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The advantages offered by structured reporting have already been highlighted in the literature. However, there is still no evidence on the validity of this reporting method for the study of abdominal wall defects. This study aims to show the experience of the Trentino Hernia Team (THT) multidisciplinary group in the development and use of a structured CT scan report for the study of abdominal wall defects. A regional multidisciplinary team (THT group) used a Delphi method to identify and select the most important CT scan parameters needed to describe and stage abdominal wall defects for correct preoperative planning. Based on the selected parameters, a CT scan structured report was worked out and collectively accepted. The first 20 structured reports obtained were individually tested for compilation speed and homogeneity of the data reported by five distinct radiologists. The reports were then evaluated by five different surgeons to test the simplicity of interpretation. We produced a model of a structured report for the study of the abdominal wall defects and tested it in our hospital network on the first 20 reports. The average completion time was 18 min (range 12–25). There was no heterogeneity among the reported data. The reports were analysed by five distinct surgeons to evaluate completeness and simplicity of interpretation. Each surgeon used a Likert scale from 0 to 5 to evaluate each report, producing average scores of 4.8 and 4.1 for completeness and comprehensibility respectively, with a mean combined total score of 8.9 out of 10. Our structured report represents a fundamental tool capable of providing the surgeon with all the measurements of the parameters necessary for correct preoperative planning. At the same time, it is of crucial help for the radiologists representing an easy and fast way to report all the needed parameters using the same standards.

We propose the use of statistical measures to quantify robustness, uncertainty, and significance for Markovian models of large magnitude seismic systems, and we also propose a method for choosing the best of different models by using the normalized measures in a discriminant function. We tested the proposed methods on earthquakes occurring in an area around Japan, divided into four regions; modeling the system as having four states, where each state corresponds to the region where the latest large earthquake, larger than a given threshold moment magnitude, has occurred. Our results show that for the 7.0–7.3 threshold magnitude range the seismicity of this region does occur according to a Markovian process, with optimum results for threshold magnitude 7.1, whereas for magnitudes outside this range seismicity is less Markovian.

The Aki-Utsu method of Gutenberg-Richter (G-R) b value estimation is often misapplied so that estimations not using the G-R histogram are often meaningless because they are not based on adequate samples. We propose a method to estimate the likelihood Pr(bǀb m , N, M 1, M 2) that an observed b m estimate, based on a sample of N magnitudes within an [M 1 − ≤ ΔM/2, M 2 + ΔM/2) range, where ΔM = 0.1 is the usual rounding applied to magnitudes, is due to a “true” source b value, b, and use these likelihoods to estimate source b ranges corresponding to various confidence levels. As an example of application of the method, we estimate the b values before and after the occurrence of a 7.4-magnitude earthquake in the Mexican subduction zone, and find a difference of 0.82 between them with 100% confidence that the b values are different.

Aki’s maximum likelihood method of Gutenberg–Richter b-value estimation is based on the premise that magnitudes above a given threshold are distributed exponentially and presupposes that sampling is adequate for all these magnitudes; clearly, sampling will not be adequate for magnitudes having average recurrence times longer than the observation time. Thus, for any given sample, there is a higher-magnitude completeness threshold. The importance of considering this higher-magnitude threshold is discussed, the effect of incomplete large-magnitude sampling is evaluated, and a method is given for correcting the observations.

Large earthquakes have semi-periodic behavior as a result of critically self-organized processes of stress accumulation and release in seismogenic regions. Hence, large earthquakes in a given region constitute semi-periodic sequences with recurrence times varying slightly from periodicity. In previous papers, it has been shown that it is possible to identify these sequences through Fourier analysis of the occurrence time series of large earthquakes from a given region, by realizing that not all earthquakes in the region need belong to the same sequence, since there can be more than one process of stress accumulation and release in the region. Sequence identification can be used to forecast earthquake occurrence with well determined confidence bounds. This paper presents improvements on the above mentioned sequence identification and forecasting method: the influence of earthquake size on the spectral analysis, and its importance in semi-periodic events identification are considered, which means that earthquake occurrence times are treated as a labeled point process; a revised estimation of non-randomness probability is used; a better estimation of appropriate upper limit uncertainties to use in forecasts is introduced; and the use of Bayesian analysis to evaluate the posterior forecast performance is applied. This improved method was successfully tested on synthetic data and subsequently applied to real data from some specific regions. As an example of application, we show the analysis of data from the northeastern Japan Arc region, in which one semi-periodic sequence of four earthquakes with M ≥ 8.0, having high non-randomness probability was identified. We compare the results of this analysis with those of the unlabeled point process analysis.

