Neuropsychologists are increasingly called upon to conduct evaluations with individuals involved in personal injury litigation. While the inclusion of measures of effort within a test battery may help clinicians determine whether a client has put forth full effort, attorney coaching may allow dishonest clients to circumvent these efforts. The purpose of this study was to determine the degree to which frequently used measures of effort are susceptible to coaching, as well as to explore and classify strategies undertaken by coached malingering simulators. Overall, coached simulators performed significantly better on 7 of 14 measured variables. Potential improvements in the external validity of the simulation design were also explored.

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The amount of recorded seismic event data is rapidly growing, and manual processing by trained human experts to infer hypocenter, source parameters, and moment tensor solutions is therefore no longer feasible. Automated procedures are required to process data efficiently and include quality-control measures that allow for outlier detection. We present a modular cross-correlation location (CCLoc) algorithm for induced seismicity that uses cross correlations of either raw seismograms or characteristic functions derived from them followed by a reverse migration procedure. The novelty of this approach is the inclusion of cross pairs of P and S arrivals and the inclusion of autocorrelations, both of which add a distance constraint to the hypocenter estimation. The algorithm is modular in the sense that preprocessing can be tailored to specific data or task. Nine months of seismic data from an underground hard-rock tin mine are processed in a fully automated mode using a machine-learning approach for seismic phase arrival detection and using the estimated arrival functions as input for CCLoc. Making use of the average cross-correlation value as a quality constraint, CCLoc can successfully infer source information on 92% of previously manually processed data. The accuracy of automatic processing is demonstrated by comparing hypocenter, source parameter, and moment tensor solutions between the two datasets. The algorithm will potentially aid the analysis of induced or other seismicity and is particularly well suited to use in the case of large numbers of seismic sensors recording many events.

Microseismic monitoring is routinely used by block and panel caving mines worldwide to track cave shape development. The standard practice is to use the cloud of seismic event locations as a proxy for cave growth below. Careful seismic sensor array planning overcomes problems with event location reliability due to seismic-ray bending around the unknown cave. However, the aseismic gap-the (unknown) zone between actual cave back and bottom of the seismic event cloud-complicates estimation of cave geometry. More information than simple seismic event locations can be used to infer the cave shape and position. Since seismic events slip according to the stresses acting locally at the source locations, an analysis of source mechanisms yields indications of principal stresses around the cave. These stresses are the result of the virgin stresses perturbed by the cave shape, and this yields information about the cave shape. The apparent seismic wave arrivals under the straight-ray assumption are retarded when rays bend around the cave. These anomalous travel times are used to create 'spaghetti'-3D curved lines indicating the likely ray path followed. Analysis of these curved rays in space and time suggests where the cave is and where it is growing. All of this indirect information is now routinely used at Cadia East mine in Australia to estimate the cave shape, helping geotechnical engineers to better manage the cave. A direct verification of the estimated cave shape with an open hole drilled to the cave back in early 2017 provided some validation of these seismic data techniques.