Erosion and deposition vulnerability of small (<5,000 km2) tropical islands

The tropics are naturally vulnerable to watershed erosion. This region is rapidly growing (projected to be 50% of the global population by 2050) which exacerbates erosional issues by the subsequent land use change. The issue is particularly of interest on the many (~45,000) small tropical (<5,000 km2) islands, and their >115M residents, where ecotourism and sediment intolerant ecosystems such as coral reefs are the main driver of their economies. However, vulnerability to erosion and deposition is poorly quantified in these regions due to the misclassification or exclusion of small islands in coarse global analyses. We use the only vulnerability assessment method that connects watershed erosion and coastal deposition to compare locally sourced, high-resolution datasets (5 x 5 m) to satellite-collected, remotely sensed low-resolution datasets (463 x 463 m). We find that on the island scale (~52 km2) the difference in vulnerability calculated by the two methods is minor. On the watershed scale however, low-resolution datasets fail to accurately demonstrate watershed and coastal deposition vulnerability when compared to high-resolution analysis. Specifically, we find that anthropogenic development (roads and buildings) is poorly constrained at a global scale. Structures and roads are difficult to identify in heavily forested regions using satellite algorithms and the rapid, ongoing rate of development aggravates the issue. We recommend that end-users of this method obtain locally sourced anthropogenic development datasets for the best results while using low resolution datasets for the other variables. Fortunately, anthropogenic development data can be easily collected using community-based research or identified using satellite imagery by any level of user. Using high-resolution results, we identify a development trend across St. John and regions that are both high risk and possible targets for future development. Previously published modeled and measured sedimentation rates demonstrate the method is accurate when using low-resolution or high-resolution data but, anthropogenic development, watershed slope, and earthquake probability datasets should be of the highest resolution depending on the region specified.

West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations

Recent advances in statistical correlations between strong earthquakes and several non-seismic phenomena have opened the possibility of formulating warnings within days or even hours. The retrieved correlations have been discovered for those ionospheric physical observations which lasted a long time and realized using the same instruments, including multi-satellite recordings. One of those regarded the electron burst phenomena detected by NOAA, for which the conditional probability of a seismic event was calculated. Then an earthquake probability greater than its frequency was assigned when a satellite realized such a phenomenological observation. This approach refers to the correlations obtained between high-energy electrons detected using the NOAA POES and strong Indonesian and Philippine earthquakes. It is reformulated here to realize a test of earthquake forecasting. The fundamental step is obtained by using a unique electron L-shell interval of 1.21 ≤ L ≤ 1.31, which decouples the electron parameters from the earthquake parameters. Then, the optimized correlation was recalculated to be 1.5–3.5 h early, between electron bursts and an increased number of seismic events with M ≥ 6, therein improving the significance too. Moreover, this methodology is reconnected to the frequency theory, and to Molchan’s error diagram, by the probability gain, where a comparison among the significances of various methods is given. The previously proposed physical link between the crust and the ionosphere through magnetic interaction, presumably operating 4–6 h before strong earthquakes, is examined quantitatively on the basis of recent magnetic pulse measurements. Consequently, the probability gain of earthquake forecasting is hypothetically calculated for both the dependent measurements of electron bursts using NOAA satellites and possible ground-based magnetic pulse detection. This method of combining probability gains for earthquake forecasting is general enough that it can be applied to any pair of observables from space and the ground.

Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management.

Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India

The eastern region of India, including the coastal state of Odisha, is a moderately seismic-prone area under seismic zones II and III. However, no major studies have been conducted on earthquake probability (EPA) and hazard assessment (EHA) in Odisha. This paper had two main objectives: (1) to assess the susceptibility of seismic wave amplification (SSA) and (2) to estimate EPA in Odisha. In total, 12 indicators were employed to assess the SSA and EPA. Firstly, using the historical earthquake catalog, the peak ground acceleration (PGA) and intensity variation was observed for the Indian subcontinent. We identified high amplitude and frequency locations for estimated PGA and the periodograms were plotted. Secondly, several indicators such as slope, elevation, curvature, and amplification values of rocks were used to generate SSA using predefined weights of layers. Thirdly, 10 indicators were implemented in a developed recurrent neural network (RNN) model to create an earthquake probability map (EPM). According to the results, recent to quaternary unconsolidated sedimentary rocks and alluvial deposits have great potential to amplify earthquake intensity and consequently lead to acute ground motion. High intensity was observed in coastal and central parts of the state. Complicated morphometric structures along with high intensity variation could be other parameters that influence deposits in the Mahanadi River and its delta with high potential. The RNN model was employed to create a probability map (EPM) for the state. Results show that the Mahanadi basin has dominant structural control on earthquakes that could be found in the western parts of the state. Major faults were pointed towards a direction of WNW–ESE, NE–SW, and NNW–SSE, which may lead to isoseismic patterns. Results also show that the western part is highly probable for events while the eastern coastal part is highly susceptible to seismic amplification. The RNN model achieved an accuracy of 0.94, precision (0.94), recall (0.97), F1 score (0.96), critical success index (CSI) (0.92), and a Fowlkes–Mallows index (FM) (0.95).

Modeling of the Pohang Earthquake Probability Using the Seismogenic Index

&lt;p&gt;A strong earthquake of Mw5.5 occurred on 15 November 2017, shortly after finishing borehole fluid injections performed for the geothermal development of the Pohang Enhanced Geothermal System. With a high probability, the earthquake was triggered by these operations. In this work we consider the Pohang Earthquake in the frame of the Seismogenic Index Model. We attempt to estimate the triggering probability of this event as well as a general &amp;#160;probability of triggering of arbitrary-magnitude earthquakes at the Pohang site before and after the termination of the fluid injections. A fluid injection in a point of an infinite continuum is taken here as a prototype of the Pohang situation.&lt;/p&gt;&lt;p&gt;The seismogenic index of the Pohang site is approximately between -2 and -1. During the injection operations, one can observe &amp;#160;a tendency of the&lt;br&gt;seismogenic index to increase with time. This was possibly &amp;#160;an indication of a gradual involvement of seismically more active zones in the stimulated domain. Especially alarming was the event of Mw3.3 on April 15th of 2017. Probably, this event indicated a jump of the seismogenic index to -1. All injection operations in both boreholes should be stopped after this event.&lt;/p&gt;&lt;p&gt;Our estimate of the probability of the Pohang earthquake is approximately 15%. One of &amp;#160;decisive factors for &amp;#160;this relatively high probability was the low b value. A combination of a low b-value and a rather high seismogenic index made the probability of a hazardous event significant. A termination of all injection operations after the occurrence of the event of M_w3.3 would significantly reduce the probability of an M_w5.5 event down to approximately 3%. An injection termination at M_w2.3 would reduce it down to approximately 1%.&lt;/p&gt;&lt;p&gt;The Pohang earthquake has a clear character of a triggered event. A real-time well developed seismic observation system permitting a precise 3-D event location and a monitoring of the temporal evolution of the geometry of the stimulated volume and of the seismogenic index could potentially help to prevent or to delay the occurrence of such an &amp;#160;earthquake.&lt;/p&gt;&lt;p&gt;This paper provides a simplified consideration based on analytical formulations for an effective homogeneous porous medium and monotonic injection operations. Numerical simulations of more realistic injection configurations, &amp;#160;an analysis of modeling results along the indicated here directions, further enhanced processing and analysis of seismologic records are required for more detailed understanding of processes led to the Pohang event.&amp;#160;&lt;/p&gt;