The Aki-Utsu maximum likelihood method is widely used for estimation of the Gutenberg-Richter b-value, but not all authors are conscious of the method’s limitations and implicit requirements. The Aki/Utsu method requires a representative estimate of the population mean magnitude; a requirement seldom satisfied in b-value studies, particularly in those that use data from small geographic and/or time windows, such as b-mapping and b-vs-time studies. Monte Carlo simulation methods are used to determine how large a sample is necessary to achieve representativity, particularly for rounded magnitudes. The size of a representative sample weakly depends on the actual b-value. It is shown that, for commonly used precisions, small samples give meaningless estimations of b. Our results give estimates on the probabilities of getting correct estimates of b for a given desired precision for samples of different sizes. We submit that all published studies reporting b-value estimations should include information about the size of the samples used.

Ground deformation and seismicity in Mexicali Valley, Baja California, Mexico, the southern part of the Mexicali-Imperial valley, are influenced by active tectonics and human activity. In this study, data from two successive leveling surveys in 2006 and 2009/2010 are used to estimate the total deformation occurred in Mexicali Valley during 2006–2009. The leveling data span more than 3.5 years and include deformation from several natural and anthropogenic sources that acted at different temporal and spatial scales during the analyzed period. Because of its large magnitude, the aseismic anthropogenic deformation caused by fluid extraction in the Cerro Prieto geothermal field obscures the deformation caused by other mechanisms and sources. The method of differential interferograms stacking was used to estimate the aseismic (interseismic tectonic and anthropogenic) components of the observed displacement, using SAR images, taken in 2007 during a period when no significant seismicity occurred in the study area. After removing the estimated aseismic signal from the leveling data, residual vertical displacement remained, and to identify possible sources and mechanisms of this displacement, a detailed analysis of records from tiltmeters and creepmeters was performed. The results of this analysis suggest that the residual displacement is mainly caused by moderate-sized seismicity in the area of study. Modeling of the vertical ground deformation caused by the coseismic slip on source fault (primary mechanism) of the two most important earthquakes, May 24, 2006 (Mw = 5.4) and December 30, 2009 (Mw = 5.8), was performed. The modeling results, together with the analysis of geotechnical instruments data, suggests that this moderate-sized seismicity influences the deformation in the study area by coseismic slip on the source fault, triggered slip on secondary faults, and soft sediments deformation.

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela.

On a global scale, large earthquakes are Poisson-distributed in time which implies that the inter-earthquake intervals are exponentially distributed. Thus, a simple-minded estimation of the most probable interval could conclude that the most probable occurrence time for a large earthquake would be now! This thesis is unsupported by observations. The apparent paradox is explained when characteristics of the interval cumulative distributions are explored for different seismicity rates.

We explore fractal properties of two observed seismicity distributions prior to the 2003 M w 7.4 Colima, Mexico and 1992 M w 7.3 Landers, USA earthquakes, together with several mathematical fractal distributions and two non-fractal ones, in order to estimate minimum reliable sample sizes, determine whether fractality for observed seismicity is essentially different from random uniform distributions, and explore the possibility of extracting premonitory information from fractal characteristics of seismicity before large earthquakes. Sample sizes above 800 events for whole catalogs appear to be sufficient to maintain ordered multifractality and to yield dimension estimates that vary smoothly and reliably. Fractal estimates appear to be best for whole catalogs that include aftershocks. The fractal characteristics of spatial distributions of seismicity are essentially different from those of the uniform random distribution, which is the null hypothesis of a non-fractal distribution with minimum information. The fractal dimensions and afractality measures of seismicity distributions change with time and show distinctive behaviors associated with foreshocks and main events, although these behaviors are different for each example. Results suggest the possibility of a priori identification of foreshocks to large earthquakes. A combination of fractal dimension and afractality measures over time may be helpful in large earthquake premonitory studies.

