detection and rapid characterisation of earthquake parameters such as magnitude are of prime importance in seismology, particularly in applications such as Earthquake Early Warning (EEW). Traditionally, algorithms such as short-term average/long-term average (STA/LTA) are used for event detection, while frequency or amplitude domain parameters calculated from 1-3 seconds of first P-arrival data are sometimes used to provide a first estimate of (body-wave) magnitude. Owing to extensive involvement of human experts in parameter determination, these approaches are often found to be insufficient. Moreover, these methods are sensitive to the signal-to-noise ratio and may often lead to false or missed alarms depending on the choice of parameters. We, therefore, propose a multi-tasking deep learning model-the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (i) detects the first earthquake signal, from background seismic noise, (ii) determines first P-arrival time as well as (iii) estimates the magnitude using the raw 3-component waveform data from a single station as model input. Considering, that speed is of the essence in EEW, we use up to two seconds of P-wave information which, to the best of our knowledge, is a significantly smaller data window (5 second window with up to 2 seconds arXiv:2204.02924v1 [physics.geo-ph] 6 Apr 2022 of P-wave data) compared to the previous studies. To examine the robustness of CREIME we test it on two independent datasets and find that it achieves an average accuracy of 98% for event-vs-noise discrimination and is able to estimate first P-arrival time and local magnitude with average root mean squared errors of 0.13 seconds and 0.65 units, respectively. We also compare CREIME architecture with the architectures of other baseline models, by training them on the same data, and also with traditional algorithms such as STA/LTA, and show that our architecture outperforms these methods.

Frontiers in Earth Science, 2022

Many active volcanoes exhibit Strombolian activity, which is typically characterized by relativel... more Many active volcanoes exhibit Strombolian activity, which is typically characterized by relatively frequent mild volcanic explosions and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of volcanoes and the underlying physical and chemical processes. Catalogs of these eruptions and, specifically, seismo-volcanic events may be generated using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative user-friendly, time-saving, automated approach labelled as: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This strategy of creating seismo-volcanic event catalogs c...

Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized... more Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of the volcano and the underlying physical and chemical processes. Catalogs of volcanic eruptions may be established using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative automated approach: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This process of creating event catalogs consists of three main steps: (i) identification of potential volcanic events based on square...

The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Eart... more The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P-wave arrival– 1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes– "noise", "low-magnitude events" and "high-magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high-magnitude. We show that the variation in the results produced by ...

Earthquakes with magnitude M ≥ 6.5 are potentially destructive events which may cause tremendous ... more Earthquakes with magnitude M ≥ 6.5 are potentially destructive events which may cause tremendous devastation, huge economic loss and large numbers of casualties. Models with predictive or forecasting power are still lacking. Nevertheless, the spatial and temporal information of these seismic events can provide important information about the seismic history and the potential future of a region. This paper analyzes the recently updated International Seismological Centre earthquake catalog of body-wave magnitudes, mb, reported for ~313,500 events in the Sunda-arc region during the last 56 years, i.e., from 1964 to 2020. Based on the data, we report a hitherto unreported strong increase in seismicity during the last two decades associated with strong earthquakes with mb ≥ 6.5. A Gaussian Process Regression Analysis of these ISC-data suggests a continuation of this strong rise in number and strength of events with mb ≥ 6.5, in the region. These yearly maxima in the magnitude of the eart...

Earthquake detection and seismic phase picking not only play a crucial role in travel-time estima... more Earthquake detection and seismic phase picking not only play a crucial role in travel-time estimation of body-waves (P and S waves) but also in the localisation of the epicenter of the corresponding event. Generally, manual phase picking is a trustworthy and the optimum method to determine the phase-arrival time, however, its capacity is restricted by available resources and time. Moreover, noisy seismic data renders an additional critical challenge for fast and accurate phase picking. In this study, a deep learning-based model, EPick, is proposed which benefits both from U-shaped neural network (also called UNet) and attention mechanism, as a strong alternative for seismic event detection and phase picking. On one hand, the utilization of UNet structure enables addressing different levels of deep features. On the other hand, attention mechanism promotes the decoder in the UNet structure to focus on the efficient exploitation of the low-resolution features learned from the encoder p...

Marine and Petroleum Geology

Structures hidden at depth can be assessed through drill cores recovered from them. A crucial par... more Structures hidden at depth can be assessed through drill cores recovered from them. A crucial part of petroleum geology study is to interpret structures from these cores collected from vertical and inclined drilling operations reaching different depths. We deduce analytically ideal structures in unrolled/unwrapped images of cylindrical drill cores. These structures are: (i) orthogonal and conjugate fractures, (ii) single generation periodic folds and type-1 and type-2 superposed folds, (iii) listric faults and (iv) different types of angular unconformities. The sinuous curves as found in unrolled images are not always characteristic of the structures. Therefore accurate identification of structures would require additional information, such as the drill core itself.

Marine and Petroleum Geology