The probability of large earthquakes cannot be calculated from seismicity rates

&lt;p&gt;The hypothesis that extrapolation of the Gutenberg-Richter (GR) relationship allows estimates of the probability of large earthquakes is incorrect. For nearly 200 faults for which the recurrence time, T&lt;sub&gt;r&lt;/sub&gt; (1/probability of occurrence), is known from trenching and geodetically measured deformation rates, it has been shown that T&lt;sub&gt;r&lt;/sub&gt; based on seismicity is overestimated typically by one order of magnitude or more. The reason for this is that there are not enough earthquakes along major faults. In some cases there are too few earthquakes for the fault to be mapped based on seismicity. Some examples are the following rupture segments of great faults: the 1717 Alpine Fault, the 1856 San Andreas, the 1906 San Andreas, the 2001 Denali earthquakes, for which geological Tr are 100 years to 300 years and seismicity T&lt;sub&gt;r&lt;/sub&gt; are 10,000 to 100,000 years. In addition, the hypothesis leads to impossible results when one considers the dependence of the b-value on stress. It has been shown that thrusts, strike-slip and normal faults have low, intermediate and high b-values, respectively. This implies that, regardless of local slip rates, the probability of large earthquakes predicted by the hypothesis is high, intermediate and low in thrust, strike-slip, and normal faulting, respectively. Measurements of recurrence probability show a different dependence: earthquake probability depends on slip rate. Finally, the hypothesis predicts different probabilities for large earthquakes, depending on the magnitude scale used. For the 1906 rupture segment, the difference in probability of an M8 earthquake is approximately a factor of 50, using the two available catalogs. Various countries measure earthquake magnitude on their own scale that is intended to agree with the M&lt;sub&gt;L&lt;/sub&gt; scale of California or the M&lt;sub&gt;S&lt;/sub&gt; scale of the USGS. However, it is not trivial to match a scale that is valid for a different region with different attenuation of seismic waves. As a result, some regional M-scales differ from the global M&lt;sub&gt;S&lt;/sub&gt; scale, which yields different T&lt;sub&gt;r&lt;/sub&gt; for the same Mmax in the same region, depending on whether the global or local magnitude scale is used. Based on the aforementioned facts, the hypothesis that probabilities of large earthquakes can be estimated by extrapolating the GR relationship has to be abandoned.&lt;/p&gt;

Evaluation of Phenomena Preceding Earthquakes and Earthquake Predictability

Unusual phenomena sometimes precede a large earthquake and are considered by some as a telltale sign of that earthquake. Judging whether the phenomenon was indeed related to the earthquake is difficult for individual cases. However, the accumulation of data over time allows for statistical evaluation to determine whether there is a correlation between the occurrence of a certain type of phenomena prior to an earthquake. The focus of this study is to review such statistical evaluation. The aspects considered in this study include seismicity, crustal deformation, slow slip, crustal fluids, crustal properties, electromagnetic phenomena, and animal behaviors. The lead times range from minutes to a few decades. The magnitude of the earthquake-preceding tendency can be universally measured by the probability gain G, which is the enhancement ratio of earthquake probability suggested by the occurrence of the phenomenon. A preceding tendency is considered to exist if G is > 1 with reasonable statistical significance. Short-term foreshock activity, that is, temporarily heightened seismicity, produces by far the highest G > 100, sometimes exceeding 10000. While this strongly contributes to empirical forecasting, a considerable part of the predictive power of foreshocks is likely to derive from the mere aftershock triggering mechanism. This enhances the probability of small and large earthquakes by the same factor. It is fundamentally different from traditional expectations that foreshock activity signifies the underlying nucleation process of the forthcoming (large) earthquake. Earthquake-preceding tendency has also been proven significant for a number of other phenomena not ascribable to the aftershock-triggering effect. Some phenomena may be indicators of physical conditions favorable for large earthquakes, while some (e.g., slow slip) may represent triggering effects other than aftershock triggering. Phenomena not ascribable to aftershock triggering have a modest G of < 20 so far. However, these phenomena, including higher-order features of foreshocks, can be combined with the high G from aftershock-triggering effect, sometimes yielding a fairly scaring level of forecast. For example, say ∼10% chance of an M7 earthquake in a week in a few hundred km radius.