Deep fluid extraction in the Cerro Prieto geothermal field (CPGF) has caused subsidence and induced slip on tectonic faults in the Mexicali Valley (Baja California, Mexico). The Mexicali Valley is located in the southern part of the Salton Trough, at the boundary between the Pacific and North American plates. The Valley is characterized by being a zone of continuous tectonic deformation, geothermal activity, and seismicity. Within the Cerro Prieto pull-apart basin, seismicity is concentrated mainly in swarms, while strong earthquakes have occurred in the Imperial and Cerro Prieto transform faults, that are the eastern and western bound of the basin. Since 1973, fluid extraction at the CPGF has influenced deformation in the area, accelerating the subsidence and causing rupture (frequently as vertical slip or creep) on the surface traces of tectonic faults. Both subsidence and fault slip are causing damage to infrastructure like roads, railroad tracks, irrigation channels, and agricultural fields. Currently, accelerated extraction in the eastern part of CPGF has shifted eastwards the area of most pronounced subsidence rate; this accelerated subsidence can be observed at the Saltillo fault, a southern branch of the Imperial fault in the Mexicali Valley. Published leveling data, together with field data from geological surveys, geotechnical instruments, and new InSAR images were used to model the observed deformation in the area in terms of fluid extraction. Since the electricity production in the CPGF is an indispensable part of Baja California economy, extraction is sure to continue and may probably increase, so that the problem of damages caused by subsidence will likely increase in the future.

A previous work introduced a new method for seismic hazard evaluation in a system (a geographic area with distinct, but related seismogenic regions) based on modeling the transition probabilities of states (patterns of presence or absence of seismicity, with magnitude greater or equal to a threshold magnitude M r, in the regions of the system, during a time interval Δt) as a Markov chain. Application of this direct method to the Japan area gave very good results. Given that the most important limitation of the direct method is the relative scarcity of large magnitude events, we decided to explore the possibility that seismicity with magnitude M ≥ M r m contains information about the future occurrence of earthquakes with M M r m > M r m . This mixed Markov chain method estimates the probabilities of occurrence of a system state for M ≥ M r M on the basis of the observed state for M ≥ M r m in the previous Δt. Application of the mixed method to the area of Japan gives better hazard estimations than the direct method; in particular for large earthquakes. As part of this study, the problem of performance evaluation of hazard estimation methods is addressed, leading to the use of grading functions.

Previous works have shown that ground deformation and seismicity in the Cerro Prieto geothermal field (CPGF) are due to both tectonics and field exploitation. Here, we use information about current tectonics and data from precision leveling surveys, to model tectonic and anthropogenic subsidence. Our results show that tectonic subsidence constitutes only ∼4% of the measured subsidence. Anthropogenic subsidence was evaluated using a model of rectangular tensional cracks, based on the hydrological model of the field, together with the Coulomb 2.0 program. From the resulting values of the fissure parameters and from extraction and injection data, we calculate that the volume changes caused by closure of the geothermal and cold water reservoirs account for only ∼3% and ∼7%, respectively, of the volume change which should occur due to extraction. Since 18% of the extracted fluids are reinjected, external recharge must compensate for about 72% of the expected volume reduction. An analysis of the changes in Coulomb stress caused by exploitation of the geothermal field suggest that even though the anthropogenic stresses account for only a fraction of tectonic stresses, they are large enough to trigger seismicity.

Seismogenic regions within some geographic area are interrelated through tectonics and seismic history, although this relation is usually complex, so that seismicity in a given region cannot be predicted in a straightforward manner from the activity in other region(s). We present a new statistical method for seismic hazard evaluation based on modeling the transition probabilities of seismicity patterns in the regions of a geographic area during a time interval, as a Markov chain. Application of the method to the Japan area renders good results, considering the occurrence of a high probability transition as a successful forecast. For magnitudes M≥5.5 and time intervals Δ t=0.10 year, the method yields a 78% aftcast (forecast of data already used to evaluate the hazard) success rate for the entire catalog, and an indicative 80% forecast success rate for the last 10 transitions in the catalog. A byproduct of the method, regional occurrence probabilities determined from the transition probabilities, also provides good results; aftcasts of regional activity have a 98% success rate, and those of activity in the highest probability region about 80.5% success rate. All results are superior to those from the null hypotheses (a memory-less Poissonian, fixed-rate, or uniform system) and have vanishingly small probabilities of resulting from purely random guessing.

—Crackmeter data from the southern tip of the Imperial fault in the Mexicali-Imperial Valley in northern Baja California, Mexico, show slip events that tend to group in episodes, or suites, that last over a few days, with months of quiescence between them. The events in these suites are apparently triggered and stopped by ground strain related to temperature changes. A characteristic decrease in peak extension velocities during the slip event suites indicates the presence of viscoelastic afterworking which contributes the strain necessary for succeeding events with diminishing driving stresses, and which can be roughly modeled as a series of Kelvin-Voigt viscoelastic solids. Occurrence of very small events towards the end of the suites plus gaps between suites indicate that for the sediments significant fault healing requires a minimum of several days without slip.

Ten years after the last effusive eruption and at least 15 years of seismic quiescence, volcanic seismic activity started at Colima volcano on 14 February 1991, with a seismic crisis which reached counts of more than 100 per day and showed a diversity of earthquake types. Four other distinct seismic crises followed, before a mild effusive eruption in April 1991. The second crisis preceded the extrusion of an andesitic scoriaceous lava lobe, first reported on 1 March; during this crisis an interesting temporary concentration of seismic foci below the crater was observed shortly before the extrusion was detected. The third crisis was constituted by shallow seismicity, featuring possible mild degassing explosion-induced activity in the form of hiccups (episodes of simple wavelets that repeat with diminishing amplitude), and accompanied by increased fumarolic activity. The growth of the new lava dome was accompanied by changing seismicity. On 16 April during the fifth crisis which consisted of some relatively large, shallow, volcanic earthquakes and numerous avalanches of older dome material, part of the newly extruded dome, which had grown towards the edge of the old dome, collapsed, producing the largest avalanches and ash flows. Afterwards, block lava began to flow slowly along the SW flank of the volcano, generating frequent small incandescent avalanches. The seismicity associated with the stages of this eruptive activity shows some interesting features: most earthquake foci were located north of the summit, some of them relatively deep (7–11 km below the summit level), underneath the saddle between the Colima and the older Nevado volcanoes. An apparently seismic quiet region appears between 4 and 7 km below the summit level. In June, harmonic tremors were detected for the first time, but no changes in the eruptive activity could be correlated with them. After June, the seismicity decreasing trend was established, and the effusive activity stopped on September 1991.

The determination of the spectral component of a signal corresponding to the Nyquist frequencyfN is not reliable. A rule is proposed to determine the highest reliable frequencyfR for a given sampling rate Δt and time windowN Δt (whereN is the total number of samples) asfR=fN(1−1/2N).

The problem of automatic detection of seismic waves by large telemetered seismic networks such as the Mexican Continental Aperture Seismic Network (RESMAC), is extended here to include determination of seismic first-arrival and S-phase-arrival times. A short general outline of the detection problem background and a small introduction to the autoregressive model (AR) concept are presented. Several automatic detection algorithms were implemented and compared with a newly developed autoregressive algorithm. Careful consideration of the advantages and disadvantages of each method determined that a mixed detection scheme is optimal and suitable for RESMAC. A few examples are shown that illustrate the relative performances of the methods tried here. The proposed detection scheme has the following characteristics: (a) First-arrival detection, based on a simple (average of squared input) characteristic function, and a trigger criterion that uses as a distortion measure the long-average-to-short-average ratio of the characteristic function, checked using a duration criterion; (b) use of two threshold values, one for triggering, and another for beginning the backward search for the phase arrival time; (c) use of the autoregressive model (AR) method, with the Itakura-Saito distortion measure, for S-phase detection, checked using both duration and amplitude criteria; and (d) characterization of the reliability of the determinations for their subsequent use in automatic location programs, alarms, etc. The automatic detection scheme has proved effective.